Comparative Study of Indocyanine Green Fluorescence Imaging in Lung Cancer with Near-Infrared-I/II Windows.

BACKGROUND: We compare the application of intravenous indocyanine green (ICG) fluorescence imaging in lung cancer with near-infrared-I (NIR-I) and near-infrared-II (NIR-II) windows. METHODS: From March to December 2022, we enrolled patients who received an intravenous injection of ICG (5 mg/kg) 1 day before the planned lung cancer surgery. The lung cancer nodules were imaged by NIR-I/II fluorescence imaging systems, and the tumor-to-normal-tissue ratio (TNR) was calculated. In addition, the fluorescence intensity and signal-to-background ratio (SBR) of capillary glass tubes containing ICG covered with different thicknesses of lung tissue were measured by NIR-I/II fluorescence imaging systems. RESULTS: In this study, 102 patients were enrolled, and the mean age was 59.9 ± 9.2 years. A total of 96 (94.1%) and 98 (96.1%) lung nodules were successfully imaged with NIR-I and NIR-II fluorescence, and the TNR of NIR-II was significantly higher than that of NIR-I (3.9 ± 1.3 versus 2.4 ± 0.6, P < 0.001). In multiple linear regression, solid nodules (P < 0.001) and squamous cell carcinoma (P < 0.001) were independent predictors of a higher TNR of NIR-I/II. When capillary glass tubes were covered with lung tissue whose thickness was more than 2 mm, the fluorescence intensity and the SBR of NIR-II were significantly higher than those of NIR-I. CONCLUSIONS: We verified the feasibility of NIR-II fluorescence imaging in intravenous ICG lung cancer imaging for the first time. NIR-II fluorescence can improve the TNR and penetration depth of lung cancer with promising clinical prospects.

Deficiency in the frequency and function of Tr1 cells in IgAV and the possible role of IL-27.

OBJECTIVE: Type 1 regulatory T (Tr1) cells are involved in the pathogenesis of numerous immune-mediated diseases. However, little is known about whether and how Tr1 cells affect the development of IgA vasculitis (IgAV). We aimed to investigate this question in IgAV patients. METHODS: . Tr1 cells in peripheral blood and kidney tissue of IgAV patients were analysed by multi-parametric flow cytometry and immunofluorescence techniques. An in vitro assay of suppression of T cell proliferation and cytokine release was performed to evaluate the function of Tr1 cells. Real-time PCR and cell stimulation in vitro were used to explore the roles of IL-27 and early growth response gene 2 (EGR2). RESULTS: The frequency of Tr1 cells was decreased in peripheral blood but increased in kidney tissue from IgAV patients. A defective suppressive function of Tr1 cells in IgAV was observed. The frequency of Tr1 cells and the cytokines secreted by them were up-regulated in the presence of recombinant IL-27 in vitro. Moreover, IL-27 also increased the expression of EGR2. Furthermore, lower frequency of Tr1 cells during remission had a higher recurrence rate. CONCLUSION: Tr1 cells are involved in the pathogenesis of IgAV. The low IL-27 in IgAV is responsible for impaired frequency and function of Tr1 cells, and EGR2 may be the specific transcription factor involved in the progression. Tr1 may be a risk factor for IgAV recurrence.

Association between alcohol intake and the risk of systemic lupus erythematosus: A systematic review and meta-analysis.

OBJECTIVES: Previous studies have reported inconsistent results on the relationship between alcohol intake and the risk of systemic lupus erythematosus (SLE). Therefore, we conducted a systematic review and meta-analysis to illustrate the potential role of alcohol intake on the progression of SLE. METHODS: An electronic search of the PubMed, EmBase, and the Cochrane library databases was conducted from their inception up to March 2020. Observational studies that investigated the role of alcohol intake on the risk of SLE were eligible for inclusion in this study. The pooled odds ratio (OR) with 95% confidence interval (CI) was calculated as an effect estimate using the random-effects model. RESULTS: Seven case-control studies (n = 3, 251) and three cohort studies (n = 322, 479) were selected for the final meta-analysis. Mild (OR: 0.85; 95% CI: 0.53-1.38; p = 0.515) or heavy (OR: 0.63; 95% CI: 0.37-1.09; p = 0.102) alcohol intake were not associated with the risk of SLE, while moderate alcohol intake could protect against the risk of SLE (OR: 0.71; 95% CI: 0.55-0.93; p = 0.012). Sensitivity analysis suggested that heavy alcohol intake was associated with a reduced risk of SLE (OR: 0.47; 95% CI: 0.32-0.67; p < 0.001). CONCLUSIONS: This study found that moderate alcohol intake could protect against the risk of SLE, while mild or heavy alcohol intake did not significantly affect the risk of SLE.

Severe sepsis caused by serious gastrointestinal infection in sJIA patients treated with IL-6 receptor antagonist: a case report.

BACKGROUND: Interleukin (IL)-6 plays an essential role in the pathogenesis of systemic juvenile idiopathic arthritis (sJIA). Tocilizumab (TCZ), a kind of biological agent against both membrane and soluble IL-6 receptor, is the only biological agent approved for the treatment of sJIA in China. Infections are the most common adverse events during TCZ therapy, and most of infections are mild or moderate. Severe sepsis originated from gastrointestinal infection is rarely reported. CASE PRESENTATION: In this article, we reported two 13-year-old sJIA patients who suffered from life-threatening infections after TCZ administration. Within one day, both of them presented rapidly progressive conditions that included fever, abdominal pain, dizziness, diarrhea and vomiting, and laboratory tests showed multi-organ dysfunctions. They were diagnosed with severe sepsis and septic shock that were supposed to be caused by the pathogens from the gastrointestinal tract, and they were eventually rescued by timely treatment. In addition, we also reviewed the literature about serious gastrointestinal infections and sepsis in sJIA patients receiving TCZ therapy. CONCLUSIONS: In summary, for sJIA patients with TCZ therapy, invading pathogens from the gastrointestinal tract can cause an intensely systemic infection that may even be fatal. Therefore, it is essential to pay attention to the gastrointestinal management of sJIA patients as well as remind them of their intestinal hygiene.

Variation in IL-21-secreting circulating follicular helper T cells in Kawasaki disease.

OBJECTIVE: Circulating follicular helper T (cTfh) cells are a specialized subset of CD4+ T cells that express the CXC-chemokine receptor 5 (CXCR5). These cells exhibit immune activities by inducing B cell differentiation and proliferation via the secretion of interleukin (IL)-21. Multiple studies have demonstrated that cTfh cells are associated with the progression and severity of numerous diseases. To investigate the role of cTfh cells in the development of Kawasaki disease (KD), we analyzed the distinct subpopulations of cTfh cells and serum IL-21 levels in different phases of KD. METHODS: According to the differential expression of inducible co-stimulator (ICOS) and programmed cell death protein 1 (PD-1), cTfh cells were divided into distinct subsets. We used flow cytometry and flow cytometric bead arrays (CBA) to analyze subsets of CD4+CXCR5+ T cells and serum IL-21 levels. The samples were collected from control subjects and Kawasaki disease patients in the acute and remission phases. RESULTS: In the acute phase (AP), the percentages of ICOShighPD-1high, ICOS+PD-1+, ICOS-PD-1+, CD45RA-IL-21+ cTfh cells and serum IL-21 levels significantly increased. Furthermore, the percentages of ICOShighPD-1high and ICOS+PD-1+ cTfh cells positively correlated with erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) values, whereas the percentage of ICOS-PD-1+ cTfh cells indicated negative correlations. The percentages of ICOS+PD-1+, ICOShighPD-1high and CD45RA-IL-21+ cTfh cells correlated positively with serum IL-21 levels. In the remission phase (RP), the percentages of ICOS-PD-1+, CD45RA-IL-21+ cTfh cells and serum IL-21 levels were significantly decreased. In contrast, the percentages of ICOS+PD-1+, ICOShighPD-1high, and ICOS+PD-1- cTfh cells were further increased. Among these subsets, only CD45RA-IL-21+ cTfh cells correlated positively with serum IL-21 levels. CONCLUSIONS: The present study is the first investigation that examined the distribution of circulating cTfh cell subsets in Kawasaki disease. Both cTfh cells and serum IL-21 are essential to the pathogenesis of KD. Our study provides further understanding of the immune response involved in KD and offers novel insights in the pathogenetic mechanism of this disease.

Role and mechanism of Lactobacillus casei in the modulation of alcohol preference in mice.

BACKGROUND: Prolonged alcohol consumption may lead to gastrointestinal tract dysfunction and cause abnormalities in the associated nervous system activity, thereby increasing the body's craving for alcohol. Lactobacillus casei is a probiotic that has been shown to reduce the incidence of alcohol-related diseases. However, it is unclear whether Lactobacillus casei can delay the development of alcohol dependence. METHODS: The chronic intermittent active drinking method was used to establish a mouse alcohol dependence model. The mice were randomly divided into 4 treatment groups, as follows: (1) Control group: two bottles of distilled water alternately, 0.2 mL/d saline gavage. (2) Alcohol group: alternating water and alcohol, 0.2 mL/d saline gavage. (3) Low group: alternating water and alcohol, 0.2 mL/d 1 × 108CFU of Lactobacillus casei by gavage. (4) High group: alternating water and alcohol, 0.2 mL/d 1 × 109CFU of Lactobacillus casei by gavage. The daily water consumption (mL), alcohol consumption (mL) and body weight of each mouse were recorded. After that, pathological changes in the intestines, brain tissues and serum of the experimental animals were detected, while changes in the intestinal flora of the mice were analysed by 16S rRNA sequencing. RESULTS: The Lactobacillus casei intervention did not produce a significant effect on body weight in alcohol-exposed mice (P>0.05), but significantly reduced alcohol preference in alcohol-exposed mice (P<0.05). Subsequent analyses showed that Lactobacillus casei significantly ameliorated intestinal, brain tissue, and systemic inflammatory responses in alcohol-exposed mice (P<0.05). 16S rRNA sequencing showed that alcohol-exposed mice treated with Lactobacillus casei exhibited a richer composition of intestinal microorganisms, such as f__Rikenellaceae, g__Alistipes_A_871400, and g__Bacteroides_H genera showed relative enrichment in the High group. CONCLUSION: By showing that Lactobacillus casei slows down alcohol preference and alleviates gut and brain tissue inflammation in alcohol-exposed mice, our findings provide a possible strategy: Lactobacillus casei may be able to serve as a potential target for the prevention and treatment of alcohol dependence.

RETNet: Resolution enhancement Transformer network for magnetic particle imaging based on X-space.

Magnetic Particle Imaging (MPI) can visualize the concentration distribution of superparamagnetic iron-oxide nanoparticles (SPIONs) in tissues with the advantages of high sensitivity and high temporal resolution. However, the low spatial resolution of MPI limits its application. Increasing the gradient strength of the selection field can improve the resolution of MPI, but also increase power consumption and noise. A feasible and cost-effective method to address this limitation is to reconstruct high gradient (HG) image from low gradient (LG) image using algorithms. Deep learning has been a powerful tool for improving the resolution of medical imaging techniques. In this study, we propose a Resolution Enhancement Transformer Network (RETNet) for reconstructing HG image with high-resolution from LG image with low-resolution as input, avoiding high power consumption and high noise in the system with HG field. RETNet leverages a shallow feature extractor to capture shallow features, a cross-scale-Transformer (CST) to focus on textural features, a residual-swin-Transformer (RST) to focus on structural features, and an image reconstruction module to aggregate these three types of features and reconstruct the HG image. Textural and structural features extracted can ensure the integrity of the details and the realization of high definition in the reconstructed image. Ablation experiments demonstrate the significant contribution of these two modules to reconstruct the HG image. Comparative experiments, including experiments at noise-free and multiple noise levels, confirm the high robustness of RETNet. Simulation, phantom, and in vivo experiments consistently demonstrate that RETNet outperforms competing methods and effectively improves the resolution of MPI.

Intermittent pulses of methylprednisolone with low-dose prednisone attenuate lupus symptoms in B6.MRL-Faslpr/J mice with fewer glucocorticoid side effects.

Glucocorticoids (GCs) are potent anti-inflammatory and immunosuppressant medications and remain the cornerstone of systemic lupus erythematosus (SLE) therapy. However, ongoing exposure to GCs has the potential to elicit multiple adverse effects. Considering the irreplaceability of GCs in SLE therapy, it is important to explore the optimal regimen of GCs. Here, we compared the long-term efficacy and safety of pulsed and oral GC therapy in a lupus-prone mouse model. Mice were grouped using a randomized block design. We monitored survival rates, proteinuria, serum autoantibodies, and complement 3 (C3) levels up to 28 weeks of age, and assessed renal damage, bone quality, lipid deposition in the liver and marrow, glucose metabolic parameters, and levels of hormones of the hypothalamic-pituitary-adrenal (HPA) axis. Finally, we explored the mechanisms underlying the superior efficacy of the pulse regimen over oral prednisone regimen. We found that both GC regimens alleviated the poor survival rate, proteinuria, and glomerulonephritis, while also reducing serum autoantibodies and increasing the level of C3. The pulsed GC regimen showed less resistance to insulin, less suppression of the HPA axis, less bone loss, and less bone marrow fat deposition than the oral GC regimen. Additionally, GC-induced leucine zipper (GILZ) was significantly overexpressed in the GC pulse group. These results suggest that the GC pulse regimen ameliorated symptoms in lupus-prone mice, with fewer side effects, which may be related to GILZ overexpression. Our findings offer a potentially promising GC treatment option for SLE.

Adversarial learning-based domain adaptation algorithm for intracranial artery stenosis detection on multi-source datasets.

Intracranial arterial stenosis (ICAS) is characterized by the pathological narrowing or occlusion of the inner lumen of intracranial blood vessels. However, the retina can indirectly react to cerebrovascular disease. Therefore, retinal fundus images (RFI) serve as valuable noninvasive and easily accessible screening tools for early detection and diagnosis of ICAS. This paper introduces an adversarial learning-based domain adaptation algorithm (ALDA) specifically designed for ICAS detection in multi-source datasets. The primary objective is to achieve accurate detection and enhanced generalization of ICAS based on RFI. Given the limitations of traditional algorithms in meeting the accuracy and generalization requirements, ALDA overcomes these challenges by leveraging RFI datasets from multiple sources and employing the concept of adversarial learning to facilitate feature representation sharing and distinguishability learning. In order to evaluate the performance of the ALDA algorithm, we conducted experimental validation on multi-source datasets. We compared its results with those obtained from other deep learning algorithms in the ICAS detection task. Furthermore, we validated the potential of ALDA for detecting diabetic retinopathy. The experimental results clearly demonstrate the significant improvements achieved by the ALDA algorithm. By leveraging information from diverse datasets, ALDA learns feature representations that exhibit enhanced generalizability and robustness. This makes it a reliable auxiliary diagnostic tool for clinicians, thereby facilitating the prevention and treatment of cerebrovascular diseases.

A systematic 3-D magnetic particle imaging simulation model for quantitative analysis of reconstruction image quality.

BACKGROUND AND OBJECTIVE: Magnetic particle imaging (MPI) is an emerging imaging technology in medical tomography that utilizes the nonlinear magnetization response of superparamagnetic iron oxide (SPIO) particles to determine the in vivo spatial distribution of nanoparticle contrast agents. The reconstruction image quality of MPI is determined by the characteristics of magnetic particles, the setting of the MPI scanner parameters, and the hardware interference of MPI systems. We explore a feasible method to systematically and quickly analyze the impact of these factors on MPI reconstruction image quality. METHODS: We propose a systematic 3-D MPI simulation model. The MPI simulation model has the capability of quickly producing the simulated reconstruction images of a scanned phantom, and quantitative analysis of MPI reconstruction image quality can be achieved by comparing the differences between the input image and output image. These factors are mainly classified as imaging parameters and interference parameters in our model. In order to reduce the computational time of the simulation model, we introduce GPU parallel programming to accelerate the processing of large complex matrix data. For ease of use, we also construct a reliable, high-performance, and open-source 3-D MPI simulation software tool based on our model. The efficiency of our model is evaluated by using OpenMPIData. To demonstrate the capabilities of our model, we conduct simulation experiments using parameters consistent with a real MPI scanner for improving MPI image quality. RESULTS: The experimental results show that our simulation model can systematically and quickly evaluate the impact of imaging parameters and interference parameters on MPI reconstruction image quality. CONCLUSIONS: We developed an easy-to-use and open-source 3-D MPI simulation software tool based on our simulation model incorporating all the stages of MPI formation, from signal acquisition to image reconstruction. In the future, our simulation model has potential guiding significance to practical MPI images.

Orbital Adipose Tissue: The Optimal Control for Back-Table Fluorescence Imaging of Orbital Tumors.

Control tissue is essential for ensuring the precision of semiquantitative analysis in back-table fluorescence imaging. However, there remains a lack of agreement on the appropriate selection of control tissues. To evaluate the back-table fluorescence imaging performance of different normal tissues and identify the optimal normal tissue, a cohort of 39 patients with orbital tumors were enrolled in the study. Prior to surgery, these patients received indocyanine green (ICG) and following resection, 43 normal control tissues (34 adipose tissues, 3 skin tissues, 3 periosteal tissues, and 3 muscle tissues) were examined using back-table fluorescence imaging. The skin tissue demonstrated significantly elevated fluorescence intensity in comparison to the diseased tissue, whereas the muscle tissue exhibited a broad range and standard deviation of fluorescence signal intensity. Conversely, the adipose and periosteum displayed weak fluorescence signals with a relatively consistent distribution. Additionally, no significant correlations were found between the signal-to-background ratio (SBR) of adipose tissue and patients' ages, genders, weights, disease duration, tumor origins, dosing of administration of ICG infusion, and the time interval between ICG infusion and surgery. However, a positive correlation was observed between the SBR of adipose tissue and its size, with larger adipose tissues (>1 cm) showing an average SBR 27% higher than smaller adipose tissues (≤1 cm). In conclusion, the findings of this study demonstrated that adipose tissue consistently exhibited homogeneous hypofluorescence during back-table fluorescence imaging, regardless of patient clinical variables or imaging parameters. The size of the adipose tissue was identified as the primary factor influencing its fluorescence imaging characteristics, supporting its utility as an ideal control tissue for back-table fluorescence imaging.

NIR-IIb Fluorescence Molecular Tomography of Glioblastomas Based on Heterogeneous Mouse Models and Adaptive Projection Match Pursuit Method.

OBJECTIVE: Fluorescence molecular tomography (FMT) using fluorescence of the second near-infrared window (NIR-II) has been proved to outperform conventional FMT using fluorescence of the first near-infrared window (NIR-I). However, it was still a challenge to achieve a satisfactory reconstructed light source using NIR-II FMT as the NIR-IIa (1300-1400 nm) fluorescence in the NIR-II spectrum used in the previous NIR-II FMT study was still suffering from prominent absorption and scattering of tissue. METHODS: A novel NIR-IIb (1500-1700 nm) FMT method was proposed and applied in the reconstruction of glioblastomas in animal models. Optical parameters that describe the effect of different tissue on the NIR-IIb photons were calculated to construct a light propagation model of NIR-IIb light to form the forward model. Besides, a novel adaptive projection matching pursuit (APMP) method was further adopted to accurately solve the inverse problem. Location error and Dice coefficient were used to evaluate the accuracy of reconstruction. Simulation experiments using single-source and dual-source and in vivo experiments were conducted to evaluate the reconstructed light source. RESULTS: Both simulation and in vivo experiments demonstrated that NIR-IIb FMT achieved reduced location error and improved shape similarity compared with NIR-IIa. NIR-IIb FMT of orthotopic mouse models further achieved a location error of 0.65 mm and a Dice coefficient of 0.56, which both outperformed NIR-IIa. CONCLUSION: The results demonstrated that NIR-IIb has better reconstruction performance for positioning accuracy and shape recovery. SIGNIFICANCE: The inspiring results in this study demonstrate the effectiveness and advantages of NIR-IIb FMT in precise tumor positioning.

Childhood-onset systemic lupus erythematosus: characteristics and the prospect of glucocorticoid pulse therapy.

Childhood-onset systemic lupus erythematosus (cSLE) is an autoimmune disease that results in significant damage and often needs more aggressive treatment. Compared to adult-onset SLE, cSLE has a stronger genetic background and more prevalent elevated type I Interferon expression. The management of cSLE is more challenging because the disease itself and treatment can affect physical, psychological and emotional growth and development. High dose oral glucocorticoid (GC) has become the rule for treating moderate to severe cSLE activity. However, GC-related side effects and potential toxicities are problems that cannot be ignored. Recent studies have suggested that GC pulse therapy can achieve disease remission rapidly and reduce GC-related side effects with a reduction in oral prednisone doses. This article reviews characteristics, including pathogenesis and manifestations of cSLE, and summarized the existing evidence on GC therapy, especially on GC pulse therapy in cSLE, followed by our proposal for GC therapy according to the clinical effects and pathogenesis.

MIL-CT: Multiple Instance Learning via a Cross-Scale Transformer for Enhanced Arterial Light Reflex Detection.

One of the early manifestations of systemic atherosclerosis, which leads to blood circulation issues, is the enhanced arterial light reflex (EALR). Fundus images are commonly used for regular screening purposes to intervene and assess the severity of systemic atherosclerosis in a timely manner. However, there is a lack of automated methods that can meet the demands of large-scale population screening. Therefore, this study introduces a novel cross-scale transformer-based multi-instance learning method, named MIL-CT, for the detection of early arterial lesions (e.g., EALR) in fundus images. MIL-CT utilizes the cross-scale vision transformer to extract retinal features in a multi-granularity perceptual domain. It incorporates a multi-head cross-scale attention fusion module to enhance global perceptual capability and feature representation. By integrating information from different scales and minimizing information loss, the method significantly improves the performance of the EALR detection task. Furthermore, a multi-instance learning module is implemented to enable the model to better comprehend local details and features in fundus images, facilitating the classification of patch tokens related to retinal lesions. To effectively learn the features associated with retinal lesions, we utilize weights pre-trained on a large fundus image Kaggle dataset. Our validation and comparison experiments conducted on our collected EALR dataset demonstrate the effectiveness of the MIL-CT method in reducing generalization errors while maintaining efficient attention to retinal vascular details. Moreover, the method surpasses existing models in EALR detection, achieving an accuracy, precision, sensitivity, specificity, and F1 score of 97.62%, 97.63%, 97.05%, 96.48%, and 97.62%, respectively. These results exhibit the significant enhancement in diagnostic accuracy of fundus images brought about by the MIL-CT method. Thus, it holds potential for various applications, particularly in the early screening of cardiovascular diseases such as hypertension and atherosclerosis.

CLRD: Collaborative Learning for Retinopathy Detection Using Fundus Images.

Retinopathy, a prevalent disease causing visual impairment and sometimes blindness, affects many individuals in the population. Early detection and treatment of the disease can be facilitated by monitoring the retina using fundus imaging. Nonetheless, the limited availability of fundus images and the imbalanced datasets warrant the development of more precise and efficient algorithms to enhance diagnostic performance. This study presents a novel online knowledge distillation framework, called CLRD, which employs a collaborative learning approach for detecting retinopathy. By combining student models with varying scales and architectures, the CLRD framework extracts crucial pathological information from fundus images. The transfer of knowledge is accomplished by developing distortion information particular to fundus images, thereby enhancing model invariance. Our selection of student models includes the Transformer-based BEiT and the CNN-based ConvNeXt, which achieve accuracies of 98.77% and 96.88%, respectively. Furthermore, the proposed method has 5.69-23.13%, 5.37-23.73%, 5.74-23.17%, 11.24-45.21%, and 5.87-24.96% higher accuracy, precision, recall, specificity, and F1 score, respectively, compared to the advanced visual model. The results of our study indicate that the CLRD framework can effectively minimize generalization errors without compromising independent predictions made by student models, offering novel directions for further investigations into detecting retinopathy.

Excitation-based fully connected network for precise NIR-II fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) is a novel imaging modality to obtain fluorescence biomarkers' three-dimensional (3D) distribution. However, the simplified mathematical model and complicated inverse problem limit it to achieving precise results. In this study, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering and reduce noise interference. An excitation-based fully connected network was proposed to model the inverse process of NIR-II photon propagation and directly obtain the 3D distribution of the light source. An excitation block was embedded in the network allowing it to autonomously pay more attention to neurons related to the light source. The barycenter error was added to the loss function to improve the localization accuracy of the light source. Both numerical simulation and in vivo experiments showed the superiority of the novel NIR-II FMT reconstruction strategy over the baseline methods. This strategy was expected to facilitate the application of machine learning in biomedical research.

A novel weighted auxiliary set matching pursuit method for glioma in Cerenkov luminescence tomography reconstruction.

Cerenkov luminescence tomography (CLT) is a promising three-dimensional imaging technology that has been actively investigated in preclinical studies. However, because of the ill-posedness in the inverse problem of CLT reconstruction, the reconstruction performance is still not satisfactory for broad biomedical applications. In this study, a novel weighted auxiliary set matching pursuit (WASMP) method was explored to enhance the accuracy of CLT reconstruction. The numerical simulations and in vivo imaging studies using tumor-bearing mice models were conducted to evaluate the performance of the WASMP method. The results of the above experiments proved that the WASMP method achieved superior reconstruction performance than other approaches in terms of positional accuracy and shape recovery. It further demonstrates that the atom selection strategy proposed in this study has a positive effect on improving the accuracy of atoms. The proposed WASMP improves the accuracy for CLT reconstruction for biomedical applications.

Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography.

Cerenkov luminescence tomography (CLT) is a novel and highly sensitive imaging technique, which could obtain the three-dimensional distribution of radioactive probes to achieve accurate tumor detection. However, the simplified radiative transfer equation and ill-conditioned inverse problem cause a reconstruction error. In this study, a novel attention mechanism based locally connected (AMLC) network was proposed to reduce barycenter error and improve morphological restorability. The proposed AMLC network consisted of two main parts: a fully connected sub-network for providing a coarse reconstruction result, and a locally connected sub-network based on an attention matrix for refinement. Both numerical simulations and in vivo experiments were conducted to show the superiority of the AMLC network in accuracy and stability over existing methods (MFCNN, KNN-LC network). This method improved CLT reconstruction performance and promoted the application of machine learning in optical imaging research.

Analysis of potential strategies for cadmium stress tolerance revealed by transcriptome analysis of upland cotton.

In recent years, heavy metal pollution has become a more serious global problem, and all countries are actively engaged in finding methods to remediate heavy metal-contaminated soil. We conducted transcriptome sequencing of the roots of cotton grown under three different cadmium concentrations, and analysed the potential strategies for coping with cadmium stress. Through Gene Ontology analysis, we found that most of the genes differentially regulated under cadmium stress were associated with catalytic activity and binding action, especially metal iron binding, and specific metabolic and cellular processes. The genes responsive to cadmium stress were mainly related to membrane and response to stimulus. The KEGG pathways enriched differentially expressed genes were associated with secondary metabolite production, Starch and sucrose metabolism, flavonoid biosynthesis, phenylalanina metalism and biosynthesis, in order to improve the activity of antioxidant system, repair systems and transport system and reduction of cadmium toxicity. There are three main mechanisms by which cotton responds to cadmium stress: thickening of physical barriers, oxidation resistance and detoxification complexation. Meanwhile, identified a potential cotton-specific stress response pathway involving brassinolide, and ethylene signaling pathways. Further investigation is needed to define the specific molecular mechanisms underlying cotton tolerance to cadmium stress. In this study potential coping strategies of cotton root under cadmium stress were revealed. Our findings can guide the selection of cotton breeds that absorb high levels of cadmium.