Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

An excess of amyloid beta (Aβ) led to a rise in ROS production, which in turn caused inflammatory reactions and mitochondrial dysfunction, both of which accelerate the progression of Alzheimer’s disease (AD).Natural flavonoids are proposed as possible agents for neurodegeneration. Pectolinarin is an important flavone mainly found in Cirsium species. In this study, we explored the potential neuroprotective effect of pectolinarin in Aβ25-35 -induced SH-SY5Y cells. The result demonstrated that pectolinarin enhanced cell viability. Pectolinarin treatment inhibited Aβ25-35 -induced ROS generation. Pectolinarin also suppressed NO generation by inhibiting the translocation of NF-ĸB and downregulating protein expression of iNOS and COX-2. Moreover, the expression of Bcl-2 increased while BAX protein decreased when the cells were exposed to pectolinarin, resulting in a decrease in the BAX/Bcl-2 ratio. Pectolinarin treatment also increased BDNF and its receptor TrkB protein expression. In conclusion, pectolinarin neuroprotected Aβ25-35 -induced inflammation and apoptosis. These findings suggest that pectolinarin may be a promising neuroprotective functional food in the protection of the neurodegenerative diseases, including AD.

An excess of amyloid beta (Aβ) led to a rise in ROS production, which in turn caused inflammatory reactions and mitochondrial dysfunction, both of which accelerate the progression of Alzheimer’s disease (AD).Natural flavonoids are proposed as possible agents for neurodegeneration. Pectolinarin is an important flavone mainly found in Cirsium species. In this study, we explored the potential neuroprotective effect of pectolinarin in Aβ25-35 -induced SH-SY5Y cells. The result demonstrated that pectolinarin enhanced cell viability. Pectolinarin treatment inhibited Aβ25-35 -induced ROS generation. Pectolinarin also suppressed NO generation by inhibiting the translocation of NF-ĸB and downregulating protein expression of iNOS and COX-2. Moreover, the expression of Bcl-2 increased while BAX protein decreased when the cells were exposed to pectolinarin, resulting in a decrease in the BAX/Bcl-2 ratio. Pectolinarin treatment also increased BDNF and its receptor TrkB protein expression. In conclusion, pectolinarin neuroprotected Aβ25-35 -induced inflammation and apoptosis. These findings suggest that pectolinarin may be a promising neuroprotective functional food in the protection of the neurodegenerative diseases, including AD.

An excess of amyloid beta (Aβ) led to a rise in ROS production, which in turn caused inflammatory reactions and mitochondrial dysfunction, both of which accelerate the progression of Alzheimer’s disease (AD).Natural flavonoids are proposed as possible agents for neurodegeneration. Pectolinarin is an important flavone mainly found in Cirsium species. In this study, we explored the potential neuroprotective effect of pectolinarin in Aβ25-35 -induced SH-SY5Y cells. The result demonstrated that pectolinarin enhanced cell viability. Pectolinarin treatment inhibited Aβ25-35 -induced ROS generation. Pectolinarin also suppressed NO generation by inhibiting the translocation of NF-ĸB and downregulating protein expression of iNOS and COX-2. Moreover, the expression of Bcl-2 increased while BAX protein decreased when the cells were exposed to pectolinarin, resulting in a decrease in the BAX/Bcl-2 ratio. Pectolinarin treatment also increased BDNF and its receptor TrkB protein expression. In conclusion, pectolinarin neuroprotected Aβ25-35 -induced inflammation and apoptosis. These findings suggest that pectolinarin may be a promising neuroprotective functional food in the protection of the neurodegenerative diseases, including AD.

An excess of amyloid beta (Aβ) led to a rise in ROS production, which in turn caused inflammatory reactions and mitochondrial dysfunction, both of which accelerate the progression of Alzheimer’s disease (AD).Natural flavonoids are proposed as possible agents for neurodegeneration. Pectolinarin is an important flavone mainly found in Cirsium species. In this study, we explored the potential neuroprotective effect of pectolinarin in Aβ25-35 -induced SH-SY5Y cells. The result demonstrated that pectolinarin enhanced cell viability. Pectolinarin treatment inhibited Aβ25-35 -induced ROS generation. Pectolinarin also suppressed NO generation by inhibiting the translocation of NF-ĸB and downregulating protein expression of iNOS and COX-2. Moreover, the expression of Bcl-2 increased while BAX protein decreased when the cells were exposed to pectolinarin, resulting in a decrease in the BAX/Bcl-2 ratio. Pectolinarin treatment also increased BDNF and its receptor TrkB protein expression. In conclusion, pectolinarin neuroprotected Aβ25-35 -induced inflammation and apoptosis. These findings suggest that pectolinarin may be a promising neuroprotective functional food in the protection of the neurodegenerative diseases, including AD.

An excess of amyloid beta (Aβ) led to a rise in ROS production, which in turn caused inflammatory reactions and mitochondrial dysfunction, both of which accelerate the progression of Alzheimer’s disease (AD).Natural flavonoids are proposed as possible agents for neurodegeneration. Pectolinarin is an important flavone mainly found in Cirsium species. In this study, we explored the potential neuroprotective effect of pectolinarin in Aβ25-35 -induced SH-SY5Y cells. The result demonstrated that pectolinarin enhanced cell viability. Pectolinarin treatment inhibited Aβ25-35 -induced ROS generation. Pectolinarin also suppressed NO generation by inhibiting the translocation of NF-ĸB and downregulating protein expression of iNOS and COX-2. Moreover, the expression of Bcl-2 increased while BAX protein decreased when the cells were exposed to pectolinarin, resulting in a decrease in the BAX/Bcl-2 ratio. Pectolinarin treatment also increased BDNF and its receptor TrkB protein expression. In conclusion, pectolinarin neuroprotected Aβ25-35 -induced inflammation and apoptosis. These findings suggest that pectolinarin may be a promising neuroprotective functional food in the protection of the neurodegenerative diseases, including AD.

Purpose: Achilles tendon rupture is a frequently encountered ankle pathology associated with a substantial burden of intense pain and functional deficits. Chronic Achilles tendon ruptures with considerable defects pose intricate repair challenges that are often marred by complications such as re-rupture and persistent pain. Various treatment methods, including allograft transplantation, have been proposed, but the literature on this technique is limited. In this study, we propose a surgical approach utilizing allotendon transplantation and a calcaneal tunneling technique and provide clinical evaluation details. Materials and Methods: Fifteen patients with chronic Achilles tendon ruptures treated with allotendon between 2020 and 2022 were included in the study. Patients were evaluated at 1, 3, and 6 months postoperatively using Visual Analog Scale (VAS) scores and Achilles Tendon Total Rupture Scores (ATRSs). Complications were assessed postoperatively. Results: The average VAS score was 7 before surgery, 7.3 immediately after surgery, and 4.3 at 1 month, 2.5 at 3 months, and 1.3 at 6 months after surgery. Because the sample was limited to 15 individuals and distributions were non-normal, the analysis was conducted using the non-parametric Wilcoxon’s signed-rank test, and statistical significance was accepted for p-values<0.05. Results showed a significant improvement in ATRS and VAS scores versus preoperative and immediate postoperative values. VAS scores showed a decreasing trend after surgery, whereas average ATRS scores increased from 14 before surgery, 33.8 at 1 month, 82.7 at 3 months, and 93.9 at 6 months. Conclusion This study suggests that allograft transplantation using the described calcaneal tunnel technique provides an effective treatment for chronic Achilles tendon ruptures. However, extensive research and long-term clinical trials are required to validate and better understand the technique’s efficacy.