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: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

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: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Interferon stimulating factor STING, a transmembrane protein residing in the endoplasmic reticulum, is extensively involved in the sensing and transduction of intracellular signals and serves as a crucial component of the innate immune system. STING is capable of directly or indirectly responding to abnormal DNA originating from diverse sources within the cytoplasm, thereby fulfilling its classical antiviral and antitumor functions. Structurally, STING is composed of 4 transmembrane helices, a cytoplasmic ligand binding domain (LBD), and a C terminal tail structure (CTT). The transmembrane domain (TM), which is formed by the transmembrane helical structures, anchors STING to the endoplasmic reticulum, while the LBD is in charge of binding to cyclic dinucleotides (CDNs). The classical second messenger, cyclic guanosine monophosphate-adenosine monophosphate (cGAMP), represents a key upstream molecule for STING activation. Once cGAMP binds to LBD, STING experiences conformational alterations, which subsequently lead to the recruitment of Tank-binding kinase 1 (TBK1) via the CTT domain. This, in turn, mediates interferon secretion and promotes the activation and migration of dendritic cells, T cells, and natural killer cells. Additionally, STING is able to activate nuclear factor-κB (NF-κB), thereby initiating the synthesis and release of inflammatory factors and augmenting the body’s immune response. In recent years, an increasing number of studies have disclosed the non-classical functions of STING. It has been found that STING plays a significant role in organelle regulation. STING is not only implicated in the quality control systems of organelles such as mitochondria and endoplasmic reticulum but also modulates the functions of these organelles. For instance, STING can influence key aspects of organelle quality control, including mitochondrial fission and fusion, mitophagy, and endoplasmic reticulum stress. This regulatory effect is not unidirectional; rather, it is subject to organelle feedback regulation, thereby forming a complex interaction network. STING also exerts a monitoring function on the nucleus and ribosomes, which further enhances the role of the cGAS-STING pathway in infection-related immunity. The interaction mechanism between STING and organelles is highly intricate, which, within a certain range, enhances the cells’ capacity to respond to external stimuli and survival pressure. However, once the balance of this interaction is disrupted, it may result in the occurrence and development of inflammatory diseases, such as aseptic inflammation and autoimmune diseases. Excessive activation or malfunction of STING may trigger an over-exuberant inflammatory response, which subsequently leads to tissue damage and pathological states. This review recapitulates the recent interactions between STING and diverse organelles, encompassing its multifarious functions in antiviral, antitumor, organelle regulation, and immune regulation. These investigations not only deepen the comprehension of molecular mechanisms underlying STING but also offer novel concepts for the exploration of human disease pathogenesis and the development of potential treatment strategies. In the future, with further probing into STING function and its regulatory mechanisms, it is anticipated to pioneer new approaches for the treatment of complex diseases such as inflammatory diseases and tumors.

Background: En bloc resection is recommended for the treatment of malignant and aggressive benign spinal tumors; however, it often requires a combined anterior-posterior approach, which is usually accompanied by longer surgical duration, increased blood loss, larger trauma, and surgical complexity. The present study describes a novel rotation-reversion technique for en bloc resection of the thoracic and upper lumbar spinal tumors using a posterior-only approach and evaluate its safety and efficacy. Methods: Thirteen patients with thoracic and upper lumbar (L1-L3) spinal tumors were treated with en bloc resection using the rotation-reversion technique through a posterior-only approach at our institution between 2015 and 2023. The clinical characteristics and surgical results of the patients were reviewed and analyzed. Results: Posterior-only en bloc resection was performed successfully in all 13 patients using the rotation-reversion technique, with a median follow-up of 30.4 months (range, 6–74 months). The average maximum size of these 13 tumors was 5.7 × 5.8 × 4.8 cm.The mean operation time and blood loss were 458.5 minutes (range, 220–880 minutes) and 3,146.2 mL (range, 1,000–6,000 mL), respectively, with 4 of the 13 patients (30.8%) experiencing perioperative complications. Negative margins were achieved in all the 13 patients (100%). One patient experienced local recurrence (7.7%) and 1 patient experienced instrumentation failures. Interbody fusion was confirmed in 11 of the 13 patients (84.6%), with a median fusion time of 6.9 months. All of the 13 patients experienced varying degrees of mild postoperative neurological deficits owing to resection of the nerve roots affected by tumor invasion of the vertebrae. No vessel injury or postoperative neurological paralysis occurred, except 1 patient who had been completely paralyzed before surgery. Conclusions The rotation-reversion technique is an effective procedure for en bloc resection of selected thoracic and upper lumbar spinal tumors through the posterior-only approach.

Background: En bloc resection is recommended for the treatment of malignant and aggressive benign spinal tumors; however, it often requires a combined anterior-posterior approach, which is usually accompanied by longer surgical duration, increased blood loss, larger trauma, and surgical complexity. The present study describes a novel rotation-reversion technique for en bloc resection of the thoracic and upper lumbar spinal tumors using a posterior-only approach and evaluate its safety and efficacy. Methods: Thirteen patients with thoracic and upper lumbar (L1-L3) spinal tumors were treated with en bloc resection using the rotation-reversion technique through a posterior-only approach at our institution between 2015 and 2023. The clinical characteristics and surgical results of the patients were reviewed and analyzed. Results: Posterior-only en bloc resection was performed successfully in all 13 patients using the rotation-reversion technique, with a median follow-up of 30.4 months (range, 6–74 months). The average maximum size of these 13 tumors was 5.7 × 5.8 × 4.8 cm.The mean operation time and blood loss were 458.5 minutes (range, 220–880 minutes) and 3,146.2 mL (range, 1,000–6,000 mL), respectively, with 4 of the 13 patients (30.8%) experiencing perioperative complications. Negative margins were achieved in all the 13 patients (100%). One patient experienced local recurrence (7.7%) and 1 patient experienced instrumentation failures. Interbody fusion was confirmed in 11 of the 13 patients (84.6%), with a median fusion time of 6.9 months. All of the 13 patients experienced varying degrees of mild postoperative neurological deficits owing to resection of the nerve roots affected by tumor invasion of the vertebrae. No vessel injury or postoperative neurological paralysis occurred, except 1 patient who had been completely paralyzed before surgery. Conclusions The rotation-reversion technique is an effective procedure for en bloc resection of selected thoracic and upper lumbar spinal tumors through the posterior-only approach.

Background: En bloc resection is recommended for the treatment of malignant and aggressive benign spinal tumors; however, it often requires a combined anterior-posterior approach, which is usually accompanied by longer surgical duration, increased blood loss, larger trauma, and surgical complexity. The present study describes a novel rotation-reversion technique for en bloc resection of the thoracic and upper lumbar spinal tumors using a posterior-only approach and evaluate its safety and efficacy. Methods: Thirteen patients with thoracic and upper lumbar (L1-L3) spinal tumors were treated with en bloc resection using the rotation-reversion technique through a posterior-only approach at our institution between 2015 and 2023. The clinical characteristics and surgical results of the patients were reviewed and analyzed. Results: Posterior-only en bloc resection was performed successfully in all 13 patients using the rotation-reversion technique, with a median follow-up of 30.4 months (range, 6–74 months). The average maximum size of these 13 tumors was 5.7 × 5.8 × 4.8 cm.The mean operation time and blood loss were 458.5 minutes (range, 220–880 minutes) and 3,146.2 mL (range, 1,000–6,000 mL), respectively, with 4 of the 13 patients (30.8%) experiencing perioperative complications. Negative margins were achieved in all the 13 patients (100%). One patient experienced local recurrence (7.7%) and 1 patient experienced instrumentation failures. Interbody fusion was confirmed in 11 of the 13 patients (84.6%), with a median fusion time of 6.9 months. All of the 13 patients experienced varying degrees of mild postoperative neurological deficits owing to resection of the nerve roots affected by tumor invasion of the vertebrae. No vessel injury or postoperative neurological paralysis occurred, except 1 patient who had been completely paralyzed before surgery. Conclusions The rotation-reversion technique is an effective procedure for en bloc resection of selected thoracic and upper lumbar spinal tumors through the posterior-only approach.

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: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.