Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance.

Background: Data-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules. Objective: To evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) for inexperienced readers to diagnose thyroid nodules. Methods: Between February and August 2023, a total of 26 readers (less than 1 year of experience in thyroid US from various departments) from 6 hospitals participated in this study. Readers completed an online learning session comprising 3,000 thyroid nodules annotated as benign or malignant independently. They were asked to assess a test set consisting of 120 thyroid nodules with known surgical pathology before and after a learning session. Then, they referred to AI-CAD and made their final decisions on the thyroid nodules. Diagnostic performances before and after self-training and with AI-CAD assistance were evaluated and compared between radiology residents and readers from different specialties. Results: AUC (area under the receiver operating characteristic curve) improved after the self-learning session, and it improved further after radiologists referred to AI-CAD (0.679 vs 0.713 vs 0.758, p<0.05). Although the 18 radiology residents showed improved AUC (0.7 to 0.743, p=0.016) and accuracy (69.9% to 74.2%, p=0.013) after self-learning, the readers from other departments did not. With AI-CAD assistance, sensitivity (radiology 70.3% to 74.9%, others 67.9% to 82.3%, all p<0.05) and accuracy (radiology 74.2% to 77.1%, others 64.4% to 72.8%, all p <0.05) improved in all readers. Conclusion: While AI-CAD assistance helps improve the diagnostic performance of all inexperienced readers for thyroid nodules, self-learning was only effective for radiology residents with more background knowledge of ultrasonography. Clinical Impact: Online self-learning, along with AI-CAD assistance, can effectively enhance the diagnostic performance of radiology residents in thyroid cancer.

Mineral trioxide aggregate in membrane form as a barrier membrane in guided bone regeneration.

Background/purpose: In the field of conservative dentistry and endodontics, mineral trioxide aggregate (MTA), commonly used, possesses advantages such as biocompatibility, antimicrobial properties and osteogenic potential. This study investigated the feasibility of utilizing membrane form mineral trioxide aggregate (MTA) as a barrier membrane in guided bone regeneration (GBR) procedures. Materials and methods: Membranes were electrospun from three different formulations: 15 w/v% Polycaprolactone (PCL), 13 w/v% PCL + 2 w/v% MTA (2MTA), and 11 w/v% PCL + 4 w/v% MTA (4MTA). Physicochemical and mechanical properties of the electrospun membrane were compared, encompassing parameters such as surface morphology, fiber diameter distribution, chemical composition, phase identification, tensile stress, pH variation, and water contact angle. Moreover, the antimicrobial properties against of the electrospun membranes were assessed through direct exposure to streptococcus aureus (S. aureus) and candida albicans (C. albicans). Additionally, on the 7th day, biocompatibility and cell attachment were investigated with respect to L929 (fibroblast) and MC3T3 (pre-osteoblast) cells. Inhibition of L929 cell infiltration and the expression of osteogenic related genes including osteocalcin (OCN), alkaline phosphatase (ALP), and runt related transcription factor 2 (RUNX2) in MC3T3 cells on 7th and 14th days were also investigated. Results: PCL, 2MTA, and 4MTA exhibited no statistically differences in fiber diameter distribution and tensile stress. However, as the MTA content increased, wettability and pH also increased. Due to the elevated pH, 4MTA demonstrated the lowest viability S.aureus and C.albicans. All membranes were highly biocompatibility and promoted cell attachment, while effectively preventing L929 cell infiltration. Lastly 4MTA showed increase in OCN, ALP, and RUNX2 expression on both 7th and 14th day. Conclusion: The membrane form MTA possessed characteristics essential for a novel barrier membrane.

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.

Species identification and pyrethroid resistance genotyping of recently resurgent Cimex lectularius and Cimex hemipterus in Korea.

The global resurgence of bed bug infestations, exacerbated by increasing international travel, trade, and insecticide resistance, has significantly impacted Korea. This study identified the bed bug species and performed pyrethroid resistance genotyping of recently resurgent bed bugs in Korea. Thirty-one regional bed bug samples were collected from 5 administrative regions: Gyeonggi-do (n=14), Seoul (n=13), Busan (n=2), Jeonllanam-do (n=1), and Chungcheongbuk-do (n=1). The samples underwent morphological and molecular identification. Twenty-four regional samples (77.4%) were identified as the tropical bed bug, Cimex hemipterus, and the remaining 7 regional samples (22.6%) were identified as the common bed bug, Cimex lectularius. The C. hemipterus regional samples carried at least three mutations associated with knockdown resistance (kdr), including 2 super-kdr mutations. The 7 C. lectularius regional samples possessed at least one of the 3 kdr-related mutations associated with pyrethroid resistance. This study confirms that the prevalent bed bug species recently in Korea is C. hemipterus, replacing the previously endemic C. lectularius. Additionally, the rise in bed bug populations with pyrethroid resistance underscores the necessity of introducing alternative insecticides.

Clinical Outcomes of Clipping and Coil Embolization for Ruptured Intracranial Aneurysms Categorized by Region and Hospital Size: A Nationwide Cohort Study in Korea.

BACKGROUND: To analyze the outcomes of clipping and coiling for ruptured intracranial aneurysms (RIAs) based on data from the National Health Insurance Service in South Korea, with a focus on variations according to region and hospital size. METHODS: This study analyzed the one-year mortality rates for patients with RIAs who underwent clipping or coiling in 2018. Coiling was further categorized into non-stent assisted coiling (NSAC) and stent assisted coiling (SAC). Hospitals were classified as tertiary referral general hospitals (TRGHs), general hospitals (GHs), or semi-general hospitals (sGHs) based on size. South Korea's administrative districts were divided into 15 regions for analysis. RESULTS: In 2018, there were 2,194 (33.1%) clipping procedures (TRGH, 985; GH, 827; sGH, 382) and 4,431 (66.9%) coiling procedures (TRGH, 1,642; GH, 2076; sGH, 713) performed for RIAs treatment. Among hospitals performing more than 20 treatments, the one-year mortality rates following clipping or coiling were 11.2% and 16.0%, respectively, with no significant difference observed. However, there was a significant difference in one-year mortality between NSAC and SAC (14.3% vs. 19.5%, P = 0.034), with clipping also showing significantly lower mortality compared to SAC (P = 0.019). No significant differences in other treatment modalities were observed according to hospital size, but clipping at TRGHs had significantly lower mortality than at GHs (P = 0.042). While no significant correlation was found between the number of treatments and outcomes at GHs, at TRGHs, a higher volume of clipping procedures was significantly associated with lower total mortality (P = 0.023) and mortality after clipping (P = 0.022). CONCLUSION: Using Korea NHIS data, mortality rates for RIAs showed no significant variation by hospital size due to coiling's prevalence. However, differences in clipping outcomes by hospital size and volume in TRGH highlight the need for national efforts to improve clipping skills and standardization. Additionally, the higher mortality rate with SAC emphasizes the importance of precise indications for its application.

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort.

This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significant features that influenced the model. A multicenter, patient-based registry cohort study in South Korea between January 1, 2019, and December 31, 2020. Delirium was identified by reviewing the medical records based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The study dataset included 165 patients with delirium among 2314 patients with advanced cancer admitted to the acute palliative care unit. Seven machine learning models, including extreme gradient boosting, adaptive boosting, gradient boosting, light gradient boosting, logistic regression, support vector machine, and random forest, were evaluated to predict delirium in patients with advanced cancer admitted to the acute palliative care unit. An ensemble approach was adopted to determine the optimal model. For k-fold cross-validation, the combination of extreme gradient boosting and random forest provided the best performance, achieving the following accuracy metrics: 68.83% sensitivity, 70.85% specificity, 69.84% balanced accuracy, and 74.55% area under the receiver operating characteristic curve. The performance of the isolated testing dataset was also validated, and the machine learning model was successfully deployed on a public website ( http://ai-wm.khu.ac.kr/Delirium/ ) to provide public access to delirium prediction results in patients with advanced cancer. Furthermore, using feature importance analysis, sex was determined to be the top contributor in predicting delirium, followed by a history of delirium, chemotherapy, smoking status, alcohol consumption, and living with family. Based on a large-scale, multicenter, patient-based registry cohort, a machine learning prediction model for delirium in patients with advanced cancer was developed in South Korea. We believe that this model will assist healthcare providers in treating patients with delirium and advanced cancer.

Testing the Reliability and Validity of the Korean Version of the Pittsburgh Sleep Quality Index Using Fitbit Devices: A Cross-Sectional Analysis.

Background: Sleep disorders and insomnia are prevalent worldwide, with negative health outcomes. The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-report assessment tool for evaluating sleep quality, comprising seven subdomains. The Korean version of the PSQI (PSQI-K) has been tested for reliability and validity in small sample sizes but lacks large-scale validation using objective measures. Methods: This study was conducted with 268 Korean adults attending health check programs. Participants completed the PSQI-K questionnaire and wore Fitbit devices (Fitbit Inc., USA) to ascertain sleep parameters. Reliability was analyzed using the Cronbach's α coefficient, and construct validity was determined through factor analysis. Criteria validity was assessed by correlating their index scores with Fitbit sleep parameters. We identified the optimal cutoff for detecting sleep disorders. Results: The Cronbach's α coefficient was 0.61, indicating adequate internal consistency. Factor analysis revealed three factors, explaining 48.2% of sleep quality variance. The index scores were negatively correlated with Fitbit sleep efficiency, total sleep time, and number of awakenings (P<0.05). The optimal cutoff point for identifying sleep disorder groups was ≥6. Conclusion: The PSQI-K demonstrated good reliability and validity when correlated with Fitbit sleep parameters, offering a practical screening tool for identifying sleep disorders among Korean adults. Cutoff scores can help identify patients for sleep interventions. However, further large-scale studies are required to validate these findings.

Prediction of postoperative cerebral infarction after combined bypass surgery in adult moyamoya disease: combining quantitative parameters on RAPID perfusion CT with clinically related factors.

OBJECTIVE: The aim of this study was to identify predictive factors of postoperative cerebral infarction (PostCI) following combined bypass (CB) surgery in adult patients with moyamoya disease (MMD) using quantitative parameters from the rapid processing of perfusion and diffusion (RAPID) perfusion CT (PCT) software. METHODS: The authors retrospectively reviewed 276 total hemispheres in patients with MMD who underwent CB. Preoperative volumes of time-to-maximum (Tmax) > 4 sec and > 6 sec were obtained from the RAPID analysis of PCT. These quantitative parameters, along with other clinical and angiographic factors, were statistically analyzed to determine the significant predictors for PostCI following CB. RESULTS: PostCI occurred in 17 hemispheres (6.16%). PCA involvement (p = 0.016), and the volume of Tmax > 6 sec (p < 0.001) and Tmax > 4 sec (p < 0.001), were identified as variables related to PostCI in the univariable analysis. In the multivariable analysis, the volume of Tmax > 6 sec (OR 1.013, 95% confidence interval 1.007-1.019, p < 0.001) was determined to be an independent predictive factor significantly associated with PostCI after CB in adult patients with MMD. In the receiver operating characteristic (ROC) curve, the cutoff value of the preoperative volume of Tmax > 6 sec was determined to be 59.5 ml (sensitivity 82.4%, specificity 71.9%, area under the ROC curve 0.811). CONCLUSIONS: For adult patients with MMD and a large volume of Tmax > 6 sec over 59.5 ml, more caution is required when deciding to undergo bypass surgery and in postoperative management.

The application of enhanced recovery after surgery protocol after distal gastrectomy for patients with gastric cancer: a prospective randomized clinical trial.

BACKGROUND: This study aimed to evaluate the clinical outcomes and efficacy of enhanced recovery after surgery (ERAS) protocol in patients undergoing distal gastrectomy for gastric cancer (GC). METHODS: Patients were randomly assigned to the ERAS group (EG) and the conventional care group (CG) by stratified randomization according to age and sex. The primary endpoint was adjusted postoperative hospital stay, calculated using discharge criteria developed to evaluate recovery. Nutritional data and quality of life (QoL) (European Organisation for Research and Treatment of Cancer [EORTC] C30 and STO22) during the perioperative period were also analyzed. RESULTS: We enrolled 198 eligible patients with GC for the study between June 2017 and January 2019. A total of 147 patients were finally enrolled in this study (full analysis set) and were assigned to EG (n = 71) and CG (n = 76). First flatus was faster significantly in EG (3.6 ± 1.5 vs 4.1 ± 1.2 days, P = .019). EG showed a faster start of the sips and soft diet than CG (1.3 ± 0.7 vs 3.1 ± 0.4 days, P < .001; 2.4 ± 0.9 vs 5.2 ± 0.7 days, P < .001) according to the protocol. The recorded hospital stay was not significantly different; however, adjusted hospital stay was significantly shorter in EG than in CG (6.5 ± 3.1 vs 7.8 ± 2.1 days, P = .005). There was no difference in morbidity, and no mortality occurred in both groups. EG did not show significant superiority in nutritional outcome and QoL improvement, except for pain scale in EORTC-STO22. CONCLUSION: The application of the ERAS protocol could reduce the adjusted hospital stay without an increase in postoperative complications. There was no significant difference in long-term nutritional outcome and QoL of the 2 groups.

Opioid use and subsequent delirium risk in patients with advanced cancer in palliative care: a multicenter registry study.

The prevalent use of opioids for pain management in patients with advanced cancer underscores the need for research on their neuropsychiatric impacts, particularly delirium. Therefore, we aimed to investigate the potential association between opioid use and the risk of delirium in patients with advanced cancer admitted to the acute palliative care unit. We conducted a retrospective observational study utilizing a multicenter, patient-based registry cohort by collecting the data from January 1, 2019, to December 31, 2020, in South Korea. All data regarding exposures, outcomes, and covariates were obtained through retrospective chart reviews by a team of specialized medical professionals with expertise in oncology. Full unmatched and 1:1 propensity-score matched cohorts were formed, and stratification analysis was conducted. The primary outcome, delirium, was defined and diagnosed by the DSM-IV. Of the 2,066 patients with advanced cancer, we identified 42.8% (mean [SD] age, 64.4 [13.3] years; 60.8% male) non-opioid users and 57.2% (62.8 [12.5] years; 55.9% male) opioid users, respectively. Opioid use was significantly associated with an increased occurrence of delirium in patients with advanced cancer (OR, 2.02 [95% CI 1.22-3.35]). The risk of delirium in patients with advanced cancer showed increasing trends in a dose-dependent manner. High-dose opioid users showed an increased risk of delirium in patients with advanced cancer compared to non-opioid users (low-dose user: OR, 2.21 [95% CI 1.27-3.84]; high-dose user: OR, 5.75 [95% CI 2.81-11.77]; ratio of OR, 2.60 [95% CI 1.05-6.44]). Patients with old age, male sex, absence of chemotherapy during hospitalization, and non-obese status were more susceptible to increased risk of delirium in patients with cancer. In this multicenter patient-based registry cohort study, we found a significant, dose-dependent association between opioid use and increased risk of delirium in patients with advanced cancer. We also identified specific patient groups more susceptible to delirium. These findings highlight the importance of opioid prescription in these patients with advanced cancer, balancing effective doses for pain management and adverse dose-inducing delirium.

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.

Feasibility and usefulness of cognitive monitoring using a new home-based cognitive test in mild cognitive impairment: a prospective single arm study.

BACKGROUND: The risk of dementia is increased in subjects with mild cognitive impairment (MCI). Despite the plethora of in-person cognitive tests, those that can be administered over the phone are lacking. We hypothesized that a home-based cognitive test (HCT) using phone calls would be feasible and useful in non-demented elderly. We aimed to assess feasibility and validity of a new HCT as an optional cognitive monitoring tool without visiting hospitals. METHODS: Our study was conducted in a prospective design during 24 weeks. We developed a new HCT consisting of 20 questions (score range 0-30). Participants with MCI (n = 38) were consecutively enrolled and underwent regular HCTs during 24 weeks. Associations between HCT scores and in-person cognitive scores and Alzheimer's disease (AD) biomarkers were evaluated. In addition, HCT scores in MCI participants were cross-sectionally compared with age-matched cognitively normal (n = 30) and mild AD dementia (n = 17) participants for discriminative ability of the HCT. RESULTS: HCT had good intra-class reliability (test-retest Cronbach's alpha 0.839). HCT scores were correlated with the Mini-Mental State Examination (MMSE), verbal memory delayed recall, and Stroop test scores but not associated with AD biomarkers. HCT scores significantly differed among cognitively normal, MCI, and mild dementia participants, indicating its discriminative ability. Finally, 32 MCI participants completed follow-up evaluations, and 8 progressed to dementia. Baseline HCT scores in dementia progressors were lower than those in non-progressors (p = 0.001). CONCLUSION: The feasibility and usefulness of the HCT were demonstrated in elderly subjects with MCI. HCT could be an alternative option to monitor cognitive decline in early stages without dementia.

Optimal blood pressure for stroke prevention in healthy adults below 65 years: A nationwide 10-year observational study.

BACKGROUND: There have been few reports on the preventative value of intensive blood pressure (BP) management for stroke since the American College of Cardiology/American Heart Association (ACC/AHA) announced the new criteria for hypertension (HTN) in 2017. This study aimed to identify optimal BP for prevention of stroke in healthy adults under 65 years. METHODS: We conducted a 10-year observational study on the risk of stroke, subclassified as hemorrhagic stroke (HS) or ischemic stroke (IS), according to BP categories (low-normal BP, high-normal BP, elevated BP, stage 1 HTN, and stage 2 HTN) using the National Health Insurance Service Database. RESULTS: In 2008, a health checkup was conducted for a total of 8,327,751 participants, and 949,551 of them enrolled in this study. The risk of HS increased from stage 2 HTN (adjusted hazard ratio [AHR], 3.036 [95 % confidence interval [CI], 2.159-4.252]) in men to stage 1 HTN (AHR, 2.972; 95 % CI, 2.256-3.897) in women. The risk of IS increased in both men (AHR, 1.404 [95 % CI, 1.164-1.693]) and women (AHR, 2.012 [95 % CI, 1.603-2.526]) with stage 1 HTN. The overall risk of stroke increased in men (AHR, 1.386; [95 % CI, 1.180-1.629]) and women (AHR, 2.363 [95 % CI, 1.973-2.830]) with stage 1 HTN. CONCLUSION: This study underscores the importance of maintaining BP below Stage 1 HTN to prevent stroke in healthy adults aged below 65 years. These findings highlight the significance of the new HTN guidelines established by the ACC/AHA in 2017.

Relationship between in-stent restenosis following carotid artery stenting and platelet reactivity to clopidogrel.

OBJECTIVE: To analyze the relationship between in-stent restenosis (ISR) following carotid artery stenting (CAS) and platelet clopidogrel reactivity confirmed by the P2Y12 reaction unit (PRU) and inhibition rate (IR). METHODS: We retrospectively analyzed 171 patients who underwent CAS with extracranial carotid stenosis from January 2016 to December 2019. Dual antiplatelet therapy with 100 mg aspirin and 75 mg clopidogrel was started ≥5 days before CAS. Clopidogrel resistance was measured with the PRU and IR the day before CAS. The ISR degree was classified into R1, R2, and R3 (moderate to severe luminal stenosis of ≥50% or occlusion) by carotid CT angiography after 24-30 months. The degree of quantitative association between platelet reactivity and ISR R3 was determined by the receiver operating characteristic curve method. The optimal cut-off values of PRU and IR were derived using the maximum Youden index. RESULTS: There were 33 R3 degrees of ISR (19.3%) and nine ipsilateral ischemic strokes (5.3%). The PRU and IR were different between R1+R2 degrees (176.4±50.1, 27.5±18.7%) and R3 degree (247.5±55.0, 10.3±13.4%) (P<0.001). The areas under the curves of PRU and IR were 0.841 and 0.781, and the optimal cut-off values were 220.0 and 14.5%, respectively. Multivariate logistic regression analysis showed that PRU ≥220 and IR ≤14.5% were significant predictive factors for ISR R3 (P<0.001 and P=0.017, respectively). ISR R3 was independently associated with ipsilateral ischemic stroke after CAS (P=0.012). CONCLUSIONS: High PRU (≥220) and low IR (≤14.5%) are related to ISR R3 following CAS, which may cause ipsilateral ischemic stroke.

Performance of ChatGPT on the National Korean Occupational Therapy Licensing Examination.

Background: ChatGPT is an artificial intelligence-based large language model (LLM). ChatGPT has been widely applied in medicine, but its application in occupational therapy has been lacking. Objective: This study examined the accuracy of ChatGPT on the National Korean Occupational Therapy Licensing Examination (NKOTLE) and investigated its potential for application in the field of occupational therapy. Methods: ChatGPT 3.5 was used during the five years of the NKOTLE with Korean prompts. Multiple choice questions were entered manually by three dependent encoders, and scored according to the number of correct answers. Results: During the most recent five years, ChatGPT did not achieve a passing score of 60% accuracy and exhibited interrater agreement of 0.6 or higher. Conclusion: ChatGPT could not pass the NKOTLE but demonstrated a high level of agreement between raters. Even though the potential of ChatGPT to pass the NKOTLE is currently inadequate, it performed very close to the passing level even with only Korean prompts.

Comparison of the Usefulness of Computer-Assisted Three-Dimensional Analysis and Weight-Bearing Radiographs in Ankle Osteoarthritis.

Background: To evaluate the degree of deformation in patients with ankle osteoarthritis (OA), it is essential to measure the three-dimensional (3D), in other words, stereoscopic alignment of the ankle, subtalar, and foot arches. Generally, measurement of radiological parameters use two-dimensional (2D) anteroposterior and lateral radiographs in a weight-bearing state; however, computer-aided 3D analysis (Disior) using weight-bearing cone-beam computed tomography (CBCT) has recently been introduced. Methods: In this study, we compared the 2D human radiographic method with a stereoscopic image in patients with ankle arthritis. We enrolled 57 patients diagnosed with OA (28 left and 29 right) and obtained both standing radiographs and weight-bearing CBCT. Patients were divided by the Takakura stage. The interclass correlation coefficient (ICC) for each result was confirmed. Results: On the ICC between 2D radiographs and 3D analysis, the tibiotalar surface angle and lateral talo-1st metatarsal angle showed a good ICC grade (> 0.6), while other parameters did not have significant ICC results. Three-dimension was superior to radiographs in terms of statistical significance. Conclusions: We demonstrated that 2D and stereoscopic images are useful for the diagnosis of OA. Our study also confirmed that the radiographic features affected by ankle OA varied. However, according to the results, the typical radiography is not sufficient to diagnose and determine a treatment plan for ankle OA. Therefore, the method of using 3D images should be considered.

Development of a Prediction Model for Delirium in Hospitalized Patients with Advanced Cancer.

Purpose: Delirium is a common neurocognitive disorder in patients with advanced cancer and is associated with poor clinical outcomes. As a potentially reversible phenomenon, early recognition of delirium by identifying the risk factors demands attention. To develop a model to predict the occurrence of delirium in hospitalized patients with advanced cancer. Materials and Methods: This retrospective study included patients with advanced cancer admitted to the oncology ward of four tertiary cancer centers in Korea for supportive cares and excluded those discharged due to death. The primary endpoint was occurrence of delirium. Sociodemographic characteristics, clinical characteristics, laboratory findings, and concomitant medication were investigated for associating variables. The predictive model developed using multivariate logistic regression was internally validated by bootstrapping. Results: From January 2019 to December 2020, 2,152 patients were enrolled. The median age of patients was 64 years, and 58.4% were male. A total of 127 patients (5.9%) developed delirium during hospitalization. In multivariate logistic regression, age, body mass index, hearing impairment, previous delirium history, length of hospitalization, chemotherapy during hospitalization, blood urea nitrogen and calcium levels, and concomitant anti-depressant use were significantly associated with the occurrence of delirium. The predictive model combining all four categorized variables showed the best performance among the developed models (area under the curve 0.831, sensitivity 80.3%, and specificity 72.0%). The calibration plot showed optimal agreement between predicted and actual probabilities through internal validation of the final model. Conclusion: We proposed a successful predictive model for the risk of delirium in hospitalized patients with advanced cancer.

Enhancing transcriptome analysis in medicinal plants: multiple unigene sets in Astragalus membranaceus.

Astragalus membranaceus is a medicinal plant mainly used in East Asia and contains abundant secondary metabolites. Despite the importance of this plant, the available genomic and genetic information is still limited. De novo transcriptome construction is recognized as an essential method for transcriptome research when reference genome information is incomplete. In this study, we constructed three individual transcriptome sets (unigene sets) for detailed analysis of the phenylpropanoid biosynthesis pathway, a major metabolite of A. membranaceus. Set-1 was a circular consensus sequence (CCS) generated using PacBio sequencing (PacBio-seq). Set-2 consisted of hybridized assembled unigenes with Illumina sequencing (Illumina-seq) reads and PacBio CCS using rnaSPAdes. Set-3 unigenes were assembled from Illumina-seq reads using the Trinity software. Construction of multiple unigene sets provides several advantages for transcriptome analysis. First, it provides an appropriate expression filtering threshold for assembly-based unigenes: a threshold transcripts per million (TPM) ≥ 5 removed more than 88% of assembly-based unigenes, which were mostly short and low-expressing unigenes. Second, assembly-based unigenes compensated for the incomplete length of PacBio CCSs: the ends of the 5`/3` untranslated regions of phenylpropanoid-related unigenes derived from set-1 were incomplete, which suggests that PacBio CCSs are unlikely to be full-length transcripts. Third, more isoform unigenes could be obtained from multiple unigene sets; isoform unigenes missing in Set-1 were detected in set-2 and set-3. Finally, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that phenylpropanoid biosynthesis and carbohydrate metabolism were highly activated in A. membranaceus roots. Various sequencing technologies and assemblers have been developed for de novo transcriptome analysis. However, no technique is perfect for de novo transcriptome analysis, suggesting the need to construct multiple unigene sets. This method enables efficient transcript filtering and detection of longer and more diverse transcripts.

Efficacy and safety of enavogliflozin vs. dapagliflozin as add-on therapy in patients with type 2 diabetes mellitus based on renal function: a pooled analysis of two randomized controlled trials.

BACKGROUND: We assessed the efficacy and safety of enavogliflozin (0.3 mg), a newly developed SGLT-2 inhibitor, in patients with type 2 diabetes mellitus based on kidney function via pooled analysis of two 24-week, randomized, double-blind phase III trials. METHODS: Data from 470 patients were included (enavogliflozin: 0.3 mg/day, n = 235; dapagliflozin: 10 mg/day, n = 235). The subjects were classified by mildly reduced (60 ≤ eGFR < 90 mL/min/1.73 m², n = 247) or normal eGFR (≥ 90 mL/min/1.73 m², n = 223). RESULTS: In the mildly reduced eGFR group, enavogliflozin significantly reduced the adjusted mean change of HbA1c and fasting plasma glucose levels at week 24 compared to dapagliflozin (- 0.94% vs. -0.77%, P = 0.0196). Enavogliflozin exhibited a more pronounced glucose-lowering effect by HbA1c when combined with dipeptidyl peptidase-4 inhibitors than that observed in their absence. Enavogliflozin showed potent blood glucose-lowering effects regardless of renal function. Conversely, dapagliflozin showed a significant decrease in the glucose-lowering efficacy as the renal function decreased. Enavogliflozin showed a higher urinary glucose excretion rate in both groups. The homeostatic model assessment showed that enavogliflozin markedly decreased the insulin resistance. The blood pressure, weight loss, or homeostasis model assessment of beta-cell function values did not differ significantly between enavogliflozin and dapagliflozin. Adverse events were similar between both drugs. CONCLUSIONS: The glucose-lowering efficacy of enavogliflozin is superior to that of dapagliflozin in patients with type 2 diabetes mellitus with mild renal function impairment; this is attributed to its potent urinary glucose excretion-promoting ability. The emergence of new and potent SGLT-2 inhibitors is considered an attractive option for patients with inadequate glycemic control and decreased renal function. TRIAL REGISTRATION: Not applicable (pooled analysis).