Long-term acclimation to organic carbon enhances the production of loliolide from Scenedesmus deserticola.

Production of functional biocompounds from microalgae has garnered interest from different industrial sectors; however, their overall productivity must be substantially improved for commercialization. Herein, long-term acclimation of Scenedesmus deserticola was conducted using glucose as an organic carbon source to enhance its heterotrophic capabilities and the production potential of loliolide. A year-long acclimation on agar plates led to the selection of S. deserticola HS4, which exhibited at least 2-fold increase in loliolide production potential; S. deserticola HS4 was subjected to further screening of its cultivation conditions and fed-batch cultivation was subsequently performed in liter-scale reactors. While S. deserticola HS4 exhibited shifts in cellular morphology and biochemical composition, the results suggested a substantial increase in its loliolide productivity regardless of trophic modes. Collectively, these results highlight the potential of long-term acclimation as an effective strategy for improving microalgal crops to align with industrial production practices.

Citrus peel powder alters the rheological properties and 3D printing performance of potato starch gel.

Owing to the growing interest in sustainable resource utilization, the current study explores the potential replacement of pectin with citrus peel powder (CP) in starch-based 3D food printing ink formulations. The effect of different concentrations of pectin (1 %, 2 %, 3 %) and CP (1 %, 2 %, 3 %) on the printing fidelity, microstructure, rheological and textural properties of potato starch gel were investigated. The results showed that the 3D printing performance of CP-added inks was higher than that of pectin-added inks at all tested concentrations. The storage modulus of CP-added ink was higher than that of pectin-added ink proving higher printing fidelity of CP-added inks. Additionally, hardness, gumminess, springiness and chewiness of food ink increased with an increase in the concentration of CP while decreased with an increase in concentration of pectin. Interestingly, pectin and CP-added inks displayed similar in vitro digestibility, suggesting an insignificant effect of replacing pectin with CP on in vitro glucose release. Moreover, the antioxidant activity of CP-added ink was higher than pectin-added ink demonstrating the potential applications of CP-added ink in functional ink development. Therefore, this study claims for effective replacement of pectin with CP in starch-based 3D food printing ink formulations as a promising sustainable additive.

Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence.

This study evaluated the positive predictive value (PPV) of artificial intelligence (AI) in detecting pneumothorax on chest radiographs (CXRs) and its affecting factors. Patients determined to have pneumothorax on CXR by a commercial AI software from March to December 2021 were included retrospectively. The PPV was evaluated according to the true-positive (TP) and false-positive (FP) diagnosis determined by radiologists. To know the factors that might influence the results, logistic regression with generalized estimating equation was used. Among a total of 87,658 CXRs, 308 CXRs with 331 pneumothoraces from 283 patients were finally included. The overall PPV of AI about pneumothorax was 41.1% (TF:FP = 136:195). The PA view (odds ratio [OR], 29.837; 95% confidence interval [CI], 15.062-59.107), high abnormality score (OR, 1.081; 95% CI, 1.066-1.097), large amount of pneumothorax (OR, 1.005; 95% CI, 1.003-1.007), presence of ipsilateral atelectasis (OR, 3.508; 95% CI, 1.509-8.156) and a small amount of ipsilateral pleural effusion (OR, 5.277; 95% CI, 2.55-10.919) had significant effects on the increasing PPV. Therefore, PPV for pneumothorax diagnosis using AI can vary based on patients' factors, image-acquisition protocols, and the presence of concurrent lesions on CXR.

Artificial intelligence-based computer-aided diagnosis abnormality score trends in the serial mammography of patients with breast cancer.

PURPOSE: To explore the abnormality score trends of artificial intelligence-based computer-aided diagnosis (AI-CAD) in the serial mammography of patients until a final diagnosis of breast cancer. METHOD: From 2015 to 2019, 126 breast cancer patients who had at least two previous mammograms obtained from 2008 up to cancer diagnosis were included. AI-CAD was retrospectively applied to 487 previous mammograms and all the abnormality scores calculated by AI-CAD were obtained. The contralateral breast of each affected breast was defined as the control group. We divided all mammograms by 6-month intervals from cancer diagnosis in reverse chronological order. The random coefficient model was used to estimate whether the chronological trend of AI-CAD abnormality scores differed between cancer and normal breasts. Subgroup analyses were performed according to mammographic visibility, invasiveness and molecular subtype of the invasive cancer. RESULTS: Mean period from initial examination to cancer diagnosis was 6.0 years (range 1.7-10.7 years). The abnormality scores of breasts diagnosed with cancer showed a significantly increasing trend during the previous examination period (slope 0.6 per 6 months, p for the slope < 0.001), while the contralateral normal breast showed no trend (slope 0.03, p = 0.776). The difference in slope between the cancerous and contralateral breasts was significant (p < 0.001). For mammography-visible cancers, the abnormality scores in cancerous breasts showed a significant increasing trend (slope 0.8, p < 0.001), while for mammography-occult cancers, the trend was not significant (slope 0.1, p = 0.6). For invasive cancers, the slope of the abnormality scores showed a significant increasing trend (slope 1.4, p = 0.002), unlike ductal carcinoma in situ (DCIS) which showed no significant trend. There was no significant difference in the slope of abnormality scores among the subtypes of invasive cancers (p = 0.418). CONCLUSION: Breasts diagnosed with cancer showed an increase in AI-CAD abnormality scores in previous serial mammograms, suggesting that AI-CAD could be useful for early detection of breast cancer.

Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results.

This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia between March 2020 and August 2021 were included. We developed prognostic models, including an AI-based consolidation score in addition to the conventional CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥ 65) and pneumonia severity index (PSI) for predicting pneumonia outcomes, defined as 30-day mortality during admission. A total of 489 patients, including 310 and 179 patients in training and test sets, were included. In the training set, the AI-based consolidation score on CXR was a significant variable for predicting the outcome (hazard ratio 1.016, 95% confidence interval [CI] 1.001-1.031). The model that combined CURB-65, initial O2 requirement, intubation, and the AI-based consolidation score showed a significantly high C-index of 0.692 (95% CI 0.628-0.757) compared to other models. In the test set, this model also demonstrated a significantly high C-index of 0.726 (95% CI 0.644-0.809) compared to the conventional CURB-65 and PSI (p < 0.001 and 0.017, respectively). Therefore, a new prognostic model incorporating AI-based CXR results along with traditional pneumonia severity score could be a simple and useful tool for predicting pneumonia outcomes in clinical practice.

Bioelectrical impedance parameters add incremental value to waist-to-hip ratio for prediction of metabolic dysfunction associated steatotic liver disease in youth with overweight and obesity.

Introduction: Metabolic dysfunction-associated steatotic liver disease (MASLD) presents a growing health concern in pediatric populations due to its association with obesity and metabolic syndrome. Bioelectrical impedance analysis (BIA) offers a non-invasive and potentially effective alternative for identifying MASLD risk in youth with overweight or obesity. Therefore, this study aimed to assess the utility of BIA for screening for MASLD in the youth. Method: This retrospective, cross-sectional study included 206 children and adolescents aged <20 years who were overweight and obese. The correlations between anthropometric measurements and BIA parameters and alanine aminotransferase (ALT) levels were assessed using Pearson's correlation analysis. Logistic regression analysis was performed to examine the associations between these parameters and ALT level elevation and MASLD score. Receiver operating characteristic (ROC) curves were generated to assess the predictive ability of the parameters for MASLD. Results: Pearson's correlation analysis revealed that waist-to-hip ratio (WHR), percentage body fat (PBF), and BIA parameters combined with anthropometric measurements were correlated with ALT level. Logistic regression revealed that WHR, skeletal muscle mass/WHR, PBF-WHR, fat-free mass/WHR, and appendicular skeletal muscle mass/WHR were correlated with ALT level elevation after adjusting for age, sex, and puberty. WHR, PBF-WHR, and visceral fat area (VFA)-WHR were positively correlated with the MASLD score in the total population after adjusting for age, sex, and puberty. PBF-WHR and VFA-WHR were correlated with the MASLD score even in youth with a normal ALT level. The cutoff points and area under the ROC curves were 34.6 and 0.69 for PBF-WHR, respectively, and 86.6 and 0.79 for VFA-WHR, respectively. Discussion: This study highlights the utility of combining BIA parameters and WHR in identifying the risk of MASLD in overweight and obese youth, even in those with a normal ALT level. BIA-based screening offers a less burdensome and more efficient alternative to conventional MASLD screening methods, facilitating early detection and intervention in youth at risk of MASLD.

The effect of inhaler prescription on the development of lung cancer in COPD: a nationwide population-based study.

BACKGROUND: COPD is associated with the development of lung cancer. A protective effect of inhaled corticosteroids (ICS) on lung cancer is still controversial. Hence, this study investigated the development of lung cancer according to inhaler prescription and comorbidties in COPD. METHODS: A retrospective cohort study was conducted based on the Korean Health Insurance Review and Assessment Service database. The development of lung cancer was investigated from the index date to December 31, 2020. This cohort included COPD patients (≥ 40 years) with new prescription of inhalers. Patients with a previous history of any cancer during screening period or a switch of inhaler after the index date were excluded. RESULTS: Of the 63,442 eligible patients, 39,588 patients (62.4%) were in the long-acting muscarinic antagonist (LAMA) and long-acting ß2-agonist (LABA) group, 22,718 (35.8%) in the ICS/LABA group, and 1,136 (1.8%) in the LABA group. Multivariate analysis showed no significant difference in the development of lung cancer according to inhaler prescription. Multivariate analysis, adjusted for age, sex, and significant factors in the univariate analysis, demonstrated that diffuse interstitial lung disease (DILD) (HR = 2.68; 95%CI = 1.86-3.85), a higher Charlson Comorbidity Index score (HR = 1.05; 95%CI = 1.01-1.08), and two or more hospitalizations during screening period (HR = 1.19; 95%CI = 1.01-1.39), along with older age and male sex, were independently associated with the development of lung cancer. CONCLUSION: Our data suggest that the development of lung cancer is not independently associated with inhaler prescription, but with coexisting DILD, a higher Charlson Comorbidity Index score, and frequent hospitalization.

Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time.

Purpose To develop an artificial intelligence (AI) model for the diagnosis of breast cancer on digital breast tomosynthesis (DBT) images and to investigate whether it could improve diagnostic accuracy and reduce radiologist reading time. Materials and Methods A deep learning AI algorithm was developed and validated for DBT with retrospectively collected examinations (January 2010 to December 2021) from 14 institutions in the United States and South Korea. A multicenter reader study was performed to compare the performance of 15 radiologists (seven breast specialists, eight general radiologists) in interpreting DBT examinations in 258 women (mean age, 56 years ± 13.41 [SD]), including 65 cancer cases, with and without the use of AI. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and reading time were evaluated. Results The AUC for stand-alone AI performance was 0.93 (95% CI: 0.92, 0.94). With AI, radiologists' AUC improved from 0.90 (95% CI: 0.86, 0.93) to 0.92 (95% CI: 0.88, 0.96) (P = .003) in the reader study. AI showed higher specificity (89.64% [95% CI: 85.34%, 93.94%]) than radiologists (77.34% [95% CI: 75.82%, 78.87%]) (P < .001). When reading with AI, radiologists' sensitivity increased from 85.44% (95% CI: 83.22%, 87.65%) to 87.69% (95% CI: 85.63%, 89.75%) (P = .04), with no evidence of a difference in specificity. Reading time decreased from 54.41 seconds (95% CI: 52.56, 56.27) without AI to 48.52 seconds (95% CI: 46.79, 50.25) with AI (P < .001). Interreader agreement measured by Fleiss κ increased from 0.59 to 0.62. Conclusion The AI model showed better diagnostic accuracy than radiologists in breast cancer detection, as well as reduced reading times. The concurrent use of AI in DBT interpretation could improve both accuracy and efficiency. Keywords: Breast, Computer-Aided Diagnosis (CAD), Tomosynthesis, Artificial Intelligence, Digital Breast Tomosynthesis, Breast Cancer, Computer-Aided Detection, Screening Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Bae in this issue.

Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography.

OBJECTIVE: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. MATERIALS AND METHODS: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. RESULTS: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). CONCLUSION: PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.

Different particle sizes of Momordica charantia leaf powder modify the rheological and textural properties of corn starch-based 3D food printing ink.

The study determined the effect of incorporating Momordica charantia leaf powder (MCLP) into corn-starch 3D food-printing ink as a functional ingredient. The effects of the particle size (75, 131, and 200 µm) and quantity of MCLP on 3D printing performance, structural, textural, and rheological properties of corn starch gel were evaluated with different concentrations (5, 10, and 15 % (w/w)) of corn starch. The viscoelastic properties of food inks were determined considering their behavior during extrusion and self-recovery after printing. Scanning electron microscope was used to characterize the microstructure. Based on the results, a high starch content (15 %) with 5 % MCLP was more favorable for 3D food printing. In addition, 3D printing performance, textural and rheological properties of formulated ink was mainly governed by the particle size of MCLP. The food ink with a 5 % mass fraction of 200 µm MCLP had the highest printing precision and the best masticatory properties.

Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography.

Purpose: To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods: This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC). Results: Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD. Conclusion: Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.

Cardiovascular Events According to Inhaler Therapy and Comorbidities in Chronic Obstructive Pulmonary Disease.

Background: COPD coexists with many concurrent comorbidities. Cardiovascular complications are deemed to be major causes of death in COPD. Although inhaler therapy is the main therapeutic intervention in COPD, cardiovascular events accompanying inhaler therapy require further investigation. Therefore, this study aimed to investigate new development of cardiovascular events according to each inhaler therapy and comorbidities. Methods: This study analyzed COPD patients (age ≥ 40 years, N = 199,772) from the Health Insurance Review and Assessment Service (HIRA) database in Korea. The development of cardiovascular events, from the index date to December 31, 2020, was investigated. The cohort was eventually divided into three arms: the LAMA/LABA group (N = 28,322), the ICS/LABA group (N = 11,812), and the triple group (LAMA/ICS/LABA therapy, N = 6174). Results: Multivariable Cox analyses demonstrated that, compared to ICS/LABA therapy, triple therapy was independently associated with the development of ischemic heart disease (HR: 1.22, 95% CI: 1.04-1.43), heart failure (HR: 1.45, 95% CI: 1.14-1.84), arrhythmia (HR: 1.72, 95% CI: 1.41-2.09), and atrial fibrillation/flutter (HR: 2.31, 95% CI: 1.64-3.25), whereas the LAMA/LABA therapy did not show a significant association. Furthermore, emergency room visit during covariate assessment window was independently associated with the development of ischemic heart disease, heart failure, arrhythmia, and atrial fibrillation/flutter (p < 0.05). Conclusion: Our data suggest that cardiovascular risk should be considered in COPD patients receiving triple therapy, despite the confounding bias resulting from disparities in each group.

Trends and Clinical Characteristics of Next-Generation Sequencing-Based Genetic Panel Tests: An Analysis of Korean Nationwide Claims Data.

PURPOSE: In the modern era of precision medicine, next-generation sequencing (NGS) is employed for a variety of clinical purposes. The aim of this study was to investigate the trends and clinical characteristics of NGS testing in South Korea. MATERIALS AND METHODS: This nationwide, population-based, retrospective cohort study examined National Health Insurance Service claims data from 2017 to 2021 for NGS and from 2008 to 2021 for gene-targeted anticancer drugs. RESULTS: Among the total 98,748 claims, there were 51,407 (52.1%) solid cancer panels, 30,173 (30.5%) hereditary disease panels, and 17,168 (17.4%) hematolymphoid cancer panels. The number of annual claims showed a persistent upward trend, exhibiting a 5.4-fold increase, from 5,436 in 2017 to 29,557 in 2021. In the solid cancer panel, colorectal cancer was the most common (19.2%), followed by lung cancer (18.8%). The annual claims for targeted cancer drugs have increased 25.7-fold, from 3,932 in 2008 to 101,211 in 2020. Drugs for the treatment of lung cancer accounted for 488,819 (71.9%) claims. The number of patients who received non-hereditary NGS testing has substantially increased, and among them, the count of patients prescribed targeted anticancer drugs consistently rose from 508 (13.9%) in 2017 to 2,245 (12.3%) in 2020. CONCLUSION: This study highlights the rising nationwide demand for comprehensive genetic testing for disease diagnosis and treatment following NGS reimbursement by the National Health Insurance in South Korea, in addition to the need for greater utilization of targeted anticancer drugs.

Clinical Impact of Genomic and Pathway Alterations in Stage I EGFR-Mutant Lung Adenocarcinoma.

PURPOSE: We investigated the clinical impact of genomic and pathway alterations in stage I epidermal growth factor receptor (EGFR)-mutant lung adenocarcinomas, which have a high recurrence rate despite complete surgical resection. MATERIALS AND METHODS: Out of the initial cohort of 257 patients with completely resected stage I EGFR-mutant lung adenocarcinoma, tumor samples from 105 patients were subjected to analysis using large-panel next-generation sequencing. We analyzed 11 canonical oncogenic pathways and determined the number of pathway alterations (NPA). Survival analyses were performed based on co-occurring alterations and NPA in three patient groups: all patients, patients with International Association for the Study of Lung Cancer (IASLC) pathology grade 2, and patients with recurrent tumors treated with EGFR-tyrosine kinase inhibitor (TKI). RESULTS: In the univariate analysis, pathological stage, IASLC grade, TP53 mutation, NPA, phosphoinositide 3-kinase pathway, p53 pathway, and cell cycle pathway exhibited significant associations with worse recurrence-free survival (RFS). Moreover, RPS6KB1 or EGFR amplifications were linked to a poorer RFS. Multivariate analysis revealed that pathologic stage, IASLC grade, and cell cycle pathway alteration were independent poor prognostic factors for RFS (p=0.002, p < 0.001, and p=0.006, respectively). In the grade 2 subgroup, higher NPA was independently associated with worse RFS (p=0.003). Additionally, in patients with recurrence treated with EGFR-TKIs, co-occurring TP53 mutations were linked to shorter progression-free survival (p=0.025). CONCLUSION: Genomic and pathway alterations, particularly cell cycle alterations, high NPA, and TP53 mutations, were associated with worse clinical outcomes in stage I EGFR-mutant lung adenocarcinoma. These findings may have implications for risk stratification and the development of new therapeutic strategies in early-stage EGFR-mutant lung cancer patients.

Chlorophyll a and novel synthetic derivatives alleviate atopic dermatitis by suppressing Th2 cell differentiation via IL-4 receptor modulation.

Atopic dermatitis (AD) treatment has largely relied on non-specific broad immunosuppressants despite their long-term toxicities until the approval of dupilumab, which blocks IL-4 signaling to target Th2 cell responses. Here, we report the discovery of compound 4aa, a novel compound derived from the structure of chlorophyll a, and the efficacy of chlorophyll a to alleviate AD symptoms by oral administration in human AD patients. 4aa downregulated GATA3 and IL-4 in differentiating Th2 cells by potently blocking IL-4 receptor dimerization. In the murine model, oral administration of 4aa reduced the clinical severity of symptoms and scratching behavior by 76% and 72%, respectively. Notably, the elevated serum levels of Th2 cytokines reduced to levels similar to those in the normal group after oral administration of 4aa. Additionally, the toxicological studies showed favorable safety profiles and good tolerance. In conclusion, 4aa may be applied for novel therapeutic developments for patients with AD.

3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net.

Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at high risk for developing breast cancer. As a supplemental screening tool, ultrasonography is a widely adopted imaging modality to standard mammography, especially for dense breasts. Lately, automated breast ultrasound imaging has gained attention due to its advantages over hand-held ultrasound imaging. However, automated breast ultrasound imaging requires considerable time and effort for reading because of the lengthy data. Hence, developing a computer-aided nodule detection system for automated breast ultrasound is invaluable and impactful practically. This study proposes a three-dimensional breast nodule detection system based on a simple two-dimensional deep-learning model exploiting automated breast ultrasound. Additionally, we provide several postprocessing steps to reduce false positives. In our experiments using the in-house automated breast ultrasound datasets, a sensitivity of [Formula: see text] with 8.6 false positives is achieved on unseen test data at best.

Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence.

This study evaluated how often clinically significant lung nodules were detected unexpectedly on chest radiographs (CXR) by artificial intelligence (AI)-based detection software, and whether co-existing findings can aid in differential diagnosis of lung nodules. Patients (> 18 years old) with AI-detected lung nodules at their first visit from March 2021 to February 2022, except for those in the pulmonology or thoracic surgery departments, were retrospectively included. Three radiologists categorized nodules into malignancy, active inflammation, post-inflammatory sequelae, or "other" groups. Characteristics of the nodule and abnormality scores of co-existing lung lesions were compared. Approximately 1% of patients (152/14,563) had unexpected lung nodules. Among 73 patients with follow-up exams, 69.9% had true positive nodules. Increased abnormality scores for nodules were significantly associated with malignancy (odds ratio [OR] 1.076, P = 0.001). Increased abnormality scores for consolidation (OR 1.033, P = 0.040) and pleural effusion (OR 1.025, P = 0.041) were significantly correlated with active inflammation-type nodules. Abnormality scores for fibrosis (OR 1.036, P = 0.013) and nodules (OR 0.940, P = 0.001) were significantly associated with post-inflammatory sequelae categorization. AI-based lesion-detection software of CXRs in daily practice can help identify clinically significant incidental lung nodules, and referring accompanying lung lesions may help classify the nodule.

Salvia miltiorrhiza Bunge Ameliorates Benign Prostatic Hyperplasia through Regulation of Oxidative Stress via Nrf-2/ HO-1 Activation.

Oxidative stress is a key factor in the pathogenesis of benign prostatic hyperplasia (BPH) that leads to inflammation. This study aimed to evaluate the ameliorative effects of Salvia miltiorrhiza Bunge extract (HLT-101) on BPH through the regulation of oxidative stress and inflammation. A testosterone propionate (TP)-induced BPH rat model was orally administered HLT-101 (20, 40, or 80 mg/kg), and its effects on oxidative stress- and inflammation-related gene expression were examined. Further, HLT-101 was assessed for its effect on reactive oxygen species (ROS) levels and Nrf-2/HO-1 signaling pathways in BPH-1 cells. HLT-101 decreased testosterone-induced excessive free radical production and inflammatory factor activation. Moreover, HLT-101 treatment significantly decreased the intracellular ROS level in the TNF-α and IFN-γ treated BPH-1 cells through the activation of Nrf-2. In addition, HLT-101 treatment inhibited the NF-κB pathway and androgen receptor (AR) signaling, which is highly linked to the pathogenesis of BPH. Therefore, HLT-101 has the potential to be an effective treatment reagent for BPH because of its ability to reduce inflammation and oxidative stress via Nrf-2/HO-1 signaling.

Design of a Bioinspired Robust Three-Dimensional Cross-Linked Polymer Binder for High-Performance Li-Ion Battery Applications.

Si has the highest theoretical capacity (4200 mA h g-1) among conventional anode materials, such as graphite (372 mA h g-1), but its large volume expansion leads to deterioration of the battery performance. To overcome this problem (issue), we investigated the use of polysaccharide-based 3D cross-linked network binders for Si anodes, in which the polysaccharide formed an effective 3D cross-linked network around Si particles via cross-linking of polysaccharide with citric acid (CA). Sodium alginate (SA), a natural polysaccharide extracted from brown algae, is a suitable binder material for Si anodes because its abundant hydroxyl (-OH) and carboxyl (-COOH) groups form hydrogen and covalent bonds with the -OH groups present on the Si surface. We found that CA-cross-linked (CA-SA) could effectively prevent the volume expansion of Si anodes through the formation of 3D cross-linked network structures. In addition, the CA-SA binders provide enhanced adhesion strength, enabling the fabrication of more robust electrodes than those prepared using binders with linear structures ("linear binders"). In particular, the fabricated Si-based electrode (high mass loading of 1.5 mg cm-2) with CA-SA binder exhibited outstanding areal capacity (∼2.7 mA h cm-2) and excellent cycle retention (∼100% after 100 cycles).

Combined effect of heating temperature and content of pectin on the textural properties, rheology, and 3D printability of potato starch gel.

Three-dimensional (3D) printing is one of the emerging techniques which fabricates customized foods with desired sensory characteristics. Rheological properties of 3D printing materials are vitally important in printability which govern the flowability and structural stability. Due to its unique gel-forming characteristics, potato starch has been extensively used in myriad food applications, such as 3D printing. However, little attention has been paid to the combined effect of heating temperature and pectin addition on the properties of potato starch gels. Thus, this study investigated the impact of different pectin contents (1, 1.5, and 2 %) on printability and the rheological and textural properties of potato starch gels heated at different temperatures (70, 80, and 90 °C). The gel heating temperature governs pectin-driven modifications in potato starch gels. Pectin addition increased the 3D printability, viscosity, storage modulus, hardness, gumminess, and springiness of starch gel at higher temperatures (80 °C and 90 °C). In contrast, at lower temperatures (70 °C), pectin addition decreased printability, viscosity, storage modulus, hardness, gumminess, and springiness. Therefore, the gel heating temperature influences the impact of pectin on printability, rheology, and textural properties. Accordingly, the combined effects of pectin and heating temperature should be considered in pectin-based 3D food-printing ink formulations.