Performance of ChatGPT on Stage 1 of the Taiwanese medical licensing exam.

Introduction: Since its release by OpenAI in November 2022, numerous studies have subjected ChatGPT to various tests to evaluate its performance in medical exams. The objective of this study is to evaluate ChatGPT's accuracy and logical reasoning across all 10 subjects featured in Stage 1 of Senior Professional and Technical Examinations for Medical Doctors (SPTEMD) in Taiwan, with questions that encompass both Chinese and English. Methods: In this study, we tested ChatGPT-4 to complete SPTEMD Stage 1. The model was presented with multiple-choice questions extracted from three separate tests conducted in February 2022, July 2022, and February 2023. These questions encompass 10 subjects, namely biochemistry and molecular biology, anatomy, embryology and developmental biology, histology, physiology, microbiology and immunology, parasitology, pharmacology, pathology, and public health. Subsequently, we analyzed the model's accuracy for each subject. Result: In all three tests, ChatGPT achieved scores surpassing the 60% passing threshold, resulting in an overall average score of 87.8%. Notably, its best performance was in biochemistry, where it garnered an average score of 93.8%. Conversely, the performance of the generative pre-trained transformer (GPT)-4 assistant on anatomy, parasitology, and embryology was not as good. In addition, its scores were highly variable in embryology and parasitology. Conclusion: ChatGPT has the potential to facilitate not only exam preparation but also improve the accessibility of medical education and support continuous education for medical professionals. In conclusion, this study has demonstrated ChatGPT's potential competence across various subjects within the SPTEMD Stage 1 and suggests that it could be a helpful tool for learning and exam preparation for medical students and professionals.

Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography.

OBJECTIVES: A deep learning (DL) model using image data from pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F] FDG)-PET or computed tomography (CT) augmented with a novel imaging augmentation approach was developed for the early prediction of distant metastases in patients with locally advanced uterine cervical cancer. METHODS: This study used baseline [18F]FDG-PET/CT images of newly diagnosed uterine cervical cancer patients. Data from 186 to 25 patients were analyzed for training and validation cohort, respectively. All patients received chemoradiotherapy (CRT) and follow-up. PET and CT images were augmented by using three-dimensional techniques. The proposed model employed DL to predict distant metastases. Receiver operating characteristic (ROC) curve analysis was performed to measure the model's predictive performance. RESULTS: The area under the ROC curves of the training and validation cohorts were 0.818 and 0.830 for predicting distant metastasis, respectively. In the training cohort, the sensitivity, specificity, and accuracy were 80.0%, 78.0%, and 78.5%, whereas, the sensitivity, specificity, and accuracy for distant failure were 73.3%, 75.5%, and 75.2% in the validation cohort, respectively. CONCLUSION: Through the use of baseline [ 18 F]FDG-PET/CT images, the proposed DL model can predict the development of distant metastases for patients with locally advanced uterine cervical cancer treatment by CRT. External validation must be conducted to determine the model's predictive performance.

The use of ultrasound for monitoring reduction and ulnar nerve subluxation in pediatric humeral supracondylar fractures.

BACKGROUND: Traditional treatment for displaced humeral supracondylar fractures (SCFs) in children involves closed reduction (CR) under fluoroscopic guidance, percutaneous pinning, and immobilization with a long-arm cast. This study aims to explore the viability of using radiation-free ultrasound (US) for guiding CR and tracking ulnar nerve dynamics during medial pinning, contrasting the US method with the conventional cross pinning technique. MATERIALS AND METHODS: We assessed 70 children with acute displaced SCFs. The US group (n = 30) underwent US-guided reduction, whereas the traditional group (n = 40) underwent fluoroscopy-guided reduction. Both groups received percutaneous cross pinning and subsequent cast immobilization. Postoperative outcomes were compared between the two methods after a 6-month follow-up. In the US group, ultrasonography assessed fracture displacement distances before and after CR. The angle at which the ulnar nerve relocated to the cubital tunnel during elbow extension was documented using real-time US monitoring during medial pinning. RESULTS: The US group demonstrated improved reduction accuracy, increased range of motion, superior restoration of both Baumann and Humeroulnar angles, and a decreased incidence of malunions compared to the traditional group (all p < 0.05). The ultrasonographic measurement of fracture displacement was comparable with that of fluoroscopy (intraclass correlation coefficient > 0.90). In the US group, no ulnar nerve injury was noted, compared to 2.5 % in the traditional group, and real-time US observations revealed ulnar nerve hypermobility, with 53.3 % of patients exhibiting anterior ulnar nerve subluxation at 120° elbow flexion, 40 % at 90°, 16.7 % at 60°, and none at 30° flexion. CONCLUSION: Ultrasound is as reliable as fluoroscopy for evaluating fracture reductions. The use of intra-operative ultrasound significantly improves reduction accuracy and radiographic outcomes while reducing the risk of ulnar nerve injury.

Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer.

BACKGROUND: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients' clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival in local or locally advanced prostate cancer (PCa) patients after the initial treatment. METHODS: A total of 354 patients who met the eligibility criteria were analyzed and used to train the model. Clinical information and radiomics features of BS were obtained. Survival-related clinical features and radiomics features were included in the ML model training. Using the pyradiomics software, 128 radiomics features from each BS image's region of interest, validated by experts, were extracted. Four textural matrices were also calculated: GLCM, NGLDM, GLRLM, and GLSZM. Five training models (Logistic Regression, Naive Bayes, Random Forest, Support Vector Classification, and XGBoost) were applied using K-fold cross-validation. Recurrence was defined as either a rise in PSA levels, radiographic progression, or death. To assess the classifier's effectiveness, the ROC curve area and confusion matrix were employed. RESULTS: Of the 354 patients, 101 patients were categorized into the recurrence group with more advanced disease status compared to the non-recurrence group. Key clinical features including tumor stage, radical prostatectomy, initial PSA, Gleason Score primary pattern, and radiotherapy were used for model training. Random Forest (RF) was the best-performing model, with a sensitivity of 0.81, specificity of 0.87, and accuracy of 0.85. The ROC curve analysis showed that predictions from RF outperformed predictions from other ML models with a final AUC of 0.94 and a p-value of <0.001. The other models had accuracy ranges from 0.52 to 0.78 and AUC ranges from 0.67 to 0.84. CONCLUSIONS: The study showed that ML based on clinical features and radiomics features of BS improves the prediction of PCa recurrence after initial treatment. These findings highlight the added value of ML techniques for risk classification in PCa based on clinical features and radiomics features of BS.

Imaging prediction of KRAS mutation in patients with rectal cancer through deep metric learning using pretreatment [18F]Fluorodeoxyglucose positron emission tomography/computed tomography.

OBJECTIVES: To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML). METHODS: This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis. This model combined ML with the deep-learning framework to analyze PET data with or without CT images. The Batch Balance Wrapper framework and K-fold cross-validation were employed during the learning process. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. RESULTS: Genetic alterations in KRAS were identified in 82 (51%) tumors. Both PET and CT images were used, and the proposed model had an area under the ROC curve of 0.836 for its ability to predict a mutation status. The sensitivity, specificity, and accuracy were 75.3%, 79.3%, and 77.5%, respectively. When PET images alone were used, the area under the curve was 0.817, whereas the sensitivity, specificity, and accuracy were 73.2%, 79.6%, and 76.2%, respectively. CONCLUSIONS: The ML model presented herein revealed that baseline 18F-FDG PET/CT images could provide supplemental information to determine KRAS mutation in RC. Additional studies are required to maximize the predictive accuracy. ADVANCES IN KNOWLEDGE: The results of the ML model presented herein indicate that baseline 18F-FDG PET/CT images could provide supplemental information for determining KRAS mutation in RC.The predictive accuracy of the model was 77.5% when both image types were used and 76.2% when PET images alone were used. Additional studies are required to maximize the predictive accuracy.

The Critical Role of Equilibrative Nucleoside Transporter-2 in Modulating Cerebral Damage and Vascular Dysfunction in Mice with Brain Ischemia-Reperfusion.

BACKGROUND: Cerebral vascular protection is critical for stroke treatment. Adenosine modulates vascular flow and exhibits neuroprotective effects, in which brain extracellular concentration of adenosine is dramatically increased during ischemic events and ischemia-reperfusion. Since the equilibrative nucleoside transporter-2 (Ent2) is important in regulating brain adenosine homeostasis, the present study aimed to investigate the role of Ent2 in mice with cerebral ischemia-reperfusion. METHODS: Cerebral ischemia-reperfusion injury was examined in mice with transient middle cerebral artery occlusion (tMCAO) for 90 minutes, followed by 24-hour reperfusion. Infarct volume, brain edema, neuroinflammation, microvascular structure, regional cerebral blood flow (rCBF), cerebral metabolic rate of oxygen (CMRO2), and the production of reactive oxygen species (ROS) were examined following the reperfusion. RESULTS: Ent2 deletion reduced the infarct volume, brain edema, and neuroinflammation in mice with cerebral ischemia-reperfusion. tMCAO-induced disruption of brain microvessels was ameliorated in Ent2-/- mice, with a reduced expression of matrix metalloproteinases-9 and aquaporin-4 proteins. Following the reperfusion, the rCBF of the wild-type (WT) mice was quickly restored to the baseline, whereas, in Ent2-/- mice, rCBF was slowly recovered initially, but was then higher than that in the WT mice at the later phase of reperfusion. The improved CMRO2 and reduced ROS level support the beneficial effects caused by the changes in the rCBF of Ent2-/- mice. Further studies showed that the protective effects of Ent2 deletion in mice with tMCAO involve adenosine receptor A2AR. CONCLUSIONS: Ent2 plays a critical role in modulating cerebral collateral circulation and ameliorating pathological events of brain ischemia and reperfusion injury.

Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease.

BACKGROUND: Lumbar degenerative disc disease (LDDD) is a leading cause of chronic lower back pain; however, a lack of clear diagnostic criteria and solid LDDD interventional therapies have made predicting the benefits of therapeutic strategies challenging. Our goal is to develop machine learning (ML)-based radiomic models based on pre-treatment imaging for predicting the outcomes of lumbar nucleoplasty (LNP), which is one of the interventional therapies for LDDD. METHODS: The input data included general patient characteristics, perioperative medical and surgical details, and pre-operative magnetic resonance imaging (MRI) results from 181 LDDD patients receiving lumbar nucleoplasty. Post-treatment pain improvements were categorized as clinically significant (defined as a ≥80% decrease in the visual analog scale) or non-significant. To develop the ML models, T2-weighted MRI images were subjected to radiomic feature extraction, which was combined with physiological clinical parameters. After data processing, we developed five ML models: support vector machine, light gradient boosting machine, extreme gradient boosting, extreme gradient boosting random forest, and improved random forest. Model performance was measured by evaluating indicators, such as the confusion matrix, accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC), which were acquired using an 8:2 allocation of training to testing sequences. RESULTS: Among the five ML models, the improved random forest algorithm had the best performance, with an accuracy of 0.76, a sensitivity of 0.69, a specificity of 0.83, an F1 score of 0.73, and an AUC of 0.77. The most influential clinical features included in the ML models were pre-operative VAS and age. In contrast, the most influential radiomic features had the correlation coefficient and gray-scale co-occurrence matrix. CONCLUSIONS: We developed an ML-based model for predicting pain improvement after LNP for patients with LDDD. We hope this tool will provide both doctors and patients with better information for therapeutic planning and decision-making.

Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation.

Positron emission tomography and computed tomography with 18F-fluorodeoxyglucose (18F-FDG PET-CT) were used to predict outcomes after liver transplantation in patients with hepatocellular carcinoma (HCC). However, few approaches for prediction based on 18F-FDG PET-CT images that leverage automatic liver segmentation and deep learning were proposed. This study evaluated the performance of deep learning from 18F-FDG PET-CT images to predict overall survival in HCC patients before liver transplantation (LT). We retrospectively included 304 patients with HCC who underwent 18F-FDG PET/CT before LT between January 2010 and December 2016. The hepatic areas of 273 of the patients were segmented by software, while the other 31 were delineated manually. We analyzed the predictive value of the deep learning model from both FDG PET/CT images and CT images alone. The results of the developed prognostic model were obtained by combining FDG PET-CT images and combining FDG CT images (0.807 AUC vs. 0.743 AUC). The model based on FDG PET-CT images achieved somewhat better sensitivity than the model based on CT images alone (0.571 SEN vs. 0.432 SEN). Automatic liver segmentation from 18F-FDG PET-CT images is feasible and can be utilized to train deep-learning models. The proposed predictive tool can effectively determine prognosis (i.e., overall survival) and, thereby, select an optimal candidate of LT for patients with HCC.

Deletion of equilibrative nucleoside transporter 2 disturbs energy metabolism and exacerbates disease progression in an experimental model of Huntington's disease.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disease, characterized by motor dysfunction and abnormal energy metabolism. Equilibrative nucleoside transporter 1 (ENT1) and ENT2 are the major nucleoside transporters in cellular plasma membrane of the brain. Yet, unlike ENT1 whose function has been better investigated in HD, the role of ENT2 in HD remains unclear. The present study aimed to investigate the impacts of ENT2 deletion on HD using a well-characterized mouse model (R6/2). Microarray analysis, quantitative real-time polymerase chain reaction, and immunostaining of ENT2 in postmortem human brain tissues were conducted. R6/2 mice with or without genetic deletion of ENT2 were generated. Motor functions, including rotarod performance and limb-clasping test, were examined at the age of 7 to 12 weeks. Biochemical changes were evaluated by immunofluorescence staining and immunoblotting at the age of 12 to 13 weeks. In regard to energy metabolism, levels of striatal metabolites were determined by liquid chromatography coupled with the fluorescence detector or quadrupole time-of-flight mass spectrometer. Mitochondrial bioenergetics was assessed by the Seahorse assay. The results showed that ENT2 protein was detected in the neurons and astrocytes of human brains and the levels in the postmortem brain tended to be higher in patients with HD. In mice, ENT2 deletion did not alter the phenotype of the non-HD controls. Yet, ENT2 deletion deteriorated motor function and increased the number of aggregated mutant huntingtin in the striatum of R6/2 mice. Notably, disturbed energy metabolism with decreased ATP level and increased AMP/ ATP ratio was observed in R6/2-Ent2-/- mice, compared with R6/2-Ent2+/+ mice, resulting in the activation of AMPK in the late disease stage. Furthermore, ENT2 deletion reduced the NAD+/NADH ratio and impaired mitochondrial respiration in the striatum of R6/2 mice. Taken together, these findings indicate the crucial role of ENT2 in energy homeostasis, in which ENT2 deletion further impairs mitochondrial bioenergetics and deteriorates motor function in R6/2 mice.

Equilibrative Nucleoside Transporter 1 is a Target to Modulate Neuroinflammation and Improve Functional Recovery in Mice with Spinal Cord Injury.

Neuroinflammation plays a critical role in the neurological recovery of spinal cord injury (SCI). Adenosine can modulate neuroinflammation, whose uptake is mediated by nucleoside transporters. This study aimed to investigate the roles of equilibrative nucleoside transporter 1 (Ent1) in the inflammatory responses and functional recovery of SCI. Spinal cord contusion at the T10 dorsal portion was induced in mice to cause partial paralysis of the hindlimbs. Genetic deletion and pharmacological inhibition of Ent1 were used to evaluate the role of Ent1 in SCI. The outcomes were evaluated in terms of the Basso Mouse Scale (BMS), gait analysis, astrogliosis, microgliosis, and cytokine levels on day 14 post-injury. As a result, Ent1 deletion reduced neuroinflammation and improved the BMS score (4.88 ± 0.35 in Ent1-/- vs. 3.78 ± 1.09 in Ent1+/+) and stride length (3.74 ± 0.48 cm in Ent1-/- vs. 2.82 ± 0.78 cm in Ent1+/+) of mice with SCI. Along with the reduced lesion size, more preserved neurons were identified in the perilesional area of mice with Ent1 deletion (102 ± 23 in Ent1-/- vs. 73 ± 10 in Ent1+/+). The results of pharmacological inhibition were consistent with the findings of genetic deletion. Moreover, Ent1 inhibition decreased the protein level of complement 3 (an A1 marker), but increased the levels of S100 calcium-binding protein a10 (an A2 marker) and transforming growth factor-ß, without changing the levels of inducible nitric oxide synthase (a M1 marker) and arginase 1 (a M2 marker) at the injured site. These findings indicate the important role of Ent1 in the pathogenesis and treatment of SCI.

Ibuprofen treatment ameliorates memory deficits in rats with collagen-induced arthritis by normalizing aberrant MAPK/NF-κB and glutamatergic pathways.

Many studies have indicated that the risk of cognitive impairment is higher in patients with rheumatoid arthritis (RA). Additionally, patients with RA may have a lower incidence of cognitive impairment with long-term use of ibuprofen. This study was aimed at investigating the impacts of RA on memory function and the mechanisms that ibuprofen may exhibit to improve memory function in rats with collagen-induced arthritis (CIA). Ibuprofen (30 mg/kg) was given twice daily to CIA rats for two weeks starting from Day 18 following the first immunization. Memory function was measured by the Morris water maze (MWM) test and long-term potentiation (LTP). The proinflammatory cytokine levels and downstream signaling pathways, including mitogen-activated protein kinase (MAPK) and nuclear factor kappa B (NF-κB), were examined. Furthermore, the glutamatergic system, including glutamate transporters/receptors and brain extracellular levels of glutamate, was investigated. The results showed that the impaired learning memory in CIA rats, examined by the MWM test and LTP, can be ameliorated by ibuprofen treatment. Along with the improvement in memory deficits, ibuprofen attenuated both neuroinflammation and the associated elevated levels of phosphorylated p38, JNK, and p65 in the hippocampus of CIA rats. In addition, the decreased excitatory amino acid transporter 2 level, the increased extracellular glutamate, and the upregulated hippocampal NMDA receptor 2B of CIA rats were all normalized by ibuprofen treatment. These findings suggest that the effect of ibuprofen on the memory improvement in CIA rats is associated with the normalization of the activated MAPK and NF-κB pathways and the aberrant glutamatergic system.

The Correlation of Carpal Tunnel Pressure with Clinical Outcomes following Ultrasonographically-Guided Percutaneous Carpal Tunnel Release.

Background: To evaluate the correlation between carpal tunnel pressure (CTP) and the clinical presentations, and to explore the possible predictors for the postoperative recovery pattern in patients with carpal tunnel syndrome (CTS). Materials and Methods: Consecutive patients with idiopathic CTS following percutaneous ultrasound-guided carpal tunnel release (UCTR) were enrolled. CTP was measured preoperatively and immediately after operation. The Boston Carpal Tunnel Questionnaire (BCTQ) and the cross-sectional area (CSA) of median nerve were recorded preoperatively and at 1, 3, and 12 months postoperatively. Results: 37 patients (37 hands; 8 men and 29 females; median age, 59.0 years) were enrolled. CTP significantly decreased immediately from 40.0 (28.0−58.0) to 13.0 (8.0−20.0) mmHg after UCTR. BCTQ scores significantly improved at 1 month postoperatively, and the improvement trend persisted until 12 months postoperatively (p < 0.001). Preoperative CTP was positively correlated with preoperative CSA and preoperative BCTQ scores (p < 0.05, all). Using group-based trajectory modeling, all patients were categorized into the "gradual recovery" or "fast recovery" group. Higher preoperative CTP was significantly associated with a faster recovery pattern (odds ratio: 1.32). Conclusions: Preoperative CTP was well correlated with the clinical presentations and might be a useful predictor for the postoperative clinical recovery pattern.

Differentiation Between Malignant and Benign Pulmonary Nodules by Using Automated Three-Dimensional High-Resolution Representation Learning With Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography.

Background: The investigation of incidental pulmonary nodules has rapidly become one of the main indications for 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), currently combined with computed tomography (PET-CT). There is also a growing trend to use artificial Intelligence for optimization and interpretation of PET-CT Images. Therefore, we proposed a novel deep learning model that aided in the automatic differentiation between malignant and benign pulmonary nodules on FDG PET-CT. Methods: In total, 112 participants with pulmonary nodules who underwent FDG PET-CT before surgery were enrolled retrospectively. We designed a novel deep learning three-dimensional (3D) high-resolution representation learning (HRRL) model for the automated classification of pulmonary nodules based on FDG PET-CT images without manual annotation by experts. For the images to be localized more precisely, we defined the territories of the lungs through a novel artificial intelligence-driven image-processing algorithm, instead of the conventional segmentation method, without the aid of an expert; this algorithm is based on deep HRRL, which is used to perform high-resolution classification. In addition, the 2D model was converted to a 3D model. Results: All pulmonary lesions were confirmed through pathological studies (79 malignant and 33 benign). We evaluated its diagnostic performance in the differentiation of malignant and benign nodules. The area under the receiver operating characteristic curve (AUC) of the deep learning model was used to indicate classification performance in an evaluation using fivefold cross-validation. The nodule-based prediction performance of the model had an AUC, sensitivity, specificity, and accuracy of 78.1, 89.9, 54.5, and 79.4%, respectively. Conclusion: Our results suggest that a deep learning algorithm using HRRL without manual annotation from experts might aid in the classification of pulmonary nodules discovered through clinical FDG PET-CT images.

Applying Machine Learning to Carotid Sonographic Features for Recurrent Stroke in Patients With Acute Stroke.

BACKGROUND: Although carotid sonographic features have been used as predictors of recurrent stroke, few large-scale studies have explored the use of machine learning analysis of carotid sonographic features for the prediction of recurrent stroke. METHODS: We retrospectively collected electronic medical records of enrolled patients from the data warehouse of China Medical University Hospital, a tertiary medical center in central Taiwan, from January 2012 to November 2018. We included patients who underwent a documented carotid ultrasound within 30 days of experiencing an acute first stroke during the study period. We classified these participants into two groups: those with non-recurrent stroke (those who has not been diagnosed with acute stroke again during the study period) and those with recurrent stoke (those who has been diagnosed with acute stroke during the study period). A total of 1,235 carotid sonographic parameters were analyzed. Data on the patients' demographic characteristics and comorbidities were also collected. Python 3.7 was used as the programming language, and the scikit-learn toolkit was used to complete the derivation and verification of the machine learning methods. RESULTS: In total, 2,411 patients were enrolled in this study, of whom 1,896 and 515 had non-recurrent and recurrent stroke, respectively. After extraction, 43 features of carotid sonography (36 carotid sonographic parameters and seven transcranial color Doppler sonographic parameter) were analyzed. For predicting recurrent stroke, CatBoost achieved the highest area under the curve (0.844, CIs 95% 0.824-0.868), followed by the Light Gradient Boosting Machine (0.832, CIs 95% 0.813-0.851), random forest (0.819, CIs 95% 0.802-0.846), support-vector machine (0.759, CIs 95% 0.739-0.781), logistic regression (0.781, CIs 95% 0.764-0.800), and decision tree (0.735, CIs 95% 0.717-0.755) models. CONCLUSION: When using the CatBoost model, the top three features for predicting recurrent stroke were determined to be the use of anticoagulation medications, the use of NSAID medications, and the resistive index of the left subclavian artery. The CatBoost model demonstrated efficiency and achieved optimal performance in the predictive classification of non-recurrent and recurrent stroke.

Safe Zones for Percutaneous Carpal Tunnel Release.

Carpal tunnel release (CTR) is an effective procedure used in open, endoscopic, or ultrasound-guided methods. The complications are rare but potentially devasting. Most complications come from errors related to intraoperative technique, especially in the minimally invasive approach. An understanding of the "safe zones" is essential to perform percutaneous CTR safely. This article reviews the anatomy of safe zones and the ultrasound-guided CTR (UCTR) techniques in an attempt to prevent intraoperative complications. In strict accordance with the concepts of safe zones, UCTR is an effective and reliable procedure. Substantial experience for ultrasound-guided injection and surgery is required.

Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer with Metric Learning Using Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography.

OBJECTIVES: Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the mainstay of treatment for patients with locally advanced rectal cancer. Based on baseline 18F-fluorodeoxyglucose ([18F]-FDG)-positron emission tomography (PET)/computed tomography (CT), a new artificial intelligence model using metric learning (ML) was introduced to predict responses to NCRT. PATIENTS AND METHODS: This study used the data of 236 patients with newly diagnosed rectal cancer; the data of 202 and 34 patients were for training and validation, respectively. All patients received pretreatment [18F]FDG-PET/CT, NCRT, and surgery. The treatment response was scored by Dworak tumor regression grade (TRG); TRG3 and TRG4 indicated favorable responses. The model employed ML combined with the Uniform Manifold Approximation and Projection for dimensionality reduction. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. RESULTS: In the training cohort, 115 patients (57%) achieved TRG3 or TRG4 responses. The area under the ROC curve was 0.96 for the prediction of a favorable response. The sensitivity, specificity, and accuracy were 98.3%, 96.5%, and 97.5%, respectively. The sensitivity, specificity, and accuracy for the validation cohort were 95.0%, 100%, and 98.8%, respectively. CONCLUSIONS: The new ML model presented herein was used to determined that baseline 18F[FDG]-PET/CT images could predict a favorable response to NCRT in patients with rectal cancer. External validation is required to verify the model's predictive value.

Emerging roles of dysregulated adenosine homeostasis in brain disorders with a specific focus on neurodegenerative diseases.

In modern societies, with an increase in the older population, age-related neurodegenerative diseases have progressively become greater socioeconomic burdens. To date, despite the tremendous effort devoted to understanding neurodegenerative diseases in recent decades, treatment to delay disease progression is largely ineffective and is in urgent demand. The development of new strategies targeting these pathological features is a timely topic. It is important to note that most degenerative diseases are associated with the accumulation of specific misfolded proteins, which is facilitated by several common features of neurodegenerative diseases (including poor energy homeostasis and mitochondrial dysfunction). Adenosine is a purine nucleoside and neuromodulator in the brain. It is also an essential component of energy production pathways, cellular metabolism, and gene regulation in brain cells. The levels of intracellular and extracellular adenosine are thus tightly controlled by a handful of proteins (including adenosine metabolic enzymes and transporters) to maintain proper adenosine homeostasis. Notably, disruption of adenosine homeostasis in the brain under various pathophysiological conditions has been documented. In the past two decades, adenosine receptors (particularly A1 and A2A adenosine receptors) have been actively investigated as important drug targets in major degenerative diseases. Unfortunately, except for an A2A antagonist (istradefylline) administered as an adjuvant treatment with levodopa for Parkinson's disease, no effective drug based on adenosine receptors has been developed for neurodegenerative diseases. In this review, we summarize the emerging findings on proteins involved in the control of adenosine homeostasis in the brain and discuss the challenges and future prospects for the development of new therapeutic treatments for neurodegenerative diseases and their associated disorders based on the understanding of adenosine homeostasis.

Equilibrative nucleoside transporter 1 inhibition rescues energy dysfunction and pathology in a model of tauopathy.

Tau pathology is instrumental in the gradual loss of neuronal functions and cognitive decline in tauopathies, including Alzheimer's disease (AD). Earlier reports showed that adenosine metabolism is abnormal in the brain of AD patients while consequences remained ill-defined. Herein, we aimed at investigating whether manipulation of adenosine tone would impact Tau pathology, associated molecular alterations and subsequent neurodegeneration. We demonstrated that treatment with an inhibitor (J4) of equilibrative nucleoside transporter 1 (ENT1) exerted beneficial effects in a mouse model of Tauopathy. Treatment with J4 not only reduced Tau hyperphosphorylation but also rescued memory deficits, mitochondrial dysfunction, synaptic loss, and abnormal expression of immune-related gene signatures. These beneficial effects were particularly ascribed to the ability of J4 to suppress the overactivation of AMPK (an energy reduction sensor), suggesting that normalization of energy dysfunction mitigates neuronal dysfunctions in Tauopathy. Collectively, these data highlight that targeting adenosine metabolism is a novel strategy for tauopathies.

Amelioration of lipopolysaccharide-induced memory impairment in equilibrative nucleoside transporter-2 knockout mice is accompanied by the changes in glutamatergic pathways.

Neuroinflammation has been implicated in cognitive deficits in neurological and neurodegenerative diseases. Lipopolysaccharide (LPS)-induced neuroinflammation and the breakdown of the blood-brain barrier can be attenuated in mice with equilibrative nucleoside transporter-2 (ENT2/Ent2) deletion. The present study was aimed to investigate the role of ENT2 in cognitive and neuronal functions under physiological and inflammatory conditions, in terms of behavioral performance and synaptic plasticity in saline- and LPS-treated Ent2 knockout (KO) mice and their wild-type (WT) littermate controls. Repeated administrations of LPS significantly impaired spatial memory formation in Morris water maze and hippocampal-dependent long-term potentiation (LTP) in WT mice. The LPS-treated WT mice exhibited significant synaptic and neuronal damage in the hippocampus. Notably, the LPS-induced impairment in spatial memory and LTP performance were attenuated in Ent2 KO mice, along with the preservation of neuronal survival. The beneficial effects were accompanied by the normalization of excessive extracellular glutamate and aberrant downstream signaling of glutamate receptor activation, including the upregulation of phosphorylated p38 mitogen-activated protein kinase and the downregulation of phosphorylated cyclic adenosine monophosphate-response element-binding protein. There was no significant difference in behavioral outcome and all tested parameters between these two genotypes under physiological condition. These results suggest that ENT2 plays an important role in regulating inflammation-associated cognitive decline and neuronal damage.

Changes of blood-brain-barrier function and transfer of amyloid beta in rats with collagen-induced arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is characterized by synovial inflammation, cartilage damage, and systemic inflammation. RA is also associated with the occurrence of neuroinflammation and neurodegenerative diseases. In this study, the impacts of RA on the function of the blood-brain barrier (BBB) and the disposition of amyloid beta (Aß), including BBB transport and peripheral clearance of Aß, were investigated in rats with collagen-induced arthritis (CIA), an animal model with similarity to clinical and pathological features of human RA. METHODS: CIA was induced in female Lewis rats. In addition to neuroinflammation, the integrity and function of the BBB were examined. The expression of Aß-transporting proteins at brain blood vessels was measured. Blood-to-brain influx and plasma clearance of Aß were determined. RESULTS: Both microgliosis and astrogliosis were significantly increased in the brain of CIA rats, compared with controls. In terms of BBB function, the BBB permeability of sodium fluorescein, a marker compound for BBB integrity, was significantly increased in CIA rats. Moreover, increased expression of matrix metalloproteinase-3 (MMP-3) and MMP-9 and decreased expression of tight junction proteins, zonula occludens-1 (ZO-1) and occludin, were observed in brain microvessels of CIA rats. In related to BBB transport of Aß, protein expression of the receptor of advanced glycation end product (RAGE) and P-glycoprotein (P-gp) was significantly increased in brain microvessels of CIA rats. Notably, much higher expression of RAGE was identified at the arterioles of the hippocampus of CIA rats. Following an intravenous injection of human Aß, significant higher brain influx of Aß was observed in the hippocampus of CIA rats. CONCLUSIONS: Neuroinflammation and the changes of BBB function were observed in CIA rats. The increased RAGE expression at cerebral blood vessels and enhanced blood-to-brain influx of Aß indicate the imbalanced BBB clearance of Aß in RA.