Validity of the Korean Version of the Brief Irritability Test in Psychiatric Patients.

OBJECTIVE: This study aimed to investigate the validity and reliability of the Korean version of the Brief Irritability Test (BITe) and adapt it for Korean patients with psychiatric disorders. METHODS: A total of 296 patients at the Department of Psychiatry of Hanyang University Guri Hospital completed the BITe, Korean Beck Depression Inventory-II, Korean Beck Anxiety Inventory, the Korean version of the State-Trait Anger Expression Inventory, and Barratt Impulsiveness Scale-11. Construct validity was confirmed through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Reliability testing included assessments of internal consistency (Cronbach's α) and item-total correlations. Convergent validity was examined through correlational analyses with variables such as anxiety, depression, anger, and impulsivity. RESULTS: First, the scale had good internal consistency with a Cronbach's α of 0.88. Second, EFA indicated a single dimensionality of the BITe, and CFA demonstrated a reasonable fit for the single-factor model (comparative fit index=0.97, Tucker-Lewis Index=0.95, normed fit index=0.97, goodness-of-fit index=0.96, root mean square error of approximation=0.12, standardized root mean residual=0.03). Finally, the convergent validity analysis revealed a significant positive correlation with depression, anxiety, and anger, except for anger control, which is a sub-variable of anger. CONCLUSION: The results showed that the Korean version of the BITe had good psychometric properties, and might serve as a valuable tool for assessing irritability in Korean patients with psychiatric disorders.

Comparison of Absolute and Relative Grip Strength to Predict Incidence of Diabetes Mellitus in Korea: A Prospective Cohort Study.

Background: This study aimed to investigate the association between absolute grip strength (AGS), relative grip strength (RGS) levels, and the risk of developing diabetes in middle-aged adults, utilizing longitudinal data. Methods: A total of 1935 participants, 51-81 years of age, were engaged in surveys both in 2017-2018 and during the subsequent follow-up survey in 2019-2020. Diabetes diagnosis and treatment were the criteria for categorizing individuals as "diabetic." Grip strength was measured using the JAMA-5030J1 equipment (SAEHAN, Korea), and AGS and RGS were also categorized into tertiles by gender. A proportional hazards Cox regression model was used to evaluate the relationship among AGS, RGS, and the risk of developing diabetes. Subsequently, we computed the hazard ratio (HR) and 95% confidence interval (95% CI) for the risk of developing diabetes. Results: After adjusting for various confounding variables, a significant reduction in diabetes risk was observed in the high grip strength group, adjusted for body mass index (RGS1) and body weight (RGS2), compared with the low RGS1 and RGS2 groups (RGS1 HR = 0.54, 95% CI = 0.36-0.80; RGS2 HR = 0.50, 95% CI = 0.34-0.73). However, there were no significant associations between AGS and diabetes risk. Furthermore, significant differences in the relationship among AGS, RGS levels, and the risk of developing diabetes were found across gender and insulin resistance levels. Conclusion: This study underscores the importance of RGS levels in predicting the incidence of diabetes, with AGS, RGS1, and RGS2 emerging as significant predictive indicators for assessing diabetes risk.

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.

How are new nurses satisfied with their jobs? From the work value perspective of Generations Y and Z nurses.

BACKGROUND: Job satisfaction has garnered significant interest across multiple disciplines as it plays a vital role in shaping human resource strategies. In the field of nursing, enhancing job satisfaction can help prevent workforce shortages. Work values and job-related characteristics are significant predictors of job satisfaction. However, the influence of factors may change as younger generations join the nursing workforce. Although research on generational commonalities and differences in work values is increasing, there is insufficient information on generational differences in the interplay between work values and job satisfaction. This study investigated the factors associated with job satisfaction of new nurses in each generational group based on a work value perspective. METHODS: A total of 280 new nurses (151 from Generation Y and 129 from Generation Z) were selected from the Graduates Occupational Mobility Survey. Multiple linear regression analyses were performed to determine the factors associated with job satisfaction in both groups. RESULTS: Most participants graduated with a diploma (61.1%), were paid less than the average salary of each group (60.4%), and conducted shift (72.9%) and overtime work (64.3%). Work values and job satisfaction levels were not significantly different between the two groups. Multiple linear regression analyses showed that career growth and task work values were associated with job satisfaction for Generation Z, while task, reputation, and environment work values were associated with job satisfaction for Generation Y. Among the job-related characteristics, nurses' job tenure was associated with job satisfaction in both groups; salary and overtime had varying relationships with job satisfaction between the two generations. CONCLUSIONS: Understanding generational differences is crucial for improving the effective management of new generational nurses. Our study findings support that different work value dimensions and job-related characteristics were associated with job satisfaction in each generation. Accordingly, it is essential to develop distinct initiatives, such as a well-structured program, to support the continued career growth of the new Generation Z nurses, thereby enhancing their job satisfaction. Furthermore, providing a conducive working environment that helps new-generation nurses overcome challenges and ensures personal lives should be considered.

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.

A Nationwide Study on HER2-low Breast Cancer in South Korea: Its Incidence of 2022 Real World Data and the Importance of Immunohistochemical Staining Protocols.

Purpose: Notable effectiveness of trastuzumab deruxtecan (T-DXd) in patients with HER2-low advanced breast cancer (BC) has focused pathologists' attention. We studied the incidence and clinicopathologic characteristics of HER2-low BC, and the effects of immunohistochemistry (IHC) associated factors on HER2 IHC results. Materials and Methods: The Breast Pathology Study Group of the Korean Society of Pathologists conducted a nationwide study using real-world data on HER2 status generated between January 2022 and December 2022. Information on HER2 IHC protocols at each participating institution was also collected. Results: Total 11,416 patients from twenty-five institutions included in this study. Of these patients, 40.7% (range: 6.0%-76.3%) were classified as HER2-zero, 41.7% (range: 10.5%-69.1%) as HER2-low, and 17.5% (range: 6.7%-34.0%) as HER2-positive. HER2-low tumors were associated with positive ER and PR statuses (p<0.001 and p<0.001, respectively). Antigen retrieval times (≥ 36 min vs. < 36 min) and antibody incubation times (≥ 12 min vs. < 12 min) affected on the frequency of HER2 IHC 1+ BC at institutions using the PATHWAY HER2 (4B5) IHC assay and BenchMark XT or Ultra staining instruments. Furthermore, discordant results between core needle biopsy (CNB) and subsequent resection specimen HER2 statuses were observed in 24.1% (787/3259) of the patients. Conclusion: The overall incidence of HER2-low BC in South Korea concurs with those reported in previously published studies. Significant inter-institutional differences in HER2 IHC protocols were observed, and it may have impact on HER2-low status. Thus, we recommend standardizing HER2 IHC conditions to ensure precise patient selection for targeted therapy.

Digital Literacy and Associated Factors in Older Adults Living in Urban South Korea: A Qualitative Study.

This study aimed to explore digital literacy among community-dwelling older adults in urban South Korea. A semistructured interview guide was developed using the Digital Competence ( 2.0 framework, which emphasizes the competencies for full digital participation in five categories: information and data literacy, communication and collaboration, content creation, safety, and problem-solving. The data were analyzed using combined inductive and deductive content analysis. Inductive analysis identified three main categories: perceived ability to use digital technology, responses to digital technology, and contextual factors. In the results of deductive analysis, participants reported varying abilities in using digital technologies for information and data literacy, communication or collaboration, and problem-solving. However, their abilities were limited in handling the safety or security of digital technology and lacked in creating digital content. Responses to digital technology contain subcategories of perception (positive or negative) and behavior (trying or avoidance). Regarding contextual factors, aging-related physical and cognitive changes were identified as barriers to digital literacy. The influence of families or peers was viewed as both a facilitator and a barrier. Our participants recognized the importance of using digital devices to keep up with the trend of digitalization, but their digital literacy was mostly limited to relatively simple levels.

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.

Comparison of Absolute and Relative Grip Strength to Predict Incidence of Metabolic Syndrome: Eight-Year Follow-Up Study in Korea.

Background: This study aimed to conduct an analysis of longitudinal study to investigate the association of absolute grip strength, and relative grip strength with incidence of metabolic syndrome. Materials and Methods: Participants who participated in the Korean Genome and Epidemiology Study, a chronic screening program conducted in Ahnseong-si, Gyeonggi-do, a primary survey conducted from 2013 to 2014 were selected. The presence of metabolic syndrome was classified using the standards of the International Diabetes Foundation following previous studies. Grip strength was measured using a JAMA 5030J1 (Saehan, Korea) and calculated the absolute grip strength and relative grip strength. To evaluate the relationship between the absolute grip strength, relative grip strength, and incidence of metabolic syndrome, independent hazard ratios (HRs) and 95% confidence intervals (CIs) for metabolic syndrome were calculated according to absolute and relative grip strength levels using a multivariate extended Cox regression model. Results: The incidence of metabolic syndrome was reduced by 38% (HR = 0.62, 95% CI = 0.43-0.88) for the high absolute grip strength group, compared to the low absolute grip strength group. Also, this study confirmed that the incidence of metabolic syndrome for mid relative grip strength and high relative grip strength groups were reduced by 27% (HR = 0.73, 95% CI = 0.55-0.98) and 55% (HR = 0.45, 95% CI = 0.32-0.64) respectively. Moreover, the incidence of metabolic syndrome was reduced by 45% (HR = 0.55, 95% CI = 0.37-0.82) and 57% (HR = 0.43, 95% CI = 0.29-0.65) for the low-level body mass index (BMI) group with high or low absolute grip strength, respectively. Finally, this study confirmed the association of sex, absolute grip strength, and relative grip strength according to age with incidence of metabolic syndrome was different. Conclusion: We observed that relative grip strength has a higher association with incidence of metabolic syndrome than absolute grip strength. Also, BMI has a higher association with metabolic syndrome than the absolute grip strength.

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.

Changes of neural coupling between cognitive and motor networks associated with dual-task performance in Parkinson's disease.

BACKGOUND: Although cognitive control is essential for efficient gait, the associations between cognitive and motor networks regarding gait in individuals with Parkinson's disease (PD) remain to be determined. Herein, we enrolled 28 PD and 28 controls to compare internetwork coupling among cognitive and motor networks and examine its relationship with single- and dual-task gait performance in PD. METHODS: The dorsal attention network (DAN), left and right frontoparietal control networks (FPNs), sensorimotor network, and lateral motor network were identified using resting-state functional magnetic resonance imaging data. The time taken to complete a 10-m walk test during cognitive or physical dual-tasks in PD was calculated representing gait performance. RESULTS: We observed that the internetwork couplings between the DAN and motor networks and between the motor networks decreased whereas those between the left FPN and DAN and motor networks increased in PD compared to controls using a permutation test. There was no significant correlation between the internetwork couplings and single- and dual-task gait performance in PD. Nevertheless, improved cognitive dual-task performance showed a positive correlation with the DAN and left FPN coupling and a negative correlation with the DAN and lateral motor network coupling in a good performance group. The opposite relationship was observed in the poor cognitive dual-task performance group. CONCLUSION: Our findings suggest a neural mechanism of cognitive control on gait to compensate for reduced goal-directed attention in PD who maintain cognitive dual-task performance.

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.

Data-mining analysis of media frame effects on social perception of schizophrenia renaming in Korea.

BACKGROUND: In 2011, Korean Neuropsychiatric Association renamed schizophrenia from 'mind split disorder' ('Jungshinbunyeolbyung' in Korean) to 'attunement disorder' ('Johyeonbyung' in Korean), in a strategic way to reduce social stigma toward people with schizophrenia. However, there remains an elusive consensus that how the renaming effort has contributed to changes in the social perception of schizophrenia in Korea. METHODS: With this regard, we explored whether media frames alter the social perception, in ways of respecting or disrespecting schizophrenia patients before and after the renaming. This study extensively investigated media keywords related to schizophrenia across the time by applying both language and epidemiologic analyses. RESULTS: In results, the media keywords have been negatively described for schizophrenia patients both before and after the renaming. Further, from an analysis using the regression model, a significant correlation was observed between the frequency of negative keywords and the hospitalization frequency of schizophrenia patients. CONCLUSIONS: These findings suggest that the social perception of schizophrenia has been scarcely changed, but rather remained negatively biased against schizophrenia patients, in spite of the renaming effort. Notably, the biased media frames have been demonstrated to negatively impact on the social perception, and even on the medical use patterns of general schizophrenia patients. In conclusion, we suggest that the unbiased media frames along with the renaming effort may collectively help reduce the negative social perception of schizophrenia. TRIAL REGISTRATION: This study was approved from the Institute of Review Board (IRB) of the Yoing-In Mental Hospital (IRB No. YIMH-IRB-2019-02).

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.

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).

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.

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.

Merkel Cell Carcinoma of the Trunk: Two Case Reports and Imaging Review.

Merkel cell carcinoma (MCC) is a rare malignant cutaneous tumor primarily located in the head and neck. We report the imaging features of pathologically confirmed MCC in the trunk. On US, MCC showed heterogeneous echogenicity with perpendicular hypoechoic linear bands that resembled "columns of smoke" in the skin and subcutaneous layers as well as prominent vascularity. On MRI, the tumor showed hypointensity on T1-weighted images and hyperintensity on proton density and T2-weighted images with linear low-signal bands in the skin and subcutaneous layers as well as intense enhancement on T1-enhanced images. Although MCC has nonspecific imaging features, these characteristics may be helpful for the early diagnosis of this disease.

Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.

BACKGROUND: This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. METHODS: We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values. RESULTS: The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631). CONCLUSIONS: We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.