Deciphering the Dynamic Assembling-Disassembling of Small Molecules on Solid/Liquid Interfaces within Microchannels by Pulsed Streaming Potential Measurement.

Assembling small molecules at liquid/solid interfaces is relatively common and contributes to many unique properties of the interface. However, such an assembling process can be dynamic depending on the concentration of the molecule and the properties of the solid and liquid themselves, which poses serious challenges on the accurate evaluation of the assembling processes. Herein, we report a convenient way for in situ and real-time monitoring of assembling-disassembling of small-molecule surfactants on the surface of microchannels using pulsed streaming potential (SP) measurement based on the variation of surface charge. With this technique, five distinctive kinetic regimes, each responsible for a characteristic molecular behavior, can be differentiated during a typical assembling-disassembling cycle. Significant difference of the assembling-disassembling process was clearly reflected for surfactants with hydrophobic tails of only a two -CH2- difference (C16TAB/C18TAB and D10DAB/D12DAB). The relative SP (Er) value is positively correlated with the molecular weight at a concentration of 0.1 mM for the same kinds of surfactants. Moreover, the assembling kinetics of D10DAB exhibits an "overshoot effect" at high concentration, which means morphology adjustment. The consequences of such assembling/disassembling of these molecules for electrophoretic separation, protein immobilization, and photocatalysis in a microchannel were investigated through dynamic characterization, which proves its potential as a tool for dynamic solid/liquid interface characterization.

Flexible electronics for heavy metal ion detection in water: a comprehensive review.

Flexible electronics offer a versatile, rapid, cost-effective and portable solution to monitor water contamination, which poses serious threat to the environment and human health. This review paper presents a comprehensive exploration of the versatile platforms of flexible electronics in the context of heavy metal ion detection in water systems. The review overviews of the fundamental principles of heavy metal ion detection, surveys the state-of-the-art materials and fabrication techniques for flexible sensors, analyses key performance metrics and limitations, and discusses future opportunities and challenges. By highlighting recent advances in nanomaterials, polymers, wireless integration, and sustainability, this review aims to serve as an essential resource for researchers, engineers, and policy makers seeking to address the critical challenge of heavy metal contamination in water resources. The versatile promise of flexible electronics is thoroughly elucidated to inspire continued innovation in this emerging technology arena.

Feasibility of Artificial Intelligence Constrained Compressed SENSE Accelerated 3D Isotropic T1 VISTA Sequence For Vessel Wall MR Imaging: Exploring the Potential of Higher Acceleration Factors Compared to Traditional Compressed SENSE.

RATIONALE AND OBJECTIVES: Investigate the feasibility of using deep learning-based accelerated 3D T1-weighted volumetric isotropic turbo spin-echo acquisition (VISTA) for vessel wall magnetic resonance imaging (VW-MRI), compared to traditional Compressed SENSE and optimize acceleration factor (AF) to obtain high-quality clinical images. METHODS: 40 patients with atherosclerotic plaques in the intracranial or carotid artery were prospectively enrolled in our study from October 1, 2022 to October 31, 2023 underwent high-resolution vessel wall imaging on a 3.0 T MR system using variable Compressed SENSE (CS) AFs and reconstructed by an optimized artificial intelligence constrained Compressed SENSE (CS-AI). Images were reconstructed through both traditional CS and optimized CS-AI. Two radiologists qualitatively assessed the image quality scores of CS and CS-AI across different segments and quantitatively evaluated SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) metrics. Paired t-tests, ANOVA, and Friedman tests analyzed image quality metrics. Written informed consent was obtained from all patients in this study. RESULTS: CS-AI groups demonstrated good image quality scores compared to reference scans until AF up to 12 (P < 0.05). The CS-AI 10 protocol provided the best images in the lumen of both normal and lesion sites (P < 0.05). The plaque SNR was significantly higher in CS-AI groups compared to CS groups until the AF increased to 12 (P < 0.05). CS-AI protocols had higher CNR compared to CS with whichever AF on both pre-and post-contrast T1WI (P < 0.05), The CNR was highest in the CS-AI 10 protocol on pre-contrast T1WI and in CS-AI 12 on post-contrast T1WI (P < 0.05). CONCLUSION: The study demonstrated the feasibility of using CS-AI technology to diagnose arteriosclerotic vascular disease with 3D T1 VISTA sequences. The image quality and diagnostic efficiency of CS-AI images were comparable or better than traditional CS images. Higher AFs are feasible and have potential for use in VW-MRI. The determination of standardized AFs for clinical scanning protocol is expected to help for empirical evaluation of new imaging technology.

Prevalence and influencing factors of anemia among pregnant women across first, second and third trimesters of pregnancy in monitoring areas, from 2016 to 2020: a population-based multi-center cohort study.

OBJECTIVE: To assess the prevalence of anemia among pregnant women across their entire pregnancy and the factors affecting it in the monitoring areas. METHODS: A total of 108,351 pregnant women who received antenatal health care and delivered from January 1, 2016 to December 31, 2020 in 15 monitoring counties of 8 provinces in the Maternal and Newborn Health Monitoring Program (MNHMP) of National Center for Women and Children's Health (NCWCH) were selected as the study subjects. The anemia status among the subjects across their first, second and third trimester of pregnancy and the influencing factors were analyzed. RESULTS: From 2016 to 2020, the prevalence of anemia at any stage during pregnancy in the monitoring areas was 43.59%. The prevalence of anemia among pregnant women across all three trimesters was 3.95%, and the prevalence of mild and moderate-to-severe anemia was 1.04% and 2.90%, respectively. Protective factors were living in the northern area (OR = 0.395) and being a member of an ethnic minority (OR = 0.632). The risk factors were residing in rural areas (OR = 1.207), with no more than junior high school education (OR = 1.203), having ≥ 3 gravidities (OR = 1.195) and multiple fetuses (OR = 1.478). CONCLUSIONS: Although the prevalence of anemia among pregnant women across all trimesters in the monitoring area was low, the severity of anemia was high. Since the prevalence of anemia among pregnant women across their entire pregnancy in the monitoring area is affected by many different factors, more attention should be paid to pregnant women living in rural areas, with low literacy, ≥ 3 gravidities and multiple fetuses for early intervention.

Targeting suicidal ideation in major depressive disorder with MRI-navigated Stanford accelerated intelligent neuromodulation therapy.

High suicide risk represents a serious problem in patients with major depressive disorder (MDD), yet treatment options that could safely and rapidly ameliorate suicidal ideation remain elusive. Here, we tested the feasibility and preliminary efficacy of the Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT) in reducing suicidal ideation in patients with MDD. Thirty-two MDD patients with moderate to severe suicidal ideation participated in the current study. Suicidal ideation and depression symptoms were assessed before and after 5 days of open-label SAINT. The neural pathways supporting rapid-acting antidepressant and suicide prevention effects were identified with dynamic causal modelling based on resting-state functional magnetic resonance imaging. We found that 5 days of SAINT effectively alleviated suicidal ideation in patients with MDD with a high response rate of 65.63%. Moreover, the response rates achieved 78.13% and 90.63% with 2 weeks and 4 weeks after SAINT, respectively. In addition, we found that the suicide prevention effects of SAINT were associated with the effective connectivity involving the insula and hippocampus, while the antidepressant effects were related to connections of the subgenual anterior cingulate cortex (sgACC). These results show that SAINT is a rapid-acting and effective way to reduce suicidal ideation. Our findings further suggest that distinct neural mechanisms may contribute to the rapid-acting effects on the relief of suicidal ideation and depression, respectively.

A novel method based on solid phase extraction and liquid chromatography-tandem mass spectrometry warrants occurrence of trace xanthates in water.

Huge volumes of wastewater containing organic flotation reagents such as xanthates have been released into the environment via mining activities, greatly threatening the eco-environment safety. A simple and fast method is urgently needed for accurate analysis of various xanthates in mining and environmental water. Here, a robust method is realized for simultaneous determination of three trace xanthates (i.e., potassium ethyl xanthate, potassium butyl xanthate, and potassium isopropyl xanthate) in environmental water samples, including eutrophic water and flotation wastewater using solid phase extraction (SPE) and HPLC-MS/MS. HPLC-MS/MS parameters, SPE cartridges and eluting solvents, pH values, and SPE procedures were optimized. The new method had an excellent linearity in the range of 1-1000 µg/L (R2 ≥ 0.998), low limits of detection (0.02-0.68 µg/L), and satisfactory accuracy and precision (72.9%-107.6% of average recoveries and <5% of relative standard deviations at 1, 10, 50, and 500 µg/L of xanthates). This is a first method developed for determination of trace xanthates in water samples. It was successfully applied to determine the target analytes in outdated flotation wastewater and river water samples, warranting the occurrence of trace xanthates (0.13-16.9 µg/L) in water and necessity of systematic investigation on environmental fate and risk of xanthates.

Diversity-scaling analysis of human breast milk microbiomes from population perspective.

Quantitative measuring the population-level diversity-scaling of human microbiomes is different from conventional approach to traditional individual-level diversity analysis, and it is of obvious significance. For example, it is well known that individuals are of significant heterogeneity with their microbiome diversities, and the population-level analysis can effectively capture such kind of individual differences. Here we reanalyze a dozen datasets of 2,115 human breast milk microbiome (BMM) samples with diversity-area relationship (DAR) to tackle the previous questions. Our focus on BMM is aimed to offer insights for supplementing the gut microbiome research from nutritional perspective. DAR is an extension to classic species-area relationship, which was discovered in the 19th century and established as one of a handful fundamental laws in community ecology. Our DAR modeling revealed the following numbers, all approximately: (i) The population-level potential diversity of BMM is 1,108 in terms of species richness (number of total species), and 67 in terms of typical species. (ii) On average, an individual carry 17% of population-level diversity in terms of species richness, and 61% in terms of typical species. (iii) The similarity (overlap) between individuals according to pair-wise diversity overlap (PDO) should be approximately 76% in terms of total species, and 92% in terms of typical species, which symbolizes the inter-individual heterogeneity. (iv) The average individual (alpha-) diversity of BMM is approximately 188 (total-species) and 37 (typical-species). (v) To deal with the potential difference among 12 BMM datasets, we conducted DAR modeling separately for each dataset, and then performed permutation tests for DAR parameters. It was found that the DAR scaling parameter that measures inter-individual heterogeneity in diversity is invariant (constant), but the population potential diversity is different among 30% of the pair-wise comparison between 12 BMM datasets. These results offer comprehensive biodiversity analyses of the BMM from host individual, inter-individual, and population level perspectives.

Design, synthesis and evaluation of fused hybrids with acetylcholinesterase inhibiting and Nrf2 activating functions for Alzheimer's disease.

Designing of multiple-target directed ligands (MTDLs) has emerged as an attractive strategy for Alzheimer's disease (AD). Fusing the benzylpiperidine motif from AChE inhibitor donepezil and the 1,2,4-oxadiazole core from the Nrf2 activator 25 that was previously reported, we designed and synthesized a series of multifunctional anti-AD hybrids. The optimal hybrid 15a exhibited excellent AChE inhibitory (eeAChE IC50 = 0.07 ± 0.01 µM; hAChE IC50 = 0.38 ± 0.04 µM) and significant Nrf2 inductivity. It upregulated the protein and transcription level of Nrf2 and its downstream proteins HO-1, NQO1, and GCLM and promoted Nrf2 translocation from cytoplasm into nuclei. Additionally, 15a exhibited important neuroprotective function in protecting the cells from being damaged by H2O2 and Aß1-42 aggregation and exerted antioxidant stress and anti-inflammatory activities in reducing the production of ROS and pro-inflammatory cytokines. Moreover, 15a effectively shortened the latency time and escape distance to the target, increased the arrival times, and simplified the tracks in Morris water maze test induced by scopolamine and Aß1-42. At the same time, it significantly reduced the levels of proinflammatory factors in the mice model brains. These effects of 15a in improving cognition and alleviating inflammation were even better than the combination of AChE inhibitor and Nrf2 activator, suggesting a remarkable benefit for AD treatment. 15a could serve as a novel hit compound with Nrf2 inductive activity and AChE inhibitory activity for further research.

Graph-Theory-Based Degree Centrality Combined with Machine Learning Algorithms Can Predict Response to Treatment with Antipsychotic Medications in Patients with First-Episode Schizophrenia.

Objectives: Schizophrenia (SCZ) is associated with disrupted functional brain connectivity, and antipsychotic medications are the primary and most commonly used treatment for schizophrenia. However, not all patients respond to antipsychotic medications. Methods: The study is aimed at investigating whether the graph-theory-based degree centrality (DC), derived from resting-state functional MRI (rs-fMRI), can predict the treatment outcomes. rs-fMRI data from 38 SCZ patients were collected and compared with findings from 38 age- and gender-matched healthy controls (HCs). The patients were treated with antipsychotic medications for 16 weeks before undergoing a second rs-fMRI scan. DC data were processed using DPABI and SPM12 software. Results: SCZ patients at baseline showed increased DC in the frontal and temporal gyrus, anterior cingulate cortex, and precuneus and reduced DC in bilateral subcortical gray matter structures. However, those abnormalities showed a clear renormalization after antipsychotic medication treatments. Support vector machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.2% (sensitivity 78.9%, specificity 89.5%, and area under the receiver operating characteristic curve (AUC) 0.925) for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the bilateral putamen, left inferior frontal gyrus, left middle occipital cortex, bilateral middle frontal gyrus, left cerebellum, left medial frontal gyrus, left inferior temporal gyrus, and left angular. Furthermore, the DC change within the bilateral putamen is negatively correlated with the symptom improvements after treatment. Conclusions: Our study confirmed that graph-theory-based measures, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of antipsychotic medications.

In Situ Growth Intercalation Structure MXene@Anatase/Rutile TiO2 Ternary Heterojunction with Excellent Phosphoprotein Detection in Sweat.

Abnormal protein phosphorylation may relate to diseases such as Alzheimer's, schizophrenia, and Parkinson's. Therefore, the real-time detection of phosphoproteins in sweat was of great significance for the early knowledge, detection, and treatment of neurological diseases. In this work, anatase/rutile TiO2 was in situ grown on the MXene surface to constructing the intercalation structure MXene@anatase/rutile TiO2 ternary heterostructure as a sensing platform for detecting phosphoprotein in sweat. Here, the intercalation structure of MXene acted as electron and diffusion channels for phosphoproteins. The in situ grown anatase/rutile TiO2 with n-n-type heterostructure provided specific adsorption sites for the phosphoproteins. The determination of phosphoprotein covered concentrations in sweat, with linear range from 0.01 to 1 mg/mL, along with a low LOD of 1.52 µM. It is worth noting that, since the macromolecular phosphoprotein was adsorbed on the surface of the material, the electrochemical signal gradually decreased with the increase of phosphoprotein concentration. In addition, the active sites in the MXene@anatase/rutile TiO2 ternary heterojunction and synergistic effect of the heterojunction were verified by first-principle calculations to further realize the response to phosphoproteins. Additionally, the effective diffusion capacity and mobility of phosphoprotein molecules in the ternary heterojunction structure were studied by molecular dynamics simulation. Furthermore, the constructed sensing platform showed high selectivity, repeatability, reproducibility, and stability, and this newly developed sensor can detect for phosphoprotein in actual sweat samples. This satisfactory sensing strategy could be promoted to realize the noninvasive and continuous detection of sweat.

Phosphoprotein Detection in Sweat Realized by Intercalation Structure 2D@3D g-C3N4@Fe3O4 Wearable Sensitive Motif.

Abnormal protein phosphorylation in sweat metabolites is closely related to cancer, cardiovascular disease, and other diseases. The real-time monitoring of phosphoproteins in sweat is significant for early monitoring of disease biomarkers. Here, a high-efficiency electrochemical sensor for phosphoprotein in sweat was realized by 2D@3D g-C3N4@Fe3O4 with intercalation structure. Common phosphoprotein ß-Casein was selected to demonstrate the platform's functionalities. The detection limit of g-C3N4@Fe3O4 could be as low as 9.7 µM, and the detection range was from 0.01 mg/mL to 1 mg/mL. In addition, the sensing platform showed good selectivity, reproducibility, and stability. We also investigated the effects of interface structure on adsorption properties and electronic properties of the g-C3N4 and Fe3O4 heterostructure using DFT. More electrons from Fe3O4 were transferred to g-C3N4, which increased the electrons in the energy band of N atoms and promoted the formation of stable N-H bonds with H atoms in phosphoproteins. We demonstrated phosphoprotein sensor functionality by measuring the phosphoprotein in human sweat during exercising. This work realizes a sensing platform for noninvasive and continuous detection of sweat phosphoproteins in wearable devices.

Diversity Scaling Analysis of Chinese Gut Microbiomes Across Ethnicities and Lifestyles.

Diversity scaling (changes) of human gut microbiome is important because it measures the inter-individual heterogeneity of diversity and other important parameters of population-level diversity. Understanding the heterogeneity of microbial diversity can be used as a reference for the personalized medicine of microbiome-associated diseases. Similar to diversity per se, diversity scaling may also be influenced by host factors, especially lifestyles and ethnicities. Nevertheless, this important topic regarding Chinese populations has not been addressed, to our best knowledge. Here, we fill the gap by applying a recent extension to the classic species-area relationship (SAR), i.e., diversity-area relationship (DAR), to reanalyze a large dataset of Chinese gut microbiomes covering the seven biggest Chinese ethnic groups (covering > 95% Chinese) living rural and urban lifestyles. Four DAR profiles were constructed to investigate the diversity scaling, diversity overlap, potential maximal diversity, and the ratio of local to global diversity of Chinese gut microbiomes. We discovered the following: (i) The diversity scaling parameters (z) at various taxon levels are little affected by either ethnicity or lifestyles, as exhibited by less than 0.5% differences in pairwise comparisons. (ii) The maximal accrual diversity (potential diversity) exhibited difference in only about 5% of pairwise comparisons, and all of the differences occurred in ethnicity comparisons (i.e., lifestyles had no effects). (iii) Ethnicity seems to have stronger effects than lifestyles across all taxon levels, and this may reflect the reality that China has been experiencing rapid urbanization in the last few decades, while the ethnic-related genetic background may change relatively little during the same period.

Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia.

BACKGROUND: Emerging evidence suggests structural and functional disruptions of the thalamus in schizophrenia, but whether thalamus abnormalities are able to be used for disease identification and prediction of early treatment response in schizophrenia remains to be determined. This study aims at developing and validating a method of disease identification and prediction of treatment response by multi-dimensional thalamic features derived from magnetic resonance imaging in schizophrenia patients using radiomics approaches. METHODS: A total of 390 subjects, including patients with schizophrenia and healthy controls, participated in this study, among which 109 out of 191 patients had clinical characteristics of early outcome (61 responders and 48 non-responders). Thalamus-based radiomics features were extracted and selected. The diagnostic and predictive capacity of multi-dimensional thalamic features was evaluated using radiomics approach. RESULTS: Using radiomics features, the classifier accurately discriminated patients from healthy controls, with an accuracy of 68%. The features were further confirmed in prediction and random forest of treatment response, with an accuracy of 75%. CONCLUSION: Our study demonstrates a radiomics approach by multiple thalamic features to identify schizophrenia and predict early treatment response. Thalamus-based classification could be promising to apply in schizophrenia definition and treatment selection.

Integrated diversity and shared species analyses of human viromes.

Diversity analysis has been performed routinely on microbiomes, including human viromes. Shared species analysis has been conducted only rarely, but it can be a powerful supplement to diversity analysis. In the present study, we conducted integrated diversity and shared species analyses of human viromes by reanalyzing three published datasets of human viromes with more than 250 samples from healthy vs. diseased individuals and/or rural vs. urban individuals. We found significant differences in the virome diversity measured in the Hill numbers between the healthy and diseased individuals, with diseased individuals exhibiting higher virome diversity than healthy individuals, and rural individual exhibiting higher virome diversity than urban individuals. We applied both "read randomization" and "sample randomization" algorithms to perform shared species analysis. With the more conservative sample randomization algorithm, the observed number of shared species was significantly smaller than the expected shared species in 50% (8 of 16) of the comparisons. These results suggest that integrated diversity and shared species analysis can offer more comprehensive insights in comparing human virome samples than standard diversity analysis alone with potentially powerful applications in differentiating the effects of diseases or other meta-factors.

Synthesis and activity of miconazole derivatives as dual BChE/IDO1 inhibitors for the treatment of Alzheimer's disease.

Background: Alzheimer's disease is a multifactorial neurological disorder seen in elderly people. Loss of cholinergic transmission and unbalanced tryptophan metabolism kynurenine pathway have been demonstrated in neuropsychiatric diseases. Methods & results: Among the two series of synthesized compounds, compounds 5c and 5h were identified as effective dual BChE/IDO1 inhibitors, with well-balanced micromolar activity. Compounds 5c and 5h exhibited promising ability to ameliorate behavioral impairment by Morris water maze. The safety of miconazole analogs was also validated by PC12 and SH-SY5Y cell lines. Conclusion: These results highlight the ability of 5c and 5h to treat Alzheimer's disease.

STING, a promising target for small molecular immune modulator: A review.

Stimulator of interferon genes (STING) plays a crucial role in human innate immune system, which is gradually concerned following the emerging immunotherapy. Activated STING induces the production of type I interferons (IFNs) and proinflammatory cytokines through STING-TBK1-IRF3/NF-κB pathway, which could be applied into the treatment of infection, inflammation, and tumorigenesis. Here, we provided a detailed summary of STING from its structure, function and regulation. Especially, we illustrated the canonical or noncanonical cyclic dinucleotides (CDNs) and synthetic small molecules for STING activation or inhibition and their efficacy in related diseases. Importantly, we particularly emphasized the discovery, development and modification of STING agonist or antagonist, attempting to enlighten reader's mind for enriching small molecular modulator of STING. In addition, we summarized biological evaluation methods for the assessment of small molecules activity.

Recent research progress of Uncaria spp. based on alkaloids: phytochemistry, pharmacology and structural chemistry.

Medicinal plants are well-known in affording clinically useful agents, with rich medicinal values by combining with disease targets through various mechanisms. Plant secondary metabolites as lead compounds lay the foundation for the discovery and development of new drugs in disease treatment. Genus Uncaria from Rubiaceae family is a significant plant source of active alkaloids, with anti-hypertensive, sedative, anti-Alzheimer's disease, anti-drug addiction and anti-inflammatory effects. This review summarizes and discuss the research progress of Uncaria based on alkaloids in the past 15 years, mainly in the past 5 years, including biosynthesis, phytochemistry, pharmacology and structural chemistry. Among, focusing on representative compounds rhynchophylline and isorhynchophylline, the pharmacological activities surrounding the central nervous system and cardiovascular system are described in detail. On the basis of case studies, this article provides a brief overview of the synthesis and analogues of representative compounds types. In summary, this review provides an early basis for further searching for new targets and activities, discussing the mechanisms of pharmacological activity and studying the structure-activity relationships of active molecules.

Functional Connectivity Combined With a Machine Learning Algorithm Can Classify High-Risk First-Degree Relatives of Patients With Schizophrenia and Identify Correlates of Cognitive Impairments.

Schizophrenia (SCZ) is an inherited disease, with the familial risk being among the most important factors when evaluating an individual's risk for SCZ. However, robust imaging biomarkers for the disease that can be used for diagnosis and determination of the prognosis are lacking. Here, we explore the potential of functional connectivity (FC) for use as a biomarker for the early detection of high-risk first-degree relatives (FDRs). Thirty-eight first-episode SCZ patients, 38 healthy controls (HCs), and 33 FDRs were scanned using resting-state functional magnetic resonance imaging. The subjects' brains were parcellated into 200 regions using the Craddock atlas, and the FC between each pair of regions was used as a classification feature. Multivariate pattern analysis using leave-one-out cross-validation achieved a correct classification rate of 88.15% [sensitivity 84.06%, specificity 92.18%, and area under the receiver operating characteristic curve (AUC) 0.93] for differentiating SCZ patients from HCs. FC located within the default mode, frontal-parietal, auditory, and sensorimotor networks contributed mostly to the accurate classification. The FC patterns of each FDR were input into each classification model as test data to obtain a corresponding prediction label (a total of 76 individual classification scores), and the averaged individual classification score was then used as a robust measure to characterize whether each FDR showed an SCZ-type or HC-type FC pattern. A significant negative correlation was found between the average classification scores of the FDRs and their semantic fluency scores. These findings suggest that FC combined with a machine learning algorithm could help to predict whether FDRs are likely to show an SCZ-specific or HC-specific FC pattern.

Gradually Increased Interhemispheric Functional Connectivity During One Night of Sleep Deprivation.

BACKGROUND: It is well known that circadian rhythms and sleep homeostasis contribute to a pronounced trough in sleepiness and behavioral performance at night. However, the underlying neuroimaging mechanisms remain unclear. How brain-function connectivity is modulated during sleep deprivation (SD) has been rarely examined. METHODS: By increasing the number of scanning sessions during SD, the current study used voxel-mirrored homotopic connectivity (VMHC) to investigate dynamic changes in interhemispheric communication during one night of SD. Every 2 hours from 10 pm to 06 am (session 1, 10 pm; session 2, 12 am; session 3, 2 am; session 4, 4 am; session 5, 6 am), functional magnetic resonance-imaging data and Stanford Sleepiness Scale (SSS) scores were collected from 36 healthy participants with intermediate chronotype. Dynamic changes in SSS scores and VMHC were determined using one-way repeated-measure ANOVA with the false discovery-rate method to correct for multiple comparisons. RESULTS: Significant time effects for VMHC were found mainly in the bilateral thalamus, bilateral superior temporal gyrus, and bilateral precentral gyrus. SSS scores and VMHC in these areas were both found to be monotonously increased during SD. Furthermore, significant positive associations were found between SSS valu and VMHC values in the left superior temporal and right superior gyri. CONCLUSION: These findings might represent the dynamic modulation of circadian rhythm merely or the interaction effects of both circadian rhythm and sleep homeostasis on interhemispheric connectivity within the thalamus, default-mode network, and sensorimotor network. Our study provides more comprehensive information on how SD regulates brain connectivity between hemispheres and adds new evidence of neuroimaging correlates of increased sleepiness after SD.

Associations between serum tau, neurological outcome, and cognition following traumatic brain injury.

OBJECTIVE: To investigate the dynamic change in the serum Tau protein early after acute traumatic brain injury (TBI) and its association with neurological outcome and cognitive function. SUBJECTS AND METHODS: Around 229 patients with acute TBI and 30 healthy subjects were evaluated for the serum levels of Tau protein on 1, 3, 5, 7, and 14 days after TBI. The relationships of the serum levels of Tau protein and initial GCS and GOS at 6 months post-injury were also analyzed. Further, 95 TBI patients were assessed with their cognitive function with Montreal cognitive assessment (MoCA) score. RESULTS: Serum Tau was significantly higher in patients with TBI at 1, 3, 5, 7, and 14 days. The serum Tau at each point was significantly lower respectively in the patients with mild TBI than that in medium and severe TBI. The serum Tau was significantly lower in patients with good outcome compared to the poor outcome group. The early serum Tau was negatively correlated with both GCS and GOS. In the TBI group, 39 (41%) out of 95 patients developed cognitive dysfunction assessed by MoCA. Tau protein at day 1, 3, and 5 after TBI was significantly correlated with cognitive dysfunction at 6 months after TBI. CONCLUSIONS: Acute Tau associations with neurological outcomes and cognition may implicate white matter damage and neuronal degeneration. Serum Tau may be used as a reliable biological marker for early diagnosis and cognitive recovery following TBI.