MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study

INTRODUCTION

The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20%-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions.

AIM

To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis (OCLNM) in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features.

METHODS

A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA) and survival analysis.

RESULTS

Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927) and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1 and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC.

CONCLUSION

The proposed MRI-based Resnet50 deep learning model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.

Effects of polyethylene microplastics occurrence on estrogens degradation in soil

Growing focus has been drawn to the continuous detection of high estrogens levels in the soil environment. Additionally, microplastics (MPs) are also of growing concern worldwide, which may affect the environmental behavior of estrogens. However, little is known about effects of MPs occurrence on estrogens degradation in soil. In this study, polyethylene microplastics (PE-MPs) were chosen to examine the influence on six common estrogens (estrone (E1), 17α-estradiol (17α-E2), 17β-estradiol (17β-E2), estriol (E3), diethylstilbestrol (DES), and 17α-ethinylestradiol (17α-EE2)) degradation. The results indicated that PE-MPs had little effect on the degradation of E3 and DES, and slightly affected the degradation of 17α-E2, however, significantly inhibited the degradation of E1, 17α-EE2, and 17β-E2. It was explained that (i) obvious oxidation reaction occurred on the surface of PE-MPs, indicating that PE-MPs might compete with estrogens for oxidation sites, such as redox and biological oxidation; (ii) PE-MPs significantly changed the bacterial community in soil, resulting in a decline in the abundance of some bacterial communities that biodegraded estrogens. Moreover, the rough surface of PE-MPs facilitated the estrogen-degrading bacterial species (especially for E1, E2, and EE2) to adhere, which decreased their reaction to estrogens. These findings are expected to deepen the understanding of the environmental behavior of typical estrogens in the coexisting system of MPs.

b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT

OBJECTIVE

Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance. Approach. we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact-affected images. These extracted metal artifact representations are then bidirectionally embedded into both the metal artifact generator and the metal artifact eliminator, which can simultaneously improve the performance of artifact removal and artifact generation. The artifact eliminator learns artifact removal in a supervised manner, while the artifact generator learns artifact generation in an adversarial manner. To further improve the performance of the bidirectional task networks, we propose artifact consistency loss to align the consistency of images generated by the eliminator and the generator with or without embedding artifact representations. Main results. To validate the effectiveness of our algorithm, experiments are conducted on simulated and clinical datasets containing various dental metal morphologies. Quantitative metrics are calculated to evaluate the results of the simulation tests,which demonstrate b-MAR improvements of > 1.4131 dB in PSNR, > 0.3473 HU decrements in RMSE, and > 0.0025 promotion in SSIM over the current state-of-the-art MAR methods. All results indicate that the proposed b-MAR method can remove artifacts caused by various metal morphologies and restore the structural integrity of dental tissues effectively. Significance. The proposed b-MAR method strengthens the joint learning of the artifact removal process and the artifact generation process by bidirectionally embedding artifact representations, thereby improving the model's artifact removal performance. Compared with other comparison methods, b-MAR can robustly and effectively correct metal artifacts in dental CBCT images caused by different dental metals.

Semi-supervised iterative adaptive network for low-dose CT sinogram recovery

Background.Concern has been expressed regarding the risk of carcinogenesis from medical computed tomography (CT) radiation. Lowering radiation in CT without appropriate modifications often leads to severe noise-induced artifacts in the images. The utilization of deep learning (DL) techniques has achieved promising reconstruction performance in low-dose CT (LDCT) imaging. However, most DL-based algorithms require the pre-collection of a large set of image pairs (low-dose/standard-dose) and the training of networks in an end-to-end supervised manner. Meanwhile, securing such a large volume of paired, well-registered training data in clinical practice is challenging. Moreover, these algorithms often overlook the potential to utilize the abundant information in a large collection of LDCT-only images/sinograms.Methods.In this paper, we introduce a semi-supervised iterative adaptive network (SIA-Net) for LDCT imaging, utilizing both labeled and unlabeled sinograms in a cohesive network framework, integrating supervised and unsupervised learning processes. Specifically, the supervised process captures critical features (i.e. noise distribution and tissue characteristics) latent in the paired sinograms, while the unsupervised process effectively learns these features in the unlabeled low-dose sinograms, employing a conventional weighted least-squares model with a regularization term. Furthermore, the SIA-Net method is designed to adaptively transfer the learned feature distribution from the supervised to the unsupervised process, thereby obtaining a high-fidelity sinogram through iterative adaptive learning. Finally, high-quality CT images can be reconstructed from the refined sinogram using the filtered back-projection algorithm.Results.Experimental results on two clinical datasets indicate that the proposed SIA-Net method achieves competitive performance in terms of noise reduction and structure preservation in LDCT imaging, when compared to traditional supervised learning methods.

DDT-Net: Dose-Agnostic Dual-Task Transfer Network for Simultaneous Low-Dose CT Denoising and Simulation

Deep learning (DL) algorithms have achieved unprecedented success in low-dose CT (LDCT) imaging and are expected to be a new generation of CT reconstruction technology. However, most DL-based denoising models often lack the ability to generalize to unseen dose data. And they only learn the posterior distribution of latent normal-dose CT (NDCT) images conditioned on observed LDCT images in the traditional maximum a posteriori (MAP) framework, while ignoring the noise generation process of LDCT images. Moreover, most simulation tools for LDCT typically operate on proprietary projection data, which is generally not accessible without an established collaboration with CT manufacturers. To alleviate these issues, in this work, we propose a dose-agnostic dual-task transfer network, termed DDT-Net, for simultaneous LDCT denoising and simulation. Concretely, the dual-task learning module is constructed to integrate the LDCT denoising and simulation tasks into a unified optimization framework by learning the joint distribution of LDCT and NDCT data. We approximate the joint distribution of continuous dose level data by training DDT-Net with discrete dose data, which can be generalized to denoising and simulation of unseen dose data. In particular, the mixed-dose training strategy adopted by DDT-Net can promote the denoising performance of lower-dose data. The paired dataset simulated by DDT-Net can be used for data augmentation to further restore the tissue texture of LDCT images. Experimental results on synthetic data and clinical data show that the proposed DDT-Net outperforms competing methods in terms of denoising and generalization performance at unseen dose data, and it also provides a simulation tool that can quickly simulate realistic LDCT images at arbitrary dose levels.

Temporal and cellular analysis of granuloma development in mycobacterial infected adult zebrafish

Because granulomas are a hallmark of tuberculosis pathogenesis, the study of the dynamic changes in their cellular composition and morphological character can facilitate our understanding of tuberculosis pathogenicity. Adult zebrafish infected with Mycobacterium marinum form granulomas that are similar to the granulomas in human patients with tuberculosis and therefore have been used to study host-mycobacterium interactions. Most studies of zebrafish granulomas, however, have focused on necrotic granulomas, while a systematic description of the different stages of granuloma formation in the zebrafish model is lacking. Here, we characterized the stages of granulomas in M. marinum-infected zebrafish, including early immune cell infiltration, nonnecrotizing granulomas, and necrotizing granulomas, using corresponding samples from patients with pulmonary tuberculosis as references. We combined hematoxylin and eosin staining and in situ hybridization to identify the different immune cell types and follow their spatial distribution in the different stages of granuloma development. The macrophages in zebrafish granulomas were shown to belong to distinct subtypes: epithelioid macrophages, foamy macrophages, and multinucleated giant cells. By defining the developmental stages of zebrafish granulomas and the spatial distribution of the different immune cells they contain, this work provides a reference for future studies of mycobacterial granulomas and their immune microenvironments.

Targeted Screening of Fiber Degrading Bacteria with Probiotic Function in Herbivore Feces

Cellulolytic bacteria with probiotic functions play a crucial role in promoting the intestinal health in herbivores. In this study, we aimed to correlate the 16S rRNA gene amplicon sequencing and fiber-degrading enzyme activity data from six different herbivore feces samples. By utilizing the separation and screening steps of probiotics, we targeted and screened high-efficiency fiber-degrading bacteria with probiotic functions. The animals included Maiwa Yak (MY), Holstein cow (CC), Tibetan sheep (TS), Southern Sichuan black goat (SG), Sichuan white rex rabbit (CR), and New Zealand white rabbit (ZR). The results showed that the enzymes associated with fiber degradation were higher in goat and sheep feces compared to cattle and rabbit's feces. Correlation analysis revealed that Bacillus and Fibrobacter were positively correlated with five types of fiber-degrading related enzymes. Notably, the relative abundance of Bacillus in the feces of Tibetan sheep was significantly higher than that of other five herbivores. A strain TS5 with good cellulose decomposition ability from the feces of Tibetan sheep by Congored staining, filter paper decomposition test, and enzyme activity determination was isolated. The strain was identified as Bacillus velezensis by biological characteristics, biochemical analysis, and 16S rRNA gene sequencing. To test the probiotic properties of Bacillus velezensis TS5, we evaluated its tolerance to acid and bile salt, production of digestive enzymes, antioxidants, antibacterial activity, and adhesion ability. The results showed that the strain had good tolerance to pH 2.0 and 0.3% bile salts, as well as good potential to produce cellulase, protease, amylase, and lipase. This strain also had good antioxidant capacity and the ability to antagonistic Staphylococcus aureus BJ216, Salmonella SC06, Enterotoxigenic Escherichia coli CVCC196, and Escherichia coli ATCC25922. More importantly, the strain had good self-aggregation and Caco-2 cell adhesion rate. In addition, we tested the safety of Bacillus velezensis TS5 by hemolysis test, antimicrobial susceptibility test, and acute toxicity test in mice. The results showed that the strain had no hemolytic phenotype, did not develop resistance to 19 commonly used antibiotics, had no cytotoxicity to Caco-2, and did not have acute toxic harm to mice. In summary, this study targeted isolated and screened a strain of Bacillus velezensis TS5 with high fiber-degrading ability and probiotic potency. This strain can be used as a potential probiotic for feeding microbial preparations for ruminants.

Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector

In flat-panel detector (FPD) based cone-beam computed tomography (CBCT) imaging, the native receptor array is usually binned into a smaller matrix size. By doing so, the signal readout speed could be increased by 4-9 times at the expense of a spatial resolution loss of 50%-67%. Clearly, such manipulation poses a key bottleneck in generating high spatial and high temporal resolution CBCT images at the same time. In addition, the conventional FPD is also difficult in generating dual-energy CBCT images. In this paper, we propose an innovative super resolution dual-energy CBCT imaging method, named as suRi, based on dual-layer FPD (DL-FPD) to overcome these aforementioned difficulties at once. With suRi, specifically, a 1D or 2D sub-pixel (half pixel in this study) shifted binning is applied instead of the conventionally aligned binning to double the spatial sampling rate during the dual-energy data acquisition. As a result, the suRi approach provides a new strategy to enable high spatial resolution CBCT imaging while at high readout speed. Moreover, a penalized likelihood material decomposition algorithm is developed to directly reconstruct the high resolution bases from these dual-energy CBCT projections containing sub-pixel shifts. Numerical and physical experiments are performed to validate this newly developed suRi method with phantoms and biological specimen. Results demonstrate that suRi can significantly improve the spatial resolution of the CBCT image. We believe this developed suRi method would greatly enhance the imaging performance of the DL-FPD based dual-energy CBCT systems in future.

OSCNet: Orientation-Shared Convolutional Network for CT Metal Artifact Learning

X-ray computed tomography (CT) has been broadly adopted in clinical applications for disease diagnosis and image-guided interventions. However, metals within patients always cause unfavorable artifacts in the recovered CT images. Albeit attaining promising reconstruction results for this metal artifact reduction (MAR) task, most of the existing deep-learning-based approaches have some limitations. The critical issue is that most of these methods have not fully exploited the important prior knowledge underlying this specific MAR task. Therefore, in this paper, we carefully investigate the inherent characteristics of metal artifacts which present rotationally symmetrical streaking patterns. Then we specifically propose an orientation-shared convolution representation mechanism to adapt such physical prior structures and utilize Fourier-series-expansion-based filter parametrization for modelling artifacts, which can finely separate metal artifacts from body tissues. By adopting the classical proximal gradient algorithm to solve the model and then utilizing the deep unfolding technique, we easily build the corresponding orientation-shared convolutional network, termed as OSCNet. Furthermore, considering that different sizes and types of metals would lead to different artifact patterns (e.g., intensity of the artifacts), to better improve the flexibility of artifact learning and fully exploit the reconstructed results at iterative stages for information propagation, we design a simple-yet-effective sub-network for the dynamic convolution representation of artifacts. By easily integrating the sub-network into the proposed OSCNet framework, we further construct a more flexible network structure, called OSCNet+, which improves the generalization performance. Through extensive experiments conducted on synthetic and clinical datasets, we comprehensively substantiate the effectiveness of our proposed methods. Code will be released at https://github.com/hongwang01/OSCNet.

Complete genome analysis of Bacillus velezensis TS5 and its potential as a probiotic strain in mice

Introduction

In recent years, a large number of studies have shown that Bacillus velezensis has the potential as an animal feed additive, and its potential probiotic properties have been gradually explored.

Methods

In this study, Illumina NovaSeq PE150 and Oxford Nanopore ONT sequencing platforms were used to sequence the genome of Bacillus velezensis TS5, a fiber-degrading strain isolated from Tibetan sheep. To further investigate the potential of B. velezensis TS5 as a probiotic strain, in vivo experiments were conducted using 40 five-week-old male specific pathogen-free C57BL/6J mice. The mice were randomly divided into four groups: high fiber diet control group (H group), high fiber diet probiotics group (HT group), low fiber diet control group (L group), and low fiber diet probiotics group (LT group). The H and HT groups were fed high-fiber diet (30%), while the L and LT groups were fed low-fiber diet (5%). The total bacteria amount in the vegetative forms of B. velezensis TS5 per mouse in the HT and LT groups was 1 × 109 CFU per day, mice in the H and L groups were given the same volume of sterile physiological saline daily by gavage, and the experiment period lasted for 8 weeks.

Results

The complete genome sequencing results of B. velezensis TS5 showed that it contained 3,929,788 nucleotides with a GC content of 46.50%. The strain encoded 3,873 genes that partially related to stress resistance, adhesion, and antioxidants, as well as the production of secondary metabolites, digestive enzymes, and other beneficial nutrients. The genes of this bacterium were mainly involved in carbohydrate metabolism, amino acid metabolism, vitamin and cofactor metabolism, biological process, and molecular function, as revealed by KEGG and GO databases. The results of mouse tests showed that B. velezensis TS5 could improve intestinal digestive enzyme activity, liver antioxidant capacity, small intestine morphology, and cecum microbiota structure in mice.

Conclusion

These findings confirmed the probiotic effects of B. velezensis TS5 isolated from Tibetan sheep feces and provided the theoretical basis for the clinical application and development of new feed additives.

AI's deep dive into complex pediatric inguinal hernia issues: a challenge to traditional guidelines?

OBJECTIVE

This study utilized ChatGPT, an artificial intelligence program based on large language models, to explore controversial issues in pediatric inguinal hernia surgery and compare its responses with the guidelines of the European Association of Pediatric Surgeons (EUPSA).

METHODS

Six contentious issues raised by EUPSA were submitted to ChatGPT 4.0 for analysis, for which two independent responses were generated for each issue. These generated answers were subsequently compared with systematic reviews and guidelines. To ensure content accuracy and reliability, a content analysis was conducted, and expert evaluations were solicited for validation. Content analysis evaluated the consistency or discrepancy between ChatGPT 4.0's responses and the guidelines. An expert scoring method assess the quality, reliability, and applicability of responses. The TF-IDF model tested the stability and consistency of the two responses.

RESULTS

The responses generated by ChatGPT 4.0 were mostly consistent with the guidelines. However, some differences and contradictions were noted. The average quality score was 3.33, reliability score was 2.75, and applicability score was 3.46 (out of 5). The average similarity between the two responses was 0.72 (out of 1), Content analysis and expert ratings yielded consistent conclusions, enhancing the credibility of our research.

CONCLUSION

ChatGPT can provide valuable responses to clinical questions, but it has limitations and requires further improvement. It is recommended to combine ChatGPT with other reliable data sources to improve clinical practice and decision-making.

Effect comparisons of different conditioners and microbial agents on the degradation of estrogens during dairy manure composting

To investigate the degradation efficiency of conditioners and commercial microbial agents on estrogens (E1, 17α-E2, 17β-E2, E3, EE2, and DES) in the composting process of dairy manure, seven different treatments (RHB-BF, OSP-BF, SD-BF, MR-BF, MR-FS, MR-EM, and MR-CK) under forced ventilation conditions were composted and monitored regularly for 30 days. The results indicated that the removal rates of estrogens in seven treatments ranged from 95.35% to 99.63%, meanwhile the degradation effect of the composting process on 17β-Estradiol equivalent (EEQ) was evaluated, and the removal rate of ΣEEQ ranged from 96.42% to 99.72%. With the combined addition of rice husk biochar (RHB) or oyster shell powder (OSP) and bio-bacterial fertilizer starter cultures (BF), namely RHB-BF and OSP-BF obviously promoted the rapid degradation of estrogens. 17β-E2 was completely degraded on the fifth day of composting in OSP-BF. Microbial agents have some promotional effect and enhances the microbial degradation of synthetic estrogen (EE2, DES). According to the results of RDA, pH and EC were the main environmental factors affecting on the composition and succession of estrogen-related degrading bacteria in composting system. As predominant estrogens-degrading genera, Acinetobacter, Bacillus, and Pseudomonas effected obviously on the change of estrogens contents. The research results provide a practical reference for effective composting of dairy manure to enhancing estrogens removal and decreasing ecological risk.

Pathomorphological characteristics of tuberculous placenta and its clinical implication

BACKGROUND

The study of pathologic diagnosis of placental TB is rare. The aim of this study is analyzing the pathomorphological characteristics of tuberculosis (TB) placenta during pregnancy and its clinical significance.

METHODS

Nineteen cases of placental tissue specimens during pregnancy were collected from June 2015 to February 2022 at Shanghai Public Health Clinical Center, the only inpatient center for pregnant women with TB in Shanghai, China. Hematoxylin-eosin staining, acid-fast staining, and molecular testing were applied to analyze them comprehensively in combination with clinical information.

RESULTS

Among the 19 cases, 7 cases caused intrauterine stillbirth, 3 cases received artificial abortion required by the pregnant woman, the other 9 cases received standard delivery and the infants survived, however, 3 of them were low-weight preterm infants, and another 1 case suffered mild intrauterine asphyxia. The 9 surviving infants were followed-up, of which 3 cases got congenital TB. For pathological characteristics of placental tissues under light microscopy, there were 3 cases of epithelioid granuloma formation, 13 cases of acute fetal membranitis, 4 cases of caseous necrosis, 7 cases of inflammatory necrosis, 10 cases of coagulative necrosis, and 6 cases with small focal calcifications. All placental tissues were positive for acid-fast staining and polymerase chain reaction (PCR). Molecular pathological diagnosis showed that 18 cases were positive for Mycobacterium tuberculosis, with 1 case not having received examination.

CONCLUSIONS

Combining acid-fast staining and molecular pathological testing is helpful for accurately diagnosing placental TB.

Cross-Domain Unpaired Learning for Low-Dose CT Imaging

Supervised deep-learning techniques with paired training datasets have been widely studied for low-dose computed tomography (LDCT) imaging with excellent performance. However, the paired training datasets are usually difficult to obtain in clinical routine, which restricts the wide adoption of supervised deep-learning techniques in clinical practices. To address this issue, a general idea is to construct a pseudo paired training dataset based on the widely available unpaired data, after which, supervised deep-learning techniques can be adopted for improving the LDCT imaging performance by training on the pseudo paired training dataset. However, due to the complexity of noise properties in CT imaging, the LDCT data are difficult to generate in order to construct the pseudo paired training dataset. In this article, we propose a simple yet effective cross-domain unpaired learning framework for pseudo LDCT data generation and LDCT image reconstruction, which is denoted as CrossDuL. Specifically, a dedicated pseudo LDCT sinogram generative module is constructed based on a data-dependent noise model in the sinogram domain, and then instead of in the sinogram domain, a pseudo paired dataset is constructed in the image domain to train an LDCT image restoration module. To validate the effectiveness of the proposed framework, clinical datasets are adopted. Experimental results demonstrate that the CrossDuL framework can obtain promising LDCT imaging performance in both quantitative and qualitative measurements.

The panda-derived Lactiplantibacillus plantarum BSG201683 improves LPS-induced intestinal inflammation and epithelial barrier disruption in vitro

Captive pandas are suffering from intestinal infection due to intestinal microbiota characterized by a high abundance of Enterobacteriaceae induced by long-term captivity. Probiotic supplements showed improvement in intestinal barrier function and inflammation. However, the effects of panda-derived probiotics on the intestinal epithelium and inflammation have not been elucidated. In the present study, lipopolysaccharide (LPS) impaired Caco-2 and RAW264.7 inflammatory models were applied to assess the protection of Lactiplantibacillus plantarum BSG201683 (L. plantarum G83) on barrier disruption and inflammation. The results showed that treatment with L. plantarum G83 significantly decreased the paracellular permeability to fluorescein isothiocyanate conjugated dextran (MW 4000, FITC-D4) after LPS induction. Meanwhile, L. plantarum G83 alleviated the reduction in tight junction (TJ) proteins and downregulated proinflammatory cytokines caused by LPS in Caco-2 cells. L. plantarum G83 also significantly decreased the expression and secretion of pro-inflammatory cytokines in LPS-induced RAW264.7 cells. In addition, the IL-10 increased in both Caco-2 and RAW264.7 cells after L. plantarum G83 treatment. The phagocytosis activity of RAW264.7 cells was significantly increased after L. plantarum G83 treatment. Toll-like receptor 4/ nuclear factor kappa-B (TLR4/NF-κB) signaling pathways were significantly down-regulated after L. plantarum G83 intervention, and the phosphorylation of NF-κB/p65 was consistent with this result. Our findings suggest that L. plantarum G83 improves intestinal inflammation and epithelial barrier disruption in vitro.

[A semi-supervised network-based tissue-aware contrast enhancement method for CT images]

OBJECTIVE

To propose a tissue- aware contrast enhancement network (T- ACEnet) for CT image enhancement and validate its accuracy in CT image organ segmentation tasks.

METHODS

The original CT images were mapped to generate low dynamic grayscale images with lung and soft tissue window contrasts, and the supervised sub-network learned to recognize the optimal window width and level setting of the lung and abdominal soft tissues via the lung mask. The self-supervised sub-network then used the extreme value suppression loss function to preserve more organ edge structure information. The images generated by the T-ACEnet were fed into the segmentation network to segment multiple abdominal organs.

RESULTS

The images obtained by T-ACEnet were capable of providing more window setting information in a single image, which allowed the physicians to conduct preliminary screening of the lesions. Compared with the suboptimal methods, T-ACE images achieved improvements by 0.51, 0.26, 0.10, and 14.14 in SSIM, QABF, VIFF, and PSNR metrics, respectively, with a reduced MSE by an order of magnitude. When T-ACE images were used as input for segmentation networks, the organ segmentation accuracy could be effectively improved without changing the model as compared with the original CT images. All the 5 segmentation quantitative indices were improved, with the maximum improvement of 4.16%.

CONCLUSION

The T-ACEnet can perceptually improve the contrast of organ tissues and provide more comprehensive and continuous diagnostic information, and the T-ACE images generated using this method can significantly improve the performance of organ segmentation tasks.

Fluoride disrupts intestinal epithelial tight junction integrity through intracellular calcium-mediated RhoA/ROCK signaling and myosin light chain kinase

Fluoride is a common contaminant of groundwater and agricultural commodity, which poses challenges to animal and human health. A wealth of research has demonstrated its detrimental effects on intestinal mucosal integrity; however, the underlying mechanisms remain obscure. This study aimed to investigate the role of the cytoskeleton in fluoride-induced barrier dysfunction. After sodium fluoride (NaF) treatment of the cultured Caco-2 cells, both cytotoxicity and cytomorphological changes (internal vacuoles or massive ablation) were observed. NaF lowered transepithelial electrical resistance (TEER) and enhanced paracellular permeation of fluorescein isothiocyanate dextran 4 (FD-4), indicating Caco-2 monolayers hyperpermeability. In the meantime, NaF treatment altered both the expression and distribution of the tight junction protein ZO-1. Fluoride exposure increased myosin light chain II (MLC2) phosphorylation and triggered actin filament (F-actin) remodeling. While inhibition of myosin II by Blebbistatin blocked NaF-induced barrier failure and ZO-1 discontinuity, the corresponding agonist Ionomycin had effects comparable to those of fluoride, suggesting that MLC2 serves as an effector. Given the mechanisms upstream of p-MLC2 regulation, further studies demonstrated that NaF activated RhoA/ROCK signaling pathway and myosin light chain kinase (MLCK), strikingly increasing the expression of both. Pharmacological inhibitors (Rhosin, Y-27632 and ML-7) reversed NaF-induced barrier breakdown and stress fiber formation. The role of intracellular calcium ions ([Ca²⁺]_i) in NaF effects on Rho/ROCK pathway and MLCK was investigated. We found that NaF elevated [Ca²⁺]_i, whereas chelator BAPTA-AM attenuated increased RhoA and MLCK expression as well as ZO-1 rupture, thus, restoring barrier function. Collectively, abovementioned results suggest that NaF induces barrier impairment via Ca²⁺-dependent RhoA/ROCK pathway and MLCK, which in turn triggers MLC2 phosphorylation and rearrangement of ZO-1 and F-actin. These results provide potential therapeutic targets for fluoride-induced intestinal injury.

Prolonged fluoride exposure induces spatial-memory deficit and hippocampal dysfunction by inhibiting small heat shock protein 22 in mice

Millions of residents in areas with high-fluoride drinking water supply ingest excessive levels of fluoride for long periods. This study investigated the mechanisms and impacts of lifelong exposure to naturally occurring moderate-high-fluoride drinking water on spatial-memory function by studying mice in controlled experiments. Spatial-memory deficits and disorders of hippocampal neuronal electrical activity were observed in mice exposed to 25-ppm or 50-ppm-fluoride drinking water for 56 weeks, but not in adult or old mice exposed to 50 ppm fluoride for 12 weeks. Ultrastructural analysis showed severely damaged hippocampal mitochondria, evidenced by reduced mitochondrial membrane potential and ATP content. Mitochondrial biogenesis was impaired in fluoride-exposed mice, manifesting as a significantly reduced mtDNA content, mtDNA-encoded subunits mtND6 and mtCO1, and respiratory complex activities. Fluoride reduced expression of Hsp22, a beneficial mediator of mitochondrial homeostasis, and decreased levels of signaling for the PGC-1α/TFAM pathway-which regulates mitochondrial biogenesis-and the NF-κβ/STAT3 pathway-which regulates mitochondrial respiratory chain enzyme activity. Hippocampus-specific Hsp22-overexpression improved fluoride-induced spatial-memory deficits by activating the PGC-1α/TFAM and STAT3 signaling pathways, while Hsp22-silencing aggravated the deficits by inhibiting both pathways. Downregulation of Hsp22 plays a vital role in fluoride-induced spatial-memory deficits by impacting mtDNA-encoding subsets and mitochondrial respiratory chain enzyme activity.

Fulvic acid enhanced peroxymonosulfate activation over Co-Fe binary metals for efficient degradation of emerging bisphenols

Bisphenol F (BPF) and bisphenol S (BPS) are emerging bisphenols, which have become the main substitutes for bisphenol A (BPA) in industrial production and are also considered as new environmental pollution challenges. Thus, the necessity for an effective approach to remove BPF and BPS is essential. In this study, fulvic acid (FA) was used to modify Co-Fe binary metals (CFO) for peroxymonosulfate (PMS) activation. The characterization results demonstrated that CFO changed significantly in morphology after compounding with FA, with smaller particle size and 5.6 times larger specific surface area, greatly increasing the active sites of catalyst; Moreover, humic acid-like compounds increased the surface functional groups of CFO, especially phenolic hydroxyl, which could effectively prolong the PMS activation. The concentration of all reactive species, such as SO₄•^-, •OH, O₂•^-, and ¹O₂ increased in FA@CFO/PMS system. As a result, the degradation efficiency of CFO for both BPF and BPS was significantly improved after compounding FA, which also had a wide range of pH applications. The degradation pathways of both BPF and BPS were proposed based on liquid chromatography-mass spectrometry (LC-MS) analysis and the density functional theory (DFT) calculations. Our findings are expected to provide new strategies and methods for remediation of environmental pollution caused by emerging bisphenols.

[A semi-supervised material quantitative intelligent imaging algorithm for spectral CT based on prior information perception learning]

OBJECTIVE

To propose a semi-supervised material quantitative intelligent imaging algorithm based on prior information perception learning (SLMD-Net) to improve the quality and precision of spectral CT imaging.

METHODS

The algorithm includes a supervised and a self- supervised submodule. In the supervised submodule, the mapping relationship between low and high signal-to-noise ratio (SNR) data was constructed through mean square error loss function learning based on a small labeled dataset. In the self- supervised sub-module, an image recovery model was utilized to construct the loss function incorporating the prior information from a large unlabeled low SNR basic material image dataset, and the total variation (TV) model was used to to characterize the prior information of the images. The two submodules were combined to form the SLMD-Net method, and pre-clinical simulation data were used to validate the feasibility and effectiveness of the algorithm.

RESULTS

Compared with the traditional model-driven quantitative imaging methods (FBP-DI, PWLS-PCG, and E3DTV), data-driven supervised-learning-based quantitative imaging methods (SUMD-Net and BFCNN), a material quantitative imaging method based on unsupervised learning (UNTV-Net) and semi-supervised learning-based cycle consistent generative adversarial network (Semi-CycleGAN), the proposed SLMD-Net method had better performance in both visual and quantitative assessments. For quantitative imaging of water and bone materials, the SLMD-Net method had the highest PSNR index (31.82 and 29.06), the highest FSIM index (0.95 and 0.90), and the lowest RMSE index (0.03 and 0.02), respectively) and achieved significantly higher image quality scores than the other 7 material decomposition methods (P < 0.05). The material quantitative imaging performance of SLMD-Net was close to that of the supervised network SUMD-Net trained with labeled data with a doubled size.

CONCLUSIONS

A small labeled dataset and a large unlabeled low SNR material image dataset can be fully used to suppress noise amplification and artifacts in basic material decomposition in spectral CT and reduce the dependence on labeled data-driven network, which considers more realistic scenario in clinics.

Physics-informed sinogram completion for metal artifact reduction in CT imaging

Objective.Metal artifacts in the computed tomography (CT) imaging are unavoidably adverse to the clinical diagnosis and treatment outcomes. Most metal artifact reduction (MAR) methods easily result in the over-smoothing problem and loss of structure details near the metal implants, especially for these metal implants with irregular elongated shapes. To address this problem, we present the physics-informed sinogram completion (PISC) method for MAR in CT imaging, to reduce metal artifacts and recover more structural textures.Approach.Specifically, the original uncorrected sinogram is firstly completed by the normalized linear interpolation algorithm to reduce metal artifacts. Simultaneously, the uncorrected sinogram is also corrected based on the beam-hardening correction physical model, to recover the latent structure information in metal trajectory region by leveraging the attenuation characteristics of different materials. Both corrected sinograms are fused with the pixel-wise adaptive weights, which are manually designed according to the shape and material information of metal implants. To furtherly reduce artifacts and improve the CT image quality, a post-processing frequency split algorithm is adopted to yield the final corrected CT image after reconstructing the fused sinogram.Main results.We qualitatively and quantitatively evaluated the presented PISC method on two simulated datasets and three real datasets. All results demonstrate that the presented PISC method can effectively correct the metal implants with various shapes and materials, in terms of artifact suppression and structure preservation.Significance.We proposed a sinogram-domain MAR method to compensate for the over-smoothing problem existing in most MAR methods by taking advantage of the physical prior knowledge, which has the potential to improve the performance of the deep learning based MAR approaches.

Hierarchically Organized Cocrystal of Tetra-Anionic Porphyrin and Di-Cationic Viologen: Ion Conformations, Supramolecule Interactions, and Porphyrin Arrays

Ionic co-assembly of tetra-anionic porphyrins has been extensively researched in the construction of hierarchically organized architectures with potential application value in organic semiconductors, sunlight catalysts and supramolecular chirality systems. However, such architectures are difficult to grow to a size suitable for single-crystal X-ray diffraction (SCXRD); the lack of single-crystal structures of these architectures leads to challenges in gaining deeper comprehension about that. This study reports a hierarchically organized cocrystal of meso-tetra(4-sulfonato-phenyl)-porphyrin (TSPP^4- ) and N, N'-diethyl-viologen (DEV²⁺ ), wherein wave-like and saddle-like TSPP^4- ions co-aggregate at a stoichiometric ratio of 1 : 2 to form unique porphyrin arrays; the spectrum characteristics and calculated coulombic exciton coupling energy show that these porphyrin arrays are J-aggregates. We prove that the distortion of porphyrin ring of TSPP^4- strongly correlates with the deflection of its phenyl groups. The crystal comprises six different ionic conformations, and the multiplicity of ionic conformation leads to intricate supramolecular interactions.

Learning CT projection denoising from adjacent views

BACKGROUND

Many learning-based low-dose (LD) computed tomography (CT) imaging methods require large paired full- and low-dose datasets for training, which are usually unavailable in clinic. Whereas models trained on simulated data often face the generalization problem on real clinical data.

PURPOSE

To develop an unsupervised learning technique to acquire clean CT projection from its adjacent LD projections.

METHODS

Given a sequential LD projection set, the method extracts out the middle projection as the target and treats the rest ones as the input. The model is trained with the mean absolute error with proposed inter-view gradient constraint term, which helps to suppress outliers and preserve edges in the denoised projection. The simulated low-dose CT grand challenge dataset and a real physical torso phantom dataset were employed for experiment. The peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) were calculated for quantitative evaluation.

RESULTS

In experiments with both the simulated and real datasets, visual comparisons reveal that the proposed method obtained images superior to unsupervised and supervised methods working in both image and projection domain. For numerical comparison, our method obtains larger SSIMs than other unsupervised methods at quarter and eighth dose levels. As for PSNR, our method obtains larger value at eighth dose whereas smaller value at quarter dose. The supervised models obtain better numerical results than all unsupervised models on simulated datasets.

CONCLUSION

The proposed method can reduce the noise in CT projections effectively, making it an attractive tool for practical LDCT pre-processing.

A real-world retrospective study of incidence and associated factors of endocrine adverse events related to PD-1/PD-L1 inhibitors

Background

The adverse events (AEs) related to immune checkpoint inhibitors (ICIs) have been mostly described in clinical trials, however, such trials are restricted to selection criteria and the results cannot wholly represent the real-world setting. We aimed to evaluate the real-world endocrine AEs associated with programmed death receptor-1/programmed death ligand-1 (PD-1/PD-L1) inhibitors in Chinese population.

Methods

This retrospective study included cancer patients who were treated with PD-1/PD-L1 inhibitors between January 2018 and December 2020 at Xinqiao Hospital, the Third Military Medical University. The information of 581 patients was reviewed, and data on clinical characteristics, PD-1/PD-L1 use, occurrence of endocrine AEs, and response to PD-1 blockade treatment were collated. The definition of endocrine AEs relied on diagnostic tests. Fisher's exact test or Pearson's chi-squared test was used to analyze the associations between endocrine variables and several categorical variables. Multivariate analyses were performed using a logistic regression model.

Results

Endocrine AEs were observed in 116 of the 581 patients (20.0%). The median time to onset of endocrine AEs was approximately 12 weeks. Pembrolizumab was associated with a significantly higher incidence of endocrine AEs compared to other anti-PD-1 agents (38.5%; P=0.0002); PD-1/PD-L1 inhibitor treatment combined with antiangiogenic therapy or with two other therapies (chemotherapy and antiangiogenic therapy) was associated with a significantly increased occurrence of endocrine AEs, compared to PD-1 blockade treatment alone (41.2%; P=0.015), both based on multivariate analysis. Patients who developed endocrine AEs had significantly higher overall response rates (ORRs; 33.3% vs. 23.1%, P=0.045) and disease control rates (DCRs; 91.1% vs. 79.1%, P=0.008) compared to patients without endocrine AEs. In multivariate analysis, endocrine AEs remained an independent factor for both ORR (OR: 1.764, 95% CI: 1.052-2.957, P=0.031) and DCR (OR: 2.896, 95% CI: 1.324-6.332, P=0.008) after adjusting for the confounding factors.

Conclusions

A real-world Chinese population receiving PD-1/PD-L1 treatment, pembrolizumab administrated and triple therapy treatment modalities had a higher incidence of endocrine AEs. Patients who developed endocrine AEs demonstrated a favorable response to PD-l blockade treatment.

Botulinum Toxin Type A for the Treatment of Masseter Muscle Prominence in Asian Populations

In Asian populations, an oval face shape with a slimmer lower face is considered favorable. Botulinum toxin type A (BoNT/A) treatment of the lower face for aesthetic purposes is widely performed in Asia, although it is not officially approved for this indication in any country. In this review, the authors highlight recent research describing injection techniques, mechanism of action, clinical efficacy, and key learning points to safely treat benign masseter muscle prominence (MMP) in Asian populations using BoNT/A. To support the existing literature, clinical examples are provided that demonstrate individualized treatment plans for patients with asymmetric vs symmetric MMP and an observed complication after treatment. This review provides insights for optimizing clinical outcomes and for designing future studies investigating the safety and efficacy of BoNT/A for treating MMP.

circANKRD28 inhibits cisplatin resistance in non-small-cell lung cancer through the miR-221-3p/SOCS3 axis

BACKGROUND

Non-small-cell lung cancer (NSCLC) is a common cancer. Chemotherapeutic drug resistance limits the therapeutic effect of NSCLC and leads to a poor prognosis. As a result, new specific targets may be better identified by studying the mechanism of drug resistance to cisplatin in NSCLC.

METHODS

In the present study, we performed a quantitative real-time polymerase chain reaction and western blotting to detect mRNA and protein levels. The proliferation of cells was analyzed by a Cell Counting Kit-8 and colony formation assays. Cell invasion was measured via the Transwell assay. A scratch assay was performed to measure cell migration in cisplatin (DDP)-resistant NSCLC cells. Apoptosis of cells was examined using flow cytometry.

RESULTS

We found that circANKRD28 was notably decreased in NSCLC. The results showed that circANKRD28 expression was not affected, and its half-life was more than 12 h. Functional experiments revealed that circANKRD28 overexpression inhibited DDP resistance in NSCLC cells in vitro. Mechanistic findings demonstrated that circANKRD28 regulated tumor cell progression and DDP sensitivity through the miR-221-3p/SOCS3 axis.

CONCLUSIONS

The present study revealed the regulatory effects and molecular mechanism of circANKRD28 on the development and cisplatin resistance in NSCLC, which may provide experimental basis and theoretical support to identify new targets for therapy of DDP resistance in NSCLC.

Prediction of knee pain improvement over two years for knee osteoarthritis using a dynamic nomogram based on MRI-derived radiomics: a proof-of-concept study

OBJECTIVES

To develop and validate a nomogram to detect improved knee pain in osteoarthritis (OA) by integrating magnetic resonance imaging (MRI) radiomics signature of subchondral bone and clinical characteristics.

METHODS

Participants were selected from the Vitamin D Effects on Osteoarthritis (VIDEO) study. The primary outcome was 20% improvement of knee pain score over 2 years in participants administrated either vitamin D or placebo. Radiomics features of subchondral bone and clinical characteristics from 216 participants were extracted and analyzed. The participants were randomly split into the training and validation cohorts at a ratio of 8:2. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate radiomics signatures. The optimal radiomics signature and clinical indicators were fitted into a nomogram using multivariable logistic regression model.

RESULTS

The nomogram showed favorable discrimination performance [AUC_training, 0.79 (95% CI: 0.72-0.79), AUC_validation, 0.83 (95% CI: 0.70-0.96)] as well as a good calibration. Additional contributing value of fusion radiomics signature to the nomogram was statistically significant (NRI, 0.23; IDI, 0.14, P < 0.001 in training cohort and NRI, 0.29; IDI, 0.18, P < 0.05 in validating cohort). Decision curve analysis confirmed the clinical usefulness of nomogram.

CONCLUSION

The radiomics-based nomogram comprising the MR radiomics signature and clinical variables achieves a favorable predictive efficacy and accuracy in differentiating improvement in knee pain among OA patients. This proof-of-concept study provides a promising way to predict clinically meaningful outcomes.

The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.

Screening and Identification of Croton Fruit Poisoning in Food Using GC-MS and HPLC-MS/MS

Croton fruit poisoning was successfully identified through a series of chromatographic technologies. A suspected compound named 2-methyl-butenoic acid was screened out using gas chromatography-mass spectrometry, and the hydrolysate of isoguanosine in soup sample was further detected by high performance liquid chromatography-tandem mass spectrometry. The identification of croton fruit poisoning was confirmed basing on these laboratory tests together with the clinical symptoms of the patients. Especially, the false-negative result and the interferences were successfully excluded through simulation test, spectral analysis and chromatographic separation technique. Such study is significant for croton poisoning identification and provides beneficial references for disposing untargeted food poisoning incident.

Rapid determination of 14 odorous compounds in drinking water using gas chromatography-mass spectrometry coupled with headspace solid-phase microextraction pretreatment

A sensitive and effective method was developed for the simultaneous determination of 14 odorous compounds in drinking water using gas chromatography-mass spectrometry (GC-MS) coupled with headspace solid phase microextraction (HS-SPME) pretreatment. The influencing factors including SPME fiber, ionic strength, temperature and time on the pretreatment procedure were evaluated systematically. Under the optimized conditions, 10 mL of the samples added with 1.0 g sodium chloride was extracted by CAR/PDMS fiber at 60 °C for 30 min and then desorbed at 280 °C for 3 min. The analytes achieved good linearity as the mass concentrations were in the range of 0.0020-10.0 μg L^-1 with correlation coefficients higher than 0.9990. The limits of detection (LODs) ranging between 0.2 and 50 ng L^-1 were lower than the olfactory threshold of these studied compounds. Satisfactory recoveries were obtained ranging from 84.8% to 110.6%, and good reproducibility indicated by relative standard deviation (RSD) in the range of 0.50-9.5% was obtained as well. The proposed method was convenient, sensitive and accurate, which was suitable for the routine monitoring 14 odorous compounds in water.

Structure-preserved meta-learning uniting network for improving low-dose CT quality

Objective.Deep neural network (DNN) based methods have shown promising performances for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based methods are trained on simulated labeled datasets, and the low-dose simulation algorithms are usually designed based on simple statistical models which deviate from the real clinical scenarios, which could lead to issues of overfitting, instability and poor robustness. To address these issues, in this work, we present a structure-preserved meta-learning uniting network (shorten as 'SMU-Net') to suppress noise-induced artifacts and preserve structure details in the unlabeled LDCT imaging task in real scenarios.Approach.Specifically, the presented SMU-Net contains two networks, i.e., teacher network and student network. The teacher network is trained on simulated labeled dataset and then helps the student network train with the unlabeled LDCT images via the meta-learning strategy. The student network is trained on real LDCT dataset with the pseudo-labels generated by the teacher network. Moreover, the student network adopts the Co-teaching strategy to improve the robustness of the presented SMU-Net.Main results.We validate the proposed SMU-Net method on three public datasets and one real low-dose dataset. The visual image results indicate that the proposed SMU-Net has superior performance on reducing noise-induced artifacts and preserving structure details. And the quantitative results exhibit that the presented SMU-Net method generally obtains the highest signal-to-noise ratio (PSNR), the highest structural similarity index measurement (SSIM), and the lowest root-mean-square error (RMSE) values or the lowest natural image quality evaluator (NIQE) scores.Significance.We propose a meta learning strategy to obtain high-quality CT images in the LDCT imaging task, which is designed to take advantage of unlabeled CT images to promote the reconstruction performance in the LDCT environments.

Dynamic Monitoring of Changes in Fecal Flora of Giant Pandas in Mice: Co-Occurrence Network Reconstruction

Giant pandas are uniquely vulnerable mammals in western China. It is important to develop an animal model to explore the intestinal flora of giant pandas to understand the relationship between digestive diseases and flora. Existing animal models of intestinal flora focus on human flora-associated animals, such as mice, and there is a very limited amount of knowledge regarding giant panda flora-associated animals. To fill this gap, fecal microorganisms from giant pandas were transplanted into pseudosterile and germfree mice using single and multiple gavages. Fecal samples were collected from mice at four time points after transplantation for microbial community analysis. We determined that compared to pseudosterile mice, the characteristics of intestinal flora in pandas were better reproduced in germfree mice. There was no significant difference in microbial diversity between germfree mice and giant panda gut microbes from day 3 to day 21. Germfree mice at the phylum level possessed large amounts of Firmicutes and Proteobacteria, and at the genus level, Escherichia-Shigella, Clostridium sensu stricto 1, and Streptococcus dominated the intestinal flora structure. The microbial community co-occurrence network based on indicator species indicated that germfree mice transplanted with fecal bacteria tended to form a microbial community co-occurrence network similar to that of giant pandas, while pseudosterile mice tended to restore the microbial community co-occurrence network originally present in these mice. Our data are helpful for the study of giant panda flora-associated animals and provide new insights for the in vitro study of giant panda intestinal flora. IMPORTANCE The giant panda is a unique vulnerable mammal in western China, and its main cause of death is digestive system diseases regardless of whether these animals are in the wild or in captivity. The relationship between the intestinal flora and the host exerts a significant impact on the nutrition and health of the giant pandas. However, the protected status of the giant panda has made in vivo, repeatable, and large-sample sampling studies of their intestinal flora difficult. This greatly hinders the research depth of the giant panda intestinal flora from the source. The development and utilization of specific animal models to simulate the structure and characteristics of the intestinal flora provide another means to deal with these research limitations. However, current research examining giant panda flora-associated animals is limited. This study is the first to reveal dynamic changes in the fecal flora of giant pandas in mice after transplantation.

Noise Characteristics Modeled Unsupervised Network for Robust CT Image Reconstruction

Deep learning (DL)-based methods show great potential in computed tomography (CT) imaging field. The DL-based reconstruction methods are usually evaluated on the training and testing datasets which are obtained from the same distribution, i.e., the same CT scan protocol (i.e., the region setting, kVp, mAs, etc.). In this work, we focus on analyzing the robustness of the DL-based methods against protocol-specific distribution shifts (i.e., the training and testing datasets are from different region settings, different kVp settings, or different mAs settings, respectively). The results show that the DL-based reconstruction methods are sensitive to the protocol-specific perturbations which can be attributed to the noise distribution shift between the training and testing datasets. Based on these findings, we presented a low-dose CT reconstruction method using an unsupervised strategy with the consideration of noise distribution to address the issue of protocol-specific perturbations. Specifically, unpaired sinogram data is enrolled into the network training, which represents unique information for specific imaging protocol, and a Gaussian mixture model (GMM) is introduced to characterize the noise distribution in CT images. It can be termed as GMM based unsupervised CT reconstruction network (GMM-unNet) method. Moreover, an expectation-maximization algorithm is designed to optimize the presented GMM-unNet method. Extensive experiments are performed on three datasets from different scan protocols, which demonstrate that the presented GMM-unNet method outperforms the competing methods both qualitatively and quantitatively.

Light Activates Cdc42-Mediated Needle-Shaped Filopodia Formation via the Integration of Small GTPases

Introduction

Cdc42 has been linked to multiple human cancers and is implicated in the migration of cancer cells. Cdc42 could be activated via biochemical and biophysical factors in tumor microenvironment, the precise control of Cdc42 was essential to determine its role to cell behaviors. Needle-shaped protrusions (filopodia) could sense the extracellular biochemical cues and pave the path for cell movement, which was a key structure involved in the regulation of cancer cell motility.

Methods

We used the photoactivatable Cdc42 to elucidate the breast cancer cell protrusions, the mutation of Cdc42 was to confirm the optogenetic results. We also inhibit the Cdc42, Rac or Rho respectively by the corresponding inhibitors.

Results

We identified that the activation of Cdc42 by light could greatly enhance the formation of filopodia, which was positive for the contribution of cell movement. The expression of Cdc42 active form Cdc42-Q61L in cells resulted in the longer and more filopodia while the Cdc42 inactive form Cdc42-T17N were with the shorter and less filopodia. Moreover, the inhibition of Cdc42, Rac or Rho all significantly reduced the filopodia numbers and length in the co-expression of Cdc42-Q61L, which showed that the integration of small GTPases was necessary in the formation of filopodia. Furthermore, photoactivation of Cdc42 failed to enhance the filopodia formation with the inhibition of Rac or Rho. However, with the inhibition of Cdc42, the photoactivation of Cdc42 could partially recover back the filopodia formations, which indicated that the integration of small GTPases was key for the filopodia formations.

Conclusions

Our work highlights that light activates Cdc42 is sufficient to promote filopodia formation without the destructive structures of small GTPases, it not only points out the novel technique to determine cell structure formations but also provides the experimental basis for the efficient small GTPases-based anti-cancer strategies.

[Association analysis between genetic variants of matrix metalloproteinase enzyme 2 gene and the blood pressure of children and adolescents]

Objective: To explore the association between genetic variants of matrix metalloproteinase enzyme 2 (MMP2) gene and the blood pressure of children and adolescents. Methods: This cross-sectional study was performed in 2016 and included 4 155 children and adolescents in the urban area of Guangzhou. Physical examinations (including body height, weight, and blood pressure), questionnaires (including general characteristics, physical exercise, parental educational level, household income, etc.), and blood sampling were performed. Multivariable linear regression models were used to investigate the associations of MMP2 genetic variations (rs243865, rs7201) and the genetic risk score (GRS) level with standardized blood pressure. Mediating effect of standardized body mass index (BMI) was further assessed by process analysis in the association between GRS level and blood pressure, and potential additive interaction between physical activity and GRS level was analyzed using the product term in the regression model. Results: A total of 4 155 primary and secondary schoolchildren were finally included in the analysis, consisting of 1 401 (33.7%) second grade pupils of primary school, 1 422 (34.2%) first grade pupils of middle school, and 1 332 (32.1%) first-grade students of senior high school. After adjusting for age, sex, parental educational level, and family income, as compared to the rs243865 TT genotype, the CC/CT genotype increased diastolic blood pressure (DBP) by 0.461 standard deviations (SD) (β for dominant model=0.461, 95%CI 0.199-0.723). When compared to the rs7201 CC genotype, the AA/AC genotype showed 0.147 SD higher systolic blood pressure (SBP) (β for recessive model=0.147, 95%CI 0.014-0.279) and 0.171 SD increased DBP (β for recessive model=0.171, 95%CI 0.039-0.304). For each increment of GRS level, SBP and DBP increased by 0.151 SD (β for dominant model=0.151, 95%CI 0.029-0.272) and 0.242 SD (β=0.242, 95%CI 0.120-0.363), respectively. The mediating effect of BMI accounted for 28.3% and 12.6% of the total effect of GRS on SBP and DBP, respectively. After controlling BMI, the direct effect of GRS on DBP remained statistically significant (P<0.001). The insufficient moderate-to-vigorous physical activity (<0.5 h/d) showed a significant interaction with GRS on SBP under additive scale (β for interaction=0.518, 95%CI 0.088-0.949, P=0.018). Conclusions: rs243865 and rs7201 variants in MMP2 gene are associated with the elevated blood pressure of children and adolescents. Obesity may yield a mediation role in the associations, while insufficient physical activity may have a positively additive interaction with MMP2 genetic variants.

Lactobacillus johnsonii YH1136 plays a protective role against endogenous pathogenic bacteria induced intestinal dysfunction by reconstructing gut microbiota in mice exposed at high altitude

Background

Intestinal microbiota plays an important role in maintaining the microecological balance of the gastrointestinal tract in various animals. Disturbances in the intestinal microbiota may lead to the proliferation of potentially pathogenic bacteria that become the dominant species, leading to intestinal immune disorders, intestinal inflammation, and other intestinal diseases. Numerous studies have been confirmed that high-altitude exposure affects the normal function of the intestine and the composition of the intestinal microbiota. However, it is still necessary to reveal the changes in intestinal microbiota in high-altitude exposure environments, and clarify the relationship between the proliferation of potentially pathogenic bacteria and intestinal injury in this environment. In addition, explored probiotics that may have preventive effects against intestinal diseases.

Methods and results

C57BL/6 mice were randomly divided into three groups, a high-altitude group (HA), control group (C), and high-altitude probiotic group (HAP). The HA and HAP groups were subjected to hypoxia modeling for 14 days in a low-pressure oxygen chamber with daily gavage of 0.2 mL of normal saline (HA) and Lactobacillus johnsonii YH1136 bacterial fluid (HAP), while the control group was fed normally. L. johnsonii YH1136 was isolated from feces of a healthy Tibetan girl in Baingoin county, the Nagqu region of the Tibet Autonomous Region, at an altitude of 5000 meters. Our observations revealed that gavage of YH1136 was effective in improving the damage to the intestinal barrier caused by high-altitude exposure to hypoxic environments and helped to reduce the likelihood of pathogenic bacteria infection through the intestinal barrier. It also positively regulates the intestinal microbiota to the extent of Lactobacillus being the dominant microbiome and reducing the number of pathogenic bacteria. By analyzing the expression profile of ileal microRNAs and correlation analysis with intestinal microbiota, we found that Staphylococcus and Corynebacterium1 cooperated with miR-196a-1-3p and miR-3060-3p, respectively, to play a regulatory role in the process of high-altitude hypoxia-induced intestinal injury.

Conclusion

These findings revealed the beneficial effect of L. johnsonii YH1136 in preventing potential endogenous pathogenic bacteria-induced intestinal dysfunction in high-altitude environments. The mechanism may be related to the regulation of intestinal injury from the perspective of the gut microbiota as well as miRNAs.

Surface Display of porcine circovirus type 2 antigen protein cap on the spores of bacillus subtilis 168: An effective mucosal vaccine candidate

The oral mucosal vaccine has great potential in preventing a series of diseases caused by porcine circovirus type 2 (PCV2) infection. This study constructed a recombinant Bacillus subtilis RB with PCV2 Capsid protein (Cap) on its spore surface and cotB as a fusion partner. The immune properties of the recombinant strain were evaluated in a mouse model. IgA in intestinal contents and IgG in serum were detected by enzyme-linked immunosorbent assay (ELISA). The results demonstrated that recombinant spores could activate strong specific mucosal and humoral immune responses. In addition, spores showed good mucosal immune adjuvant function, promoting the proliferation of CD3+, CD4+ and CD8+ T cells and other immune cells. We also found that the relative expression of inflammatory cytokines such as IL-1β, IL-6, IL-10, TNF-α and IFN in the small intestinal mucosa was significantly up-regulated under the stimulation of recombinant bacteriophage. These effects are important for the balance of Th1/Th2-like responses. In summary, our results suggest that recombinant B. subtilis RB as a feed additive provides a new strategy for the development of novel and safe PCV2 mucosal subunit vaccines.

Basis and current state of computed tomography perfusion imaging: a review

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

MRI-based Texture Analysis of Infrapatellar Fat Pad to Predict Knee Osteoarthritis Incidence

Background Infrapatellar fat pad (IPFP) quality has been implicated as a marker for predicting knee osteoarthritis (KOA); however, no valid quantification for subtle IPFP abnormalities has been established. Purpose To investigate whether MRI-based three-dimensional texture analysis of IPFP abnormalities could help predict incident radiographic KOA. Materials and Methods In this prospective nested case-control study, 690 participants whose knees were at risk for KOA were included from the Pivotal Osteoarthritis Initiative MRI Analyses incident osteoarthritis cohort. All knees had a Kellgren-Lawrence grade of 1 or less at baseline. During the 4-year follow-up, case participants were matched 1:1 to control participants, with incident radiographic KOA as the outcome. MRI scans were segmented at the incident time point of KOA (hereafter, P0), 1 year before P0 (hereafter, P-1), and baseline. MRI-based three-dimensional texture analysis was performed to extract IPFP texture features. Least absolute shrinkage and selection operator and multivariable logistic regressions were applied in the development cohort and evaluated in the test cohort. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative value of the clinical score, IPFP texture score, and MRI Osteoarthritis Knee Score. Results Participants were allocated to development (n = 500, 340 women; mean age, 60 years) and test (n = 190, 120 women; mean age, 61 years) cohorts. In both cohorts, IPFP texture scores (AUC ≥0.75 for all) showed greater discrimination than clinical scores (AUC ≤0.69 for all) at baseline, P-1, and P0, with significant differences in pairwise comparisons (P ≤ .002 for all). Greater predictive and concurrent validities of IPFP texture scores (AUC ≥0.75 for all) compared with MRI Osteoarthritis Knee Scores (AUC ≤0.66 for all) were also demonstrated (P < .001 for all). Conclusion MRI-based three-dimensional texture of the infrapatellar fat pad was associated with future development of knee osteoarthritis. ClinicalTrials.gov registration no.: NCT00080171 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Fischer in this issue.

High expression of HIV-1 matrix protein p17 in both lymphoma and lymph node tissues of AIDS patients

BACKGROUND

HIV-1 matrix protein p17 was found to be associated with lymphoma development in vitro. This study aimed to elucidate the pathogenetic roles of HIV-1 p17 in AIDS-related lymphoma.

METHODS

Expression of HIV-1 proteins p17, p24, nef and tat were evaluated in tumor tissue samples from 60 lymphoma patients and lymph node samples from 23 non-lymphoma patients with HIV-1 infection by immunohistochemistry. Microvascular density (MVD) determined by CD34 were also assessed in tumor tissues. Clinicopathological data of AIDS patients with lymphoma were collected retrospectively.

RESULTS

The subtypes of lymphoma among sixty AIDS patients were diffuse large B-cell lymphoma (32 cases), Burkitt lymphoma (23 cases), Hodgkin's lymphoma (4 cases), and plasmablastic lymphoma (1 case). The expression rate of HIV-1 p17 in lymphoma and non-lymphoma group was 63 % (38/60) and 61 % (14/29) respectively, with no significant difference (p = 0.835). The positive expression rate of p17 in both groups was significantly higher than that of p24, nef and tat (p < 0.05). The expression of p17 was associated with a higher MVD in the lymphoma group (p < 0.05). There were no significant differences in the 2-years overall survival between p17 positive and negative group (61 % vs. 50 %, p = 0.525).

CONCLUSION

The common expression of HIV-1 matrix protein p17 in both lymphoma and lymph node tissues of AIDS patients and the association between p17 expression and the higher MVD suggest that the accumulation and persistence of p17 in tissues may play a role in lymphoma development.

Capabilities of hepatitis B surface antigen are divergent from hepatitis B virus DNA in delimiting natural history phases of chronic hepatitis B virus infection

Objective

Quantitative hepatitis B surface antigen (HBsAg) and hepatitis B virus (HBV) DNA in the natural history of chronic HBV infection have not been rationally evaluated. This study aimed to re-characterize quantitative HBsAg and HBV DNA in the natural history phases.

Methods

A total of 595 and 651 hepatitis B e antigen (HBeAg)-positive patients and 485 and 705 HBeAg-negative patients were assigned to the early and late cohorts, respectively. Based on the ‘S-shape’ receiver operating characteristic (ROC) curves, the HBeAg-positive sub-cohorts with possibly high HBV replication (PHVR) and possibly low HBV replication (PLVR) and the HBeAg-negative sub-cohorts with possibly high HBsAg expression (PHSE) and possibly low HBsAg expression (PLSE) were designated.

Results

The areas under the ROC curve (AUCs) of HBsAg and HBV DNA in predicting HBeAg-positive significant hepatitis activity (SHA) in the early cohort, sub-cohort with PHVR, and sub-cohort with PLVR were 0.655 and 0.541, 0.720 and 0.606, and 0.553 and 0.725, respectively; those in the late cohort, sub-cohort with PHVR, and sub-cohort with PLVR were 0.646 and 0.501, 0.798 and 0.622, and 0.603 and 0.674, respectively. The AUCs of HBsAg and HBV DNA in predicting HBeAg-negative SHA in the early cohort, sub-cohort with PHSE, and sub-cohort with PLSE were 0.508 and 0.745, 0.573 and 0.780, and 0.577 and 0.729, respectively; those in the late cohort, sub-cohort with PHSE, and sub-cohort with PLSE were 0.503 and 0.761, 0.560 and 0.814, and 0.544 and 0.722, respectively. The sensitivity and specificity of HBsAg ≤4.602 log10 IU/ml in predicting HBeAg-positive SHA in the early cohort were 82.6% and 45.8%, respectively; those in the late cohort were 87.0% and 44.1%, respectively. The sensitivity and specificity of HBV DNA &gt;3.301 log10 IU/ml in predicting HBeAg-negative SHA in the early cohort were 73.4% and 60.8%, respectively; those in the late cohort were 73.6% and 64.1%, respectively.

Conclusion

Quantitative HBsAg and HBV DNA are valuable, but their capabilities are divergent in delimiting the natural history phases.

Tumor-induced erythroid precursor-differentiated myeloid cells mediate immunosuppression and curtail anti-PD-1/PD-L1 treatment efficacy

Despite the unprecedented success of immune checkpoint inhibitors (ICIs) as anti-cancer therapy, it remains a prevailing clinical need to identify additional mechanisms underlying ICI therapeutic efficacy and potential drug resistance. Here, using lineage tracking in cancer patients and tumor-bearing mice, we demonstrate that erythroid progenitor cells lose their developmental potential and switch to the myeloid lineage. Single-cell transcriptome analyses reveal that, notwithstanding quantitative differences in erythroid gene expression, erythroid differentiated myeloid cells (EDMCs) are transcriptionally indistinguishable from their myeloid-originated counterparts. EDMCs possess multifaceted machinery to curtail T cell-mediated anti-tumor responses. Consequently, EDMC content within tumor tissues is negatively associated with T cell inflammation for the majority of solid cancers; moreover, EDMC enrichment, in accordance with anemia manifestation, is predictive of poor prognosis in various cohorts of patients undergoing ICI therapy. Together, our findings reveal a feedforward mechanism by which tumors exploit anemia-triggered erythropoiesis for myeloid transdifferentiation and immunosuppression.

Noise modelling of perfusion CT images for robust hemodynamic parameter estimations

Objective. The radiation dose of cerebral perfusion computed tomography (CPCT) imaging can be reduced by lowering the milliampere-second or kilovoltage peak. However, dose reduction can decrease image quality due to excessive x-ray quanta fluctuation and reduced detector signal relative to system electronic noise, thereby influencing the accuracy of hemodynamic parameters for patients with acute stroke. Existing low-dose CPCT denoising methods, which mainly focus on specific temporal and spatial prior knowledge in low-dose CPCT images, not take the noise distribution characteristics of low-dose CPCT images into consideration. In practice, the noise of low-dose CPCT images can be much more complicated. This study first investigates the noise properties in low-dose CPCT images and proposes a perfusion deconvolution model based on the noise properties. Approach. To characterize the noise distribution in CPCT images properly, we analyze noise properties in low-dose CPCT images and find that the intra-frame noise distribution may vary in the different areas and the inter-frame noise also may vary in low-dose CPCT images. Thus, we attempt the first-ever effort to model CPCT noise with a non-independent and identical distribution (i.i.d.) mixture-of-Gaussians (MoG) model for noise assumption. Furthermore, we integrate the noise modeling strategy into a perfusion deconvolution model and present a novel perfusion deconvolution method by using self-relative structural similarity information and MoG model (named as SR-MoG) to estimate the hemodynamic parameters accurately. In the presented SR-MoG method, the self-relative structural similarity information is obtained from preprocessed low-dose CPCT images. Main results. The results show that the presented SR-MoG method can achieve promising gains over the existing deconvolution approaches. In particular, the average root-mean-square error (RMSE) of cerebral blood flow (CBF), cerebral blood volume, and mean transit time was improved by 40.3%, 69.1%, and 40.8% in the digital phantom study, and the average RMSE of CBF can be improved by 81.0% in the clinical data study, compared with tensor total variation regularization deconvolution method. Significance. The presented SR-MoG method can estimate high-accuracy hemodynamic parameters andachieve promising gains over the existing deconvolution approaches.

Chromatographic Determination of the Mycotoxin Patulin in 219 Chinese Tea Samples and Implications for Human Health

Patulin (PAT) is a mycotoxin, with several acute, chronic, and cellular level toxic effects, produced by various fungi. A limit for PAT in food of has been set by authorities to guarantee food safety. Research on PAT in tea has been very limited although tea is the second largest beverage in the world. In this paper, HPLC−DAD and GC−MS methods for analysis of PAT in different tea products, such as non-fermented (green tea), partially fermented (oolong tea, white tea, yellow tea), completely fermented (black tea), and post-fermented (dark tea and Pu-erh tea) teas were developed. The methods showed good selectivity with regard to tea pigments and 5-hydroxymethylfurfural (5-HMF) and a recovery of 90–102% for PAT at a 10–100 ppb spiking level. Limit of detection (LOD) and limit of quantification (LOQ) in tea were 1.5 ng/g and 5.0 ng/g for HPLC−UV, and 0.25 ng/g and 0.83 ng/g for GC−MS. HPLC was simpler and more robust, while GC−MS showed higher sensitivity and selectivity. GC−MS was used to validate the HPLC−UV method and prove its accuracy. The PAT content of 219 Chinese tea samples was investigated. Most tea samples contained less than 10 ng/g, ten more than 10 ng/g and two more than 50 ng/g. The results imply that tea products in China are safe with regard to their PAT content. Even an extreme daily consumption of 25 g of the tea with the highest PAT content (124 ng/g), translates to an intake of only 3 μg/person/day, which is still an order of magnitude below the maximum allowed daily intake of 30 µg for an adult.

Psychoactive Effects of Lactobacillus johnsonii BS15 on Preventing Memory Dysfunction Induced by Acute Ethanol Exposure Through Modulating Intestinal Microenvironment and Improving Alcohol Metabolic Level

The negative effects of ethanol (EtOH) abuse on the body have been widely reported in recent years. Building on the microbiota-gut-brain axis hypothesis, our study aimed to demonstrate the potential psychobiotic role of Lactobacillus johnsonii BS15 in the preventive effects of acute EtOH intake on memory impairment. We also determined whether L. johnsonii BS15 intake could effectively improve resistance to acute drinking and alleviate the adverse effects of EtOH. Male mice were fed L. johnsonii BS15 orally with (Probiotic group) or without (Control and Alcohol groups) daily dose of 0.2 × 109 CFU/ml per mouse for 28 days. Gavage with L. johnsonii BS15 significantly modified the ileal microbial ecosystem (assessed by 16S rRNA gene sequencing) in favor of Firmicutes and Lactobacillus, indicating the ability of BS15 to restore the gut microbiota. The acute EtOH exposure model (7 g/kg EtOH per mice) was established by gavage, which was administered to the alcohol and probiotic groups on day 28 of the experiment. The L. johnsonii BS15 intake effectively reduced alcohol unconsciousness time, blood alcohol concentration, and serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels. Meanwhile, the improvement of ethanol resistance time and the activities of alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) in the liver were shown by BS15 in acute alcohol-induced mice. We found that acute EtOH exposure reduced the exploration ratio (assessed by the novel object recognition test), escape latency, number of errors (assessed by passive avoidance test), and spontaneous exploration (assessed by T-maze test) in mice, which were obviously improved by L. johnsonii BS15. In the hippocampus, L. johnsonii BS15 significantly reversed the decrease in antioxidant capacity of superoxide dismutase (SOD), malondialdehyde (MDA), and glutathione (GSH) and mRNA expression of memory-related functional proteins of brain-derived neurotrophic factor (BDNF) and cyclic ampresponse element binding protein (CREB) in the hippocampal tissue after acute EtOH exposure. In conclusion, L. johnsonii BS15 intake appears as a promising psychoactive therapy to ameliorate alcohol-mediated memory impairment by increasing EtOH metabolic levels.

Dietary Probiotic Supplementation Suppresses Subclinical Necrotic Enteritis in Broiler Chickens in a Microbiota-Dependent Manner

Background

Chicken meat is one of the most consumed meats worldwide and poultry production is increasing at an exponential rate. Reducing antibiotic usage has resulted in the recurrence of subclinical necrotic enteritis again and influenced global poultry production. Probiotics are potential antibiotic substitutes that can be used to prevent subclinical necrotic enteriti. However, the precise mechanism of action of probiotics and information on which gut microbes confer this efficacy remain elusive.

Methods and results

The subclinical necrotic enteritis animal model was used to reveal the mechanism underlying the effect of probiotics on intestinal health through RNA sequencing and 16S rDNA amplicon sequencing. Bacillus licheniformis H2 feeding significantly reduced the relative abundance of Clostridium perfringens in the ileum and markedly ameliorated the pathological damage in the ileum and liver. In addition, oral administration of B. licheniformis H2 contributed to the enhancement of the intestinal barrier function and epithelial renewal, reducing energy consumption, and improving enteral nutrition absorption. Probiotic B. licheniformis H2 also ameliorated the inflammatory response and increased the immunity of subclinical necrotic enteritis infected broilers. Finally, B. licheniformis H2 feeding regulated liver gene expression to suppress immune response and promoted growth and metabolism depending on the gut microbiota.

Conclusions

These results indicated the mechanism of probiotic action of B. licheniformis H2 in maintaining intestinal health and thus promoting growth and B. licheniformis H2 may serve as an antibiotic substitute to prevent subclinical necrotic enteritis in poultry farming.

An Interactive Evolutionary Design Method for Mobile Product Customization and Validation of Its Application

Product customization is a means that effectively caters to personal needs, and as such, has increasingly caught the attention of both consumers and manufacturers. With technological advancements, the customization of products is now being made available through mobile applications. However, mobile apps need to be easy to use and operate, which presents some challenges for mobile app designers. In response, this study proposes an interactive evolutionary design method for mobile apps, based on an interactive genetic algorithm, to help consumers generate high-quality designs and enhance their retail experience by optimizing synthetic fitness and reducing the user’s fatigue from evaluation. Firstly, a human–computer interaction model for mobile interactive evolutionary design was launched to solve the screen space problem and simplify the evaluation process. Secondly, to accelerate the convergence of the algorithm, this paper combines hesitation patterns to obtain accurate individual fitness. Thirdly, an ongoing prediction and replacement mechanism were presented to improve user experience. After addressing these items, the proposed method is applied to a customization system that involves traditional brocade patterns of the Zhuang ethnic group in southwestern China and validated using a conventional interactive evolutionary design system with an interactive genetic algorithm. The experimental results show that the proposed method increases the designs’ efficiency, and can help consumers effectively customize their product purchases on mobile devices.