A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source modalities as inputs. The output of our model includes reconstructed images of the source modalities and high-quality image synthesized in the target modality. Our proposed model is formulated as a variational problem that leverages several learnable modality-specific feature extractors and a multimodal synthesis module. We propose a learnable optimization algorithm to solve this model, which induces a multi-phase network whose parameters can be trained using multi-modal MRI data. Moreover, a bilevel-optimization framework is employed for robust parameter training. We demonstrate the effectiveness of our approach using extensive numerical experiments.Comment: 12 page

Acute rejection is associated with antibodies to non-Gal antigens in baboons using Gal-knockout pig kidneys

We transplanted kidneys from α1,3-galactosyltransferase knockout (GalT-KO) pigs into six baboons using two different immunosuppressive regimens, but most of the baboons died from severe acute humoral xenograft rejection. Circulating induced antibodies to non-Gal antigens were markedly elevated at rejection, which mediated strong complement-dependent cytotoxicity against GalT-KO porcine target cells. These data suggest that antibodies to non-Gal antigens will present an additional barrier to transplantation of organs from GalT-KO pigs to humans. © 2005 Nature Publishing Group

High Frequency of CD4+CXCR5+ TFH Cells in Patients with Immune-Active Chronic Hepatitis B

BACKGROUND: T follicular helper (TFH) cells are a special subpopulation of T helper cells and can regulate humoral immune responses. This study examined whether the frequency of CD4(+)CXCR5(+) TFH cells could be associated with active immunity in chronic hepatitis B (CHB) patients. METHODOLOGY AND FINDINGS: The frequencies of peripheral blood CD4(+)CXCR5(+) TFH cells, inducible T cell costimulator (ICOS), and/or programmed death 1 (PD-1) positive CD4(+)CXCR5(+) TFH cells in immune-active (IA), immune-tolerant (IT) CHB, and healthy controls (HC) were characterized by flow cytometry analysis. The effect of adevofir dipivoxil treatment on the frequency of CD4(+)CXCR5(+) TFH cells, the concentrations of serum IL-2, IFN-γ, TNF-α, IL-4, IL-6, IL-10, IL-21, ALT, AST, HBsAg, HBsAb, HBeAg, HBeAb and HBV loads in IA patients were determined. The potential association of the frequency of CD4(+)CXCR5(+) TFH cells with clinical measures was analyzed. In addition, the frequency of splenic and liver CD4(+)CXCR5(+) TFH cells in HBV-transgenic mice was examined. We found that the frequency of CD4(+)CXCR5(+) TFH cells in IA patients was significantly higher than that of IT patients and HC, and the percentages of CD4(+)CXCR5(+) TFH in IA patients were positively correlated with AST. Furthermore, the percentages of ICOS(+), PD-1(+), and ICOS(+)PD-1(+) in CD4(+)CXCR5(+) TFH cells in CHB patients were significantly higher than that of HC. Treatment with adefovir dipivoxil reduced the frequency of CD4(+)CXCR5(+) TFH, PD-1(+)CD4(+)CXCR5(+) TFH cells and the concentrations of HBsAg and HBeAg, but increased the concentrations of HBsAb, HBeAb, IL-2 and IFN-γ in IA patients. Moreover, the frequency of splenic and liver CD4(+)CXCR5(+) TFH cells in HBV-transgenic mice was higher than that of wild-type controls. CONCLUSIONS: These data indicate that CD4(+)CXCR5(+) TFH cells may participate in the HBV-related immune responses and that high frequency of CD4(+)CXCR5(+) TFH cells may be a biomarker for the evaluation of active immune stage of CHB patients

A Lightweight YOLOv5 Optimization of Coordinate Attention

As Machine Learning technologies evolve, there is a desire to add vision capabilities to all devices within the IoT in order to enable a wider range of artificial intelligence. However, for most mobile devices, their computing power and storage space are affected by factors such as cost and the tight supply of relevant chips, making it impossible to effectively deploy complex network models to small processors with limited resources and to perform efficient real-time detection. In this paper, YOLOv5 is studied to achieve the goal of lightweight devices by reducing the number of original network channels. Then detection accuracy is guaranteed by adding a detection head and CA attention mechanism. The YOLOv5-RC model proposed in this paper is 30% smaller and lighter than YOLOv5s, but still maintains good detection accuracy. YOLOv5-RC network models can achieve a good balance between detection accuracy and detection speed, with potential for its widespread use in industry

A Lightweight YOLOv5 Optimization of Coordinate Attention

As Machine Learning technologies evolve, there is a desire to add vision capabilities to all devices within the IoT in order to enable a wider range of artificial intelligence. However, for most mobile devices, their computing power and storage space are affected by factors such as cost and the tight supply of relevant chips, making it impossible to effectively deploy complex network models to small processors with limited resources and to perform efficient real-time detection. In this paper, YOLOv5 is studied to achieve the goal of lightweight devices by reducing the number of original network channels. Then detection accuracy is guaranteed by adding a detection head and CA attention mechanism. The YOLOv5-RC model proposed in this paper is 30% smaller and lighter than YOLOv5s, but still maintains good detection accuracy. YOLOv5-RC network models can achieve a good balance between detection accuracy and detection speed, with potential for its widespread use in industry

Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study

Abstract Background Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies. Methods This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients. Results Preoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p  4.12, p < 0.0001) were most significant in the postoperative period. Postoperative predictive models specifically targeting the glioblastoma and IDH wildtype subgroups were also performed, with an AUC of 0.76 and 0.80, respectively. The 50 combinations of distinct risk factors accommodate diverse recurrence risks among glioma patients, and the nomograms visualizes the results for clinical practice. A stratified Cox model identified many prognostic factors for long-term recurrence, thereby facilitating the enhanced formulation of perioperative care plans for patients, and glioblastoma patients displayed a median progression-free survival (PFS) of only 11 months. Conclusion The constructed preoperative and postoperative models reliably predicted short-term postoperative glioma recurrence in a substantial patient cohort. The combinations risk factors and nomograms enhance the operability of personalized therapeutic strategies and care regimens. Particular emphasis should be placed on patients with recurrence within six months post-surgery, and the corresponding treatment strategies require comprehensive clinical investigation

Sulfation and Its Effect on the Bioactivity of Magnolol, the Main Active Ingredient of Magnolia Officinalis

Magnolol, the main active ingredient of Magnolia officinalis, has been reported to display anti-inflammatory activity. Sulfation plays an important role in the metabolism of magnolol. The magnolol sulfated metabolite was identified by the ultra-performance liquid chromatography to quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and a proton nuclear magnetic resonance (1H-NMR). The magnolol sulfation activity of seven major recombinant sulfotransferases (SULTs) isoforms (SULT1A1*1, SULT1A1*2, SULT1A2, SULT1A3, SULT1B1, SULT1E1, and SULT2A1) was analyzed. The metabolic profile of magnolol was investigated in liver S9 fractions from human (HLS9), rat (RLS9), and mouse (MLS9). The anti-inflammatory effects of magnolol and its sulfated metabolite were evaluated in RAW264.7 cells stimulated by lipopolysaccharide (LPS). Magnolol was metabolized into a mono-sulfated metabolite by SULTs. Of the seven recombinant SULT isoforms examined, SULT1B1 exhibited the highest magnolol sulfation activity. In liver S9 fractions from different species, the CLint value of magnolol sulfation in HLS9 (0.96 &micro;L/min/mg) was similar to that in RLS9 (0.99 &micro;L/min/mg) but significantly higher than that in MLS9 (0.30 &micro;L/min/mg). Magnolol and its sulfated metabolite both significantly downregulated the production of inflammatory mediators (IL-1&beta;, IL-6 and TNF-&alpha;) stimulated by LPS (p &lt; 0.001). These results indicated that SULT1B1 was the major enzyme responsible for the sulfation of magnolol and that the magnolol sulfated metabolite exhibited potential anti-inflammatory effects

Additional file 1 of Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study

Supplementary Material