The changes of kernel nutritive components and seed vigor in F1 seeds of sh2 sweet corn during seed development stage were investigated and the relationships between them were analyzed by time series regression (TSR) analysis. The results show that total soluble sugar and reducing sugar contents gradually declined, while starch and soluble protein contents increased throughout the seed development stages. Germination percentage, energy of germination, germination index and vigor index gradually increased along with seed development and reached the highest levels at 38 d after pollination (DAP). The TSR showed that, during 14 to 42 DAP, total soluble sugar content was independent of the vigor parameters determined in present experiment, while the reducing sugar content had a significant effect on seed vigor. TSR equations between seed reducing sugar and seed vigor were also developed. There were negative correlations between the seed reducing sugar content and the germination percentage, energy of germination, germination index and vigor index, respectively. It is suggested that the seed germination, energy of germination, germination index and vigor index could be predicted by the content of reducing sugar in sweet corn seeds during seed development stages.
Carbohydrates ; analysis ; Germination ; Seeds ; growth & development ; Zea mays ; chemistry ; growth & development

Purpose: Gastric cancer (GC) is the second most lethal cancer globally and is associated with poor prognosis. Fatty acid-binding proteins (FABPs) can regulate biological properties of carcinoma cells. FABP5 is overexpressed in many types of cancers; however, the role and mechanisms of action of FABP5 in GC remain unclear. In this study, we aimed to evaluate the clinical and biological functions of FABP5 in GC. Materials and Methods: We assessed FABP5 expression using immunohistochemical analysis in 79 patients with GC and evaluated its biological functions following in vitro and in vivo ectopic expression. FABP5 targets relevant to GC progression were determined using RNA sequencing (RNA-seq). Results: Elevated FABP5 expression was closely associated with poor outcomes, and ectopic expression of FABP5 promoted proliferation, invasion, migration, and carcinogenicity of GC cells, thus suggesting its potential tumor-promoting role in GC. Additionally, RNA-seq analysis indicated that FABP5 activates immune-related pathways, including cytokinecytokine receptor interaction pathways, interleukin-17 signaling, and tumor necrosis factor signaling, suggesting an important rationale for the possible development of therapies that combine FABP5-targeted drugs with immunotherapeutics. Conclusions These findings highlight the biological mechanisms and clinical implications of FABP5 in GC and suggest its potential as an adverse prognostic factor and/or therapeutic target.

This study was aimed to investigate the expression levels of CXCR4 and VEGF in serum of patients with DLBCL and their clinical significances. The peripheral blood of 44 patients with newly diagnosed DLBCL and 20 healthy adults as a control group were chosen for study. And the expression levels of CXCR4 and VEGF in serum were detected by ELISA. The results showed that the expressions of VEGF and CXCR4 in DLBCL patients were higher than those in the control group (P < 0.05). The expression of VEGF was positively correlated with the expression of CXCR4 in DLBCL patients, and the correlation coefficient was 0.743 (P < 0.05). The VEGF expression in DLBCL patients was correlated with LDH, immunotyping, the number of extranodal involvements, Ann Arbor staging and ECOG performance score; while the expression of CXCR4 was correlated with LDH, immunotyping, the number of extranodal involvements and Ann Arbor staging. Univariate analysis showed that LDH, extranodal involvements, immunotyping, Ann Arbor staging, CXCR4 and VEGF were associated with OS. Multivariate analysis showed that the immunotyping and CXCR4 expression independently associated with OS. It is concluded that both expression levels of VEGF and CXCR4 are significant higher than those in the control group. CXCR4 expression positively correlates with VEGF expression and displays a prognostic significance for OS. This study suggests that combined targeting VEGF and CXCR4 may become a novel therapeutic strategy for diffuse large B cell lymphoma.
Adolescent ; Adult ; Aged ; Case-Control Studies ; Female ; Humans ; Lymphoma, Large B-Cell, Diffuse ; metabolism ; pathology ; Male ; Middle Aged ; Prognosis ; Receptors, CXCR4 ; metabolism ; Vascular Endothelial Growth Factor A ; metabolism ; Young Adult

Objective To investigate the genetic data of the 19 X-STR loci in three ethnicities of China ( Han, Gelao,Miao) and to evaluate the application in forensic science.Methods The DNA samples of unrelated individ-ual in Han (n=308), Gelao (n=398), Miao (n=323) ethnicities were amplified using MicroreaderTM19X ID System kit, and the PCR products were analyzed by electrophoresis through 3500XL genetic analyzer. The fragment sizes of alleles were taken subsequently by GeneMapper? ID-X. Allele frequencies and national genetics parameters of the 19 X-STR were analyzed by statistics. The allele frequencies were compared among the three nationalities and were compared with available data of other Han ethnicities from different regions. Results After the Bonferroni correction at a 95% significance level, no significant departures from the Hardy-Weinberg equilibrium was observed. Linkage disequilibrium test showed no significant allelic association between all 19 X-STR loci after Bonferroni’s correction. The cumulative discrimination power in females and in males were greater than 0.999 999 999 99 and 0.999 999 999 94,respectively. The combined power of exclusion in trios and in duos were greater than 0.999 999 999 36 and 0.999 999 52,respectively. The p values,calculated throuth Arle-quin v3.5 software,there were significantly different as detected at loci of X-STR among the different nationalities. Conclusions This panel of X-STR is highly polymorphic in China’s three ethnicities and can be served as a supple-mentary to the current STR system for individual identification.

UNLABELLEDOBJECTIVE To verify the correlation between the points of eye acupuncture and zang-fu function so as to provide the theoretical evidence for the principle of point selection in eye acupuncture therapy.

METHODSSixty cases of cerebral infarction were treated with different points according to syndrome differentiation of Chinese medicine.

MAIN POINTSupper energizer area and lower energizer area. Supplementary points: liver area, kidney area and spleen area for hyperactivity of wind, phlegm and fire; liver area and spleen area for blockage of wind, phlegm and stasis; stomach area and large intestine area for excess fu syndrome due to phlegm heat; heart area and spleen area for qi deficiency and blood stasis; liver area and kidney area for yin deficiency and wind stirring. The single photon emission computed tomography (SPECT) was adopted to observe the changes in blood flow in local foci before and after treatment with eye acupuncture.

RESULTSAfter the treatment with eye acupuncture therapy, the intake ratio of region of interest (ROI) between the lesion area and corresponding area on the opposite side was 0.74 +/- 0.12 before eye acupuncture and was 0.91 +/- 0.08 after treatment, indicating significant statistical difference in comparison (P < 0.05). After eye acupuncture, cerebral blood flow increased apparently.

CONCLUSIONThe point selection according to syndrome differentiation in eye acupuncture therapy may increase local brain blood flow in the patients with cerebral infarction and improve the state of brain ischemia so that the correlation can be proved between the points of eye acupuncture and zang-fu function.

Acupuncture Points ; Acupuncture Therapy ; Adult ; Aged ; Brain ; diagnostic imaging ; Cerebral Infarction ; diagnostic imaging ; physiopathology ; therapy ; Cerebrovascular Circulation ; Eye ; Female ; Humans ; Male ; Middle Aged ; Tomography, Emission-Computed, Single-Photon

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.
Male ; Female ; Humans ; Young Adult ; Adult ; Artificial Intelligence ; Deep Learning ; Cephalometry ; Maxilla ; Mandible/diagnostic imaging*

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.
Humans ; Male ; Female ; Child, Preschool ; Child ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Deep Learning ; Reproducibility of Results ; Radiography ; Algorithms ; Cone-Beam Computed Tomography

The purpose of this study was to explore the differences in interhemispheric functional connectivity in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) based on a triple network model consisting of the default mode network (DMN), salience network (SN), and executive control network (ECN). The technique of voxel-mirrored homotopic connectivity (VMHC) analysis was applied to explore the aberrant connectivity of all patients. The results showed that: (1) the statistically significant connections of interhemispheric brain regions included DMN-related brain regions (i.e. precuneus, calcarine, fusiform, cuneus, lingual gyrus, temporal inferior gyrus, and hippocampus), SN-related brain regions (i.e. frontoinsular cortex), and ECN-related brain regions (i.e. frontal middle gyrus and frontal inferior); (2) the precuneus and frontal middle gyrus in the AD group exhibited lower VMHC values than those in the aMCI and healthy control (HC) groups, but no significant difference was observed between the aMCI and HC groups; and (3) significant correlations were found between peak VMHC results from the precuneus and Mini Mental State Examination (MMSE) and Montreal Cognitive Scale (MOCA) scores and their factor scores in the AD, aMCI, and AD plus aMCI groups, and between the results from the frontal middle gyrus and MOCA factor scores in the aMCI group. These findings indicated that impaired interhemispheric functional connectivity was observed in AD and could be a sensitive neuroimaging biomarker for AD. More specifically, the DMN was inhibited, while the SN and ECN were excited. VMHC results were correlated with MMSE and MOCA scores, highlighting that VMHC could be a sensitive neuroimaging biomarker for AD and the progression from aMCI to AD.
Aged ; Aged, 80 and over ; Alzheimer Disease/physiopathology* ; Brain/diagnostic imaging* ; Brain Mapping ; Cognitive Dysfunction/physiopathology* ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Memory ; Middle Aged ; Models, Neurological ; Nerve Net