Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: To investigate the outcomes of brolucizumab reinjection after intraocular inflammation (IOI) development. Methods: This retrospective study analyzed patients with brolucizumab injections from April 2021 to January 2024. Patients who developed IOI after brolucizumab were included and categorized into subgroups depending on reinjection, discontinuation, and further IOI development. Results: A total of 472 eyes of 432 patients received brolucizumab injections. Thirty-eight cases developed IOI at least once, and 25 continued brolucizumab. Sixteen cases had no more IOI events, and nine experienced a second or more IOI events. Among the nine cases, three maintained brolucizumab injections despite IOI recurrence. The incidence of IOI was 8.1% based on the number of eyes (38 of 472 eyes) and 2.0% based on the number of brolucizumab injections (50 of 2,468 injections). The incidence of occlusive retinal vasculitis was 0.2% (1 of 472 eyes). The recurrence rate was 23.7% (9 of 38 eyes). The average number of injections between the first brolucizumab injection and the injection date on which IOI first developed was 2.15 times in the no-reinjection group, 3.44 times in the no-IOI-recurrence group, and 2.0 times in the second-IOI-episode group. Time to IOI occurrence in cases with first IOI episode was 18.60 ± 16.73 days, with 15 cases developing IOI within 1 week. Conclusions This study elucidates the real-world incidence of brolucizumab associated IOIs, with a description of information related to reinjections after the IOI episodes. A comprehensive understanding of brolucizumab reinjection is essential for its optimal utilization.

Purpose: To investigate the outcomes of brolucizumab reinjection after intraocular inflammation (IOI) development. Methods: This retrospective study analyzed patients with brolucizumab injections from April 2021 to January 2024. Patients who developed IOI after brolucizumab were included and categorized into subgroups depending on reinjection, discontinuation, and further IOI development. Results: A total of 472 eyes of 432 patients received brolucizumab injections. Thirty-eight cases developed IOI at least once, and 25 continued brolucizumab. Sixteen cases had no more IOI events, and nine experienced a second or more IOI events. Among the nine cases, three maintained brolucizumab injections despite IOI recurrence. The incidence of IOI was 8.1% based on the number of eyes (38 of 472 eyes) and 2.0% based on the number of brolucizumab injections (50 of 2,468 injections). The incidence of occlusive retinal vasculitis was 0.2% (1 of 472 eyes). The recurrence rate was 23.7% (9 of 38 eyes). The average number of injections between the first brolucizumab injection and the injection date on which IOI first developed was 2.15 times in the no-reinjection group, 3.44 times in the no-IOI-recurrence group, and 2.0 times in the second-IOI-episode group. Time to IOI occurrence in cases with first IOI episode was 18.60 ± 16.73 days, with 15 cases developing IOI within 1 week. Conclusions This study elucidates the real-world incidence of brolucizumab associated IOIs, with a description of information related to reinjections after the IOI episodes. A comprehensive understanding of brolucizumab reinjection is essential for its optimal utilization.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Microplastics (MP) are pervasive environmental pollutants with potential adverse effects on human health, particularly concerning neurotoxicity. This study investigates the accumulation and neurotoxic effects of MP in cerebral organoids and mouse brains. Utilizing in vitro cerebral organoids and in vivo mouse models, we examined the penetration of MP, revealing that smaller MP (50 nm) infiltrated deeper into the organoids compared to larger ones (100 nm). Exposure to 50 nm MP resulted in a significant reduction in organoid viability. Furthermore, total RNA sequencing indicated substantial alterations in neurotoxicity-related gene expression.In vivo, MP-treated mice exhibited notable DNA fragmentation in the hippocampus and cortex, alongside elevated levels of inflammatory markers and neurotoxic metabolites, such as kynurenine (KYN) and 3-hydroxykynurenine (3-HK). Our findings suggest that MP may promote neurotoxicity through the kynurenine pathway, leading to heightened levels of neurotoxic compounds like quinolinic acid. This research highlights the potential for MP to induce neuroinflammatory responses and disrupt normal brain function, underscoring the need for further investigation into the long-term effects of MP exposure on neurological health.

Purpose: To investigate the outcomes of brolucizumab reinjection after intraocular inflammation (IOI) development. Methods: This retrospective study analyzed patients with brolucizumab injections from April 2021 to January 2024. Patients who developed IOI after brolucizumab were included and categorized into subgroups depending on reinjection, discontinuation, and further IOI development. Results: A total of 472 eyes of 432 patients received brolucizumab injections. Thirty-eight cases developed IOI at least once, and 25 continued brolucizumab. Sixteen cases had no more IOI events, and nine experienced a second or more IOI events. Among the nine cases, three maintained brolucizumab injections despite IOI recurrence. The incidence of IOI was 8.1% based on the number of eyes (38 of 472 eyes) and 2.0% based on the number of brolucizumab injections (50 of 2,468 injections). The incidence of occlusive retinal vasculitis was 0.2% (1 of 472 eyes). The recurrence rate was 23.7% (9 of 38 eyes). The average number of injections between the first brolucizumab injection and the injection date on which IOI first developed was 2.15 times in the no-reinjection group, 3.44 times in the no-IOI-recurrence group, and 2.0 times in the second-IOI-episode group. Time to IOI occurrence in cases with first IOI episode was 18.60 ± 16.73 days, with 15 cases developing IOI within 1 week. Conclusions This study elucidates the real-world incidence of brolucizumab associated IOIs, with a description of information related to reinjections after the IOI episodes. A comprehensive understanding of brolucizumab reinjection is essential for its optimal utilization.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Microplastics (MP) are pervasive environmental pollutants with potential adverse effects on human health, particularly concerning neurotoxicity. This study investigates the accumulation and neurotoxic effects of MP in cerebral organoids and mouse brains. Utilizing in vitro cerebral organoids and in vivo mouse models, we examined the penetration of MP, revealing that smaller MP (50 nm) infiltrated deeper into the organoids compared to larger ones (100 nm). Exposure to 50 nm MP resulted in a significant reduction in organoid viability. Furthermore, total RNA sequencing indicated substantial alterations in neurotoxicity-related gene expression.In vivo, MP-treated mice exhibited notable DNA fragmentation in the hippocampus and cortex, alongside elevated levels of inflammatory markers and neurotoxic metabolites, such as kynurenine (KYN) and 3-hydroxykynurenine (3-HK). Our findings suggest that MP may promote neurotoxicity through the kynurenine pathway, leading to heightened levels of neurotoxic compounds like quinolinic acid. This research highlights the potential for MP to induce neuroinflammatory responses and disrupt normal brain function, underscoring the need for further investigation into the long-term effects of MP exposure on neurological health.

Purpose: To investigate the outcomes of brolucizumab reinjection after intraocular inflammation (IOI) development. Methods: This retrospective study analyzed patients with brolucizumab injections from April 2021 to January 2024. Patients who developed IOI after brolucizumab were included and categorized into subgroups depending on reinjection, discontinuation, and further IOI development. Results: A total of 472 eyes of 432 patients received brolucizumab injections. Thirty-eight cases developed IOI at least once, and 25 continued brolucizumab. Sixteen cases had no more IOI events, and nine experienced a second or more IOI events. Among the nine cases, three maintained brolucizumab injections despite IOI recurrence. The incidence of IOI was 8.1% based on the number of eyes (38 of 472 eyes) and 2.0% based on the number of brolucizumab injections (50 of 2,468 injections). The incidence of occlusive retinal vasculitis was 0.2% (1 of 472 eyes). The recurrence rate was 23.7% (9 of 38 eyes). The average number of injections between the first brolucizumab injection and the injection date on which IOI first developed was 2.15 times in the no-reinjection group, 3.44 times in the no-IOI-recurrence group, and 2.0 times in the second-IOI-episode group. Time to IOI occurrence in cases with first IOI episode was 18.60 ± 16.73 days, with 15 cases developing IOI within 1 week. Conclusions This study elucidates the real-world incidence of brolucizumab associated IOIs, with a description of information related to reinjections after the IOI episodes. A comprehensive understanding of brolucizumab reinjection is essential for its optimal utilization.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)