Twenty-too patients were treated with Naaxia. Of these, 11 patients had vernal conjunctivitis and 8 patients had chronic allergic conjunctivitis. 19 patients(86.4%) achieved beneficial effect. The most frequent side effect was mild burning sensation after instillation and no systemic side effect was found.
Burns ; Conjunctivitis, Allergic* ; Humans ; Sensation

BACKGROUND/OBJECTIVES: Excessive alcohol consumption has harmful health effects, including alcohol hangovers and alcohol-related liver disease. Therefore, methods to accelerate the alcohol metabolism are needed. Laurus nobilis is a spice, flavoring agent, and traditional herbal medicine against various diseases. This study examined whether the standardized aqueous extract of L. nobilis leaves (LN) accelerates the alcohol metabolism and protects against liver damage in single-ethanol binge Sprague-Dawley (SD) rats.MATERIALS/METHODS: LN was administered orally to SD rats 1 h before ethanol administration (3 g/kg body weight [BW]) at 100 and 300 mg/kg BW. Blood samples were collected 0.5, 1, 2, and 4 h after ethanol administration. The livers were excised 1 h after ethanol administration to determine the hepatic enzyme activity. The alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), superoxide dismutase (SOD), and glutathione peroxidase (GPx) activities in the liver tissue were measured. RESULTS: LN decreased the serum ethanol and acetaldehyde levels in ethanol-administered rats. LN increased the hepatic ADH and ALDH activities but decreased the alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase activities in the ethanol-administered rats. In addition, LN inhibited lipid peroxidation and increased the activities of SOD and GPx. CONCLUSIONS LN modulates the mediators of various etiological effects of excessive alcohol consumption and enhances the alcohol metabolism and antioxidant activity, making it a potential candidate for hangover treatments.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

There have been ongoing social discussions on revision of the abortion law since the Constitutional Court ruled it nonconformity to the constitution on April 11, 2019. Thus, Korean Society of Obstetrics and Gynecology, Korean Society of Maternal Fetal Medicine, Korean College of Obstetricians and Gynecologists, and Korean Association of Obstetricians and Gynecologists organized 'special committee for abortion law' to support social discussion on the revision of the abortion law, and they prepared official opinions of obstetrics for medical issues including doctors' right to refuse abortion. In the future, the committee will continue to adapt to changes of medical environment especially after the revision of the abortion law by collecting its members' feedbacks.

There have been ongoing social discussions on revision of the abortion law since the Constitutional Court ruled it nonconformity to the constitution on April 11, 2019. Thus, Korean Society of Obstetrics and Gynecology, Korean Society of Maternal Fetal Medicine, Korean College of Obstetricians and Gynecologists, and Korean Association of Obstetricians and Gynecologists organized 'special committee for abortion law' to support social discussion on the revision of the abortion law, and they prepared official opinions of obstetrics for medical issues including doctors' right to refuse abortion. In the future, the committee will continue to adapt to changes of medical environment especially after the revision of the abortion law by collecting its members' feedbacks.