White-matter structure in the right hemisphere predicts Mandarin Chinese learning success

AbstractSecond language learning becomes increasingly difficult with age, but some adults learn more successfully than others. We examined whether inter-subject variability in the microstructure of white matter pathways, as measured by diffusion tensor imaging (DTI), would predict native English speakers' outcomes in learning Mandarin Chinese. Twenty-one adults were scanned before participating in an intensive 4-week Mandarin course. At the end of the Mandarin course, participants completed a final exam that assessed their skills in both spoken and written Mandarin. Individual participants' white-matter tracts were reconstructed from their native DTI data and related to final-exam performance. Superior language learning was correlated with DTI measures in the right hemisphere, but not in the left hemisphere. In particular, greater initial fractional anisotropy (FA) in both the right superior longitudinal fasciculus (parietal bundle) and the right inferior longitudinal fasciculus was associated with more successful Mandarin learning. The relation between white-matter structure in the right hemisphere of native English speakers and successful initial language learning may reflect the tonal and visuo-spatial properties, respectively, of spoken and written Mandarin Chinese

Shared Neuroanatomical Substrates of Impaired Phonological Working Memory Across Reading Disability and Autism

Background Individuals with reading disability and individuals with autism spectrum disorder (ASD) are characterized, respectively, by their difficulties in reading and social communication, but both groups often have impaired phonological working memory (PWM). It is not known whether the impaired PWM reflects distinct or shared neuroanatomical abnormalities in these two diagnostic groups. Methods White-matter structural connectivity via diffusion weighted imaging was examined in 64 children, age 5 to 17 years, with reading disability, ASD, or typical development, who were matched on age, gender, intelligence, and diffusion data quality. Results Children with reading disability and children with ASD exhibited reduced PWM compared with children with typical development. The two diagnostic groups showed altered white matter microstructure in the temporoparietal portion of the left arcuate fasciculus and in the occipitotemporal portion of the right inferior longitudinal fasciculus (ILF), as indexed by reduced fractional anisotropy and increased radial diffusivity. Moreover, the structural integrity of the right ILF was positively correlated with PWM ability in the two diagnostic groups but not in the typically developing group. Conclusions These findings suggest that impaired PWM is transdiagnostically associated with shared neuroanatomical abnormalities in ASD and reading disability. Microstructural characteristics in left arcuate fasciculus and right ILF may play important roles in the development of PWM. The right ILF may support a compensatory mechanism for children with impaired PWM

Selection of bone graft type for the surgical treatment of thoracolumbar spinal tuberculosis based on the spinal instability neoplastic score: a retrospective single-center cohort study

Abstract Objectives This study aimed to establish a standard for selecting bone graft type for thoracolumbar spinal tuberculosis surgery based on the spinal instability neoplastic score (SINS). Methods Patients with thoracolumbar tuberculosis who underwent one-stage debridement posteriorly and instrumentation were divided into a structural bone graft group (SBG) (51 cases) and a non-structural bone graft group (NSBG) (54 cases) according to their SINS. SBG was performed when the SINS was ≥ 13 and NSBG was performed when it was 7 ≤ SINS ≤ 12. Baseline data, clinical outcomes, and imaging outcomes were collected and statistically analyzed between the two groups. Results Significant improvements in clinical and imaging outcomes were achieved in both groups. Compared to the SBG group, the operation time of the NSBG group was shorter, the intraoperative blood loss of the NSBG group was less, the bone fusion time of the NSBG group was faster. Conclusion Non-structural and structural bone grafting can achieve comparable therapeutic effects in patients with spinal tuberculosis, and a suitable selection of bone grafts based on quantitative SINS will make full use of the advantages of different bone grafts

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

Abstract Background The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM. Aim Our study presents a novel and significant contribution by developing an interpretable fusion model that effectively integrates both free‐text medical record data and structured laboratory data to predict LM in postoperative CRC patients. Methods We used a robust dataset of 1463 patients and leveraged state‐of‐the‐art natural language processing (NLP) and machine learning techniques to construct a two‐layer fusion framework that demonstrates superior predictive performance compared to single modal models. Our innovative two‐tier algorithm fuses the results from different data modalities, achieving balanced prediction results on test data and significantly enhancing the predictive ability of the model. To increase interpretability, we employed Shapley additive explanations to elucidate the contributions of free‐text clinical data and structured clinical data to the final model. Furthermore, we translated our findings into practical clinical applications by creating a novel NLP score‐based nomogram using the top 13 valid predictors identified in our study. Results The proposed fusion models demonstrated superior predictive performance with an accuracy of 80.8%, precision of 80.3%, recall of 80.5%, and an F1 score of 80.8% in predicting LMs. Conclusion This fusion model represents a notable advancement in predicting LMs for postoperative CRC patients, offering the potential to enhance patient outcomes and support clinical decision‐making

Altered Resting-State Functional Networks in Nondialysis Patients with Stage 5 Chronic Kidney Disease: A Graph–Theoretical Analysis

This study aimed to investigate the topological characteristics of the resting-state functional network and the underlying pathological mechanism in nondialysis patients with stage 5 chronic kidney disease (CKD5 ND). Eighty-five subjects (21 patients with CKD5 ND, 32 patients with CKD on maintenance hemodialysis (HD), and 32 healthy controls (HCs)) underwent laboratory examinations, neuropsychological tests, and brain magnetic resonance imaging. The topological characteristics of networks were compared with a graph–theoretical approach, and correlations between neuropsychological scores and network properties were analyzed. All participants exhibited networks with small-world attributes, and global topological attributes were impaired in both groups of patients with CKD 5 (ND and HD) compared with HCs (p p p p p p = 0.01) was positively correlated with MoCA scores. In conclusion, all CKD5 ND patients exhibited changes in functional network topological properties and were closely associated with mild cognitive impairment. More interestingly, the topological property changes in CKD5 ND patients were dominated by basal ganglia areas, which may be more helpful to understand and possibly reveal the underlying pathological mechanisms of cognitive impairment in CKD5 ND

Speech processing and plasticity in the right hemisphere predict variation in adult foreign language learning

Foreign language learning in adulthood often takes place in classrooms where learning outcomes vary widely among students, for both initial learning and long-term retention. Despite the fundamental role of speech perception in first language acquisition, its role in foreign language learning outcomes remains unknown. Using a speech discrimination functional magnetic resonance imaging (fMRI) task and resting-state fMRI before and after an intensive, classroom-based, Mandarin Chinese course, we examined how variations in pre-training organization and pre-to-post reorganization of brain functions predicted successful language learning in male and female native English-speakers. Greater pre-training activation in right inferior frontal gyrus (IFG) to Mandarin speech was associated with better Mandarin attainment at the end of the course. After four weeks of class, learners showed overall increased activation in left IFG and left superior parietal lobule (SPL) to Mandarin speech, but in neither region was variation related to learning outcomes. Immediate attainment was associated with greater pre-to-post reduction of right IFG activation to Mandarin speech but also greater enhancement of resting-state connectivity between this region and both left IFG and left SPL. Long-term retention of Mandarin skills measured three months later was more accurately predicted by models using features of neural preparedness (pre-training activation) and neural plasticity (pre-to-post activation change) than models using behavior preparedness and plasticity features (pre-training speech discrimination accuracy and Mandarin attainment, respectively). These findings suggest that successful holistic foreign language acquisition in human adulthood requires right IFG engagement during initial learning but right IFG disengagement for long-term retention of language skills

Computed tomography-based body composition parameters can predict short-term prognosis in ulcerative colitis patients

Abstract Objectives Emerging evidence suggests a potential relationship between body composition and short-term prognosis of ulcerative colitis (UC). Early and accurate assessment of rapid remission based on conventional therapy via abdominal computed tomography (CT) images has rarely been investigated. This study aimed to build a prediction model using CT-based body composition parameters for UC risk stratification. Methods In total, 138 patients with abdominal CT images were enrolled. Eleven quantitative parameters related to body composition involving skeletal muscle mass, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were measured and calculated using a semi-automated segmentation method. A prediction model was established with significant parameters using a multivariable logistic regression. The receiver operating characteristic (ROC) curve was plotted to evaluate prediction performance. Subgroup analyses were implemented to evaluate the diagnostic efficiency of the prediction model between different disease locations, centers, and CT scanners. The Delong test was used for statistical comparison of ROC curves. Results VAT density, SAT density, gender, and visceral obesity were significantly statistically different between remission and invalidation groups (all p  0.05). Conclusions The predicting model constructed with CT-based body composition parameters is a potential non-invasive approach for short-term prognosis identification and risk stratification. Additionally, VAT density was an independent predictor for escalating therapeutic regimens in UC cohorts. Critical relevance statement The CT images were used for evaluating body composition and risk stratification of ulcerative colitis patients, and a potential non-invasive prediction model was constructed to identify non-responders with conventional therapy for making therapeutic regimens timely and accurately. Key points • CT-based prediction models help divide patients into invalidation and remission groups in UC. • Results of the subgroup analysis confirmed the stability of the prediction model with a high AUC (all > 0.820). • The visceral adipose tissue density was an independent predictor of bad short-term prognosis in UC. Graphical Abstrac

DataSheet_1_Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer.xlsx

BackgroundMedical imaging is critical in clinical practice, and high value radiological reports can positively assist clinicians. However, there is a lack of methods for determining the value of reports.ObjectiveThe purpose of this study was to establish an ensemble learning classification model using natural language processing (NLP) applied to the Chinese free text of radiological reports to determine their value for liver lesion detection in patients with colorectal cancer (CRC).MethodsRadiological reports of upper abdominal computed tomography (CT) and magnetic resonance imaging (MRI) were divided into five categories according to the results of liver lesion detection in patients with CRC. The NLP methods including word segmentation, stop word removal, and n-gram language model establishment were applied for each dataset. Then, a word-bag model was built, high-frequency words were selected as features, and an ensemble learning classification model was constructed. Several machine learning methods were applied, including logistic regression (LR), random forest (RF), and so on. We compared the accuracy between priori choosing pertinent word strings and our machine language methodologies.ResultsThe dataset of 2790 patients included CT without contrast (10.2%), CT with/without contrast (73.3%), MRI without contrast (1.8%), and MRI with/without contrast (14.6%). The ensemble learning classification model determined the value of reports effectively, reaching 95.91% in the CT with/without contrast dataset using XGBoost. The logistic regression, random forest, and support vector machine also achieved good classification accuracy, reaching 95.89%, 95.04%, and 95.00% respectively. The results of XGBoost were visualized using a confusion matrix. The numbers of errors in categories I, II and V were very small. ELI5 was used to select important words for each category. Words such as “no abnormality”, “suggest”, “fatty liver”, and “transfer” showed a relatively large degree of positive correlation with classification accuracy. The accuracy based on string pattern search method model was lower than that of machine learning.ConclusionsThe learning classification model based on NLP was an effective tool for determining the value of radiological reports focused on liver lesions. The study made it possible to analyze the value of medical imaging examinations on a large scale.</p