No QTc prolongation with CDK 4/6 inhibitor FCN-437c: results of a concentration-QTc analysis from a dedicated study in adult healthy subjects.

Cardiotoxicity and QT interval prolongation have been a common cause of withdrawal of drugs from the market. FCN-437c is an oral, second-generation, potent, and selective CDK4/6 inhibitor for the treatment of patients with HR+/HER2- metastatic breast cancer. A single-center, double-blind, randomized, and placebo-controlled clinical study in healthy subjects was conducted to investigate the QTc prolongation potential of FCN-437c utilizing Concentration-QTc (C-QTc) modeling approach. FCN-437c was administered at doses of 300, and 400 mg with single oral administration, along with placebo, in 18 healthy subjects. Electrocardiograms (ECGs) through 24 h holter monitor and blood samples were collected. The Cmax of 400 mg single dose in healthy subjects is similar to that from therapeutic dose 200 mg QD at steady state in patients with cancer. The 90% CI upper limit of ΔΔQTcF at the Cmax geometric mean in both dose groups were <10 ms. It is concluded that FCN-437c has low risk of prolonging the QT interval at therapeutic dose. Systematic Review Registration: https://clinicaltrials.gov/study/NCT06290466?term=NCT06290466&rank=1, identifier [NCT06290466].

Multi-omics evaluation of the prognostic value and immune signature of FCN1 in pan-cancer and its relationship with proliferation and apoptosis in acute myeloid leukemia.

Background: The FCN1 gene encodes the ficolin-1 protein, implicated in the pathogenesis of various diseases, though its precise role in tumorigenesis remains elusive. This study aims to elucidate the prognostic significance, immune signature, and treatment response associated with FCN1 across diverse cancer types. Methods: Employing multi-omics data, we conducted a comprehensive assessment, encompassing tissue-specific and single-cell-specific expression disparities, pan-cancer expression patterns, epigenetic modifications affecting FCN1 expression, and the immune microenvironment. Our investigation primarily focused on the clinical prognostic attributes, immune profiles, potential molecular mechanisms, and candidate therapeutic agents concerning FCN1 and acute myeloid leukemia (AML). Additionally, in vitro experiments were performed to scrutinize the impact of FCN1 knockdown on cell proliferation, apoptosis, and cell cycle dynamics within the AML cell line U937 and NB4. Results: FCN1 expression exhibits widespread dysregulation across various cancers. Through both univariate and multivariate Cox regression analyses, FCN1 has been identified as an independent prognostic indicator for AML. Immunological investigations elucidate FCN1's involvement in modulating inflammatory responses within the tumor microenvironment and its correlation with treatment efficacy. Remarkably, the deletion of FCN1 influences the proliferation, apoptosis, and cell cycle dynamics of U937 cells and NB4 cells. Conclusion: These findings underscore FCN1 as a promising pan-cancer biomarker indicative of macrophage infiltration, intimately linked with the tumor microenvironment and treatment responsiveness, and pivotal for cellular mechanisms within AML cell lines.

Advanced Global Prototypical Segmentation Framework for Few-Shot Hyperspectral Image Classification.

With the advancement of deep learning, related networks have shown strong performance for Hyperspectral Image (HSI) classification. However, these methods face two main challenges in HSI classification: (1) the inability to capture global information of HSI due to the restriction of patch input and (2) insufficient utilization of information from limited labeled samples. To overcome these challenges, we propose an Advanced Global Prototypical Segmentation (AGPS) framework. Within the AGPS framework, we design a patch-free feature extractor segmentation network (SegNet) based on a fully convolutional network (FCN), which processes the entire HSI to capture global information. To enrich the global information extracted by SegNet, we propose a Fusion of Lateral Connection (FLC) structure that fuses the low-level detailed features of the encoder output with the high-level features of the decoder output. Additionally, we propose an Atrous Spatial Pyramid Pooling-Position Attention (ASPP-PA) module to capture multi-scale spatial positional information. Finally, to explore more valuable information from limited labeled samples, we propose an advanced global prototypical representation learning strategy. Building upon the dual constraints of the global prototypical representation learning strategy, we introduce supervised contrastive learning (CL), which optimizes our network with three different constraints. The experimental results of three public datasets demonstrate that our method outperforms the existing state-of-the-art methods.

Exploring the Impact of Noise and Image Quality on Deep Learning Performance in DXA Images.

BACKGROUND AND OBJECTIVE: Segmentation of the femur in Dual-Energy X-ray (DXA) images poses challenges due to reduced contrast, noise, bone shape variations, and inconsistent X-ray beam penetration. In this study, we investigate the relationship between noise and certain deep learning (DL) techniques for semantic segmentation of the femur to enhance segmentation and bone mineral density (BMD) accuracy by incorporating noise reduction methods into DL models. METHODS: Convolutional neural network (CNN)-based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and their effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods. RESULTS: Our study demonstrated that the FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis. CONCLUSIONS: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging.

Quality improvement of frozen cooked noodles by protein addition.

This study investigated the impact of protein enrichment on the physicochemical, cooking, textural, and color properties of frozen cooked noodles (FCN) stored for 0-3 weeks at -18 °C. Incorporating casein, egg white protein, and soy protein into the noodles significantly increased moisture content, with casein-enriched noodles showing the highest initial moisture levels. The addition of proteins also led to increased ash content, indicating improved nutritional quality. Protein enrichment resulted in reduced cooking loss and enhanced water retention during cooking and frozen storage. Casein-enriched noodles exhibited the highest water absorption capacity and the most substantial enhancement in textural properties, maintaining cohesiveness, gumminess, and elasticity better than egg white protein and soy protein during storage. The results indicated that egg white protein promotes intermolecular interactions, leading to enhanced color stability over time. These findings suggest that enriching with the protein could be a viable approach to elevate the overall quality of FCN.

Multi-organ segmentation of organ-at-risk (OAR's) of head and neck site using ensemble learning technique.

INTRODUCTION: This paper presents a novel approach to automate the segmentation of Organ-at-Risk (OAR) in Head and Neck cancer patients using Deep Learning models combined with Ensemble Learning techniques. The study aims to improve the accuracy and efficiency of OAR segmentation, essential for radiotherapy treatment planning. METHODS: The dataset comprised computed tomography (CT) scans of 182 patients in DICOM format, obtained from an institutional image bank. Experienced Radiation Oncologists manually segmented seven OARs for each scan. Two models, 3D U-Net and 3D DenseNet-FCN, were trained on reduced CT scans (192 × 192 x 128) due to memory limitations. Ensemble Learning techniques were employed to enhance accuracy and segmentation metrics. Testing was conducted on 78 patients from the institutional dataset and an open-source dataset (TCGA-HNSC and Head-Neck Cetuximab) consisting of 31 patient scans. RESULTS: Using the Ensemble Learning technique, the average dice similarity coefficient for OARs ranged from 0.990 to 0.994, indicating high segmentation accuracy. The 95% Hausdorff distance (mm) ranged from 1.3 to 2.1, demonstrating precise segmentation boundaries. CONCLUSION: The proposed automated segmentation method achieved efficient and accurate OAR segmentation, surpassing human expert performance in terms of time and accuracy. IMPLICATIONS FOR PRACTICE: This approach has implications for improving treatment planning and patient care in radiotherapy. By reducing manual segmentation reliance, the proposed method offers significant time savings and potential improvements in treatment planning efficiency and precision for head and neck cancer patients.

S-Net: a multiple cross aggregation convolutional architecture for automatic segmentation of small/thin structures for cardiovascular applications.

With the success of U-Net or its variants in automatic medical image segmentation, building a fully convolutional network (FCN) based on an encoder-decoder structure has become an effective end-to-end learning approach. However, the intrinsic property of FCNs is that as the encoder deepens, higher-level features are learned, and the receptive field size of the network increases, which results in unsatisfactory performance for detecting low-level small/thin structures such as atrial walls and small arteries. To address this issue, we propose to keep the different encoding layer features at their original sizes to constrain the receptive field from increasing as the network goes deeper. Accordingly, we develop a novel S-shaped multiple cross-aggregation segmentation architecture named S-Net, which has two branches in the encoding stage, i.e., a resampling branch to capture low-level fine-grained details and thin/small structures and a downsampling branch to learn high-level discriminative knowledge. In particular, these two branches learn complementary features by residual cross-aggregation; the fusion of the complementary features from different decoding layers can be effectively accomplished through lateral connections. Meanwhile, we perform supervised prediction at all decoding layers to incorporate coarse-level features with high semantic meaning and fine-level features with high localization capability to detect multi-scale structures, especially for small/thin volumes fully. To validate the effectiveness of our S-Net, we conducted extensive experiments on the segmentation of cardiac wall and intracranial aneurysm (IA) vasculature, and quantitative and qualitative evaluations demonstrated the superior performance of our method for predicting small/thin structures in medical images.

Excess lifetime cancer risk due to ingestion of fish from a dam reservoir associated with a nuclear facility in Brazil.

Fish from a Funil dam reservoir associated with a Nuclear Fuel Factory were sampled aiming to assess the radiological risk due to ingestion. Funil dam reservoir is a strategic site, once it receives effluent from the industrial complex that performs isotopic enrichment of uranium and conversion of UF6. The mean activity concentrations obtained for 40K, 226Ra, 228Ra and 228Th were 57.81, 0.41, 0.92 and 0.49 Bq·kg-1, respectively. Lifetime cancer risk was estimated in ∼10-5 and no action needs to be taken.

Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification.

Functional connectivity network (FCN) has become a popular tool to identify potential biomarkers for brain dysfunction, such as autism spectrum disorder (ASD). Due to its importance, researchers have proposed many methods to estimate FCNs from resting-state functional MRI (rs-fMRI) data. However, the existing FCN estimation methods usually only capture a single relationship between brain regions of interest (ROIs), e.g., linear correlation, nonlinear correlation, or higher-order correlation, thus failing to model the complex interaction among ROIs in the brain. Additionally, such traditional methods estimate FCNs in an unsupervised way, and the estimation process is independent of the downstream tasks, which makes it difficult to guarantee the optimal performance for ASD identification. To address these issues, in this paper, we propose a multi-FCN fusion framework for rs-fMRI-based ASD classification. Specifically, for each subject, we first estimate multiple FCNs using different methods to encode rich interactions among ROIs from different perspectives. Then, we use the label information (ASD vs. healthy control (HC)) to learn a set of fusion weights for measuring the importance/discrimination of those estimated FCNs. Finally, we apply the adaptively weighted fused FCN on the ABIDE dataset to identify subjects with ASD from HCs. The proposed FCN fusion framework is straightforward to implement and can significantly improve diagnostic accuracy compared to traditional and state-of-the-art methods.

Phase 1 dose-escalation study to evaluate the safety, tolerability, pharmacokinetics, and anti-tumor activity of FCN-159 in adults with neurofibromatosis type 1-related unresectable plexiform neurofibromas.

BACKGROUND: Surgery is a common treatment strategy for patients with neurofibromatosis type 1 (NF1)-related plexiform neurofibroma (PN) and has limited efficacy. FCN-159 is a novel anti-tumorigenic drug via selective inhibition of MEK1/2. This study assesses the safety and efficacy of FCN-159 in patients with NF1-related PN. METHODS: This is a multicenter, open-label, single-arm, phase I dose-escalation study. Patients with NF1-related PN that was non-resectable or unsuitable for surgery were enrolled; they received FCN-159 monotherapy daily in 28-day cycles. RESULTS: Nineteen adults were enrolled in the study, 3 in 4 mg, 4 in 6 mg, 8 in 8 mg, and 4 in 12 mg. Among patients included in dose-limiting toxicity (DLT) analysis, DLTs (grade 3 folliculitis) were reported in 1 of 8 patients (16.7%) receiving 8 mg and 3 of 3 (100%) patients receiving 12 mg. The maximum tolerated dose was determined to be 8 mg. FCN-159-related treatment-emergent adverse events (TEAEs) were observed in 19 patients (100%); most of which were grade 1 or 2. Nine (47.4%) patients reported grade 3 study-drug-related TEAEs across all dose levels, including four experiencing paronychia and five experiencing folliculitis. Of the 16 patients analyzed, all (100%) had reduced tumor size and six (37.5%) achieved partial responses; the largest reduction in tumor size was 84.2%. The pharmacokinetic profile was approximately linear between 4 and 12 mg, and the half-life supported once daily dosing. CONCLUSIONS: FCN-159 was well tolerated up to 8 mg daily with manageable adverse events and showed promising anti-tumorigenic activity in patients with NF1-related PN, warranting further investigation in this indication. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04954001. Registered 08 July 2021.

The Soluble Lectin Families as Novel Biomarkers for COVID-19 Pneumonia.

BACKGROUND/AIM: The lung-specific soluble lectins, SP-A and SP-D have been clinically used to diagnose interstitial lung disease, but their clinical significance in COVID-19 remains controversial. This study was undertaken to determine their association with other lectins (MBL and FCN1), disease severity, and radiographs in COVID-19 patients. PATIENTS AND METHODS: A total of 131 patients with COVID-19 admitted in the Sapporo Medical University Hospital between May 22 and September 19, 2021, were enrolled in the study. Data including demographics, medical history, symptoms, signs, laboratory findings, and radiological images were collected from the patients' medical records. Chest computed tomography (CT) scanning was performed at admission. Serum levels of surfactant protein A and D (SP-A and SP-D), mannose-binding lectin (MBL) and ficolin1 (FCN1) were measured using enzyme-linked immunosorbent assay (ELISA) kits. RESULTS: Compared to the control group, the COVID-19 group had significantly higher serum SP-A and FCN1 levels on admission (SP-A: 59.60±38.89 vs. 35.61±11.22 ng/ml; p<0.01, FCN1: 542.45±506.04 vs. 250.6±161.1 ng/ml; p<0.01). The severe group in COVID-19 had significantly higher serum SP-D and lower MBL levels than the non-severe group (SP-D: 141.7±155.7 vs. 61.41±54.54 ng/ml; p<0.01, MBL: 1,670±1,240 vs. 2,170±1,140 ng/ml; p<0.05). SP-D strongly reflected the degree of imaging findings, whereas SP-A showed a significant correlation, albeit slightly weaker than SP-D. Conversely, MBL and FNC1 were not significantly correlated with imaging findings. CONCLUSION: Among soluble serum lectins, SP-A and SP-D may be more sensitive to CT findings than reported disease biomarkers such as IL-6, LDH, and CRP due to their lung-specific characteristics.

Hounsfield Unit Variations-Based Liver Lesions Detection and Classification Using Deep Learning.

BACKGROUND: Deep learning-based diagnosis systems are useful to identify abnormalities in medical images with the greatly increased workload of doctors. Specifically, the rate of new cases and deaths from malignancies is rising for liver diseases. Early detection of liver lesions plays an extremely important role in effective treatment and gives a higher chance of survival for patients. Therefore, automatic detection and classification of common liver lesions are essential for doctors. In fact, radiologists mainly rely on Hounsfield Units to locate liver lesions but previous studies often pay little attention to this factor. METHODS: In this paper, we propose an improved method for the automatic classification of common liver lesions based on deep learning techniques and the variation of Hounsfield Unit densities on CT images with and without contrast. Hounsfield Unit is used to locate liver lesions accurately and support data labeling for classification. We construct a multi-phase classification model developed on the deep neural networks of Faster R-CNN, R-FCN, SSD, and Mask R-CNN with the transfer learning approach. RESULTS: The experiments are conducted on six scenarios with multi-phase CT images of common liver lesions. Experimental results show that the proposed method improves the detection and classification of liver lesions compared with recent methods because its accuracy achieves up to 97.4%. CONCLUSION: The proposed models are very useful to assist doctors in the automatic segmentation and classification of liver lesions to solve the problem of depending on the clinician's experience in the diagnosis and treatment of liver lesions.

Identification of FCN1 as a novel macrophage infiltration-associated biomarker for diagnosis of pediatric inflammatory bowel diseases.

BACKGROUND: The incidence of pediatric inflammatory bowel disease (PIBD) has been steadily increasing globally. Delayed diagnosis of PIBD increases the risk of complications and contributes to growth retardation. To improve long-term outcomes, there is a pressing need to identify novel markers for early diagnosis of PIBD. METHODS: The candidate biomarkers for PIBD were identified from the GSE117993 dataset by two machine learning algorithms, namely LASSO and mSVM-RFE, and externally validated in the GSE126124 dataset and our PIBD cohort. The role of ficolin-1 (FCN1) in PIBD and its association with macrophage infiltration was investigated using the CIBERSORT method and enrichment analysis of the single-cell dataset GSE121380, and further validated using immunoblotting, qRT-PCR, and immunostaining in colon biopsies from PIBD patients, a juvenile murine DSS-induced colitis model, and THP-1-derived macrophages. RESULTS: FCN1 showed great diagnostic performance for PIBD in an independent clinical cohort with the AUC of 0.986. FCN1 expression was upregulated in both colorectal biopsies and blood samples from PIBD patients. Functionally, FCN1 was associated with immune-related processes in the colonic mucosa of PIBD patients, and correlated with increased proinflammatory M1 macrophage infiltration. Furthermore, single-cell transcriptome analysis and immunostaining revealed that FCN1 was almost exclusively expressed in macrophages infiltrating the colonic mucosa of PIBD patients, and these FCN1+ macrophages were related to hyper-inflammation. Notably, proinflammatory M1 macrophages derived from THP-1 expressed high levels of FCN1 and IL-1ß, and FCN1 overexpression in THP-1-derived macrophages strongly promoted LPS-induced activation of the proinflammatory cytokine IL-1ß via the NLRP3-caspase-1 axis. CONCLUSIONS: FCN1 is a novel and promising diagnostic biomarker for PIBD. FCN1+ macrophages enriched in the colonic mucosa of PIBD exhibit proinflammatory phenotypes, and FCN1 promotes IL-1ß maturation in macrophages via the NLRP3-caspase-1 axis.

Development of a compressed FCN architecture for semantic segmentation using Particle Swarm Optimization.

Researchers have adapted the conventional deep learning classification networks to generate Fully Conventional Networks (FCN) for carrying out accurate semantic segmentation. However, such models are expensive both in terms of storage and inference time and not readily employable on edge devices. In this paper, a compressed version of VGG16-based Fully Convolution Network (FCN) has been developed using Particle Swarm Optimization. It has been shown that the developed model can offer tremendous saving in storage space and also faster inference time, and can be implemented on edge devices. The efficacy of the proposed approach has been tested using potato late blight leaf images from publicly available PlantVillage dataset, street scene image dataset and lungs X-Ray dataset and it has been shown that it approaches the accuracies offered by standard FCN even after 851× compression.

A large-scale plasma proteome Mendelian randomization study identifies novel causal plasma proteins related to primary biliary cholangitis.

Background and aims: Primary biliary cholangitis (PBC) is a progressive chronic autoimmune cholestatic liver disease characterized by the destruction of small intrahepatic bile ducts leading to biliary cirrhosis. Liver biopsy is required in the diagnosis of Antimitochondrial antibody-negative patients. Therefore, novel biomarkers are needed for the non-invasive diagnosis of PBC. To identify novel biomarkers for PBC, we conducted large-scale plasma proteome Mendelian randomization (MR). Methods: A total of 21,593 protein quantitative trait loci (pQTLs) for 2297 circulating proteins were used and classified into four different groups. MR analyses were conducted in the four groups separately. Furthermore, the results were discovered and replicated in two different cohorts of PBC. Colocalization analysis and enrichment analysis were also conducted. Results: Three plasma proteins (ficolin-1, CD40 and protein FAM177A1) were identified and replicated as being associated with PBC. All of them showed significant protective effects against PBC. An increase in ficolin-1 (OR=0.890 [0.843-0.941], p=3.50×10-5), CD40 (OR=0.814 [0.741-0.895], p=1.96×10-5) and protein FAM177A1 (OR=0.822 [0.754-0.897], p=9.75×10-6) reduced the incidence of PBC. Ficolin-1 (PP4 = 0.994) and protein FAM177A1 (PP4 = 0.995) colocalized with the expression of the genes FCN1 and FAM177A1 in whole blood, respectively. Furthermore, CD40 (PP4 = 0.977) and protein FAM177A1 (PP4 = 0.897) strongly colocalized with PBC. Conclusions: We expand the current biomarkers for PBC. In total, three (ficolin-1, CD40, and protein FAM177A1) plasma proteins were identified and replicated as being associated with PBC in MR analysis. All of them showed significant protective effects against PBC. These proteins can be potential biomarkers or drug targets for PBC.

Association of low ficolin-2 concentration in cord serum with respiratory distress syndrome in preterm newborns.

Introduction: Ficolin-2 is a serum pattern recognition molecule, involved in complement activation via the lectin pathway. This study aimed to investigate the association of ficolin-2 concentration in cord blood serum with complications related to premature birth. Methods: 546 premature neonates were included. The concentration of ficolin-2 in cord blood serum was determined by a sandwich TRIFMA method. FCN2 genetic variants were analysed with RFLP-PCR, allele-specific PCR, Sanger sequencing or allelic discrimination using TaqMan probes method. Findings: Cord blood serum ficolin-2 concentration correlated positively with Apgar score and inversely with the length of hospitalisation and stay at Neonatal Intensive Care Unit (NICU). Multivariate logistic regression analysis indicated that low ficolin-2 increased the possibility of respiratory distress syndrome (RDS) diagnosis [OR=2.05, 95% CI (1.24-3.37), p=0.005]. Median ficolin-2 concentration was significantly lower in neonates with RDS than in premature babies without this complication, irrespective of FCN2 gene polymorphisms localised to promoter and 3'untranslated regions: for patients born <33 GA: 1471 ng/ml vs. 2115 ng/ml (p=0.0003), and for patients born ≥33 GA 1610 ng/ml vs. 2081 ng/ml (p=0.012). Ficolin-2 level was also significantly lower in neonates requiring intubation in the delivery room (1461 ng/ml vs. 1938 ng/ml, p=0.023) and inversely correlated weakly with the duration of respiratory support (R=-0.154, p<0.001). Interestingly, in the neonates born at GA <33, ficolin-2 concentration permitted differentiation of those with/without RDS [AUC=0.712, 95% CI (0.612-0.817), p<0.001] and effective separation of babies with mild RDS from those with moderate/severe form of the disease [AUC=0.807, 95% CI (0.644-0.97), p=0.0002]. Conclusion: Low cord serum ficolin-2 concentration (especially in neonates born at GA <33 weeks) is associated with a higher risk of developing moderate/severe RDS, requiring respiratory support and intensive care.

Population pharmacokinetics of FCN-159, a MEK1/2 inhibitor, in adult patients with advanced melanoma and neurofibromatosis type 1 (NF1) and model informed dosing recommendations for NF1 pediatrics.

Objective: FCN-159 is a highly active mitogen-activated extracellular signal-regulated kinase 1/2 (MEK1/2) inhibitor in patients with advanced melanoma and neurofibromatosis type 1 (NF1). We report a population pharmacokinetic (PopPK) model-based analysis of FCN-159 and its application to inform dose selection for NF1 pediatric trials. Methods: PK data collected from patients with advanced melanoma and NF1 in two clinical studies (NCT03932253 and NCT04954001) were analyzed using a non-linear mixed effects model. The adult model was adapted by incorporating allometric scaling for PK projection in 2-17 years old children. Pediatric exposure in different body surface area (BSA) bins was simulated to identify nominal doses (i.e., dose amounts given as integers) and BSA bin cutoffs to achieve exposure comparable to adults' optimal exposure across the entire pediatric BSA range. Results: The final dataset consisted of 45 subjects with a total of 1030 PK samples. The PK of FCN-159 was well-described by a 2-compartment model with first-order linear elimination and delayed first-order absorption. Covariates, including BSA, age, sex, albumin, total protein, and cancer type, were identified as statistically significant predictors of FCN-159 disposition. Simulations based on the final model projected daily doses of 4 mg/m2 QD with optimized BSA bin cutoffs would allow fixed nominal doses within each bin and result in steady state exposure approximating the adult exposure observed at the recommended phase 2 dose (RP2D) in NF1, which is 8 mg QD. Conclusion: The developed population PK model adequately described the PK profile of FCN-159, which was adapted using allometric scaling to inform dose selection for NF1 pediatric trials.

Selected SNPs of FCN2 Associated with Chronic Tonsillitis in the Polish Adult Population.

Chronic tonsillitis is a problem related to bacterial and viral infections. Ficolins play a key role in the defence against various pathogens. In the present study, we investigated the associations between the selected single nucleotide polymorphisms (SNPs) of the FCN2 gene and chronic tonsillitis in the Polish population. The study included 101 patients with chronic tonsillitis and 101 healthy individuals. The selected SNPs of FCN2 (rs3124953, rs17514136 and rs3124954) were genotyped using TaqMan SNP Genotyping Assays (Applied Biosystem, Foster City, CA, USA). The analysis of rs17514136 and rs3124953 showed no significant differences in genotype frequencies between the chronic tonsillitis patients and controls (p > 0.01). The CT genotype of rs3124954 was significantly more frequent, while the CC genotype was less frequent in chronic tonsillitis patients (p = 0.003 and p = 0.001, respectively). The frequency of the A/G/T haplotype (rs17514136/rs3124953/rs3124954) was significantly more common in chronic tonsillitis patients (p = 0.0011). Moreover, the FCN2 CT genotype of rs3124954 was associated with a higher risk of chronic tonsillitis, while the CC genotype of rs3124954 decreased this risk. Our findings demonstrate that FCN2 rs3124954 may be associated with chronic tonsillitis in the Polish adult population.

FCN3 inhibits the progression of hepatocellular carcinoma by suppressing SBDS-mediated blockade of the p53 pathway.

Hepatocellular carcinoma (HCC) is the third-leading cause of cancer deaths globally. Although considerable progress has been made in the treatment, clinical outcomes of HCC patients are still poor. Therefore, it is necessary to find novel prognostic factors upon which prevention and treatment strategies can be formulated. Ficolin-3 (FCN3) protein is a member of the human ficolin family. It activates complement through pathways associated with mannose-binding lectin-associated serine proteases. Herein, we identified that FCN3 was downregulated in HCC tissues and decreased FCN3 expression was closely related to poor prognosis. Overexpression of FCN3 induced apoptosis and inhibited cell proliferation via the p53 signaling pathway. Mechanistically, FCN3 modulated the nuclear translocation of eukaryotic initiation factor 6 (EIF6) by binding ribosome maturation factor (SBDS), which induced ribosomal stress and activation of the p53 pathway. In addition, Y-Box Binding Protein 1 (YBX1) involved in the transcription and translation level regulation of FCN3 to SBDS. Besides, a negative feedback loop in the downstream of FCN3 involving p53, YBX1 and SBDS was identified.

Effect of Food (Low and High Fat) on Pharmacokinetics of FCN-159, a Selective MEK Inhibitor, in Healthy Chinese Males.

INTRODUCTION: FCN-159 is a novel, oral, potent, selective MEK1/2 inhibitor in clinical development for the treatment of NRAS-mutant advanced melanoma and neurofibromatosis type 1. We investigated the effect of food on the pharmacokinetics (PK), safety, and tolerability of FCN-159. METHODS: In this single-center, open-label, phase 1 study with a three-period, three-sequence, crossover design, healthy Chinese male subjects (n = 24) were randomized (1:1:1) to receive a single, oral 8 mg dose of FCN-159 in the fasted state (overnight, > 10 h), and with a low-fat and a high-fat meal, separated by a 10-day washout. PK parameters including time to maximum plasma concentration (Cmax) and area under the concentration-time curve (AUC) were compared using geometric least-squares mean ratios (GLSMR), with the fasted state as the reference. A 90% CI for the GLSMR within 80-125% indicated no significant food effect. RESULTS: A low-fat meal (n = 23) did not affect the PK profile of FCN-159: G LSMR for AUC from time 0 to t (AUC0-t), 106.9% (90% CI 99.9-114.4%); AUC from time 0 to infinity (AUC0-∞), 106.8% (90% CI 100.0-114.0%); Cmax, 96.4% (90% CI 83.9-110.8%). A high-fat meal (n = 24) did not affect exposure to FCN-159 (GLSMR for AUC0-t, 99.4%; 90% CI 99.0-106.3%; AUC0-∞, 99.5 5%; 90% CI 93.2-106.1%), but modestly reduced Cmax by 15% (GLSMR 84.9%; 90% CI 74.0-97.3%). Both the low-fat and high-fat meals slightly prolonged the median time to Cmax by 0.5 h (90% CI 0.5-1.0 h). FCN-159 was generally well tolerated, with a lower incidence of treatment-emergent adverse events following administration in the fasted state than with a low-fat or high-fat meal (20.8%, 39.1%, and 37.5%, respectively). CONCLUSION: Food did not affect the PK profile of FCN-159 to a clinically meaningful extent compared with administration in the fasted state.