Prospective Comparison of [18F]FDG and [18F]AIF-FAPI-74 PET/CT in the Evaluation of Potentially Resectable Pancreatic Ductal Adenocarcinoma.

PURPOSE: Accurate clinical staging of potentially resectable pancreatic ductal adenocarcinoma (PDAC) is critical for establishing optimal treatment strategies. While the efficacy of fluorine-18-fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) in clinical staging is unclear, PET/CT detecting fibroblast-activation protein (FAP) expression has recently received considerable attention for detecting various tumors, including PDAC, with high sensitivity. We explored the efficacy of [18F]FDG and [18F]AIF-FAPI-74 PET/CT in the initial evaluation of potentially resectable PDAC. PROCEDURES: Between 2021 and 2022, twenty participants with newly diagnosed potentially resectable PDAC were enrolled. After the initial evaluation with pancreatic CT, [18F]FDG PET/CT, and [18F]AIF-FAPI-74 PET/CT, treatment strategies were determined considering the participant's general status, clinical staging, and resectability. Pathological information from the surgical specimens was only available in 17 participants who underwent curative-intent surgery. Head-to-head comparisons of quantitative radiotracer uptake and diagnostic performance were performed among imaging modalities. RESULTS: [18F]AIF-FAPI-74 PET/CT showed a significantly higher maximum standardized uptake value than [18F]FDG PET/CT did in evaluating primary pancreatic lesions (median [interquartile range]; 12.6 [10.7-13.7] vs. 6.3 [4.8-9.2]; P < 0.001). In contrast, [18F]AIF-FAPI-74 PET/CT showed a significantly lower mean standardized uptake value than [18F]FDG PET/CT did in evaluating background organ (median [interquartile range]) 0.8 [0.7-0.9] vs. 2.6 [2.3-2.7]; P < 0.001). In addition, the sensitivity of [18F]AIF-FAPI-74 PET/CT in detecting metastatic lymph nodes was higher than that of [18F]FDG PET/CT (50.0% vs. 0.0%; P = 0.026). CONCLUSION: This study demonstrated that [18F]AIF-FAPI-74 PET/CT could improve the clinical staging of potentially resectable PDAC.

Aldosterone promotes calcification of vascular smooth muscle cells in mice through the AIF-1/Wnt/ß-catenin signaling pathway.

OBJECTIVE: This study aimed to investigate the impact of aldosterone on calcification in murine vascular smooth muscle cells (VSMCs) via the allograft inflammatory factor-1 (AIF-1)/Wnt/ß-catenin signaling pathway. METHODS: Mouse VSMCs were cultured in vitro, and calcification was induced by treatment with 100 nM aldosterone. The level of calcification in mouse VSMCs was evaluated using colorimetric assays to assess ALP activity and qRT-PCR to identify the expression of calcification-related markers, such as Runx2, α-SMA, OCN, and ALP mRNA. Western blot analysis was performed to determine the protein expression levels associated with the Wnt/ß-catenin pathway (LRP6, p-LRP6, GSK3ß, p-GSK3ß, ß-catenin) and AIF-1. Plasmid transfection techniques were utilized to either knock down or overexpress AIF-1, and the subsequent alterations in these markers were observed. RESULTS: (1) Compared to the control group, the aldosterone treatment group with exhibited a significant increase in ALP. Concurrently, Runx2, OCN, and ALP mRNA levels increased, as did LRP6, p-LRP6, GSK3ß, p-GSK3ß, ß-catenin, and AIF-1 protein levels. Additionally, a significant decrease in the expression of α-SMA mRNA was observed (P < 0.05). (2) The aldosterone + oe-AIF-1 group showed significant increases in ALP activity compared to the aldosterone + oe-NC group, whereas the aldosterone + sh-AIF-1 group showed significant decreases (P < 0.05). (3) The aldosterone + oe-AIF-1 group exhibited significantly upregulated expression of AIF-1, p-LRP6/LRP6, p-GSK3ß/GSK3ß, and ß-catenin proteins relative to the aldosterone + oe-NC group (P < 0.05). This was concurrent with increased mRNA expression of Runx2, OCN, and ALP, and decreased α-SMA mRNA expression (P < 0.05). CONCLUSION: Aldosterone affects the calcification process in mouse VSMCs, and the activation of the AIF-1/Wnt/ß-catenin signaling pathway is the mechanism behind its action.

Elucidating the causal nexus between antibody-mediated immunity and autoimmune diseases: Insights from bidirectional mendelian randomization, gene expression profiling, and drug sensitivity analysis.

OBJECTIVE: This study aimed to elucidate the causal relationships between antibodies and autoimmune diseases using Mendelian randomization (MR). METHODS: Data on 46 antibodies were obtained from genome-wide association studies (GWAS). Autoimmune disease data were sourced from the FinnGen consortium and the IEU OpenGWAS project. Inverse-variance weighted (IVW) analysis was the primary method, supplemented by heterogeneity and sensitivity analyses. We also examined gene expression near significant SNPs and conducted drug sensitivity analyses. RESULTS: Antibodies and autoimmune diseases exhibit diverse interactions. Antibodies produced after Polyomavirus infection tend to increase the risk of several autoimmune diseases, while those following Human herpesvirus 6 infection generally reduce it. The impact of Helicobacter pylori infection varies, with different antibodies affecting autoimmune diseases in distinct ways. Overall, antibodies significantly influence the risk of developing autoimmune diseases, whereas autoimmune diseases have a lesser impact on antibody levels. Gene expression and drug sensitivity analyses identified multiple genes and drugs as potential treatment options for ankylosing spondylitis (AS), with the AIF1 gene being particularly promising. CONCLUSIONS: Bidirectional MR analysis confirms complex causal relationships between various antibodies and autoimmune diseases, revealing intricate patterns of post-infection antibody interactions. Several drugs and genes, notably AIF1, show potential as candidates for AS treatment, offering new avenues for research. Further exploration of the underlying mechanisms is necessary.

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging.

INTRODUCTION: We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation. METHODS: Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to incorporatea priori knowledge about the AIF's functional form and shape, enabling precise predictions of the concentrations of [11C]PBR28 in whole blood and the free tracer in metabolite-corrected plasma. RESULTS: We found a robust linear correlation between our model's predicted AIF curves and those obtained through traditional, invasive measurements. We achieved an average cross-validated Pearson correlation of 0.86 for whole blood and 0.89 for parent plasma curves. Moreover, our method's ability to estimate the volumes of distribution across several key brain regions - without significant differences between the use of predicted versus actual AIFs in a two-tissue compartmental model - successfully captures the intrinsic variability related to sex, the binding affinity of the translocator protein (18 kDa), and age. CONCLUSIONS: These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.

ATBS1-INTERACTING FACTOR 2 Positively Regulates Freezing Tolerance via INDUCER OF CBF EXPRESSION 1/C-REPEAT BINDING FACTOR-Induced Cold Acclimation Pathway.

The INDUCER OF CBF EXPRESSION 1/C-REPEAT BINDING FACTOR (ICE1/CBF) pathway plays a crucial role in plant responses to cold stress, impacting growth and development. Here, we demonstrated that ATBS1-INTERACTING FACTOR 2 (AIF2), a non-DNA-binding basic helix-loop-helix transcription factor, positively regulates freezing tolerance through the ICE1/CBF-induced cold tolerance pathway in Arabidopsis. Cold stress transcriptionally upregulated AIF2 expression and induced AIF2 phosphorylation, thereby stabilizing the AIF2 protein during early stages of cold acclimation. The AIF2 loss-of-function mutant, aif2-1, exhibited heightened sensitivity to freezing before and after cold acclimation. In contrast, ectopic expression of AIF2, but not the C-terminal-deleted AIF2 variant, restored freezing tolerance. AIF2 enhanced ICE1 stability during cold acclimation and promoted the transcriptional expression of CBFs and downstream cold-responsive genes, ultimately enhancing plant tolerance to freezing stress. MITOGEN-ACTIVATED PROTEIN KINASES 3 and 6 (MPK3/6), known negative regulators of freezing tolerance, interacted with and phosphorylated AIF2, subjecting it to protein degradation. Furthermore, transient co-expression of MPK3/6 with AIF2 and ICE1 downregulated AIF2/ICE1-induced transactivation of CBF2 expression. AIF2 interacted preferentially with BRASSINOSTEROID-INSENSITIVE 2 (BIN2) and MPK3/6 during the early and later stages of cold acclimation, respectively, thereby differentially regulating AIF2 activity in a cold acclimation time-dependent manner. Moreover, AIF2 acted additively in a gain-of-function mutant of BRASSINAZOLE-RESISTANT 1 (BZR1; bzr1-1D) and a triple knockout mutant of BIN2 and its homologs (bin2bil1bil2) to induce CBFs-mediated freezing tolerance. This suggests that cold-induced AIF2 coordinates freezing tolerance along with BZR1 and BIN2, key positive and negative components, respectively, of brassinosteroid signaling pathways.

Association between type 2 diabetes, alcohol intake frequency, age at menarche, and gallbladder cancer: a two-sample Mendelian randomization study.

Background: Gallbladder cancer (GBC) is a rare malignancy of the digestive tract, characterized by a remarkably poor prognosis. Currently, there is a controversy on the relationship between type 2 diabetes (T2D) and GBC. Additionally, no definitive conclusions were established regarding the causal relationships between alcohol intake frequency (AIF), age at menarche (AAM) and GBC. The objective of this study was to elucidate the causal association between T2D, AIF, AAM, and GBC. Methods: Single-nucleotide polymorphisms (SNPs) associated with exposures and outcomes were sourced from the Integrative Epidemiology Unit (IEU) Open Genome-Wide Association Study (GWAS) database. Specifically, the data of GBC comprised 907 East Asians (pathological results of all cases were registered into Biobank Japan) and 425,707 SNPs; T2D comprised 655,666 Europeans with 5,030,727 SNPs; AIF comprised 462,346 Europeans and 9,851,867 SNPs; AAM comprised 243,944 Europeans and 9,851,867 SNPs. The measurement of exposure traits is collected uniformly from the UK Biobank (UKB) database and presented in the form of standard deviation (SD) or the logarithmic form of the odds ratio (logOR). We employed a two-sample Mendelian randomization (MR) analysis to discern the causalities between T2D, AIF, AAM, and GBC. Sensitivity analyses were conducted to identify and address potential heterogeneity, horizontal pleiotropy, and outliers. Results: Our findings indicated that T2D reduced GBC risk [odds ratio (OR) =0.044; 95% confidence interval (CI): 0.004-0.55; P=0.015, inverse variance-weighted (IVW)]. However, no causal relationship was observed between AIF (OR =0.158; 95% CI: 5.33E-05 to 466.84; P=0.65, IVW), AAM (OR =0.19; 95% CI: 0.0003-140.34; P=0.62, IVW), and GBC. Sensitivity analysis revealed no evidence of horizontal pleiotropy, heterogeneity, or outliers, suggesting the robustness and reliability of our conclusions. Conclusions: T2D emerged as a potentially protective factor against GBC, whereas neither AIF nor AAM demonstrated a causal relationship with GBC risk. Regulation of glucose metabolism may be one of the methods for preventing GBC.

Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network.

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time-concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and ktrans results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from -23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding ktrans error was reduced from -13.5 ± 8.8% to -0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding ktrans error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and ktrans error (-2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset.

Validation of cardiac image-derived input functions for functional PET quantification.

PURPOSE: Functional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations. METHODS: Twenty healthy individuals underwent fPET/MR scans using [18F]FDG or 6-[18F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard. RESULTS: For both radiotracer cohorts, moderate to high agreement (r: 0.60-0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87-0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975-0.998) with AIF-derived measurements. CONCLUSION: Our proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.

Automated extraction of the arterial input function from brain images for parametric PET studies.

BACKGROUND: Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images. RESULTS: Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07. CONCLUSIONS: Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.

Network pharmacology combined with experimental validation to investigate the effect of Rongjin Niantong Fang on chondrocyte apoptosis in knee osteoarthritis.

Knee osteoarthritis (KOA) is a chronic degenerative disease that affects the quality of life of middle­aged and elderly individuals, and is one of the major factors leading to disability. Rongjin Niantong Fang (RJNTF) can alleviate the clinical symptoms of patients with KOA, but the molecular mechanism underlying its beneficial effects on KOA remains unknown. Using pharmacological analysis and in vitro experiments, the active components of RJNTF were analyzed to explore their potential therapeutic targets and mechanisms in KOA. The potential targets and core signaling pathways by which RJNTF exerts its effects on KOA were obtained from databases such as Gene Expression Omnibus, Traditional Chinese Medicine Systems Pharmacology and Analysis Platform. Subsequently, chondrocyte apoptosis was modeled using hydrogen peroxide (H2O2). Cell Counting Kit­8 assay involving a poly [ADP­ribose] polymerase­1 (PARP1) inhibitor, DAPI staining, reverse transcription­quantitative PCR, Annexin V­FITC/PI staining and flow cytometry, western blotting and co­immunoprecipitation analysis were used to determine the therapeutic efficacy of RJNTF on KOA and to uncover the molecular mechanism. It was found that PARP1­knockdown lentivirus, incubation with PARP1 inhibitor PJ34, medium and high doses of RJNTF significantly reduced H2O2­induced chondrocyte apoptosis. Medium and high doses of RJNTF downregulated the expression of cleaved caspase­3, cleaved PARP1 and PAR total proteins, as well as nucleus proteins of apoptosis­inducing factor (AIF) and migration inhibitory factor (MIF), and upregulated the expression of caspase­3, PARP1 total protein, as well as the cytoplasmic expression of AIF and MIF, suggesting that RJNTF may inhibit chondrocyte apoptosis through the PARP1/AIF signaling pathway.

Parthanatos: Mechanisms, modulation, and therapeutic prospects in neurodegenerative disease and stroke.

Parthanatos is a cell death signaling pathway that has emerged as a compelling target for pharmaceutical intervention. It plays a pivotal role in the neuron loss and neuroinflammation that occurs in Parkinson's Disease (PD), Alzheimer's Disease (AD), Huntington's Disease (HD), Amyotrophic Lateral Sclerosis (ALS), and stroke. There are currently no treatments available to humans to prevent cell death in any of these diseases. This review provides an in-depth examination of the current understanding of the Parthanatos mechanism, with a particular focus on its implications in neuroinflammation and various diseases discussed herein. Furthermore, we thoroughly review potential intervention targets within the Parthanatos pathway. We dissect recent progress in inhibitory strategies, complimented by a detailed structural analysis of key Parthanatos executioners, PARP-1, AIF, and MIF, along with an assessment of their established inhibitors. We hope to introduce a new perspective on the feasibility of targeting components within the Parthanatos pathway, emphasizing its potential to bring about transformative outcomes in therapeutic interventions. By delineating therapeutic opportunities and known targets, we seek to emphasize the imperative of blocking Parthanatos as a precursor to developing disease-modifying treatments. This comprehensive exploration aims to catalyze a paradigm shift in our understanding of potential neurodegenerative disease therapeutics, advocating for the pursuit of effective interventions centered around Parthanatos inhibition.

AIF translocation into nucleus caused by Aifm1 R450Q mutation: generation and characterization of a mouse model for AUNX1.

Mutations in AIFM1, encoding for apoptosis-inducing factor (AIF), cause AUNX1, an X-linked neurologic disorder with late-onset auditory neuropathy (AN) and peripheral neuropathy. Despite significant research on AIF, there are limited animal models with the disrupted AIFM1 representing the corresponding phenotype of human AUNX1, characterized by late-onset hearing loss and impaired auditory pathways. Here, we generated an Aifm1 p.R450Q knock-in mouse model (KI) based on the human AIFM1 p.R451Q mutation. Hemizygote KI male mice exhibited progressive hearing loss from P30 onward, with greater severity at P60 and stabilization until P210. Additionally, muscle atrophy was observed at P210. These phenotypic changes were accompanied by a gradual reduction in the number of spiral ganglion neuron cells (SGNs) at P30 and ribbons at P60, which coincided with the translocation of AIF into the nucleus starting from P21 and P30, respectively. The SGNs of KI mice at P210 displayed loss of cytomembrane integrity, abnormal nuclear morphology, and dendritic and axonal demyelination. Furthermore, the inner hair cells and myelin sheath displayed abnormal mitochondrial morphology, while fibroblasts from KI mice showed impaired mitochondrial function. In conclusion, we successfully generated a mouse model recapitulating AUNX1. Our findings indicate that disruption of Aifm1 induced the nuclear translocation of AIF, resulting in the impairment in the auditory pathway.

Type I/II Immune Balance Contributes to the Protective Effect of AIF-1 on Hepatic Immunopathology Induced by Schistosoma japonicum in a Transgenic Mouse Model.

Schistosomiasis is the second most debilitating neglected tropical disease in the world. Liver egg granuloma and fibrosis are the main damage of schistosomiasis. In this study, the role of allograft inflammatory factor-1 (AIF-1) in liver pathology and its regulation in immune responses were investigated in a transgenic mouse infected with Schistosoma japonicum. We found that AIF-1 overexpression reduced worm burden and decreased egg granuloma sizes and serum alanine aminotransferase levels, along with inhibited hepatic collagen deposition and serum hydroxyproline levels during S. japonicum infection. Moreover, AIF-1 overexpression resulted in an increased ratio of Th1/Th2, increased levels of IFN-γ and T-bet, and lower levels of GATA-3 in the spleen, accompanied by increased M1 percentages, decreased M2 percentages, and thus a higher ratio of M1/M2 in the peritoneal cavity and liver. AIF-1 induced CD68 and iNOS mRNA expression and protein levels of cytoplasmic p-P38 and nuclear NF-κB, along with enhanced levels of TNF-α and TGF-ß in macrophages in vitro. Moreover, the hepatic pathology had a negative correlation with Th1/Th2 and M1/M2 ratios in the infected mice. The findings reveal that the beneficial role of AIF-1 in alleviating hepatic damage is related to restoring type I/II immune balance in S. japonicum infection.

Cdc73 is a major regulator of apoptosis-inducing factor 1 expression in Saccharomyces cerevisiae via H3K36 methylation.

Apoptosis-inducing factor 1 (AIF1) overexpression is intimately linked to the sensitivity of yeast cells towards hydrogen peroxide or acetic acid. Therefore, studying the mechanism of AIF1 regulation in the cell would provide a significant understanding of the factors guiding yeast apoptosis. In this report, we show the time-dependent induction of AIF1 under hydrogen peroxide stress. Additionally, we find that AIF1 expression in response to hydrogen peroxide is mediated by two transcription factors, Yap5 (DNA binding) and Cdc73 (non-DNA binding). Furthermore, substituting the H3K36 residue with another amino acid significantly abrogates AIF1 expression. However, substituting the lysine (K) in H3K4 or H3K79 with alanine (A) does not affect AIF1 expression level under hydrogen peroxide stress. Altogether, reduced AIF1 expression in cdc73Δ is plausibly due to reduced H3K36me3 levels in the cells.

Greybox: A hybrid algorithm for direct estimation of tracer kinetic parameters from undersampled DCE-MRI data.

BACKGROUND: A variety of deep learning-based and iterative approaches are available to predict Tracer Kinetic (TK) parameters from fully sampled or undersampled dynamic contrast-enhanced (DCE) MRI data. However, both the methods offer distinct benefits and drawbacks. PURPOSE: To propose a hybrid algorithm (named as 'Greybox'), using both model- as well as DL-based, for solving a multi-parametric non-linear inverse problem of directly estimating TK parameters from undersampled DCE MRI data, which is invariant to undersampling rate. METHODS: The proposed algorithm was inspired by plug-and-play algorithms used for solving linear inverse imaging problems. This technique was tested for its effectiveness in solving the nonlinear ill-posed inverse problem of generating 3D TK parameter maps from four-dimensional (4D; Spatial + Temporal) retrospectively undersampled k-space data. The algorithm learns a deep learning-based prior using UNET to estimate the K trans $\mathbf {K_{trans}}$ and V p $\mathbf {V_{p}}$ parameters based on the Patlak pharmacokinetic model, and this trained prior was utilized to estimate the TK parameter maps using an iterative gradient-based optimization scheme. Unlike the existing DL models, this network is invariant to the undersampling rate of the input data. The proposed method was compared with the total variation-based direct reconstruction technique on brain, breast, and prostate DCE-MRI datasets for various undersampling rates using the Radial Golden Angle (RGA) scheme. For the breast dataset, an indirect estimation using the Fast Composite Splitting algorithm was utilized for comparison. Undersampling rates of 8 × $\times$ , 12 × $\times$ and 20 × $\times$ were used for the experiments, and the results were compared using the PSNR and SSIM as metrics. For the breast dataset of 10 patients, data from four patients were utilized for training (1032 samples), two for validation (752 samples), and the entire volume of four patients for testing. Similarly, for the prostate dataset of 18 patients, 10 patients were utilized for training (720 samples), five for validation (216 samples), and the whole volume of three patients for testing. For the brain dataset of nineteen patients, ten patients were used for training (3152 samples), five for validation (1168 samples), and the whole volume of four patients for testing. Statistical tests were also conducted to assess the significance of the improvement in performance. RESULTS: The experiments showed that the proposed Greybox performs significantly better than other direct reconstruction methods. The proposed algorithm improved the estimated K trans $\mathbf {K_{trans}}$ and V p $\mathbf {V_{p}}$ in terms of the peak signal-to-noise ratio by up to 3 dB compared to other standard reconstruction methods. CONCLUSION: The proposed hybrid reconstruction algorithm, Greybox, can provide state-of-the-art performance in solving the nonlinear inverse problem of DCE-MRI. This is also the first of its kind to utilize convolutional neural network-based encodings as part of the plug-and-play priors to improve the performance of the reconstruction algorithm.

Histopathological Clues of Enhanced Inflammation in the Placental Tissue of Women with Chronic Venous Disease in Lower Limbs during Pregnancy.

It is estimated that approximately one in three women develop chronic venous disease (CVD) during pregnancy, a broad spectrum of morphofunctional disorders affecting the venous system in different regions of the body, including the lower limbs. A growing body of evidence supports the diverse maternofetal consequences derived from this condition, with the placenta being an organ particularly affected. Among other consequences, having CVD during pregnancy has been associated with systemic inflammation and altered cytokines and chemokine profiles in the maternal and fetal serum related to this condition. In the present work, we aimed to analyze if these inflammatory changes also occurred in the placental tissue of women with CVD, exploring by immunohistochemistry and real-time PCR (RT-qPCR) gene and protein expression of critical inflammatory markers like allograft inflammatory factor 1 (AIF-1), interleukin 10 (IL-10), IL-12A, and IL-18. Our results demonstrate an enhanced tissue expression of AIF-1, IL-12A, and IL-18, accompanied by a decrease in IL-10 in the placentas of women who had undergone CVD during pregnancy. Overall, our results suggest a possible pathophysiological role of inflammation in the placental tissue of women with CVD during pregnancy, although the precise consequences of this feature remain to be deeply analyzed.

A short 18F-FDG imaging window triple injection neuroimaging protocol for parametric mapping in PET.

BACKGROUND: In parametric PET, kinetic parameters are extracted from dynamic PET images. It is not commonly used in clinical practice because of long scan times and the requirement for an arterial input function (AIF). To address these limitations, we designed an 18F-fluorodeoxyglucose (18F-FDG) triple injection dynamic PET protocol for brain imaging with a standard field of view PET scanner using a 24-min imaging window and an input function modeled using measurements from a region of interest placed over the left ventricle. METHODS: To test the protocol in 6 healthy participants, we examined the quality of voxel-based maps of kinetic parameters in the brain generated using the two-tissue compartment model and compared estimated parameter values with previously published values. We also utilized data from a 36-min validation imaging window to compare (1) the modeled AIF against the input function measured in the validation window; and (2) the net influx rate ([Formula: see text]) computed using parameter estimates from the short imaging window against the net influx rate obtained using Patlak analysis in the validation window. RESULTS: Compared to the AIF measured in the validation window, the input function estimated from the short imaging window achieved a mean area under the curve error of 9%. The voxel-wise Pearson's correlation between [Formula: see text] estimates from the short imaging window and the validation imaging window exceeded 0.95. CONCLUSION: The proposed 24-min triple injection protocol enables parametric 18F-FDG neuroimaging with noninvasive estimation of the AIF from cardiac images using a standard field of view PET scanner.

NADH improves AIF dimerization and inhibits apoptosis in iPSCs-derived neurons from patients with auditory neuropathy spectrum disorder.

Auditory neuropathy spectrum disorder (ANSD) is a hearing impairment involving disruptions to inner hair cells (IHCs), ribbon synapses, spiral ganglion neurons (SGNs), and/or the auditory nerve itself. The outcomes of cochlear implants (CI) for ANSD are variable and dependent on the location of lesion sites. Discovering a potential therapeutic agent for ANSD remains an urgent requirement. Here, 293T stable transfection cell lines and patient induced pluripotent stem cells (iPSCs)-derived auditory neurons carrying the apoptosis inducing factor (AIF) p.R422Q variant were used to pursue a therapeutic regent for ANSD. Nicotinamide adenine dinucleotide (NADH) is a main electron donor in the electron transport chain (ETC). In 293T stable transfection cells with the p.R422Q variant, NADH treatment improved AIF dimerization, rescued mitochondrial dysfunctions, and decreased cell apoptosis. The effects of NADH were further confirmed in patient iPSCs-derived neurons. The relative level of AIF dimers was increased to 150.7 % (P = 0.026) from 59.2 % in patient-neurons upon NADH treatment. Such increased AIF dimerization promoted the mitochondrial import of coiled-coil-helix-coiled-coil-helix domain-containing protein 4 (CHCHD4), which further restored mitochondrial functions. Similarly, the content of mitochondrial calcium (mCa2+) was downregulated from 136.7 % to 102.3 % (P = 0.0024) in patient-neurons upon NADH treatment. Such decreased mCa2+ levels inhibited calpain activity, ultimately reducing the percentage of apoptotic cells from 30.5 % to 21.1 % (P = 0.021). We also compared the therapeutic effects of gene correction and NADH treatment on hereditary ANSD. NADH treatment had comparable restorative effects on functions of ANSD patient-specific cells to that of gene correction. Our findings offer evidence of the molecular mechanisms of ANSD and introduce NADH as a potential therapeutic agent for ANSD therapy.

Allograft inflammatory factor-1 enhances inflammation and oxidative stress via the NF-κB pathway of bladder urothelium in diabetic rat model.

OBJECTIVES: To explore the role of allograft inflammatory factor-1 (AIF-1) both in diabetic rat bladder urothelium and in high-glucose-treated human urothelial cell line (SV-HUC-1). METHODS: Inflammation and oxidative stress (OS) promote diabetic cystopathy (DCP), but the mechanisms are not fully understood. The expression level of AIF-1 in diabetic rat bladder urothelium and in the SV-HUC-1 cells treated with high glucose was detected using tissue immunofluorescence, immunohistochemistry and western blot assays. AIF-1 was knocked down and NF-κB was suppressed with the specific inhibitor BAY 11-7082 in high-glucose-treated SV-HUC-1 cells. RESULTS: High-glucose condition induced AIF-1 upregulation in vivo and in vitro. The up-regulated AIF-1 induced the production of inflammatory factors IL-6 and TNF-α and elevation of ROS. Informatics analysis suggested that NF-κB pathway is implicated in DCP. Through knockdown of AIF-1, we confirmed that AIF-1 simulated NF-κB pathway by enhancing the phosphorylation of IκB (p-IκB) and promoting the translocation of NF-κB p65 from cytoplasm into nucleus. Additionally, High-glucose-induced inflammation in SV-HUC-1 cells was attenuated by the addition of NF-κB inhibitor. CONCLUSIONS: This study provides novel information to understand the molecular regulation mechanisms of AIF-1 in DCP.

Role of autophagy in stress and drug-responsive cell death in Entamoeba histolytica and its cross-talk with apoptosis-inducing factor.

Cell death in unicellular protozoan parasite Entamoeba histolytica is not yet reported though it displays several features of autophagic cell death. Autophagic cell death was reported to take place in ancient protozoans under several stresses. Here we report the occurrence of autophagic cell death in the Entamoeba histolytica trophozoites under oxidative stress as well as by the treatment with metronidazole, the most-widely-used drug for amoebiasis treatment and was shown to generate oxidative stress in the trophozoites. The autophagic flux increases during nutrient deprivation and metronidazole treatment and decreases upon oxidative stress. During oxidative stress the autophagy leads to nucleophagy that is ultimately destined to be digested within the lysosomal chamber. The formation of nucleophagosome depends on the apoptosis-inducing factor (AIF) that translocates to the nucleus from cytoplasm upon oxidative stress. It was experimentally proved that ATG8 (Autophagy-related protein 8) binds with the AIF in the nucleus of the trophozoites and helps in ATG8 recruitment and autophagy initiation overall suggesting that oxidative stress-driven AIF translocation to nucleus results in binding with ATG8 and initiates nucleophagy leading to cell death.