Active whey protein/hydroxypropyl methylcellulose edible films incorporated with cinnamaldehyde: Characterization, release kinetics and application to Mongolian cheese preservation.

Edible films with modulated release of antimicrobial agents are important for food preservation. Herein, antimicrobial edible films were prepared using whey protein (WP) and hydroxypropyl methylcellulose (HM) as polymer matrix materials and cinnamaldehyde (CIN) as antimicrobial agent. The mass ratios of WP and HM were 100/0, 75/25, 50/50, 25/75 and 0/100. The release kinetics of CIN through the film was studied, applying the Fickian model, power law and Weibull model. The films were also characterized by physical and structural characteristics, and antibacterial activity. In comparison to other films, the CIN-loaded film with a WP/HM ratio of 50/50 had better moisture resistance, water vapor barrier properties and mechanical properties. High correlation factors were obtained by fitting the CIN release data with the power law (R2 > 0.96) and Weibull model (R2 > 0.97). The diffusion mechanism of CIN was pseudo-Fickian. The diffusion coefficients (D1 and D2) had a positive linear relationship with the HM ratio, suggesting that a high HM ratio was beneficial to the CIN release. Finally, the WH50-C film was successfully used to preserve Mongolian cheese. This research provides a new perspective on the design of active packaging film with sustained-release characteristics.

Dual uncertainty-guided multi-model pseudo-label learning for semi-supervised medical image segmentation.

Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However, this method relies on the quality of pseudo-labels and can lead to an unstable training process due to differences between samples. Additionally, directly generating pseudo-labels from the model itself accelerates noise accumulation, resulting in low-confidence pseudo-labels. To address these issues, we proposed a dual uncertainty-guided multi-model pseudo-label learning framework (DUMM) for semi-supervised medical image segmentation. The framework consisted of two main parts: The first part is a sample selection module based on sample-level uncertainty (SUS), intended to achieve a more stable and smooth training process. The second part is a multi-model pseudo-label generation module based on pixel-level uncertainty (PUM), intended to obtain high-quality pseudo-labels. We conducted a series of experiments on two public medical datasets, ACDC2017 and ISIC2018. Compared to the baseline, we improved the Dice scores by 6.5% and 4.0% over the two datasets, respectively. Furthermore, our results showed a clear advantage over the comparative methods. This validates the feasibility and applicability of our approach.

A self-supervised fusion network for carotid plaque ultrasound image classification.

Carotid plaque classification from ultrasound images is crucial for predicting ischemic stroke risk. While deep learning has shown effectiveness, it heavily relies on substantial labeled datasets. Achieving high performance with limited labeled images is essential for clinical use. Self-supervised learning (SSL) offers a potential solution; however, the existing works mainly focus on constructing the SSL tasks, neglecting the use of multiple tasks for pretraining. To overcome these limitations, this study proposed a self-supervised fusion network (Fusion-SSL) for carotid plaque ultrasound image classification with limited labeled data. Fusion-SSL consists of two SSL tasks: classifying image block order (Ordering) and predicting image rotation angle (Rotating). A dual-branch residual neural network was developed to fuse feature presentations learned by the two tasks, which can extract richer visual boundary shape and contour information than a single task. In this experiment, 1270 carotid plaque ultrasound images were collected from 844 patients at Zhongnan Hospital (Wuhan, China). The results showed that Fusion-SSL outperforms single SSL methods across different percentages of labeled training data, ranging from 10 to 100%. Moreover, with only 40% labeled training data, Fusion-SSL achieved comparable results to a single SSL method (predicting image rotation angle) with 100% labeled data. These results indicate that Fusion-SSL could be beneficial for the classification of carotid plaques and the early warning of a stroke in clinical practice.

Decoding the Cell Atlas and Inflammatory Features of Human Intracranial Aneurysm Wall by Single-Cell RNA Sequencing.

BACKGROUND: Intracranial aneurysm (IA) is common and occasionally results in life-threatening hemorrhagic strokes. However, the cell architecture and inflammation in the IA dome remain less understood. METHODS AND RESULTS: Single-cell RNA sequencing was performed on ruptured and unruptured human IA domes for delineating the cell atlas, gene expression perturbations, and inflammation features. Two external bulk mRNA sequencing-based data sets and serological results of 126 patients were collected for validation. As a result, a total of 21 332 qualified cells were captured. Vascular cells, including endothelial cells, smooth muscle cells, fibroblasts, and pericytes, were assigned in extremely sparse numbers (4.84%), and were confirmed by immunofluorescence staining. Pericytes, characterized by ABCC9 and HIGD1B, were identified in the IA dome for the first time. Abundant immune cells were identified, with the proportion of monocytes/macrophages and neutrophils being remarkably higher in ruptured IA. The lymphocyte compartment was also thoroughly categorized. By leveraging external data sets and machine learning algorithms, macrophages were robustly associated with IA rupture, irrespective of their polarization status. The single nucleotide polymorphism rs2280543, which is identified in East Asian populations, was associated with macrophage metabolic reprogramming through regulating TALDO1 expression. CONCLUSIONS: This study provides insights into the cellular architecture and inflammatory features in the IA dome and may enlighten novel therapeutics for unruptured IA.

An Alzheimer's Disease classification network based on MRI utilizing diffusion maps for multi-scale feature fusion in graph convolution.

Graph convolutional networks (GCN) have been widely utilized in Alzheimer's disease (AD) classification research due to its ability to automatically learn robust and powerful feature representations. Inter-patient relationships are effectively captured by constructing patients magnetic resonance imaging (MRI) data as graph data, where nodes represent individuals and edges denote the relationships between them. However, the performance of GCNs might be constrained by the construction of the graph adjacency matrix, thereby leading to learned features potentially overlooking intrinsic correlations among patients, which ultimately causes inaccurate disease classifications. To address this issue, we propose an Alzheimer's disease Classification network based on MRI utilizing diffusion maps for multi-scale feature fusion in graph convolution. This method aims to tackle the problem of features neglecting intrinsic relationships among patients while integrating features from diffusion mapping with different neighbor counts to better represent patients and achieve an accurate AD classification. Initially, the diffusion maps method conducts diffusion information in the feature space, thus breaking free from the constraints of diffusion based on the adjacency matrix. Subsequently, the diffusion features with different neighbor counts are merged, and a self-attention mechanism is employed to adaptively adjust the weights of diffusion features at different scales, thereby comprehensively and accurately capturing patient characteristics. Finally, metric learning techniques enhance the similarity of node features within the same category in the graph structure and bring node features of different categories more distant from each other. This study aims to enhance the classification accuracy of AD, by providing an effective tool for early diagnosis and intervention. It offers valuable information for clinical decisions and personalized treatment. Experimentation on the publicly accessible Alzheimer's disease neuroimaging initiative (ADNI) dataset validated our method's competitive performance across various AD-related classification tasks. Compared to existing methodologies, our approach captures patient characteristics more effectively and demonstrates superior generalization capabilities.

JoCoRank: Joint correlation learning with ranking similarity regularization for imbalanced fetal brain age regression.

Estimating fetal brain age based on sulci by magnetic resonance imaging (MRI) is clinically crucial in determining the normal development of fetal brains. Deep learning provides a possible way for fetal brain age estimation using MRI. Previous studies have mainly emphasized optimizing individual-wise correlation criteria, such as mean square error. However, they ignored the very important global and peer-wise criterion, which are essential for learning the structured relationships among regression samples. Moreover, the imbalanced label distribution introduces an adverse bias, which impairs the reliability and interpretation of correlation estimation and the model's fairness and generalizability. In this work, we propose a novel joint correlation learning with ranking similarity regularization (JoCoRank) algorithm for deep imbalanced regression of fetal brain age. Joint correlation learning concurrently captures individual, global, and peer-level valuable relationship information, and the customized optimization scheme for each criterion exhibits strong robustness against outliers and imbalanced regression. Ranking similarity regularization is designed to calibrate the biased feature representations by aligning the sorted list of neighbors in the label space with those in the feature space. A total of 1327 MRI images from 157 healthy fetuses between 22 and 34 weeks were collected at Wuhan Children's Hospital and utilized to evaluate the performance of JoCoRank in fetal brain age estimation. JoCoRank achieved promising results with an average mean absolute error of 0.693±0.064 weeks and R2 coefficient of 0.930±0.019. Our fetal brain age estimation algorithm would be useful for identifying abnormalities in fetal brain development and undertaking early intervention in clinical practice.

Synergistic effects of EDTA and lysozyme on the properties of hydroxypropyl starch nano antibacterial films.

Hydroxypropyl starch (HPS) nano antibacterial films incorporating Ethylene Diamine Tetraacetic Acid (EDTA) and lysozyme (LY) were fabricated via solvent casting method. The synergistic effects of EDTA and LY on the microstructure, component interactions, color, optical, mechanical, barrier and antibacterial properties of HPS nano antibacterial films were evaluated. The results indicated that EDTA and LY were well dispersed in the matrix of the HPS nano antibacterial films, the film-forming substrates have good compatibility, resulting in a dense multi-layer structure of the HPS nano antibacterial films. The addition of EDTA and LY increased the color parameters (L*, a*, b* and △E*) of the HPS nano antibacterial films. The synergistic effects of EDTA and LY significantly decreased the light transmission of the HPS nano antibacterial films. The presence of EDTA and LY increased the tensile strength (TS) and the elongation at break (EAB) of the HPS nano antibacterial films. The TS and EAB of E2.5L1 reached the highest values of 6.329 MPa and 50.24 %, respectively. The incorporation of EDTA and LY had positive effects on the improvement of water vapor permeability (WVP) and oxygen permeability (OP). The WVP and OP of E2.5L1 reached the highest values of 0.9350 × 10-12 g cm/cm2•s•Pa and 0.297 × 10 -2 g m/m2 •d, respectively. In addition, EDTA and LY had significant synergistic effects on the antibacterial activity against S. aureus (Gram-positive bacteria) and E. coli (Gram-negative bacteria). E2.5L1 exhibited the highest antibacterial activity and the inhibition zone diameters of S. aureus and E. coli were 3.69 mm and 4.28 mm, respectively. The HPS nano antibacterial films incorporating EDTA and LY are potential functional packaging materials.

Value of preoperative evaluation of FEV1 in patients with destroyed lung undergoing pneumonectomy - a 20-year real-world study.

BACKGROUND: Clinical guidelines recommend a preoperative forced expiratory volume in one second (FEV1) of > 2 L as an indication for left or right pneumonectomy. This study compares the safety and long-term prognosis of pneumonectomy for destroyed lung (DL) patients with FEV1 ≤ 2 L or > 2 L. METHODS: A total of 123 DL patients who underwent pneumonectomy between November 2002 and February 2023 at the Department of Thoracic Surgery, Beijing Chest Hospital were included. Patients were sorted into two groups: the FEV1 > 2 L group (n = 30) or the FEV1 ≤ 2 L group (n = 96). Clinical characteristics and rates of mortality, complications within 30 days after surgery, long-term mortality, occurrence of residual lung infection/tuberculosis (TB), bronchopleural fistula/empyema, readmission by last follow-up visit, and modified Medical Research Council (mMRC) dyspnea scores were compared between groups. RESULTS: A total of 96.7% (119/123) of patients were successfully discharged, with 75.6% (93/123) in the FEV1 ≤ 2 L group. As compared to the FEV1 > 2 L group, the FEV1 ≤ 2 L group exhibited significantly lower proportions of males, patients with smoking histories, patients with lung cavities as revealed by chest imaging findings, and patients with lower forced vital capacity as a percentage of predicted values (FVC%pred) (P values of 0.001, 0.027, and 0.023, 0.003, respectively). No significant intergroup differences were observed in rates of mortality within 30 days after surgery, incidence of postoperative complications, long-term mortality, occurrence of residual lung infection/TB, bronchopleural fistula/empyema, mMRC ≥ 1 at the last follow-up visit, and postoperative readmission (P > 0.05). CONCLUSIONS: As most DL patients planning to undergo left/right pneumonectomy have a preoperative FEV1 ≤ 2 L, the procedure is generally safe with favourable short- and long-term prognoses for these patients. Consequently, the results of this study suggest that DL patient preoperative FEV1 > 2 L should not be utilised as an exclusion criterion for pneumonectomy.

Optical Genome Mapping for Chromosomal Aberrations Detection-False-Negative Results and Contributing Factors.

Optical genome mapping (OGM) has been known as an all-in-one technology for chromosomal aberration detection. However, there are also aberrations beyond the detection range of OGM. This study aimed to report the aberrations missed by OGM and analyze the contributing factors. OGM was performed by taking both GRCh37 and GRCh38 as reference genomes. The OGM results were analyzed in blinded fashion and compared to standard assays. Quality control (QC) metrics, sample types, reference genome, effective coverage and classes and locations of aberrations were then analyzed. In total, 154 clinically reported variations from 123 samples were investigated. OGM failed to detect 10 (6.5%, 10/154) aberrations with GRCh37 assembly, including five copy number variations (CNVs), two submicroscopic balanced translocations, two pericentric inversion and one isochromosome (mosaicism). All the samples passed pre-analytical and analytical QC. With GRCh38 assembly, the false-negative rate of OGM fell to 4.5% (7/154). The breakpoints of the CNVs, balanced translocations and inversions undetected by OGM were located in segmental duplication (SD) regions or regions with no DLE-1 label. In conclusion, besides variations with centromeric breakpoints, structural variations (SVs) with breakpoints located in large repetitive sequences may also be missed by OGM. GRCh38 is recommended as the reference genome when OGM is performed. Our results highlight the necessity of fully understanding the detection range and limitation of OGM in clinical practice.

Birefringence characterization in a dual-hole microstructured optical fiber using an OFDR method.

The birefringence in a dual-hole microstructured optical fiber is numerically calculated and characterized with an optical frequency domain reflectometry (OFDR) method. Due to the asymmetric dual air holes in the cross-section, the polarized L P01x and L P01y modes propagate with different group velocities and time delays. Through a polarized coherent OFDR system in experiment, the Fresnel reflection peaks for each mode are separated in the frequency domain with their corresponding beat frequency. Thus, the group birefringence -9.68×10-4 is calculated with a beat frequency difference of 50.03 Hz between the L P01x and L P01y modes at a 6.2 m fiber end, which is in good agreement with that of -9.54×10-4 from the theoretical simulation. Our demonstration provides an accurate and flexible method for group birefringence characterization in microstructured optical fibers with complex cross-sectional structures.

Dual-process modeling of sequential decision making in the balloon analogue risk task.

People are often faced with repeated risky decisions that involve uncertainty. In sequential risk-taking tasks, like the Balloon Analogue Risk Task (BART), the underlying decision process is not yet fully understood. Dual-process theory proposes that human cognition involves two main families of processes, often referred to as System 1 (fast and automatic) and System 2 (slow and conscious). We cross models of the BART with different architectures of the two systems to yield a pool of computational dual-process models that are evaluated on multiple performance measures (e.g., parameter identifiability, model recovery, and predictive accuracy). Results show that the best-performing model configuration assumes the two systems are competitively connected, an evaluation process based on the Scaled Target Learning model of the BART, and an assessment rate that incorporates sensitivity to the trial number, pumping opportunity, and bias to engage in System 1. Findings also shed light on how modeling choices and response times in a dual-process framework can benefit our understanding of sequential risk-taking behavior.

Efficient Production of Melanin by Aureobasidium Melanogenum Using a Simplified Medium and pH-Controlled Fermentation Strategy with the Cell Morphology Analysis.

Natural melanin is a biopolymer with wide application prospects in medicine, food, cosmetics, environmental protection, agriculture, and so on. Microbial fermentation is an important and effective way to produce melanin. In this study, Aureobasidium melanogenum, known as black yeast with cellular pleomorphism, was used for the production of melanin. Based on the characteristic of A. melanogenum secreting melanin under oligotrophic stress, a simple medium containing only glucose, MgSO4·7H2O, and KCl was constructed for the production of melanin. The melanin titer of 6.64 ± 0.22 g/L was obtained after 20 days of fermentation without pH control. The cell morphological changes of A. melanogenum during the production of melanin were recorded, and the results showed that chlamydospore might be the most favorable cell morphology for melanin synthesis. Then, different fermentation strategies with cell morphology analysis were developed to further improve the production of melanin in a 5-L fermenter. Results showed that the maximum titer of melanin reached 18.50 g/L by using the fermentation strategy integrating pH control, ammonium salt addition, and H2O2 stimulation, which increased by 178.6% than that of the strategy without pH control. Furthermore, the melanin obtained from the fermentation broth was characterized as eumelanin containing an indole structure. This study provided a potentially feasible fermentation strategy for the industrial production of melanin.

An image registration-based self-supervised Su-Net for carotid plaque ultrasound image segmentation.

BACKGROUND AND OBJECTIVES: Total Plaque Area (TPA) measurement is critical for early diagnosis and intervention of carotid atherosclerosis in individuals with high risk for stroke. The delineation of the carotid plaques is necessary for TPA measurement, and deep learning methods can automatically segment the plaque and measure TPA from carotid ultrasound images. A large number of labeled images is essential for training a good deep learning model, but it is very difficult to collect such large labeled datasets for carotid image segmentation in clinical practice. Self-supervised learning can provide a possible solution to improve the deep-learning models on small labeled training datasets by designing a pretext task to pre-train the models without using the segmentation masks. However, the existing self-supervised learning methods do not consider the feature presentations of object contours. METHODS: In this paper, we propose an image registration-based self-supervised learning method and a stacked U-Net (SSL-SU-Net) for carotid plaque ultrasound image segmentation, which can better exploit the semantic features of carotid plaque contours in self-supervised task training. RESULTS: Our network was trained on different numbers of labeled images (n = 10, 33, 50 and 100 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The network trained on the entire SPARC dataset was then directly applied to an independent dataset collected in Zhongnan hospital (n = 497, Wuhan, China). For the 44 subjects tested on the SPARC dataset, our method yielded a DSC of 80.25-89.18% and the produced TPA measurements, which were strongly correlated with manual segmentation (r = 0.965-0.995, ρ< 0.0001). For the Zhongnan dataset, the DSC was 90.3% and algorithm TPAs were strongly correlated with manual TPAs (r = 0.985, ρ< 0.0001). CONCLUSIONS: The results demonstrate that our proposed method yielded excellent performance and good generalization ability when trained on a small labeled dataset, facilitating the use of deep learning in carotid ultrasound image analysis and clinical practice. The code of our algorithm is available https://github.com/a610lab/Registration-SSL.

Reproductive outcomes in couples with recurrent pregnancy loss after embryonic chromosomal microarray analysis.

BACKGROUND: Chromosomal microarray analysis (CMA) has been widely applied to explore the genetic etiology in recurrent pregnancy loss (RPL). However, the reproductive prognosis in RPL couples with different types of chromosomally abnormal miscarriage remains unclear. OBJECTIVES: The main purpose of this study was to evaluate the reproductive prognosis among RPL couples after genetic testing in products of conception (POCs) by CMA. STUDY DESIGN: In this retrospective study, 1101 RPL couples referred for genetic testing in POCs by CMA. A total of 830 couples who met the inclusion criteria were followed up for at least 24 months after the index miscarriage. The rates of live birth and adverse pregnancy events in subsequent pregnancy and cumulative pregnancies were examined. RESULTS: For couples with three or more miscarriage, compared with those with chromosomally normal miscarriage, a significantly higher subsequent live birth rate was found in couples with chromosomally abnormal miscarriage (66.9% vs 71.6%, P = .040). However, differences in cumulative live birth rate among couples with chromosomally abnormal miscarriage and normal miscarriage were nonsignificant (82.7% vs 80.2%, P = .131). Women with advanced maternal age showed a significant decrease in the live birth rate (P < 0.01) and an increase in the miscarriage rate (P < 0.01) than those aged < 35 years old, regardless of whether the miscarriage was chromosomally normal or abnormal. RPL couples with chromosomally normal miscarriage showed a significant decrease in live birth rates in subsequent pregnancy and cumulative pregnancies, when they had experienced a large number of previous miscarriages; however, no significant difference was observed in those with chromosomally abnormal miscarriage. CONCLUSION: For women with three or more previous miscarriages, RPL couples with chromosomally normal miscarriage manifested a poorer reproductive prognosis than those with chromosomally abnormal miscarriage in subsequent pregnancy, while the cumulative live birth rate was similar. Advanced maternal age was a predictor of adverse pregnancy events, regardless of embryonic chromosomal results. Furthermore, among RPL women with large numbers of previous miscarriages, the supportive care and counselling regarding individual risk is necessary for those with chromosomally normal miscarriage.

Residual risk of clinically significant copy number variations in fetuses with nasal bone absence or hypoplasia after excluding non-invasive prenatal screening-detectable findings.

BACKGROUND: It remains controversial whether prenatal screening or diagnostic testing should be offered to fetuses with nasal bone (NB) absence or hypoplasia, and there are no studies comparing the yield of chromosomal microarray analysis (CMA) to non-invasive prenatal screening (NIPS). The aim of this study was to evaluate the residual risk of clinically significant copy number variations (CNVs) in fetuses with NB absence or hypoplasia after excluding theoretically NIPS-detectable abnormalities, and to assess their clinical outcomes. METHODS: This prospective study encompassed 400 fetuses with NB absence or hypoplasia undergoing CMA testing between 2015 and 2022. Clinically significant CMA findings were categorized into three subgroups, including three-NIPS-detectable (trisomies 21, 18 and 13), five-NIPS-detectable (trisomies 21, 18 and 13 and sex chromosome aneuploidies) and genome-wide NIPS-detectable (variants over 7 Mb). We calculated the theoretical residual risk and compared it with the results of a control cohort of low-risk pregnancies. We further evaluated their clinical outcomes. RESULTS: The overall diagnostic yield in our cohort was 7.8% (31/400). The detection rate of clinically significant CMA findings in fetuses with non-isolated NB absence or hypoplasia was significantly higher than that in fetuses with isolated NB absence or hypoplasia (20.0% vs. 6.6%, P =.005). The theoretical residual risks in all NIPS models were significantly higher when compared with the control cohort. The normal infant rate in fetuses with normal CMA results was 97.9% (323/330), and a significant higher incidence was observed in fetuses with isolated NB absence or hypoplasia compared with non-isolated NB absence or hypoplasia (98.4% vs. 91.7%, P =.028). CONCLUSIONS: The residual risk of clinically significant CNVs in fetuses with NB absence or hypoplasia following the exclusion of theoretically NIPS-detectable findings was higher than that in low-risk pregnancies. This risk should be considered in genetic counseling to make a more comprehensive and precise choice regarding prenatal genetic testing.

CXCR4-overexpressed exosomes from cardiosphere-derived cells attenuate myocardial ischemia/reperfusion injury by transferring miRNA to macrophages and regulating macrophage polarization.

Cardiosphere-derived cells (CDCs) are emerging as ideal candidates for managing cardiac inflammation, albeit with some limitations. Recent literatures have indicated that exosomes secreted by CDCs with C-X-C motif chemokine receptor 4 (CXCR4) overexpression can promote cardiac function after myocardial infarction and there have been some reports of miRNAs involved in ischemia/reperfusion (I/R) therapy. Therefore, we are interested in the role of CXCR4-overexpressed CDC-derived exosomes in delivering specific miRNA after myocardial I/R injury. In this research, we first constructed CDC-derived exosomes that overexpressed CXCR4 and miR-27a-5p, miR-182, or miR-101a. Then, we co-cultured the engineered exosomes with RAW264.7 cells and injected them intravenously into myocardial I/R model mice. In vitro, results showed that proinflammatory cytokines levels in the culture supernatant were decreased and the expression of M2 phenotypic markers were increased. Administration of engineered exosomes improved cardiac function, reduced infarct size, alleviated macrophage infiltration, and regulated M2 macrophage polarization after myocardial I/R, suggesting their implications in cardiac injury repair.

PDFF-CNN: An attention-guided dynamic multi-orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset.

BACKGROUND: Fetal brain magnetic resonance imaging (MRI)-based gestational age prediction has been widely used to characterize normal fetal brain development and diagnose congenital brain malformations. PURPOSE: The uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above-mentioned problems pose great challenges for deep learning-based fetal brain MRI gestational age prediction. METHODS: In this study, a pyramid squeeze attention (PSA)-guided dynamic feature fusion CNN (PDFF-CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF-CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi-orientation feature representations in a parallel weight-sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi-orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age prediction performance. RESULTS: Evaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2-weighted MRI images from 157 subjects, PDFF-CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an R2 of 0.904. Furthermore, the attention activation maps of PDFF-CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestational stage. CONCLUSIONS: These results suggest that the proposed PDFF-CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.

[Effect of recombinant human growth hormone on serum Klotho and fibroblast growth factor 23 in children with idiopathic short stature]. / é‡ç»„äººç”Ÿé•¿æ¿€ç´ æ²»ç–—å¯¹ç‰¹å‘æ€§çŸ®å°ç—‡å„¿ç«¥è¡€æ¸ Klothoå’Œæˆçº¤ç»´ç»†èƒžç”Ÿé•¿å› å­23的影响.

OBJECTIVES: To investigate the changes in the serum levels of Klotho, fibroblast growth factor 23 (FGF23), and insulin-like growth factor-1 (IGF-1) in children with idiopathic short stature (ISS) before and after recombinant human growth hormone (rhGH) treatment, as well as the correlation of Klotho and FGF23 with the growth hormone (GH)/IGF-1 growth axis in these children. METHODS: A prospective study was conducted on 33 children who were diagnosed with ISS in the Department of Pediatrics, Hebei Provincial People's Hospital, from March 10, 2021 to December 1, 2022 (ISS group). Twenty-nine healthy children, matched for age and sex, who attended the Department of Child Healthcare during the same period, were enrolled as the healthy control group. The children in the ISS group were treated with rhGH, and the serum levels of Klotho, FGF23, and IGF-1 were measured before treatment and after 3, 6, and 9 months of treatment. A correlation analysis was conducted on these indexes. RESULTS: There were no significant differences in the serum levels of IGF-1, Klotho, and FGF23 between the ISS and healthy control groups (P>0.05). The serum levels of Klotho, FGF23, and IGF-1 increased significantly in the ISS group after 3, 6, and 9 months of rhGH treatment (P<0.05). In the ISS group, Klotho and FGF23 levels were positively correlated with the phosphate level before treatment (P<0.05). Before treatment and after 3, 6, and 9 months of rhGH treatment, the Klotho level was positively correlated with the IGF-1 level (P<0.05), the FGF23 level was positively correlated with the IGF-1 level (P<0.05), and the Klotho level was positively correlated with the FGF23 level (P<0.05), while Klotho and FGF23 levels were not correlated with the height standard deviation of point (P>0.05). CONCLUSIONS: The rhGH treatment can upregulate the levels of Klotho, FGF23, and IGF-1 and realize the catch-up growth in children with ISS. Klotho and FGF23 may not directly promote the linear growth of children with ISS, but may have indirect effects through the pathways such as IGF-1 and phosphate metabolism. The consistent changes in Klotho, FGF23 and IGF-1 levels show that there is a synergistic relationship among them in regulating the linear growth of ISS children.

Multiomic analysis reveals cellular and epigenetic plasticity in intestinal pouches of ulcerative colitis patients.

Background & Aims: Total proctocolectomy with ileal pouch anal anastomosis (IPAA) is the standard of care for patients with severe treatment resistant ulcerative colitis (UC). Despite improvements in patient outcomes, about 50% of patients will develop inflammation of the pouch within 1-2 years following surgery. Establishment of UC pouches is associated with profound histological changes of the mucosa. A detailed characterization of these changes on a cellular and molecular level is crucial for an improved understanding of pouch physiology and diseases management. Methods: We generated cell-type-resolved transcriptional and epigenetic atlases of UC pouches using scRNA-seq and scATAC-seq data from paired biopsy samples from the ileal pouch and ileal segment above the pouch (pre-pouch) of UC-IPAA patients (n=6, female=2) without symptoms. We also collected data from paired biopsies of the terminal ileum (TI) and ascending colon (AC) from healthy controls (n=6, female=3). Results: We identified novel populations of colon-like absorptive and secretory epithelial cells, constituting a significant proportion of the epithelial cell fraction in the pouch but not in matched pre-pouch samples. Pouch-specific enterocytes expressed colon-specific genes, including CEACAM5, CA2. However, in contrast to normal colonic epithelium, these cells also expressed a range of inflammatory and secretory genes, similar to previously detected gene expression signatures in IBD patients. Comparison to longitudinal bulk RNA-seq data from UC pouches demonstrated that colon-like epithelial cells are present early after pouch functionalization and independently of subsequent pouchitis. Finally, single cell chromatin accessibility revealed activation colonic transcriptional regulators, including CDX1, NFIA, and EHF. Conclusion: UC pouches are characterized by partial colonic metaplasia of the epithelium. These data constitute a resource of transcriptomic and epigenetic signatures of cell populations in the pouch and provide an anchor for understanding the underlying molecular mechanisms of pouchitis.