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There is still controversy about optimal dietary iodine intake as the Universal Salt Iodization policy enforcement in China. A modified iodine balance study was thus conducted to explore the suitable iodine intake in Chinese adult males using the iodine overflow hypothesis. In this study, thirty-eight apparently healthy males (19·1 (sd 0·6) years) were recruited and provided with designed diets. After the 14-d iodine depletion, daily iodine intake gradually increased in the 30-d iodine supplementation, consisting of six stages and each of 5 d. All foods and excreta (urine, faeces) were collected to examine daily iodine intake, iodine excretion and the changes of iodine increment in relation to those values at stage 1. The dose–response associations of iodine intake increment with excretion increment were fitted by the mixed effects models, as well as with retention increment. Daily iodine intake and excretion were 16·3 and 54·3 μg/d at stage 1, and iodine intake increment increased from 11·2 μg/d at stage 2 to 118·0 μg/d at stage 6, while excretion increment elevated from 21·5 to 95·0 μg/d. A zero iodine balance was dynamically achieved as 48·0 μg/d of iodine intake. The estimated average requirement and recommended nutrient intake were severally 48·0 and 67·2 μg/d, which could be corresponded to a daily iodine intake of 0·74 and 1·04 μg/kg per d. The results of our study indicate that roughly half of current iodine intakes recommendation could be enough in Chinese adult males, which would be beneficial for the revision of dietary reference intakes.
Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.
To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.
We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.
We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.
These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.
Schizophrenia is a severe and complex psychiatric disorder that needs treatment based on extensive experience. Antipsychotic drugs have already become the cornerstone of the treatment for schizophrenia; however, the therapeutic effect is of significant variability among patients, and only around a third of patients with schizophrenia show good efficacy. Meanwhile, drug-induced metabolic syndrome and other side-effects significantly affect treatment adherence and prognosis. Therefore, strategies for drug selection are desperately needed. In this study, we will perform pharmacogenomics research and set up an individualised preferred treatment prediction model.
We aim to create a standard clinical cohort, with multidimensional index assessment of antipsychotic treatment for patients with schizophrenia.
This trial is designed as a randomised clinical trial comparing treatment with different kinds of antipsychotics. A total sample of 2000 patients with schizophrenia will be recruited from in-patient units from five clinical research centres. Using a computer-generated program, the participants will be randomly assigned to four treatment groups: aripiprazole, olanzapine, quetiapine and risperidone. The primary outcomes will be measured as changes in the Positive and Negative Syndrome Scale of schizophrenia, which reflects the efficacy. Secondary outcomes include the measure of side-effects, such as metabolic syndromes. The efficacy evaluation and side-effects assessment will be performed at baseline, 2 weeks, 6 weeks and 3 months.
This trial will assess the efficacy and side effects of antipsychotics and create a standard clinical cohort with a multi-dimensional index assessment of antipsychotic treatment for schizophrenia patients.
This study aims to set up an individualized preferred treatment prediction model through the genetic analysis of patients using different kinds of antipsychotics.
No studies have reported on how to relieve distress or relax in medical health workers while wearing medical protective equipment in coronavirus disease 2019 (COVID-19) pandemic. The study aimed to establish which relaxation technique, among six, is the most feasible in first-line medical health workers wearing medical protective equipment.
This was a two-step study collecting data with online surveys. Step 1: 15 first-line medical health workers were trained to use six different relaxation techniques and reported the two most feasible techniques while wearing medical protective equipment. Step 2: the most two feasible relaxation techniques revealed by step 1 were quantitatively tested in a sample of 65 medical health workers in terms of efficacy, no space limitation, no time limitation, no body position requirement, no environment limitation to be done, easiness to learn, simplicity, convenience, practicality, and acceptance.
Kegel exercise and autogenic relaxation were the most feasible techniques according to step 1. In step 2, Kegel exercise outperformed autogenic relaxation on all the 10 dimensions among the 65 participants while wearing medical protective equipment (efficacy: 24 v. 15, no space limitation: 30 v. 4, no time limitation: 31 v. 4, no body position requirement: 26 v. 4, no environment limitation: 30 v. 11, easiness to learn: 28 v. 5, simplicity: 29 v. 7, convenience: 29 v. 4, practicality: 30 v. 14, acceptance: 32 v. 6).
Kegel exercise seems a promising self-relaxation technique for first-line medical health workers while wearing medical protective equipment among COVID-19 pandemic.
Data on average iodine requirements for the Chinese population are limited following implementation of long-term universal salt iodisation. We explored the minimum iodine requirements of young adults in China using a balance experiment and the ‘iodine overflow’ hypothesis proposed by our team. Sixty healthy young adults were enrolled to consume a sequential experimental diet containing low, medium and high levels of iodine (about 20, 40 and 60 μg/d, respectively). Each dose was consumed for 4 d, and daily iodine intake, excretion and retention were assessed. All participants were in negative iodine balance throughout the study. Iodine intake, excretion and retention differed among the three iodine levels (P < 0·01 for all groups). The zero-iodine balance derived from a random effect model indicated a mean iodine intake of 102 μg/d, but poor correlation coefficients between observed and predicted iodine excretion (r 0·538 for μg/d data) and retention (r 0·304 for μg/d data). As iodine intake increased from medium to high, all of the increased iodine was excreted (‘overflow’) through urine and faeces by males, and 89·5 % was excreted by females. Although the high iodine level (63·4 μg/d) might be adequate in males, the corresponding level of 61·6 μg/d in females did not meet optimal requirements. Our findings indicate that a daily iodine intake of approximately half the current recommended nutrient intake (120 μg/d) may satisfy the minimum iodine requirements of young male adults in China, while a similar level is insufficient for females based on the ‘iodine overflow’ hypothesis.
Amnestic mild cognitive impairment (aMCI) is characterized by delayed P300 latency and reduced grey matter (GM) volume, respectively. The relationship between the features in aMCI is unclear. This study was to investigate the relationship between the altered P300 latency and the GM volume in aMCI.
Thirty-four aMCI and 34 well-matched normal controls (NC) were studied using electroencephalogram during a visual oddball task and scanned with MRI. Both tests were finished in the same day.
As compared with the NC group, the aMCI group exhibited delayed P300 latency in parietal cortex and reduced GM volumes in bilateral temporal pole and left hippocampus/parahippocampal gyrus. A remarkable negative correlation was found between delayed P300 latency and reduced left hippocampal volume only in the aMCI group. Interestingly, the mediating analysis found P300 latency significantly mediated the association between right supramarginal gyrus volume and information processing speed indicated by Stroop Color and Word Test A scores.
The association between delayed P300 latency and reduced left hippocampal volume in aMCI subjects suggests that reduced left hippocampal volume may be the potential structural basis of delayed P300 latency.
Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.
To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia.
Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant.
The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus–medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia.
These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.
Heusler Ni–Mn–(Ga, In, Sn, Sb) materials can provide large magnetic-field-induced strain, giant magnetocaloric and magnetoresistance effects based on their first-order solid-state martensitic transformation. In the present work, effects of Co doping on martensitic transformation behavior in melt-spun Ni–Mn–Sn ribbons were studied by x-ray diffraction, scanning/transmission electron microscopy, and thermal analysis. Experimental results showed that both martensitic transition and austenite Curie temperatures increased linearly with Co addition to Ni49Mn39Sn12; and meanwhile, crystal structures of the martensite evolved from four-layered orthorhombic (4O) to five-layered orthorhombic (10M), and then seven-layered monoclinic (14M). The compositional dependence of the martensitic transition temperatures was well correlated with changes of valence electron concentration (e/a) and unit-cell volume of high-temperature austenite. It was proposed that both increase of valence electron concentration and shrinkage of austenite unit-cell volume with Co addition are favorable to the occurrence of martensitic transformation. In addition, the Curie temperature of austenite increases with Co addition, which was ascribed to the enhancement of ferromagnetic exchange interaction.
Blue ice extent and its geographical distribution during the three years from December 1999–January 2003 have been mapped together with rock exposure areas for the entire Lambert-Amery glacial basin at an unprecedented level of spatial detail using Landsat ETM+ images. Various geometric and shape attributes for each blue ice patch have been derived. The total area of blue ice is estimated to be 20 422 km2, accounting for 1.48% of the glacial basin. We also found that the image texture information is helpful for distinguishing different types of blue ice: rough blue ice, smooth blue ice, and level blue ice. Rough blue ice areas are mostly associated with glacial dynamics and located in relatively low elevation regions, smooth blue ice areas are often related to nunataks and mountains, steep slopes and wind blows, and level blue ice areas are formed by melt-induced lakes in the margin of the ice shelf and melt-induced ponds on the ice shelf in low elevations. The elevation and surface slope properties of different types of blue ice area are characterized through regional topographical analysis with ICESat laser altimetry data. The effect of mountain height on the blue ice extent is also examined with local topographic profile analysis based on the ASTER global digital elevation model. The wide variation of the ratio of blue ice area length to mountain height indicates that the factors controlling the formation and extent of blue ice are more complicated than we previously thought.
The basal melting and freezing rates under the Amery Ice Shelf, East Antarctica, are evaluated, and their spatial distributions mapped. Ice velocity, surface elevation and accumulation rate datasets are employed in the analysis, along with a column-averaged ice density model. Our analysis shows that the total area of basal melting is 34 700 km2, with a total annual melt of 62.5 ± 9.3 Gt and an average melting rate of 1.8 ± 0.3 m a−1. Basal freezing mainly occurs in the northwestern part of the ice shelf, over a total area of 26 100 km2 and with a maximum freezing rate of 2.4 ± 0.4 m a−1. The total marine ice that accretes to the ice-shelf base is estimated to be 16.2 ± 2.4 Gt a−1. Using a redefined grounding line and geometry of the Amery Ice Shelf, we estimate the net melt over the ice-shelf base is about 46.4 ± 6.9 G ta−1, which is higher than previous modeling and oceanographic estimates. Net basal melting accounts for about half of the total ice-shelf mass loss, with the rest being from iceberg discharge. Our basal melting and freezing distribution map provides a scientific basis for quantitative analysis of ice–ocean interaction at the ice-shelf–ocean interface.
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