[Xiaojiang River Basin Ecological Environmental Quality Spatiotemporal Pattern and Evolutionary Trend Analysis Using GEE from 1990 to 2022].

Assessment and monitoring of the quality of the ecological environment in the area is a very important fundamental task in the development of ecological civilization in the Xiaojiang River Basin in Yunnan Province, which serves as a demonstration area for ecological restoration in the upper reaches of the Yangtze River. The Landsat remote sensing images from 1990, 1995, 2000, 2005, 2010, 2014, 2018, and 2022 were chosen, and the four indexes of greenness （NDMVI）, humidity （WET）, dryness （NDBSI）, and heat （LST） were extracted. The remote sensing ecological index （RSEI） was created using the principal component analysis method, then the spatial and temporal patterns and trends of ecological quality in the Xiaojiang River Basin between 1990 and 2022 were examined using the GEE platform, ArcGIS 10.7 platform, and Python platform, combining the analysis methods of geographic information mapping, coefficient of variation, Mann-Kendall trend test, Sen's slope estimation, and Hurst's index. The findings demonstrated that： ① the ecological quality of the study area had more obvious geographic differentiation spatially, and by 2022, the areas with excellent and good ecological quality grades were primarily distributed in the areas with better alpine vegetation cover, and those with poor ecological quality were primarily distributed in the areas of the mudslide ravines with relatively low terrain. On a time scale, the study area's RSEI index increased from 0.41 in 1990 to 0.55 in 2022, with a fluctuating overall trend of ecological quality improvement and an average increase of 0.048（10 a） -1； this progress was directly related to a number of ecological construction initiatives that have been energetically carried out, such as converting farms to forests, preventing mudslides, saving soil and water, managing heavy metal contamination, etc. ② The RSEI was more appropriate for the evaluation of ecological quality in alpine ravine areas because, in comparison to the NDVI index, the NDVMI adopted in this study was more sensitive to vegetation information in topographic undulation areas, especially in shaded areas, and could more accurately and quantitatively describe the vegetation information. ③ The RSEI in the Xiaojiang River Basin had a mean coefficient of variation of 0.202. Overall, its volatility was low, and its high volatility was mostly concentrated in the mudslide gully area along both sides of the Xiaojiang River fracture zone, where the surface was made up of bare rocks and sediment that was easily impacted by the changing of the seasons, the climate, and human activity. ④ The quality of the ecological environment in the region was significantly improving, with the rising area reaching 85.72% of the total area and the declining area accounting for approximately 10.15% of the total area. The future trend of change will be dominated by ongoing improvement and future degradation, accounting for 44.75% and 39.97%, respectively. It is important to pay close attention to areas that could potentially degrade. The findings of this study can serve as a theoretical foundation for additional ecological environmental conservation, management, and sustainable development in the Xiaojiang River Basin.

Construction and validation of a risk score system for diagnosing invasive adenocarcinoma presenting as pulmonary pure ground-glass nodules: a multi-center cohort study in China.

Background: Anxiety-driven clinical interventions have been queried due to the nondeterminacy of pure ground-glass nodules (pGGNs). Although radiomics and radiogenomics aid diagnosis, standardization and reproducibility challenges persist. We aimed to assess a risk score system for invasive adenocarcinoma in pGGNs. Methods: In a retrospective, multi-center study, 772 pGGNs from 707 individuals in The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital were grouped into training (509 patients with 558 observations) and validation (198 patients with 214 observations) sets consecutively from January 2017 to November 2021. An additional test set of 143 observations in Hainan Cancer Hospital was analyzed in the same period. Computed tomography (CT) signs and clinical features were manually collected, and the quantitative parameters were achieved by artificial intelligence (AI). The positive cutoff score was ≥3. Risk scores system 3 combined carcinoma history, chronic obstructive pulmonary disease (COPD), maximum diameters, nodule volume, mean CT values, type II or III vascular supply signs, and other radiographic characteristics. The evaluation included the area under the curves (AUCs), accuracy, sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) for both the risk score systems 1, 2, 3 and the AI model. Results: The risk score system 3 [AUC, 0.840; 95% confidence interval (CI): 0.789-0.890] outperformed the AI model (AUC, 0.553; 95% CI: 0.487-0.619), risk score system 1 (AUC, 0.802; 95% CI: 0.754-0.851), and risk score system 2 (AUC, 0.816; 95% CI: 0.766-0.867), with 88.0% (0.850-0.904) accuracy, 95.6% (0.932-0.972) PPV, 0.620 (0.535-0.702) NPV, 89.6% (0.864-0.920) sensitivity, and 80.6% (0.717-0.872) specificity in the training sets. In the validation and test sets, risk score system 3 performed best with AUCs of 0.769 (0.678-0.860) and 0.801 (0.669-0.933). Conclusions: An AI-based risk scoring system using quantitative image parameters, clinical features, and radiographic characteristics effectively predicts invasive adenocarcinoma in pulmonary pGGNs.

Multiview Classification Through Learning From Interval-Valued Data.

The classification problem concerning crisp-valued data has been well resolved. However, interval-valued data, where all of the observations' features are described by intervals, are also a common data type in real-world scenarios. For example, the data extracted by many measuring devices are not exact numbers but intervals. In this article, we focus on a highly challenging problem called learning from interval-valued data (LIND), where we aim to learn a classifier with high performance on interval-valued observations. First, we obtain the estimation error bound of the LIND problem based on the Rademacher complexity. Then, we give the theoretical analysis to show the strengths of multiview learning on classification problems, which inspires us to construct a new algorithm called multiview interval information extraction (Mv-IIE) approach for improving classification accuracy on interval-valued data. The experiment comparisons with several baselines on both synthetic and real-world datasets illustrate the superiority of the proposed framework in handling interval-valued data. Moreover, we describe an application of Mv-IIE that we can prevent data privacy leakage by transforming crisp-valued (raw) data into interval-valued data.

A novel simplified sonographic approach with fluoroscopy-controlled L5 transforaminal epidural injections in patients with high iliac crest: a retrospective study.

BACKGROUND: To explore a novel ultrasound (US) modality for lumbar transforaminal epidural injections (TFEIs) in patients with low back pain (LBP) and L5 radicular pain combined with high iliac crest (HIC). METHODS: One-hundred and forty-one patients were retrospectively stratified into two groups based on the treatment they received: novel group, receiving US-guided and fluoroscopy (FL)-controlled TFEIs using a sagittal oblique approach between the superior articular process of L5 and S1, and control group, receiving US-guided TFEIs with conventional transverse approach combined with FL confirmation. Accuracy of contrast dispersing into lumbar epidural space was set as the primary endpoint. Radiation dosages, procedure time, numeric rating scale (NRS) scores, Modified Oswestry Disability Questionnaire (MODQ) scores, adverse events, and rescue analgesic requirement were also recorded. The generalized liner mixed model (GLMMs) was employed to compare the repeatedly measured variables between groups, taking individual confounding factors as covariance. RESULTS: The accuracy of TFEIs was 92.8% and 65.2% in novel and control group, with a significant difference of 26.7% (95% CI: 15.4%, 39.8%) between two modalities (p < 0.001). Significant pain relief was observed in novel group as opposed to control group after one injection. Procedure time in novel group (8.4 ± 1.6 min) was shorter than control group (15.8 ± 3.5 min) (p < 0.001) with less radiation dosage (3047 ± 5670 vs. 8808 ± 1039 µGy/m2, p < 0.001). Significantly, lower incidence of L5 paresthesia occurred in novel group. Statistical differences of NRS scores between the novel and control group were reached at 1 week after procedure (1 (IQR: - 1-3) vs. 3 (IQR: - 1-7), p = 0.006), while not reached at both 1- (1 (IQR: 0-2) vs. 1 (IQR: - 1-3), p = 0.086) or 3-month follow-up (0 (IQR: - 1-1) vs. 1 (IQR: 0-2), p = 0.094). Both groups showed similar functional improvement (F = 0.103, p = 0.749) during follow-up. CONCLUSIONS: The novel sonographic technique provided superior accuracy needle placement and better pain-relieving effect through one injection as compared to the routine transverse approach. Consequently, in situations where the HIC imposed limitations for TFEIs performance on L5, the novel technique should be recommended to consider increasing accurate puncture, minimizing radiation exposure, consuming procedure time, and reducing the risk of neuraxial injury.

De Novo Synthesis of α-Ketoamides via Pd/TBD Synergistic Catalysis.

Precisely controlling the product selectivity of a reaction is an important objective in organic synthesis. α-Ketoamides are vital intermediates in chemical transformations and privileged motifs in numerous drugs, natural products, and biologically active molecules. The selective synthesis of α-ketoamides from feedstock chemicals in a safe and operationally simple manner under mild conditions is a long-standing catalysis challenge. Herein, an unprecedented TBD-switched Pd-catalyzed double isocyanide insertion reaction for assembling ketoamides in aqueous DMSO from (hetero)aryl halides and pseudohalides under mild conditions is reported. The effectiveness and utility of this protocol are demonstrated by its diverse substrate scope (93 examples), the ability to late-stage modify pharmaceuticals, scalability to large-scale synthesis, and the synthesis of pharmaceutically active molecules. Mechanistic studies indicate that TBD is a key ligand that modulates the Pd-catalyzed double isocyanide insertion process, thereby selectively providing the desired α-ketoamides in a unique manner. In addition, the imidoylpalladium(II) complex and α-ketoimine amide are successfully isolated and determined by X-ray analysis, confirming that they are probable intermediates in the catalytic pathway.

Body-Wide Inactivation of the Myc-Like Mlx Transcription Factor Network Accelerates Aging and Increases the Lifetime Cancer Incidence.

The "Mlx" and "Myc" transcription factor networks cross-communicate and share many common gene targets. Myc's activity depends upon its heterodimerization with Max, whereas the Mlx Network requires that the Max-like factor Mlx associate with the Myc-like factors MondoA or ChREBP. The current work demonstrates that body-wide Mlx inactivation, like that of Myc, accelerates numerous aging-related phenotypes pertaining to body habitus and metabolism. The deregulation of numerous aging-related Myc target gene sets is also accelerated. Among other functions, these gene sets often regulate ribosomal and mitochondrial structure and function, genomic stability, and aging. Whereas "MycKO" mice have an extended lifespan because of a lower cancer incidence, "MlxKO" mice have normal lifespans and a higher cancer incidence. Like Myc, the expression of Mlx, MondoA, and ChREBP and their control over their target genes deteriorate with age in both mice and humans. Collectively, these findings underscore the importance of lifelong and balanced cross-talk between the two networks to maintain proper function and regulation of the many factors that can affect normal aging.

Dietary L-arabinose-induced gut dysbiosis exacerbates Salmonella infection outcome.

The gut microbiota is essential for providing colonization resistance against pathogens. Dietary sugars markedly shift the composition of the intestinal microbiota and alter host susceptibility to enteric infections. Here, we demonstrate the effect of L-arabinose on bacterial infection by using a mouse infection model with Salmonella enterica serovar Typhimurium (S. Tm). In the presence of microbiota, L-arabinose induces a dramatic expansion of Enterobacteriaceae, thereby decreasing the microbiota diversity and causing more severe systemic infection. However, L-arabinose supplementation does not alter the disease progression of Salmonella infection in a microbiota-depleted mouse model. More importantly, short-term supplementation of L-arabinose fails to exert anti-diabetic effects in Salmonella-infected hyperglycemia mice and still promotes infection. Overall, our work reveals that a high intake of dietary L-arabinose supports a bloom of Enterobacteriaceae in Salmonella-infected gut, further accelerating the process of systemic infection.IMPORTANCEL-arabinose is a promising natural sweetener and food additive for the regulation of hyperglycemia. Since diabetic subjects are more susceptible to infections, the safety of dietary L-arabinose in diabetic patients experiencing infection remains a concern. Our findings reveal that L-arabinose exacerbates Salmonella infection outcome by inducing gut microbiota dysbiosis in mice. High dietary intake of L-arabinose may be deleterious for diabetic individuals undergoing infection.

Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.

Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. With the development of artificial intelligence technology based on deep learning, the extraction of high-dimensional features in imaging can assist doctors in clinical decision-making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct.

Root plasticity improves maize nitrogen use when nitrogen is limiting - an analysis using 3D plant modelling.

Plant phenotypic plasticity plays an important role in nitrogen (N) acquisition and use under nitrogen-limited conditions. However, this role has never been quantified as a function of N availability, leaving it unclear whether plastic responses should be considered as potential targets for selection. A combined modelling and experimentation approach was adopted to quantify the role of plasticity on N uptake and plant yield. Based on a greenhouse experiment we considered plasticity in two maize traits: root-to-leaf biomass allocation ratio and emergence rate of axial roots. In a simulation experiment we individually enabled or disabled both plastic responses for maize stands grown across six N levels. Both plastic responses contributed to maintaining a higher N uptake and plant productivity as N-availability declined, compared to stands in which plastic responses were disabled. We conclude that plastic responses quantified in this study may be a potential target trait in breeding programs for greater N uptake across N levels while it may only be important for the internal use of N under N-limited conditions in maize. Given the complexity of breeding for plastic responses, an a priori model analysis is useful to identify which plastic traits to target for enhanced plant performance.

Artificial intelligence-based automatic nidus segmentation of cerebral arteriovenous malformation on time-of-flight magnetic resonance angiography.

OBJECTIVE: Accurate nidus segmentation and quantification have long been challenging but important tasks in the clinical management of Cerebral Arteriovenous Malformation (CAVM). However, there are still dilemmas in nidus segmentation, such as difficulty defining the demarcation of the nidus, observer-dependent variation and time consumption. The aim of this study isto develop an artificial intelligence model to automatically segment the nidus on Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) images. METHODS: A total of 92patients with CAVM who underwent both TOF-MRA and DSA examinations were enrolled. Two neurosurgeonsmanually segmented the nidusonTOF-MRA images,which were regarded as theground-truth reference. AU-Net-basedAImodelwascreatedfor automatic nidus detectionand segmentationonTOF-MRA images. RESULTS: The meannidus volumes of the AI segmentationmodeland the ground truthwere 5.427 ± 4.996 and 4.824 ± 4.567 mL,respectively. The meandifference in the nidus volume between the two groups was0.603 ± 1.514 mL,which wasnot statisticallysignificant (P = 0.693). The DSC,precision and recallofthe testset were 0.754 ± 0.074, 0.713 ± 0.102 and 0.816 ± 0.098, respectively. The linear correlation coefficient of the nidus volume betweenthesetwo groupswas 0.988, p < 0.001. CONCLUSION: The performance of the AI segmentationmodel is moderate consistent with that of manual segmentation. This AI model has great potential in clinical settings, such as preoperative planning, treatment efficacy evaluation, riskstratification and follow-up.

Targeting G6PD to mitigate cartilage inflammation in TMJOA: The NOX4-ROS-MAPK axis as a therapeutic avenue.

Chondrocytes, known for their metabolic adaptability in response to varying stimuli, play a significant role in osteoarthritis (OA) progression. Glucose-6-phosphate dehydrogenase (G6PD), the rate-limiting enzyme of the pentose phosphate pathway, has recently been found to upregulate in OA chondrocyte. However, the exact role of G6PD in temporomandibular joint osteoarthritis (TMJOA) and its effect on chondrocyte function remains unclear. In present study, we induced OA-like conditions in the rat temporomandibular joint via occlusal disharmony (OD), noting a marked increase in G6PD expression in the condylar cartilage. Our data show that G6PD knockdown in mandibular condylar chondrocytes (MCCs) reduces the expression of catabolic enzymes (e.g., MMP3, MMP13) and inflammatory cytokines (e.g., IL6) induced by IL-1ß. G6PD knockdown also mitigates IL-1ß-induced upregulation of ERK, JNK, and p38 phosphorylation and reduces reactive oxygen species (ROS) levels by decreasing the nicotinamide adenine dinucleotide phosphate (NADPH) and NADPH oxidases 4 (NOX4) mRNA expression. In summary, G6PD appears to regulate the inflammatory state of condylar chondrocytes via the NOX-ROS-MAPK axis, highlighting its potential as a therapeutic target for TMJOA.

FET PET provides adjunctive value to FDG PET in distinction of spinal cord tumors.

Objective: This study aimed to compare the diagnostic efficacy of O-(2-18F-fluoroethyl)-l-tyrosine (18F-FET) PET and 2-deoxy-2-[18F]fluoro-d-deoxyglucose (18F-FDG) PET for spinal cord lesions. Materials and methods: Paired preoperative 18F-FDG PET/MRI and 18F-FET PET/MRI scans were conducted on patients with suspected spinal cord tumors. Clinical manifestations and PET performance, including SUVmean, SUVmax, TBRmean, TBRmax, metabolic tumor volume (MTV), and total lesion metabolism (TLM), and tumor volume, were compared using group analysis and receiver operating characteristic (ROC) curves. Results: Thirty-five patients were categorized into three groups based on their pathological diagnosis: high-grade tumors (HGTs, n = 6), low-grade tumors (LGTs, n = 19), and non-tumor diseases (NTDs, n = 10). The background SUVmean of 18F-FET PET was significantly lower than that of 18F-FDG PET (p < 0.0001), while the delineated tumor volumes showed no significant difference (p > 0.05). The mass SUVmean, SUVmax, MTV, and TLM values of both 18F-FDG PET and 18F-FET PET were statistically different between HGTs and LGTs (p < 0.05). Similarly, the mass SUVmax, TBRmax, MTV, and TLM values of both 18F-FDG PET and 18F-FET PET, as well as the mass SUVmean of 18F-FET PET, exhibited statistical differences between HGTs and NTDs (p < 0.05). But none were able to distinguish LGTs and NTDs (p > 0.05). Notably, 18F-FET PET provided valuable supporting diagnostic evidence in 1 case of mixed neuronal-glial tumor (MNGT) and 2 cases of intramedullary inflammatory lesions. Optimal cut-off values of all measured parameters for distinguishing tumors and NTDs were determined through ROC analysis. Conclusion: 18F-FET PET presented comparable diagnostic performance to 18F-FDG PET in differentiating HGTs, LGTs, and NTDs, but exhibited particular utility in MNGT and inflammatory lesions.

Diffusion Tensor Imaging Identifies Cervical Spondylosis, Myelitis, and Spinal Cord Tumors.

BACKGROUND: Diffusion tensor imaging (DTI) has been increasingly recognized for its capability to study microstructural changes in the neuropathology of brain diseases. However, the optimal DTI metric and its diagnostic utility for a variety of spinal cord diseases are still under investigation. PURPOSE: To evaluate the diagnostic efficacy of DTI metrics for differentiating between cervical spondylosis, myelitis, and spinal tumors. METHODS: This retrospective study analyzed DTI scans from 68 patients (22 with cervical spondylosis, 23 with myelitis, and 23 with spinal tumors). DTI indicators, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD), were calculated. The Kruskal-Wallis test was used to compare these indicators, followed by Receiver Operating Characteristic (ROC) curve analysis, to evaluate the diagnostic efficacy of each indicator across disease pairs. Additionally, we explored the correlations of DTI indicators with specific clinical measurements. RESULTS: FA values were significantly lower in tumor patients compared to those with cervical spondylosis (p < 0.0001) and myelitis (p < 0.05). Additionally, tumor patients exhibited significantly elevated MD and RD values relative to the spondylosis and myelitis groups. ROC curve analysis underscored FA's superior discriminative performance, with an area under the curve (AUC) of 0.902 for differentiating tumors from cervical spondylosis, and an AUC of 0.748 for distinguishing cervical myelitis from spondylosis. Furthermore, a significant negative correlation was observed between FA values and Expanded Disability Status Scores (EDSSs) in myelitis patients (r = -0.62, p = 0.002), as well as between FA values and Ki-67 scores in tumor patients (r = -0.71, p = 0.0002). CONCLUSION: DTI indicators, especially FA, have the potential in distinguishing spondylosis, myelitis, and spinal cord tumors. The significant correlation between FA values and clinical indicators highlights the value of FA in the clinical assessment and prognosis of spinal diseases and may be applied in diagnostic protocols in the future.

Diagnostic efficacy of complexity metrics from cardiac MRI myocardial segmental motion curves in detecting late gadolinium enhancement in myocardial infarction patients.

Background: Myocardial segmental motion is associated with cardiovascular pathology, often assessed through myocardial strain features. The stability of the motion can be influenced by myocardial fibrosis. This research aimed to explore the complexity metrics (CM) of myocardial segmental motion curves, observe their correlation with late gadolinium enhancement (LGE) transmural extension (TE), and assess diagnostic efficacy combined with segmental strains in different TE segments. Methods: We included 42 myocardial infarction patients, dividing images into 672 myocardial segments (208 remote, 384 viable, and 80 unviable segments based on TE). Radial and circumferential segmental strain, along with CM for motion curves, were extracted. Correlation between CM and LGE, as well as the potential distinguishing role of CM, was evaluated using Pearson correlation, univariate linear regression (F-test), multivariate regression analysis (T-test), area under curve (AUC), machine learning models, and DeLong test. Results: All CMs showed significant linear correlation with TE (P < 0.001). Six CMs were correlated with TE (r > 0.3), with radial frequency drift (FD) displayed the strongest correlation (r = 0.496, P < 0.001). Radial and circumferential FD significantly differed in higher TE myocardium than in remote segments (P < 0.05). Radial FD had practical diagnostic efficacy (remote vs. unviable AUC = 0.89, viable vs. unviable AUC = 0.77, remote vs. viable AUC = 0.65). Combining CM with segmental strain features boosted diagnostic efficacy than models using only segmental strain features (DeLong test, P < 0.05). Conclusions: The CM of myocardial motion curves has been associated with LGE infarction, and combining CM with strain features improves the diagnosis of different myocardial LGE infarction degrees.

Areca palm velarivirus 1 infection caused disassembly of chloroplast and reduction of photosynthesis in areca palm.

The expansion of betel palm cultivation is driven by rising demand for betel nut, yet this growth is accompanied by challenges such as decreased agricultural biodiversity and the spread of infectious pathogens. Among these, Yellow Leaf Disease (YLD) emerges as a prominent threat to betel palm plantation. Areca Palm Velarivirus 1 (APV1) has been identified as a primary causative agent of YLD, precipitating leaf yellowing, stunted growth, and diminished yield. However, the precise mechanisms underlying APV1-induced damage remain elusive. Our study elucidates that APV1 infiltrates chloroplasts, instigating severe damage and consequential reductions in chlorophyll a/b and carotene levels, alongside notable declines in photosynthetic efficiency. Moreover, APV1 infection exerts broad regulatory effects on gene expression, particularly suppressing key genes implicated in chloroplast function and photosynthesis. These disruptions correlate with growth retardation, yield diminishment, and compromised nut quality. Intriguingly, the paradoxical destruction of the host's photosynthetic machinery by APV1 prompts inquiry into its evolutionary rationale, given the virus's dependence on host resources for replication and proliferation. Our findings reveal that APV1-induced leaf yellowing acts as a beacon for transmission vectors, hinting at a nuanced "host-pathogen-vector co-evolutionary" dynamic.

Low T3 syndrome is associated with the severity of myelin oligodendrocyte glycoprotein antibody-associated disease exacerbation.

Background: Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a rare autoimmune inflammatory disease of the central nervous system, (CNS) different from multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). While numerous studies have delved into the involvement of thyroid antibodies (ATAbs) and thyroid function in NMOSD and MS. The objective of this study is to explore the clinical significance of thyroid dysfunction and ATAbs abnormalities in adult patients with MOGAD. Methods: 36 adult inpatients diagnosed with MOGAD and 47 sex- and age-matched healthy controls were enrolled. Patients were divided into two groups based on the presence or absence of low T3 syndrome. Demographics, clinical characteristics, and results of auxiliary examinations were compared across the subgroups. Moreover, an analysis was conducted to explore the correlations between thyroid hormone levels and Expanded Disability Status Scale (EDSS) scores. Results: Thyroid dysfunction was notably more frequent in MOGAD patients than healthy controls (p < 0.0001), particularly low T3 syndrome (p=0.03). Furthermore, subgroup analyses revealed that the low T3 syndrome group exhibited higher EDSS scores and a higher proportion of individuals with EDSS scores > 3, in comparison to the non-low T3 syndrome group (p = 0.014, p = 0.046). However, no significant differences were observed in demographic characteristics, annual relapse rates, clinical phenotypes, laboratory and MRI results, and EEG abnormalities between the two groups. Additional Spearman's analysis showed significantly negative correlations between the TT3 and FT3 levels with EDSS scores (r = -0.367, p = 0.028; r = -0.377, p = 0.024). Typical brain lesions and paralateral ventricle lesions were significantly rare in patients with positive ATAbs compared to those with negative ATAbs (p = 0.0001, p = 0.03), although the incidence of ATAbs abnormalities did not differ significantly between MOGAD patients and healthy controls. Conclusions: Overall, this study confirmed thyroid dysfunction, especially low T3 syndrome, is frequent in adult MOGAD patients. Patients with low T3 syndrome exhibited elevated EDSS scores and a significantly higher incidence of unfavorable condition. additionally, the correlation analysis model manifests that FT3 and TT3 levels were negatively correlated with EDSS scores. These evidences indicate that low T3 syndrome is associated with the severity of MOGAD exacerbation.

Associations between renal function, hippocampal volume, and cognitive impairment in 544 outpatients.

Background: Cognitive impairment and brain atrophy are common in chronic kidney disease patients. It remains unclear whether differences in renal function, even within normal levels, influence hippocampal volume (HCV) and cognition. We aimed to investigate the association between estimated glomerular filtration rate (eGFR), HCV and cognition in outpatients. Methods: This single-center retrospective study enrolled 544 nonrenal outpatients from our hospital. All participants underwent renal function assessment and 3.0 T magnetic resonance imaging (MRI) in the same year. HCV was also measured, and cognitive assessments were obtained. The correlations between eGFR, HCV, and cognitive function were analyzed. Logistic regression analysis was performed to identify the risk factors for hippocampal atrophy and cognitive impairment. Receiver-operator curves (ROCs) were performed to find the cut-off value of HCV that predicts cognitive impairment. Results: The mean age of all participants was 66.5 ± 10.9 years. The mean eGFR of all participants was 88.5 ± 15.1 mL/min/1.73 m2. eGFR was positively correlated with HCV and with Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Univariate and multivariate logistic regression analysis showed Age ≥ 65 years, eGFR < 75 mL/min/1.73 m2, Glucose ≥6.1 mmol/L and combined cerebral microvascular diseases were independent risk factors for hippocampal atrophy and Age ≥ 65 years, left hippocampal volume (LHCV) <2,654 mm3 were independent risk factors for cognitive impairment in outpatients. Although initial unadjusted logistic regression analysis indicated that a lower eGFR (eGFR < 75 mL/min/1.73 m2) was associated with poorer cognitive function, this association was lost after adjusting for confounding variables. ROC curve analysis demonstrated that LHCV <2,654 mm3 had the highest AUROC [(0.842, 95% CI: 0.808-0.871)], indicating that LHCV had a credible prognostic value with a high sensitivity and specificity for predicting cognitive impairment compared with age in outpatients. Conclusion: Higher eGFR was associated with higher HCV and better cognitive function. eGFR < 75 mL/min/1.73 m2 was an independent risk factor for hippocampal atrophy after adjusting for age. It is suggested that even eGFR < 75 mL/min/1.73 m2, lower eGFR may still be associated with hippocampal atrophy, which is further associated with cognitive impairment. LHCV was a favorable prognostic marker for predicting cognitive impairment rather than age.

Mendelian randomization evidence for the causal effect of mental well-being on healthy aging.

Mental well-being relates to multitudinous lifestyle behaviours and morbidities and underpins healthy aging. Thus far, causal evidence on whether and in what pattern mental well-being impacts healthy aging and the underlying mediating pathways is unknown. Applying genetic instruments of the well-being spectrum and its four dimensions including life satisfaction, positive affect, neuroticism and depressive symptoms (n = 80,852 to 2,370,390), we performed two-sample Mendelian randomization analyses to estimate the causal effect of mental well-being on the genetically independent phenotype of aging (aging-GIP), a robust and representative aging phenotype, and its components including resilience, self-rated health, healthspan, parental lifespan and longevity (n = 36,745 to 1,012,240). Analyses were adjusted for income, education and occupation. All the data were from the largest available genome-wide association studies in populations of European descent. Better mental well-being spectrum (each one Z-score higher) was causally associated with a higher aging-GIP (ß [95% confidence interval (CI)] in different models ranging from 1.00 [0.82-1.18] to 1.07 [0.91-1.24] standard deviations (s.d.)) independent of socioeconomic indicators. Similar association patterns were seen for resilience (ß [95% CI] ranging from 0.97 [0.82-1.12] to 1.04 [0.91-1.17] s.d.), self-rated health (0.61 [0.43-0.79] to 0.76 [0.59-0.93] points), healthspan (odds ratio [95% CI] ranging from 1.23 [1.02-1.48] to 1.35 [1.11-1.65]) and parental lifespan (1.77 [0.010-3.54] to 2.95 [1.13-4.76] years). Two-step Mendelian randomization mediation analyses identified 33 out of 106 candidates as mediators between the well-being spectrum and the aging-GIP: mainly lifestyles (for example, TV watching and smoking), behaviours (for example, medication use) and diseases (for example, heart failure, attention-deficit hyperactivity disorder, stroke, coronary atherosclerosis and ischaemic heart disease), each exhibiting a mediation proportion of >5%. These findings underscore the importance of mental well-being in promoting healthy aging and inform preventive targets for bridging aging disparities attributable to suboptimal mental health.

Alterations in cortical thickness and volumes of subcortical structures in pediatric patients with complete spinal cord injury.

AIMS: To study the changes in cortical thickness and subcortical gray matter structures in children with complete spinal cord injury (CSCI), reveal the possible causes of dysfunction beyond sensory motor dysfunction after CSCI, and provide a possible neural basis for corresponding functional intervention training. METHODS: Thirty-seven pediatric CSCI patients and 34 age-, gender-matched healthy children as healthy controls (HCs) were recruited. The 3D high-resolution T1-weighted structural images of all subjects were obtained using a 3.0 Tesla MRI system. Statistical differences between pediatric CSCI patients and HCs in cortical thickness and volumes of subcortical gray matter structures were evaluated. Then, correlation analyses were performed to analyze the correlation between the imaging indicators and clinical characteristics. RESULTS: Compared with HCs, pediatric CSCI patients showed decreased cortical thickness in the right precentral gyrus, superior temporal gyrus, and posterior segment of the lateral sulcus, while increased cortical thickness in the right lingual gyrus and inferior occipital gyrus. The volume of the right thalamus in pediatric CSCI patients was significantly smaller than that in HCs. No significant correlation was found between the imaging indicators and the injury duration, sensory scores, and motor scores of pediatric CSCI patients. CONCLUSIONS: These findings demonstrated that the brain structural reorganizations of pediatric CSCI occurred not only in sensory motor areas but also in cognitive and visual related brain regions, which may suggest that the visual processing, cognitive abnormalities, and related early intervention therapy also deserve greater attention beyond sensory motor rehabilitation training in pediatric CSCI patients.