Modulation of High-Frequency rTMS on Reward Circuitry in Individuals with Nicotine Dependence: A Preliminary fMRI Study.

Although previous studies have shown that repetitive transcranial magnetic stimulation (rTMS) can ameliorate addictive behaviors and cravings, the underlying neural mechanisms remain unclear. This study aimed to investigate the effect of high-frequency rTMS with the left dorsolateral prefrontal cortex (L-DLPFC) as a target region on smoking addiction in nicotine-dependent individuals by detecting the change of spontaneous brain activity in the reward circuitry. We recruited 17 nicotine-dependence participants, who completed 10 sessions of 10 Hz rTMS over a 2-week period and underwent evaluation of several dependence-related scales, and resting-state fMRI scan before and after the treatment. Functional connectivity (FC) analysis was conducted with reward-related brain regions as seeds, including ventral tegmental area, bilateral nucleus accumbens (NAc), bilateral DLPFC, and bilateral amygdala. We found that, after the treatment, individuals showed reduced nicotine dependence, alleviated tobacco withdrawal symptoms, and diminished smoking cravings. The right NAc showed increased FC with right fusiform gyrus, inferior temporal gyrus (ITG), calcarine fissure and surrounding cortex, superior occipital gyrus (SOG), lingual gyrus, and bilateral cuneus. No significant FC changes were observed in other seed regions. Moreover, the changes in FC between the right NAc and the right ITG as well as SOG before and after rTMS were negatively correlated with changes in smoking scale scores. Our findings suggest that high-frequency L-DLPFC-rTMS reduces nicotine dependence and improves tobacco withdrawal symptoms, and the dysfunctional connectivity in reward circuitry may be the underlying neural mechanism for nicotine addiction and its therapeutic target.

Large-scale pretrained frame generative model enables real-time low-dose DSA imaging: An AI system development and multi-center validation study.

BACKGROUND: Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, necessitating multiple scans per procedure, which results in significant radiation exposure for both doctors and patients. Inspired by generative artificial intelligence techniques, this study proposes GenDSA, a large-scale pretrained multi-frame generative model-based real-time and low-dose DSA imaging system. METHODS: GenDSA was developed to generate 1-, 2-, and 3-frame sequences following each real frame. A large-scale dataset comprising ∼3 million DSA images from 27,117 patients across 10 hospitals was constructed to pretrain, fine-tune, and validate GenDSA. Two other datasets from 25 hospitals were used for evaluation. Objective evaluations included SSIM and PSNR. Five interventional radiologists independently assessed the quality of the generated frames using the Likert scale and visual Turing test. Scoring consistency among the radiologists was measured using the Kendall coefficient of concordance (W). The Fleiss' kappa values were used for inter-rater agreement analysis for visual Turing tests. FINDINGS: Using only one-third of the clinical radiation dose, videos generated by GenDSA were perfectly consistent with real videos. Objective evaluations demonstrated that GenDSA's performance (PSNR = 36.83, SSIM = 0.911, generation time = 0.07 s/frame) surpassed state-of-the-art algorithms. Subjective ratings and statistical results from five doctors indicated no significant difference between real and generated videos. Furthermore, the generated videos were comparable to real videos in overall quality (4.905 vs. 4.935) and lesion assessment (4.825 vs. 4.860). CONCLUSIONS: With clear clinical and translational values, the developed GenDSA can significantly reduce radiation damage to both doctors and patients during DSA-guided procedures. FUNDING: This study was supported by the National Key R&D Program and the National Natural Science Foundation of China.

Efficacy and Safety of Non-Vitamin K Antagonist Oral Anticoagulants Compared with Vitamin K Antagonists in Patients with Atrial Fibrillation and Type 2 Valvular Heart Disease: A Systematic Review and Meta-Analysis.

PURPOSE: This meta-analysis aimed to evaluate the efficacy and safety of non-vitamin K antagonist oral anticoagulants (NOACs) compared with vitamin K antagonists (VKAs) in patients with atrial fibrillation (AF) and type 2 valvular heart disease (VHD). METHODS: We searched the PubMed, LILACS, and MEDLINE databases to retrieve, randomized controlled trials (RCTs) comparing NOACs and VKAs in patients with AF and type 2 VHD, excluding mitral stenosis (moderate to severe, of rheumatic origin) or mechanical heart valves. The efficacy outcomes assessed were stroke and systemic embolism (SE), while safety outcomes included major bleeding and intracranial hemorrhage (ICH). RESULTS: Seven RCTs, including 16,070 patients with AF and type 2 VHD, were included. NOACs reduced the risk of stroke/SE (relative risk [RR], 0.75; 95% confidence interval [CI], 0.64-0.89; P = 0.0005), with no significant difference in major bleeding (RR, 0.88; 95% CI, 0.64-1.21; P = 0.43). The risk of ICH was reduced with NOACs (RR, 0.46; 95% CI, 0.27-0.77; P = 0.003). For patients with AF and bioprosthetic heart valve (five trials, 2805 patients), stroke/SE risks (RR, 0.65, 95% CI, 0.44-0.96) with NOACs were superior to VKAs. Major bleeding risks without ENVISAGE TAVI AF trial (RR, 0.53; 95% CI, 0.30-0.94; P = 0.03) with NOACs were superior to VKAs. The risks of ICH (RR, 0.61; 95% CI 0.34-1.09; P = 0.09) with NOACs were comparable to VKAs. CONCLUSIONS: NOACs demonstrate efficacy and safety in patients with AF and type 2 VHD and reduce the risk of stroke/SE and ICH when compared with those with VKAs.

Enhancing lymph node metastasis prediction in adenocarcinoma of the esophagogastric junction: A study combining radiomic with clinical features.

BACKGROUND: The incidence of adenocarcinoma of the esophagogastric junction (AEJ) is increasing, and with poor prognosis. Lymph node status (LNs) is particularly important for planning treatment and evaluating the prognosis of patients with AEJ. However, the use of radiomic based on enhanced computed tomography (CT) to predict the preoperative lymph node metastasis (PLNM) status of the AEJ has yet to be reported. PURPOSE: We sought to investigate the value of radiomic features based on enhanced CT in the accurate prediction of PLNM in patients with AEJ. METHODS: Clinical features and enhanced CT data of 235 patients with AEJ from October 2017 to May 2023 were retrospectively analyzed. The data were randomly assigned to the training cohort (n = 164) or the external testing cohort (n = 71) at a ratio of 7:3. A CT-report model, clinical model, radiomic model, and radiomic-clinical combined model were developed to predict PLNM in patients with AEJ. Univariate and multivariate logistic regression were used to screen for independent clinical risk factors. Least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomic features. Finally, a nomogram for the preoperative prediction of PLNM in AEJ was constructed by combining Radiomics-score and clinical risk factors. The models were evaluated by area under the receiver operating characteristic curve (AUC-ROC), calibration curve, and decision curve analyses. RESULTS: A total of 181 patients (181/235, 77.02%) had LNM. In the testing cohort, the AUC of the radiomic-clinical model was 0.863 [95% confidence interval (CI) = 0.738-0.957], and the radiomic model (0.816; 95% CI = 0.681-0.929), clinical model (0.792; 95% CI = 0.677-0.888), and CT-report model (0.755; 95% CI = 0.647-0.840). CONCLUSION: The radiomic-clinical model is a feasible method for predicting PLNM in patients with AEJ, helping to guide clinical decision-making and personalized treatment planning.

Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning.

INTRODUCTION: Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deep-learning (DL) features. METHODS: We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. Sixty-nine quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software, and 2,000 DL features were extracted by VGG-16 method. The logistic regression method was employed to identify AECOPD patients, and 29 patients of external validation cohort were used to access the robustness of the results. RESULTS: The model 3-B achieved an area under the receiver operating characteristic curve (AUC) of 0.933 and 0.865 in the testing cohort and external validation cohort, respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, model 7-I achieved an AUC of 0.938 and 0.872 in the testing cohort and external validation cohort, respectively. CONCLUSIONS: DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.

Quantifying cerebral blood flow changes using arterial spin labeling: A comparative study of idiopathic rapid eye movement sleep behavior disorder and Parkinson's disease.

INTRODUCTION: Idiopathic rapid eye movement sleep behavior disorder (iRBD) and Parkinson's disease (PD) have been found to have changes in cerebral perfusion and overlap of some of the lesioned brain areas. However, a consensus regarding the specific location and diagnostic significance of these cerebral blood perfusion alternations remains elusive in both iRBD and PD. The present study evaluated the patterns of cerebral blood flow changes in iRBD and PD. MATERIAL AND METHODS: A total of 59 right-handed subjects were enrolled, including 15 patients with iRBD, 20 patients with PD, and 24 healthy controls (HC). They were randomly divided into groups at a ratio of 4 to 1 for training and testing. A PASL sequence was employed to obtain quantitative cerebral blood flow (CBF) maps. The CBF values were calculated from these acquired maps. In addition, AutoGluon was employed to construct a classifier for CBF features selection and classification. An independent t-test was performed for CBF variations, with age and sex as nuisance variables. The performance of the feature was evaluated using receiver operating characteristic (ROC) curves. A significance level of P < 0.05 was considered significant. CBF in several brain regions, including the left median cingulate and paracingulate gyri and the right middle occipital gyrus (MOG), showed significant differences between PD and HC, demonstrating good classification performance. The combined model that integrates all features achieved even higher performance with an AUC of 0.9380. Additionally, CBF values in multiple brain regions, including the right MOG and the left angular gyrus, displayed significant differences between PD and iRBD. Particularly, CBF values in the left angular gyrus exhibited good performance in classifying PD and iRBD. The combined model achieved improved performance, with an AUC of 0.8533. No significant differences were found in brain regions when comparing CBF values between iRBD and HC subjects. CONCLUSIONS: ASL-based quantitative CBF change features can offer reliable biomarkers to assist in the diagnosis of PD. Regarding the characteristic of CBF in the right MOG, it is anticipated to serve as an imaging biomarker for predicting the progression of iRBD to PD.

Lower fractional dimension in Alzheimer's disease correlates with reduced locus coeruleus signal intensity.

This study aimed to determine the pattern of fractional dimension (FD) in Alzheimer's disease (AD) patients, and investigate the relationship between FD and the locus coeruleus (LC) signal intensity.A total of 27 patients with AD and 25 healthy controls (HC) were collected to estimate the pattern of fractional dimension (FD) and cortical thickness (CT) using the Computational Anatomy Toolbox (CAT12), and statistically analyze between groups on a vertex level using statistical parametric mapping 12. In addition, they were examined by neuromelanin sensitive MRI(NM-MRI) technique to calculate the locus coeruleus signal contrast ratios (LC-CRs). Additionally, correlations between the pattern of FD and LC-CRs were further examined.Compared to HC, AD patients showed widespread lower CT and FD Furthermore, significant positive correlation was found between local fractional dimension (LFD) of the left rostral middle frontal cortex and LC-CRs. Results suggest lower cortical LFD is associated with LCCRs that may reflect a reduction due to broader neurodegenerative processes. This finding may highlight the potential utility for advanced measures of cortical complexity in assessing brain health and early identification of neurodegenerative processes.

A Longitudinal Study of Sleep Habits and Leukemia Incidence Among Postmenopausal Women.

We sought to assess the relationship between sleep duration, sleep disturbance, and leukemia incidence among postmenopausal women. This study included 130,343 postmenopausal women aged 50-79 years who were enrolled in the Women's Health Initiative (WHI) during 1993-1998. Information on self-reported typical sleep duration and sleep disturbance was obtained by questionnaire at baseline, and sleep disturbance level was defined according to the Women's Health Initiative Insomnia Rating Scale (WHIIRS). WHIIRS scores of 0-4, 5-8, and 9-20 comprised 37.0%, 32.6%, and 30.4% of all women, respectively. After an average of 16.4 years (2,135,109 cumulative person-years) of follow-up, 930 of the participants were identified as having incident leukemia. Compared with women with the lowest level of sleep disturbance (WHIIRS score 0-4), women with higher sleep disturbance levels (WHIIRS scores of 5-8 and 9-20) had 22% (95% confidence interval (CI): 1.04, 1.43) and 18% (95% CI: 1.00, 1.40) excess risks of leukemia, respectively, after multivariable adjustment. A significant dose-response trend was found for the association between sleep disturbance and leukemia risk (P for trend = 0.048). In addition, women with the highest level of sleep disturbance had a higher risk of myeloid leukemia (for WHIIRS score 9-20 vs. WHIIRS score 0-4, hazard ratio = 1.39, CI: 1.05, 1.83). Higher sleep disturbance level was associated with increased risk of leukemia, especially for myeloid leukemia among postmenopausal women.

LN-Net: Perfusion Pattern-Guided Deep Learning for Lymph Node Metastasis Diagnosis Based on Contrast-Enhanced Ultrasound Videos.

OBJECTIVE: The blood flow in lymph nodes reflects important pathological features. However, most intelligent diagnosis based on contrast-enhanced ultrasound (CEUS) video focuses only on CEUS images, ignoring the process of extracting blood flow information. In the work described here, a parametric imaging method for describing blood perfusion pattern was proposed and a multimodal network (LN-Net) to predict lymph node metastasis was designed. METHODS: First, the commercially available artificial intelligence object detection model YOLOv5 was improved to detect the lymph node region. Then the correlation and inflection point matching algorithms were combined to calculate the parameters of the perfusion pattern. Finally, the Inception-V3 architecture was used to extract the image features of each modality, with the blood perfusion pattern taken as the guiding factor in fusing the features with CEUS by sub-network weighting. DISCUSSION: The average precision of the improved YOLOv5s algorithm compared with baseline was improved by 5.8%. LN-Net predicted lymph node metastasis with 84.9% accuracy, 83.7% precision and 80.3% recall. Compared with the model without blood flow feature guidance, accuracy was improved by 2.6%. The intelligent diagnosis method has good clinical interpretability. CONCLUSION: A static parametric imaging map could describe a dynamic blood flow perfusion pattern, and as a guiding factor, it could improve the classification ability of the model with respect to lymph node metastasis.

Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements.

Purpose: Quantitative computed tomography (QCT) techniques, focusing on airway anatomy and emphysema, may help to detect early structural changes of COPD disease. This retrospective study aims to identify high-risk COPD participants by using QCT measurements. Patients and Methods: We enrolled 140 participants from the Second Affiliated Hospital of Shenyang Medical College who completed inspiratory high-resolution CT scans, pulmonary function tests (PFTs), and clinical characteristics recorded. They were diagnosed Non-COPD by PFT value of FEV1/FVC >70% and divided into two groups according percentage predicted FEV1 (FEV1%), low-risk COPD group: FEV1% ≥ 95%, high-risk group: 80% < FEV1% < 95%. The QCT measurements were analyzed by the Student's t-test (or Mann-Whitney U-test) method. Then, feature candidates were identified using the LASSO method. Meanwhile, the correlation between QCT measurements and PFTs was assessed by the Spearman rank correlation test. Furthermore, support vector machine (SVM) was performed to identify high-risk COPD participants. The performance of the models was evaluated in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-score, and area under the ROC curve (AUC), with p <0.05 considered statistically significant. Results: The SVM based on QCT measurements achieved good performance in identifying high-risk COPD patients with 85.71% of ACC, 88.34% of SEN, 84.00% of SPE, 83.33% of F1-score, and 0.93 of AUC. Further, QCT measurements integration of clinical data improved the performance with an ACC of 90.48%. The emphysema index (%LAA-950) of left lower lung was negatively correlated with PFTs (P < 0.001). The airway anatomy indexes of lumen diameter (LD) were correlated with PFTs. Conclusion: QCT measurements combined with clinical information could provide an effective tool for an early diagnosis of high-risk COPD. The QCT indexes can be used to assess the pulmonary function status of high-risk COPD.

Decreased locus coeruleus signal associated with Alzheimer's disease based on neuromelanin-sensitive magnetic resonance imaging technique.

Objective: Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) technique was used to detect the changes of the locus coeruleus (LC) signals in Alzheimer's disease patients (AD), and to analyze its correlation with cognitive function. Materials and methods: A total of 27 patients with AD, 15 patients with mild cognitive impairment (MCI), and 25 healthy controls (HC) were examined by NM-MRI technique. ImageJ software was used to measure the LC signals. The locus coeruleus signal contrast ratios (LC-CRs) were calculated, along with the measurement of neuropsychological scales. Results: The LC-CRs of AD patients were significantly different from that of HC (p = 0.007, 95% CI: -0.053∼-0.007). However, such significant differences were not observed between MCI and HC (p = 1.000, 95% CI: -0.030∼0.024), AD and MCI (p = 0.050, 95% CI: -0.054∼0.000). Furthermore, a significant positive correlation was identified between LC-CRs and MMSE sub item Drawing (r = 0.484, p = 0.011) in the AD group, MoCA sub item Attention (r = 0.519, p = 0.047) in the MCI group. The area under the curve of LC-CRs in the diagnosis of AD was 0.749 (p = 0.002, 95% CI: 0.618∼0.880), with a sensitivity of 85.2% and a specificity of 56.0%. Conclusion: The NM-MRI technique could quantify the pathological degenerations of the LC in AD. Such LC degenerations can be employed to distinguish AD from healthy elderly.

Dietary selenium intake and thyroid cancer risk in postmenopausal women.

OBJECTIVE: It has been suggested that higher selenium intake and consumption of supplements protect against several cancers. To our knowledge, epidemiologic evidence is rare and inconsistent on the association of selenium level and the risk for thyroid cancer. Therefore, the aim of this study was to examine the association between selenium intake and thyroid cancer risk in postmenopausal women using the Women's Health Initiative (WHI) database. METHODS: The WHI recruited 161 808 postmenopausal women 50 to 79 y of age between September 1, 1993 and December 31, 1998. The present study included 147 348 women 63.15 y of age (SD = 7.21) at baseline. The main exposure was baseline total selenium intake including dietary selenium measured by food frequency questionnaire (FFQ) and supplemental selenium. The outcome was thyroid cancer, which was adjudicated by trained physicians. Cox proportional hazard models were used to analyze the association. RESULTS: During a mean follow-up of 16.4 y until September 30, 2020, 442 thyroid cancer cases were identified. There was no significant association between total selenium intake and thyroid cancer risk after adjusting for multiple covariates (highest versus lowest quartile: hazard ratio [HR], 0.88; 95% confidence interval [CI], 0.60-1.29). Association between total selenium intake and the risk for papillary thyroid cancer was also not significant (highest versus lowest quartile: HR, 1.02; 95% CI, 0.66-1.52). CONCLUSIONS: The present data did not support that either total or dietary selenium intake was associated with the risk for thyroid cancer or the papillary subtype in postmenopausal women ages 50 to 79 y in the United States.

Colposcopic multimodal fusion for the classification of cervical lesions.

Objective: Cervical cancer is one of the two biggest killers of women and early detection of cervical precancerous lesions can effectively improve the survival rate of patients. Manual diagnosis by combining colposcopic images and clinical examination results is the main clinical diagnosis method at present. Developing an intelligent diagnosis algorithm based on artificial intelligence is an inevitable trend to solve the objectification of diagnosis and improve the quality and efficiency of diagnosis.Approach: A colposcopic multimodal fusion convolutional neural network (CMF-CNN) was proposed for the classification of cervical lesions. Mask region convolutional neural network was used to detect the cervical region while the encoding network EfficientNet-B3 was introduced to extract the multimodal image features from the acetic image and iodine image. Finally, Squeeze-and-Excitation, Atrous Spatial Pyramid Pooling, and convolution block were also adopted to encode and fuse the patient's clinical text information.Main results: The experimental results showed that in 7106 cases of colposcopy, the accuracy, macro F1-score, macro-areas under the curve of the proposed model were 92.70%, 92.74%, 98.56%, respectively. They are superior to the mainstream unimodal image classification models.Significance: CMF-CNN proposed in this paper combines multimodal information, which has high performance in the classification of cervical lesions in colposcopy, so it can provide comprehensive diagnostic aid.

Chitosan based film reinforced with EGCG loaded melanin-like nanocomposite (EGCG@MNPs) for active food packaging.

In this study, EGCG loaded melanin-like nanoparticles (EGCG@MNPs) were incorporated into chitosan matrix to prepare an active nanocomposite food packaging film, chitosan-EGCG@MNPs (CH-EM). The influence of EGCG@MNPs on the physical and biological properties of the chitosan film was investigated. The EGCG@MNPs nanoparticles were cross-linked with chitosan through intermolecular hydrogen bonds and uniformly distributed in the matrix. Besides, the incorporation of EGCG@MNPs tremendously improved the solubility, swelling ratio and water vapor barrier properties of the film, and permitted superior ultraviolet rays blocking property. In addition, the mechanical properties, thermal stability and surface hydrophobicity have also been significantly improved. The CH-EM2.0 nanocomposite films also showed excellent oxidation resistance (58.4 ± 4.4%, DPPH and 92.4 ± 1.3%, ABTS+), and strong inhibitory ability against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). The experimental results comprehensively showed that the prepared chitosan-EGCG@MNPs nanocomposite film offering excellent potential for eco-friendly active food packaging.

T2 MRI at 3T of cartilage and menisci in patients with hyperuricemia: initial findings.

OBJECTIVE: To compare and evaluate T2 values of compartmental femorotibial cartilage and subregional menisci in patients with hyperuricemia at 3T. MATERIALS AND METHODS: Thirty-two subjects were included in this study and subdivided into two subgroups: 15 healthy controls (3 females, 12 males; mean age = 45.3 ± 10.9 years), 17 patients with hyperuricemia (2 females, 15 males; mean age = 44.4 ± 12.7 years). All subjects were assessed on a 3T MR scanner using an 8-channel phased-array knee coil (transmit-receive). Wilcoxon rank sum test and analysis of covariance (ANCOVA) were performed to determine whether there were any statistically significant differences in T2 values of compartmental femorotibial cartilage and subregional menisci between the two subgroups. RESULTS: Lateral tibial cartilage (48.6 ± 3.5 ms) in healthy subgroup had significantly lower (p < 0.05) T2 values than all subcompartments of femorotibial cartilage in hyperuricemia subgroup. Medial tibial cartilage (56.5 ± 4.3 ms) in hyperuricemia subgroup had significantly higher (p < 0.05) T2 values than all subcompartments of femorotibial cartilage except medial tibial cartilage in healthy subgroup. Medial anterior horn of meniscus (39.4 ± 2.9 ms) in healthy subgroup had significantly lower (p < 0.05) T2 values than all subregional menisci except both medial anterior horn and medial body segment of meniscus in hyperuricemia subgroup. CONCLUSION: T2 values in certain compartmental femorotibial cartilage and subregional menisci in patients with hyperuricemia are evidently and abnormally heightened compared with those in healthy subjects, to which special attention should be paid when diagnosing and treating the patients with hyperuricemia in the clinical setting. The LT cartilage had significantly lower T2 values (48.6 ± 3.5 ms) in healthy subgroup compared to all compartmental femorotibial cartilage in cohort with HU. MF cartilage had significantly lower T2 values (51.6 ± 2.9 ms) in healthy subgroup compared to both LF (54.4 ± 4.1 ms) and MT (56.5 ± 4.3 ms) in cohort with HU. MT cartilage had significantly higher T2 values (56.5 ± 4.3 ms) in cohort with HU subgroup compared to LF (52.5 ± 3.0 ms) in healthy subgroup. T2 mapping may be promising and potential sensitive discriminator of understanding and examining the early compositional and structural change in proteoglycan-collagen matrix of human femorotibial cartilage in patients with hyperuricemia.

Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study.

Accumulating evidence shows that brain functional deficits may be impacted by damage to remote brain regions. Recent advances in neuroimaging suggest that stroke impairment can be better predicted based on disruption to brain networks rather than from lesion locations or volumes only. Our aim was to explore the feasibility of predicting post-stroke somatosensory function from brain functional connectivity through the application of machine learning techniques. Somatosensory impairment was measured using the Tactile Discrimination Test. Functional connectivity was employed to model the global brain function. Behavioral measures and MRI were collected at the same timepoint. Two machine learning models (linear regression and support vector regression) were chosen to predict somatosensory impairment from disrupted networks. Along with two feature pools (i.e., low-order and high-order functional connectivity, or low-order functional connectivity only) engineered, four predictive models were built and evaluated in the present study. Forty-three chronic stroke survivors participated this study. Results showed that the regression model employing both low-order and high-order functional connectivity can predict outcomes based on correlation coefficient of r = 0.54 (p = 0.0002). A machine learning predictive approach, involving high- and low-order modelling, is feasible for the prediction of residual somatosensory function in stroke patients using functional brain networks.

Active Surveillance, Drug Resistance, and Genotypic Profiling of Staphylococcus aureus Among School-Age Children in China.

Methicillin-susceptible (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) nasal colonization predisposes individuals for endogenous infections and is a major threat to children. Recently, oxacillin/cefoxitin-susceptible mecA-positive S. aureus (OS-MRSA) has been reported worldwide. Herein, a prospective, cross-sectional study was conducted across five schools, representing three educational stages, in Guangzhou, China. Nasal swabs from 2,375 students were cultured for S. aureus and all isolates were subjected to antibiotic susceptibility testing phenotypically and confirmed by femB and mecA genetic detection; all the isolates were classified as MSSA, MRSA, or OS-MRSA. All strains were also analyzed by multi-locus sequence typing. Among the 2,375 swabs, S. aureus was detected in 744 children (31.3%, 95% CI: 25.9-36.7%), of whom 72 had MRSA (3.0%, 95% CI: 0.6-5.4%) and 4 had OS-MRSA (0.2%, 95% CI: 0.1-0.3%), of which an oxacillin- and cefoxitin-susceptible MRSA strain was identified. The prevalence of S. aureus and MRSA was higher in younger children. The highest percentage of drug resistance of the S. aureus isolates (n = 744) was to penicillin (85.5%), followed by erythromycin (43.3%) and clidamycin (41.0%). The most prevalent sequence types (STs) were ST30, ST45, and ST188 in MSSA, accounting for 38.7% of the total isolates, whereas ST45, ST59, and ST338 accounted for 74.6% of the MRSA isolates and ST338 accounted for 50.0% of the OS-MRSA isolates. The MRSA and OS-MRSA isolates (n = 76) were grouped into three clades and one singleton, with clonal complex (CC) 45 as the most predominant linkage. The top nine multi-locus sequence typing-based CCs (CC30, CC45, CC5, CC1, CC15, CC944, CC398, CC59, CC7) represented 86.7% of all S. aureus isolates. All CC30 isolates were resistant to erythromycin and clidamycin, and almost all these isolates were also resistant to penicillin (99.2%). The CC45 and CC59 isolates exhibited high resistance rates to oxacillin at 31.5 and 59.0%, respectively. This study provides updated data valuable for designing effective control strategies to mitigate the burden of disease and to improve the adequacy of empirical antimicrobial treatments for potentially harmful infections.

Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities.

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.

Structural Connectivity Remote From Lesions Correlates With Somatosensory Outcome Poststroke.

Background and Purpose: Changes in connectivity of white matter fibers remote to a stroke lesion, suggestive of structural connectional diaschisis, may impact on clinical impairment and recovery after stroke. However, until recently, we have not had tract-specific techniques to map changes in white matter tracts in vivo in humans to enable investigation of potential mechanisms and clinical impact of such remote changes. Our aim was to identify and quantify white matter tracts that are affected remote from a stroke lesion and to investigate the associations between reductions in tract-specific connectivity and impaired touch discrimination function after stroke. Methods: We applied fixel-based analysis to diffusion magnetic resonance imaging data from 37 patients with stroke (right lesion =16; left lesion =21) and 26 age-matched healthy adults. Three quantitative metrics were compared between groups: fiber density; fiber-bundle cross-section; and a combined measure of both (fiber-bundle cross-section) that reflects axonal structural connectivity. Results: Compared with healthy adults, patients with stroke showed significant common fiber-bundle cross-section and fiber density reductions in 4 regions remote from focal lesions that play roles in somatosensory and spatial information processing. Structural connectivity along the somatosensory fibers of the lesioned hemisphere was correlated with contralesional hand touch function. Touch function of the ipsilesional hand was associated with connectivity of the superior longitudinal fasciculus, and, for the right-lesion group, the corpus callosum. Conclusions: Remote tract-specific reductions in axonal connectivity indicated by diffusion imaging measures are observed in the somatosensory network after stroke. These remote white matter connectivity reductions, indicative of structural connectional diaschisis, are associated with touch impairment in patients with stroke.