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BACKGROUND: Few studies have focused on the daily hassles among adolescent siblings living with individuals with intellectual disability (IwID) and the resulting conflicts between family members. The pathway from stress (hassles) to adolescents' violent acts has been largely ignored. OBJECTIVE: This study examined the link between hassles (independent variable) and violence against parents (dependent variable), with aggression as a mediator and social support as a moderator. PARTICIPANTS: Data were collected from November 2018 to February 2019 in Seoul, Gyeonggi Province, and Incheon City. Participants were 303 adolescent siblings of IwID (male: 47.2 %; female: 52.1 %). METHODS: Descriptive analysis was conducted using SPSS 24.0 and mediating pathways were determined using SPSS Macro 25.0. To verify the moderating role of social support, the entire sample was divided into two groups based on the mean value of social support and the mediating model was analyzed. RESULTS: In the low social support group (n = 140), the direct effect was not significant (ß = 0.0111, 95 % CI = -0.0044 to 0.0266), but the indirect effect was maintained (ß = 0.0096, 95 % CI = 0.0005 to 0.0234). However, in the high support group (n = 118), neither the direct effect (ß = 0.0106, 95 % CI = -0.0162 to 0.0373) nor the indirect effect (ß = 0.0126, 95 % CI = -0.0013 to 0.0301) was significant. Social support as a moderator was statistically significant compared with different paths in the two-group analysis. CONCLUSIONS: Counselling interventions are needed to alleviate the link between hassles and adolescent violence against parents through aggression.
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Deficiência Intelectual , Irmãos , Apoio Social , Humanos , Masculino , Feminino , Adolescente , República da Coreia , Deficiência Intelectual/psicologia , Irmãos/psicologia , Pais/psicologia , Agressão/psicologia , Estresse Psicológico/psicologia , Violência Doméstica/psicologia , Violência Doméstica/estatística & dados numéricos , Relações Pais-FilhoRESUMO
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.
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Inteligência Artificial , Neoplasias da Mama , Mamografia , Humanos , Neoplasias da Mama/diagnóstico por imagem , Feminino , Mamografia/métodos , Pessoa de Meia-Idade , Estudos Retrospectivos , Idoso , Adulto , Diagnóstico por Computador/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodosRESUMO
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.
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Impedância Elétrica , Sobrepeso , Relação Cintura-Quadril , Humanos , Masculino , Feminino , Criança , Estudos Transversais , Adolescente , Estudos Retrospectivos , Sobrepeso/complicações , Obesidade Infantil/complicações , Síndrome Metabólica/complicações , Fígado Gorduroso/complicações , Composição Corporal , Índice de Massa Corporal , PrognósticoRESUMO
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.
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Inteligência Artificial , Pneumonia , Radiografia Torácica , Humanos , Masculino , Feminino , Prognóstico , Idoso , Pneumonia/diagnóstico por imagem , Pneumonia/mortalidade , Pessoa de Meia-Idade , Radiografia Torácica/métodos , Índice de Gravidade de Doença , Idoso de 80 Anos ou mais , Estudos RetrospectivosRESUMO
Since 2011, South Korea has implemented biannual vaccinations against foot-and-mouth disease (FMD) and recently, lumpy skin disease (LSD), to mitigate the spread of transboundary animal diseases. However, due to past adverse reactions, potentially linked to acute phase responses from FMD vaccinations, there is hesitancy among Korean livestock farmers regarding new strategies for simultaneous vaccinations against both FMD and LSD. This study was conducted to assess possible adverse reactions to the LSD vaccination by analyzing acute phase proteins (APPs) in three groups: cows vaccinated against FMD (G1-FMDV), LSD (G2-LSDV), and both (G3-FMDV/LSDV). In G1-FMDV, APP levels peaked on day 3 post-vaccination (p < 0.001) and returned to baseline. In G2-LSDV, APP levels increased gradually, peaking on day 10 post-vaccination. In G3-FMDV/LSDV, APP levels peaked on day 3 post-vaccination and remained high until day 10 (p < 0.001). These results indicate that LSD vaccines trigger a later immune response compared to FMD vaccines, possibly due to different adjuvants. Therefore, a longer follow-up period for monitoring adverse reactions to LSD vaccinations may be required to understand and mitigate potential risks.
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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.
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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.
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Diabetes Mellitus , Força da Mão , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , República da Coreia/epidemiologia , Idoso , Incidência , Estudos Prospectivos , Diabetes Mellitus/epidemiologia , Diabetes Mellitus/diagnóstico , Fatores de Risco , Idoso de 80 Anos ou mais , Estudos LongitudinaisRESUMO
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.
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Inteligência Artificial , Neoplasias da Mama , Mamografia , Sensibilidade e Especificidade , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Pessoa de Meia-Idade , Mamografia/métodos , Estudos Retrospectivos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , República da Coreia/epidemiologia , Aprendizado Profundo , Adulto , Fatores de Tempo , Algoritmos , Estados Unidos , Reprodutibilidade dos TestesRESUMO
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.
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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.
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Neoplasias da Mama , Mamografia , Feminino , Humanos , Adulto , Pessoa de Meia-Idade , Mamografia/métodos , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Valor Preditivo dos Testes , Estudos Retrospectivos , Inteligência Artificial , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Detecção Precoce de Câncer , ComputadoresRESUMO
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.
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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.
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Comunicação , Alfabetização , Humanos , Idoso , Pesquisa Qualitativa , Envelhecimento , República da CoreiaRESUMO
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.
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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.
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Síndrome Metabólica , Humanos , Seguimentos , Síndrome Metabólica/diagnóstico , Síndrome Metabólica/epidemiologia , Incidência , Estudos Longitudinais , Força da Mão , Redução de Peso , República da Coreia/epidemiologiaRESUMO
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.
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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.
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Imageamento por Ressonância Magnética , Doença de Parkinson , Humanos , Doença de Parkinson/fisiopatologia , Doença de Parkinson/diagnóstico por imagem , Doença de Parkinson/complicações , Masculino , Feminino , Idoso , Pessoa de Meia-Idade , Rede Nervosa/fisiopatologia , Rede Nervosa/diagnóstico por imagem , Desempenho Psicomotor/fisiologia , Cognição/fisiologia , Marcha/fisiologia , Vias Neurais/fisiopatologia , Vias Neurais/diagnóstico por imagem , Atenção/fisiologiaRESUMO
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.
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Neoplasias da Mama , Feminino , Humanos , Neoplasias da Mama/diagnóstico por imagem , Mamografia/métodos , Densidade da Mama , Mama/diagnóstico por imagem , Ultrassonografia Mamária/métodos , Redes Neurais de Computação , Detecção Precoce de Câncer/métodosRESUMO
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.
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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.