Pulmonary function, genetic predisposition, and the risk of cirrhosis: A prospective cohort study.

OBJECTIVE: Pulmonary function is associated with the development of chronic liver disease. However, evidence of the association between pulmonary function and cirrhosis risk is still lacking. This study aimed to investigate the longitudinal associations of pulmonary function with the development of cirrhosis, and to explore whether genetic predisposition to cirrhosis could modify these associations. METHODS: Of 294,835 participants free of cirrhosis and had undergone spirometry at baseline from the UK Biobank were included. Cirrhosis diagnoses were ascertained through linked hospital records and death registries. Cox proportional hazard models were employed to investigate the longitudinal associations between pulmonary function, genetic predisposition, and cirrhosis risk. RESULTS: During a median follow-up of 12.0 years, 2598 incident cirrhosis cases were documented. Compared to individuals with normal spirometry findings, those with preserved ratio impaired spirometry (PRISm) findings (hazard ratio [HR] and 95% confidence interval [CI]: 1.32 [1.18, 1.48]) and airflow obstruction (HR [95%CI]: 1.19 [1.07, 1.31]) had a higher risk of developing cirrhosis after adjustments. These associations were consistent across all categories of genetic predisposition, with no observed modifying effect of genetic predisposition. In joint exposure analyses, the highest risk was observed in individuals with both a high genetic predisposition for cirrhosis and PRISm findings (HR [95% CI]: 1.74 [1.45, 2.08]). CONCLUSIONS: Our findings indicate that worse pulmonary function is a significant risk factor of cirrhosis, irrespective of genetic predisposition. Early identification and appropriate intervention for pulmonary function may lead to more effective healthcare resource utilization and reduce the burden associated with cirrhosis.

Neuroimage analysis using artificial intelligence approaches: a systematic review.

In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023. To maintain rigor and credibility, stringent inclusion criteria, quality assessments, and precise data extraction protocols were consistently enforced throughout this review. Following a rigorous selection process, 104 studies were selected for review, focusing on diverse neuroimaging modalities with an emphasis on mental and neurological disorders. Among these, 19.2% addressed mental illness, and 80.7% focused on neurological disorders. It is found that the prevailing clinical tasks are disease classification (58.7%) and lesion segmentation (28.9%), whereas image reconstruction constituted 7.3%, and image regression and prediction tasks represented 9.6%. AI-driven neuroimaging analysis holds tremendous potential, transforming both research and clinical applications. Machine learning and deep learning algorithms outperform traditional methods, reshaping the field significantly.

Hybrid transformer convolutional neural network-based radiomics models for osteoporosis screening in routine CT.

OBJECTIVE: Early diagnosis of osteoporosis is crucial to prevent osteoporotic vertebral fracture and complications of spine surgery. We aimed to conduct a hybrid transformer convolutional neural network (HTCNN)-based radiomics model for osteoporosis screening in routine CT. METHODS: To investigate the HTCNN algorithm for vertebrae and trabecular segmentation, 92 training subjects and 45 test subjects were employed. Furthermore, we included 283 vertebral bodies and randomly divided them into the training cohort (n = 204) and test cohort (n = 79) for radiomics analysis. Area receiver operating characteristic curves (AUCs) and decision curve analysis (DCA) were applied to compare the performance and clinical value between radiomics models and Hounsfield Unit (HU) values to detect dual-energy X-ray absorptiometry (DXA) based osteoporosis. RESULTS: HTCNN algorithm revealed high precision for the segmentation of the vertebral body and trabecular compartment. In test sets, the mean dice scores reach 0.968 and 0.961. 12 features from the trabecular compartment and 15 features from the entire vertebral body were used to calculate the radiomics score (rad score). Compared with HU values and trabecular rad-score, the vertebrae rad-score suggested the best efficacy for osteoporosis and non-osteoporosis discrimination (training group: AUC = 0.95, 95%CI 0.91-0.99; test group: AUC = 0.97, 95%CI 0.93-1.00) and the differences were significant in test group according to the DeLong test (p < 0.05). CONCLUSIONS: This retrospective study demonstrated the superiority of the HTCNN-based vertebrae radiomics model for osteoporosis discrimination in routine CT.

Cortical surface analysis for focal cortical dysplasia diagnosis by using PET images.

Focal cortical dysplasia (FCD) is a neurological disorder distinguished by faulty brain cell structure and development. Repetitive and uncontrollable seizures may be linked to FCD's aberrant cortical thickness, gyrification, and sulcal depth. Quantitative cortical surface analysis is a crucial alternative to ineffective visual inspection. This study recruited 42 subjects including 22 FCD patients who underwent surgery and 20 healthy controls (HC). For the FCD patients, T1-weighted and PET images were obtained by a PET-MRI scanner, and the confirmed epileptogenic zone (EZ) was collected from postsurgical follow-up. For the HCs, CT and PET images were obtained by a PET-CT scanner. Cortical thickness, gyrification index, and sulcal depth were calculated using a computational anatomical toolbox (CAT12). A cluster-based analysis is carried out to determine each FCD patient's aberrant cortical surface. After parcellating the cerebral cortex into 68 regions by the Desikan-Killiany atlas, a region of interest (ROI) analysis was conducted to know whether the feature in the FCD group is significantly different from that in the HC group. Finally, the features of all ROIs were utilised to train a support vector machine classifier (SVM). The classification performance is evaluated by the leave-one-out cross-validation. The cluster-based analysis can localize the EZ cluster with the highest accuracy of 54.5 % (12/22) for cortical thickness, 40.9 % (9/22) and 13.6 % (3/22) for sulcal depth and gyrification, respectively. Moderate concordance (Kappa, 0.6) is observed between the confirmed EZs and identified clusters by using the cortical thickness. Fair concordance (Kappa, 0.3) and no concordance (Kappa, 0.1) is found by using sulcal depth and gyrification. Significant differences are found in 46 of 68 regions (67.7 %) for the three measures. The trained SVM classifier achieved a prediction accuracy of 95.5 % for the cortical thickness, while the sulcal depth and the gyrification obtained 86.0 % and 81.5 %. Cortical thickness, as determined by quantitative cortical surface analysis of PET data, has a greater ability than sulcal depth and gyrification to locate aberrant EZ clusters in FCD. Surface measures might be different in many regions for FCD and HC. By integrating machine learning and cortical morphologies features, individual prediction of FCD seems to be feasible.

Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis.

Objective: Epilepsy is considered as a neural network disorder. Seizure activity in epilepsy may disturb brain networks and damage brain functions. We propose using resting-state functional magnetic resonance imaging (rs-fMRI) data to characterize connectivity patterns in drug-resistant epilepsy. Methods: This study enrolled 47 participants, including 28 with drug-resistant epilepsy and 19 healthy controls. Functional and effective connectivity was employed to assess drug-resistant epilepsy patients within resting state networks. The resting state functional connectivity (FC) analysis was performed to assess connectivity between each patient and healthy controls within the default mode network (DMN) and the dorsal attention network (DAN). In addition, dynamic causal modeling was used to compute effective connectivity (EC). Finally, a statistical analysis was performed to evaluate our findings. Results: The FC analysis revealed significant connectivity changes in patients giving 64.3% (18/28) and 78.6% (22/28) for DMN and DAN, respectively. Statistical analysis of FC was significant between the medial prefrontal cortex, posterior cingulate cortex, and bilateral inferior parietal cortex for DMN. For DAN, it was significant between the left and the right intraparietal sulcus and the frontal eye field. For the DMN, the patient group showed significant EC connectivity in the right inferior parietal cortex and the medial prefrontal cortex for the DMN. There was also bilateral connectivity between the medial prefrontal cortex and the posterior cingulate cortex, as well as between the left and right inferior parietal cortex. For DAN, patients showed significant connectivity in the right frontal eye field and the right intraparietal sulcus. Bilateral connectivity was also found between the left frontal eye field and the left intraparietal sulcus, as well as between the right frontal eye field and the right intraparietal sulcus. The statistical analysis of the EC revealed a significant result in the medial prefrontal cortex and the right intraparietal cortex for the DMN. The DAN was found significant in the left frontal eye field, as well as the left and right intraparietal sulcus. Conclusion: Our results provide preliminary evidence to support that the combination of functional and effective connectivity analysis of rs-fMRI can aid in diagnosing epilepsy in the DMN and DAN networks.

The non-linear link between non-high-density lipoprotein to high-density lipoprotein ratio and the risk of stroke in middle-aged and older adults in Chinese: a prospective cohort study from the China health and retirement longitudinal study.

Objective: This study aims to assess the association between the non-HDL-c/HDL-c ratio and stroke risk among middle-aged and older adults participating in the China Health and Retirement Longitudinal Study (CHARLS). Methods: This study conducted a prospective cohort analysis, enrolling a total of 10,183 participants who met the designated criteria from CHARLS between 2011 and 2012. We then used the Cox proportional-hazards regression model to explore the relationship between baseline non-HDL-c/HDL-c ratio and stroke risk. Using a Cox proportional hazards regression with cubic spline function, we were able to identify the non-linear relationship between the non-HDL-c/HDL-c ratio and stroke occurrence. A series of sensitivity analyses were also carried out. Results: The average age of the participants included in this study was 59.16 ± 9.35 years, and 4,735 individuals (46.68%) were male. Over a median follow-up period of 7.0 years, a total of 1,191 people (11.70%) experienced a stroke. Using a Cox proportional hazards regression model that was fully adjusted, we found no statistically significant correlation between the non-HDL-c/HDL-c ratio and the risk of stroke (HR=1.022; 95% CI 0.964, 1.083). Nevertheless, we did observe a non-linear relationship and saturation effect between the non-HDL-c/HDL-c ratio and stroke. Employing a two-piece Cox proportional hazards regression model and a recursive algorithm, we determined an inflection point of 2.685 for the non-HDL-c/HDL-c ratio. In instances where the non-HDL-c/HDL-c ratio fell below 2.685, for every 1-unit decrease in the non-HDL-c/HDL-c ratio, the likelihood of stroke decreased by 21.4% (HR=1.214, 95% CI: 1.039-1.418). In contrast, when the non-HDL-c/HDL-c ratio exceeded 2.685, there was no statistically significant change in the risk of stroke for each unit decrease in the non-HDL-c/HDL-c ratio (HR: 0.967, 95% CI: 0.897-1.042). The consistency of these findings across multiple sensitivity analyses suggests their robustness. Conclusion: This study unveils a non-linear relationship between the non-HDL-c/HDL-c ratio and stroke risk in middle-aged and older adults in China. Specifically, when the non-HDL-c/HDL-c ratio was below 2.685, a significant and clearly positive association with stroke risk was observed. Additionally, maintaining the non-HDL-c/HDL-c ratio below 2.685 could potentially lead to a substantial reduction in the risk of stroke.

Epileptogenic Zone Localization in Refractory Epilepsy by FDG-PET: The Comparison of SPM and SPM-CAT With Different Parameter Settings.

Refractory epilepsy is a complex case of epileptic disease. The quantitative analysis of fluorodeoxyglucose positron emission tomography (FDG-PET) images complements visual assessment and helps localize the epileptogenic zone (EZ) for better curative treatment. Statistical parametric mapping (SPM) and its computational anatomy toolbox (SPM-CAT) are two commonly applied tools in neuroimaging analysis. This study compares SPM and SPM-CAT with different parameters to find the optimal approach for localizing EZ in refractory epilepsy. The current study enrolled 45 subjects, including 25 refractory epilepsy patients and 20 healthy controls. All of the 25 patients underwent surgical operations. Pathological results and the postoperative outcome evaluation by the Engel scale were likewise presented. SPM and SPM-CAT were used to assess FDG-PET images with three different uncorrected p-values and the corresponding cluster sizes (k), as in voxels in the cluster, namely p < 0.0002, k > 25; p < 0.001, k > 100; p < 0.005, and k > 200. When combining three settings, SPM and SPM-CAT yielded overall positive finding scores of 96.0% (24/25) and 100.0% (25/25) respectively. However, for the individual setting, SPM-CAT achieved the diverse positive finding scores of 96.0% (24/25), 96.0% (24/25), and 88.0% (22/24), which are higher than those of SPM [88.0% (22/25), 76.0% (19/25), and 72.0% (18/25)]. SPM and SPM-CAT localized EZ correctly with 28.0% (7/25) and 64.0% (16/25), respectively. SPM-CAT with parameter settings p < 0.0002 and k > 25 yielded a correct localization at 56.0% (14/25), which is slightly higher than that for the other two settings (48.0 and 20.0%). Moderate concordance was found between the confirmed and pre-surgical EZs, identified by SPM-CAT (kappa value = 0.5). Hence, SPM-CAT is more efficient than SPM in localizing EZ for refractory epilepsy by quantitative analysis of FDG-PET images. SPM-CAT with the setting of p < 0.0002 and k > 25 might perform as an objective complementary tool to the visual assessment for EZ localization.

Automatic lumbar spinal MRI image segmentation with a multi-scale attention network.

Lumbar spinal stenosis (LSS) is a lumbar disease with a high incidence in recent years. Accurate segmentation of the vertebral body, lamina and dural sac is a key step in the diagnosis of LSS. This study presents an lumbar spine magnetic resonance imaging image segmentation method based on deep learning. In addition, we define the quantitative evaluation methods of two clinical indicators (that is the anteroposterior diameter of the spinal canal and the cross-sectional area of the dural sac) to assist LSS diagnosis. To improve the segmentation performance, a dual-branch multi-scale attention module is embedded into the network. It contains multi-scale feature extraction based on three 3 × 3 convolution operators and vital information selection based on attention mechanism. In the experiment, we used lumbar datasets from the spine surgery department of Shengjing Hospital of China Medical University to evaluate the effect of the method embedded the dual-branch multi-scale attention module. Compared with other state-of-the-art methods, the average dice similarity coefficient was improved from 0.9008 to 0.9252 and the average surface distance was decreased from 6.40 to 2.71 mm.

Sensorimotor cortex atrophy in patients with cervical spondylotic myelopathy.

Previous studies have shown compensatory adaptive changes in cerebral functions before surgery in patients with cervical spondylotic myelopathy (CSM), especially in the sensorimotor cortices. However, the structural changes in the sensorimotor cortices in patients with CSM remain poorly understood. The aim of this study was to assess the volumetric changes in the sensorimotor cortices using morphological MRI and to correlate these changes with clinical scales. We hypothesize that CSM causes atrophy in the sensorimotor cortices, which results in functional changes during CSM progression. The study participants included 30 CSM patients and 25 matched healthy controls. The patients underwent brain morphological MRI before surgery. Compared with the healthy controls, the patients with CSM showed significant atrophy in the primary somatosensory cortex (S1), the primary motor cortex (M1), the somatosensory association cortex, and the supplementary motor area. The gray matter volumes in the S1 and M1 were correlated positively with the motor scores of the Japanese Orthopedic Association in patients with CSM. The change in supplementary motor area correlated with the sphincter scores of the Japanese Orthopedic Association in CSM patients. Our findings provide new insights into the compensatory reaction in CSM patients.

Fluorescence Assay for Ribonuclease H Based on Nonlabeled Substrate and DNAzyme Assisted Cascade Amplification.

As a highly conserved damage repair protein, RNase H can specifically hydrolyze RNA in DNA-RNA chimeric strands. DNAzyme, a synthetic single-stranded DNA consisting of binding and catalytic sites, can cleave RNA in the presence of cofactors. In this study, we establish a highly sensitive RNase H assay assisted with DNAzyme's cleavage property. A DNA-RNA chimeric strand, which contains DNAzyme sequences, is used as the hydrolysis substrate of RNase H. The RNase H hydrolysis of the chimeric substrate results in the release of DNAzyme. Subsegment DNAzyme digest, a molecular beacon, causes a "turn-on" fluorescence signal by disrupting its hairpin structure. Furthermore, the fluorescence signal is amplified by cyclic digestion of DNAzyme to the substrate of molecular beacon. Under the optimal conditions, the detection limit of RNase H is 0.01 U/mL, which is superior to those of several alternative approaches. Additionally, the method was further used for RNase H detection in heterogeneous biological samples as well as to investigate the effects of natural compounds on this enzyme. In summary, these results show that the method not only provides a universal platform for monitoring RNase H activity but also shows great potential in biomedical studies and drug screening.