Oral iron therapy: Current concepts and future prospects for improving efficacy and outcomes.

Iron deficiency (ID) and iron-deficiency anaemia (IDA) are global public health concerns, most commonly afflicting children, pregnant women and women of childbearing age. Pathological outcomes of ID include delayed cognitive development in children, adverse pregnancy outcomes and decreased work capacity in adults. IDA is usually treated by oral iron supplementation, typically using iron salts (e.g. FeSO4 ); however, dosing at several-fold above the RDA may be required due to less efficient absorption. Excess enteral iron causes adverse gastrointestinal side effects, thus reducing compliance, and negatively impacts the gut microbiome. Recent research has sought to identify new iron formulations with better absorption so that lower effective dosing can be utilized. This article outlines emerging research on oral iron supplementation and focuses on molecular mechanisms by which different supplemental forms of iron are transported across the intestinal epithelium and whether these transport pathways are subject to regulation by the iron-regulatory hormone hepcidin.

Distinct but cooperating brain networks supporting semantic cognition.

Semantic cognition is a complex multifaceted brain function involving multiple processes including sensory, semantic, and domain-general cognitive systems. However, it remains unclear how these systems cooperate with each other to achieve effective semantic cognition. Here, we used independent component analysis (ICA) to investigate the functional brain networks that support semantic cognition. We used a semantic judgment task and a pattern-matching control task, each with 2 levels of difficulty, to disentangle task-specific networks from domain-general networks. ICA revealed 2 task-specific networks (the left-lateralized semantic network [SN] and a bilateral, extended semantic network [ESN]) and domain-general networks including the frontoparietal network (FPN) and default mode network (DMN). SN was coupled with the ESN and FPN but decoupled from the DMN, whereas the ESN was synchronized with the FPN alone and did not show a decoupling with the DMN. The degree of decoupling between the SN and DMN was associated with semantic task performance, with the strongest decoupling for the poorest performing participants. Our findings suggest that human higher cognition is achieved by the multiple brain networks, serving distinct and shared cognitive functions depending on task demands, and that the neural dynamics between these networks may be crucial for efficient semantic cognition.

Camera-Radar Fusion with Radar Channel Extension and Dual-CBAM-FPN for Object Detection.

When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS.

Association between maternal iron status and the risk of pre-eclampsia: a case-control study.

BACKGROUND AND OBJECTIVES: This study aimed to assess the associations of maternal iron status and placental iron transport proteins expression with the risk of pre-eclampsia (PE) in Chinese pregnant women. METHODS AND STUDY DESIGN: A total of 94 subjects with PE and 112 healthy pregnant women were enrolled. Fasting blood samples were collected to detect maternal iron status. The placenta samples were collected at delivery to detect the mRNA and protein expression of divalent metal transporter 1 (DMT1) and ferroportin-1 (FPN1). Logistic analysis was used to explore the associations of maternal iron status with PE risk. The associations of placental iron transport proteins with maternal iron status were explored. RESULTS: After adjusting for covariates, dietary total iron, non-heme iron intake and serum hepcidin were negatively associated with PE, with adjusted ORs (95%CIs) were 0.40 (0.17, 0.91), 0.42 (0.18, 0.94) and 0.02 (0.002, 0.13) for the highest versus lowest tertile, respectively. For the highest tertile versus lowest tertile, serum iron (4.08 (1.58, 10.57)) and ferritin (5.61 (2.36, 13.31)) were positively associated with PE. The mRNA expressions and protein levels of DMT1 and FPN1 in placenta were up-regulated in the PE group (p < 0.05). The mRNA expressions of DMT1 and FPN1 in placenta showed a negative correlation with the serum hepcidin (r = -0.71, p < 0.001; r = -0.49, p < 0.05). CONCLUSIONS: In conclusion, the maternal iron status were closely associated with PE risk, placental DMT1 and FPN1 were upregulated in PE which may be a promising target for the prevention of PE.

Alcohol- and Low-Iron Induced Changes in Antioxidant and Energy Metabolism Associated with Protein Lys Acetylation.

Understanding the role of iron in ethanol-derived hepatic stress could help elucidate the efficacy of dietary or clinical interventions designed to minimize liver damage from chronic alcohol consumption. We hypothesized that normal levels of iron are involved in ethanol-derived liver damage and reduced dietary iron intake would lower the damage caused by ethanol. We used a pair-fed mouse model utilizing basal Lieber-DeCarli liquid diets for 22 weeks to test this hypothesis. In our mouse model, chronic ethanol exposure led to mild hepatic stress possibly characteristic of early-stage alcoholic liver disease, seen as increases in liver-to-body weight ratios. Dietary iron restriction caused a slight decrease in non-heme iron and ferritin (FeRL) expression while it increased transferrin receptor 1 (TfR1) expression without changing ferroportin 1 (FPN1) expression. It also elevated protein lysine acetylation to a more significant level than in ethanol-fed mice under normal dietary iron conditions. Interestingly, iron restriction led to an additional reduction in nicotinamide adenine dinucleotide (NAD+) and NADH levels. Consistent with this observation, the major mitochondrial NAD+-dependent deacetylase, NAD-dependent deacetylase sirtuin-3 (SIRT3), expression was significantly reduced causing increased protein lysine acetylation in ethanol-fed mice at normal and low-iron conditions. In addition, the detection of superoxide dismutase 1 and 2 levels (SOD1 and SOD2) and oxidative phosphorylation (OXPHOS) complex activities allowed us to evaluate the changes in antioxidant and energy metabolism regulated by ethanol consumption at normal and low-iron conditions. We observed that the ethanol-fed mice had mild liver damage associated with reduced energy and antioxidant metabolism. On the other hand, iron restriction may exacerbate certain activities of ethanol further, such as increased protein lysine acetylation and reduced antioxidant metabolism. This metabolic change may prove a barrier to the effectiveness of dietary reduction of iron intake as a preventative measure in chronic alcohol consumption.

18F-5-FPN: A Specific Probe for Monitoring Photothermal Therapy Response in Malignant Melanoma.

Previously, we successfully synthesized a 18F-labeled positron-emission tomography (PET) tracer, termed 18F-5-fluoro-N-(2-[diethylamino]ethyl)picolinamide (18F-5-FPN), with high specificity for melanin. In this study, we sought to investigate the value of 18F-5-FPN in assessing the response to photothermal therapy (PTT) in melanoma via comparison with 18F-fluorodeoxyglucose (18F-FDG) to reveal an early response, recognize early recurrence, and distinguish the inflammatory response during the treatment. B16F10, inflammatory, and MDA-MB-231 models were subjected to 18F-FDG PET and 18F-5-FPN PET static acquisitions. We compared quantitative data to assess the specificity of different agents for different diseases. B16F10 and MDA-MB-231subcutaneous tumor models were irradiated with an 808 nm laser for PTT. Their survival was documented to observe the efficacy of and response to PTT, using 18F-5-FPN and 18F-FDG PET. 18F-5-FPN accumulated in B16F10 cell xenografts only, whereas 18F-FDG accumulated in all three models. Melanin in B16F10 cell xenografts successfully transformed the optical energy into heat. Hematoxylin and eosin (H&E) staining at 24 h revealed destruction and extensive necrosis of tumor tissue. PTT rapidly inhibited the growth of B16F10 cell xenografts and prolonged the median survival. The mean tumor uptakes of 18F-5-FPN on day 2 (7.52 ± 3.65 %ID/g) and day 6 (10.22 ± 6.00 %ID/g) were much lower than that before treatment (18.33 ± 4.98 %ID/g, p < 0.01). However, a significant difference in 18F-FDG uptakes was not found between day 1 after PTT and before treatment. Compared with 18F-FDG, 18F-5-FPN PET could estimate PTT efficacy in melanoma, monitor minimal recurrence, and distinguish melanoma from inflammation and other carcinoma types, thanks to its high affinity to melanin. 18F-5-FPN may provide a new approach for precise and accurate evaluation of response, timely management of therapeutic regimens, and sensitive follow-up.

Mindfulness-based real-time fMRI neurofeedback: a randomized controlled trial to optimize dosing for depressed adolescents.

BACKGROUND: Adolescence is characterized by a heightened vulnerability for Major Depressive Disorder (MDD) onset, and currently, treatments are only effective for roughly half of adolescents with MDD. Accordingly, novel interventions are urgently needed. This study aims to establish mindfulness-based real-time fMRI neurofeedback (mbNF) as a non-invasive approach to downregulate the default mode network (DMN) in order to decrease ruminatory processes and depressive symptoms. METHODS: Adolescents (N = 90) with a current diagnosis of MDD ages 13-18-years-old will be randomized in a parallel group, two-arm, superiority trial to receive either 15 or 30 min of mbNF with a 1:1 allocation ratio. Real-time neurofeedback based on activation of the frontoparietal network (FPN) relative to the DMN will be displayed to participants via the movement of a ball on a computer screen while participants practice mindfulness in the scanner. We hypothesize that within-DMN (medial prefrontal cortex [mPFC] with posterior cingulate cortex [PCC]) functional connectivity will be reduced following mbNF (Aim 1: Target Engagement). Additionally, we hypothesize that participants in the 30-min mbNF condition will show greater reductions in within-DMN functional connectivity (Aim 2: Dosing Impact on Target Engagement). Aim 1 will analyze data from all participants as a single-group, and Aim 2 will leverage the randomized assignment to analyze data as a parallel-group trial. Secondary analyses will probe changes in depressive symptoms and rumination. DISCUSSION: Results of this study will determine whether mbNF reduces functional connectivity within the DMN among adolescents with MDD, and critically, will identify the optimal dosing with respect to DMN modulation as well as reduction in depressive symptoms and rumination. TRIAL REGISTRATION: This study has been registered with clinicaltrials.gov, most recently updated on July 6, 2023 (trial identifier: NCT05617495).

Brain tumor image segmentation based on improved FPN.

PURPOSE: Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. MATERIALS AND METHODS: Aiming at the problem that the traditional full convolutional neural network (FCN) has weak processing ability, which leads to the loss of details in tumor segmentation, this paper proposes a brain tumor image segmentation method based on the improved feature pyramid networks (FPN) convolutional neural network. In order to improve the segmentation effect of brain tumors, we improved the model, introduced the FPN structure into the U-Net structure, captured the context multi-scale information by using the different scale information in the U-Net model and the multi receptive field high-level features in the FPN convolutional neural network, and improved the adaptability of the model to different scale features. RESULTS: Performance evaluation indicators show that the proposed improved FPN model has 99.1% accuracy, 92% DICE rating and 86% Jaccard index. The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. CONCLUSIONS: The experimental results show that this method can effectively segment brain tumor regions and has certain generalization, and the segmentation effect is better than other networks. It has positive significance for clinical diagnosis of brain tumors.

A neurocognitive account of attentional control theory: how does trait anxiety affect the brain's attentional networks?

Attentional control theory (ACT) was proposed to account for trait anxiety's effects on cognitive performance. According to ACT, impaired processing efficiency in high anxiety is mediated through inefficient executive processes that are needed for effective attentional control. Here we review the central assumptions and predictions of ACT within the context of more recent empirical evidence from neuroimaging studies. We then attempt to provide an account of ACT within a framework of the relevant cognitive processes and their associated neural mechanisms and networks, particularly the fronto-parietal, cingular-opercula, and default mode networks. Future research directions, including whether a neuroscience-informed model of ACT can provide a platform for novel neurocognitive intervention for anxiety, are also discussed.

Lightweight Network DCR-YOLO for Surface Defect Detection on Printed Circuit Boards.

To resolve the problems associated with the small target presented by printed circuit board surface defects and the low detection accuracy of these defects, the printed circuit board surface-defect detection network DCR-YOLO is designed to meet the premise of real-time detection speed and effectively improve the detection accuracy. Firstly, the backbone feature extraction network DCR-backbone, which consists of two CR residual blocks and one common residual block, is used for small-target defect extraction on printed circuit boards. Secondly, the SDDT-FPN feature fusion module is responsible for the fusion of high-level features to low-level features while enhancing feature fusion for the feature fusion layer, where the small-target prediction head YOLO Head-P3 is located, to further enhance the low-level feature representation. The PCR module enhances the feature fusion mechanism between the backbone feature extraction network and the SDDT-FPN feature fusion module at different scales of feature layers. The C5ECA module is responsible for adaptive adjustment of feature weights and adaptive attention to the requirements of small-target defect information, further enhancing the adaptive feature extraction capability of the feature fusion module. Finally, three YOLO-Heads are responsible for predicting small-target defects for different scales. Experiments show that the DCR-YOLO network model detection map reaches 98.58%; the model size is 7.73 MB, which meets the lightweight requirement; and the detection speed reaches 103.15 fps, which meets the application requirements for real-time detection of small-target defects.

In-Field Tobacco Leaf Maturity Detection with an Enhanced MobileNetV1: Incorporating a Feature Pyramid Network and Attention Mechanism.

The maturity of tobacco leaves plays a decisive role in tobacco production, affecting the quality of the leaves and production control. Traditional recognition of tobacco leaf maturity primarily relies on manual observation and judgment, which is not only inefficient but also susceptible to subjective interference. Particularly in complex field environments, there is limited research on in situ field maturity recognition of tobacco leaves, making maturity recognition a significant challenge. In response to this problem, this study proposed a MobileNetV1 model combined with a Feature Pyramid Network (FPN) and attention mechanism for in situ field maturity recognition of tobacco leaves. By introducing the FPN structure, the model fully exploits multi-scale features and, in combination with Spatial Attention and SE attention mechanisms, further enhances the expression ability of feature map channel features. The experimental results show that this model, with a size of 13.7 M and FPS of 128.12, performed outstandingly well on the task of field maturity recognition of tobacco leaves, achieving an accuracy of 96.3%, superior to classical models such as VGG16, VGG19, ResNet50, and EfficientNetB0, while maintaining excellent computational efficiency and small memory footprint. Experiments were conducted involving noise perturbations, changes in environmental brightness, and occlusions to validate the model's robustness in dealing with the complex environments that may be encountered in actual applications. Finally, the Score-CAM algorithm was used for result visualization. Heatmaps showed that the vein and color variations of the leaves provide key feature information for maturity recognition. This indirectly validates the importance of leaf texture and color features in maturity recognition and, to some extent, enhances the credibility of the model. The model proposed in this study maintains high performance while having low storage requirements and computational complexity, making it significant for in situ field maturity recognition of tobacco leaves.

Improved YOLOv5 Network for Real-Time Object Detection in Vehicle-Mounted Camera Capture Scenarios.

Object detection in the process of driving is a convenient and efficient task. However, due to the complex transformation of the road environment and vehicle speed, the scale of the target will not only change significantly but also be accompanied by the phenomenon of motion blur, which will have a significant impact on the detection accuracy. In practical application scenarios, it is difficult for traditional methods to simultaneously take into account the need for real-time detection and high accuracy. To address the above problems, this study proposes an improved network based on YOLOv5, taking traffic signs and road cracks as detection objects and conducting separate research. This paper proposes a GS-FPN structure to replace the original feature fusion structure for road cracks. This structure integrates the convolutional block attention model (CBAM) based on bidirectional feature pyramid networks (Bi-FPN) and introduces a new lightweight convolution module (GSConv) to reduce the information loss of the feature map, enhance the expressive ability of the network, and ultimately achieve improved recognition performance. For traffic signs, a four-scale feature detection structure is used to increase the detection scale of shallow layers and improve the recognition accuracy for small targets. In addition, this study has combined various data augmentation methods to improve the robustness of the network. Through experiments using 2164 road crack datasets and 8146 traffic sign datasets made by LabelImg, compared to the baseline model (YOLOv5s), the modified YOLOv5 network improves the mean average precision (mAP) result of the road crack dataset and small targets in the traffic sign dataset by 3% and 12.2%, respectively.

Improved Mask R-CNN Multi-Target Detection and Segmentation for Autonomous Driving in Complex Scenes.

Vision-based target detection and segmentation has been an important research content for environment perception in autonomous driving, but the mainstream target detection and segmentation algorithms have the problems of low detection accuracy and poor mask segmentation quality for multi-target detection and segmentation in complex traffic scenes. To address this problem, this paper improved the Mask R-CNN by replacing the backbone network ResNet with the ResNeXt network with group convolution to further improve the feature extraction capability of the model. Furthermore, a bottom-up path enhancement strategy was added to the Feature Pyramid Network (FPN) to achieve feature fusion, while an efficient channel attention module (ECA) was added to the backbone feature extraction network to optimize the high-level low resolution semantic information graph. Finally, the bounding box regression loss function smooth L1 loss was replaced by CIoU loss to speed up the model convergence and minimize the error. The experimental results showed that the improved Mask R-CNN algorithm achieved 62.62% mAP for target detection and 57.58% mAP for segmentation accuracy on the publicly available CityScapes autonomous driving dataset, which were 4.73% and 3.96%% better than the original Mask R-CNN algorithm, respectively. The migration experiments showed that it has good detection and segmentation effects in each traffic scenario of the publicly available BDD autonomous driving dataset.

Identification and detection of microplastic particles in marine environment by using improved faster R-CNN model.

Microplastics refer to plastic particles measuring less than 5 mm, which has led to serious environmental problem and the detection of these tiny particles is crucial for understanding the corresponding distribution and impact on the marine environment. In this paper, an improved faster region-based convolutional neural network (R-CNN) model was developed for the identification and detection of microplastic particles. In the proposed model, the residual network-50 (ResNet-50) is employed as the backbone with the replacement of the traditional one to enhance the feature extraction capability and the feature pyramid networks (FPN) module is introduced together for solving the multi-scale target detection. By using the improved Faster R-CNN model, the network model performance is enhanced where the average confidence of detecting unique microplastic particles in the marine environment reaches as high as 99%. Moreover, the microparticles mixture was bounded precisely via the predicted bounding boxes without missing detection and wrong detection. In this way, the successful identification of polystyrene microplastic particles from the particles suspension with similar shapes but various conditions of backgrounds, brightness, distributions and object sizes, was achieved by employing the proposed improved Faster R-CNN model, enabling the accurate detection of microplastic particles in marine environment.

MSIA-Net: A Lightweight Infrared Target Detection Network with Efficient Information Fusion.

In order to solve the problems of infrared target detection (i.e., the large models and numerous parameters), a lightweight detection network, MSIA-Net, is proposed. Firstly, a feature extraction module named MSIA, which is based on asymmetric convolution, is proposed, and it can greatly reduce the number of parameters and improve the detection performance by reusing information. In addition, we propose a down-sampling module named DPP to reduce the information loss caused by pooling down-sampling. Finally, we propose a feature fusion structure named LIR-FPN that can shorten the information transmission path and effectively reduce the noise in the process of feature fusion. In order to improve the ability of the network to focus on the target, we introduce coordinate attention (CA) into the LIR-FPN; this integrates the location information of the target into the channel so as to obtain more expressive feature information. Finally, a comparative experiment with other SOTA methods was completed on the FLIR on-board infrared image dataset, which proved the powerful detection performance of MSIA-Net.

Iron chelates hitch a ride on PAT1.

The nicotianamine-iron chelate [NA-Fe2+], which is found in many plant-based foods, has been recently described as a new form of bioavailable iron in mice and chickens. How NA-Fe2+ is assimilated from the diet, however, remains unclear. The current investigation by Murata et al. has identified the proton-coupled amino acid transporter 1 (PAT1) as the main mechanism by which NA-Fe2+ is absorbed in the mammalian intestine. Discovery of this new form of dietary iron and elucidation of its pathway of intestinal absorption may lead to the development of improved iron supplementation approaches.

Iron overload impairs renal function and is associated with vascular calcification in rat aorta.

Vascular calcification (VC) has been associated with a risk of cardiovascular diseases. Iron may play a critical role in progressive VC. Therefore, we investigated the effects of iron overload on the aorta of rats. A rat model of iron overload was established by intraperitoneal injection of Iron-Dextran. The levels of iron, calcium, and ALP activity were detected. Von Kossa staining and Perl's staining were conducted. The expression of iron metabolism-related and calcification related factors were examined in the aortic tissue of rats. The results showed serum and aortic tissue iron were increased induced by iron overload and excessive iron induced hepatic and renal damage. In iron overload rats, the expression of divalent metal transporter 1 (DMT1) and hepcidin were higher, but ferroportin1 (FPN1) was lower. Von Kossa staining demonstrated calcium deposition in the aorta of iron overload rats. The calcium content and ALP activity in serum and aortic tissue were increased and iron level in aortic tissue highly correlated with calcium content and ALP activity. The expressions of the osteogenic markers were increased while a decrease of Alpha-smooth muscle actin (α-SMA) in the aortic tissue of iron overload rats. IL-24 was increased during the calcification process induced by iron. Overall, we demonstrated excessive iron accumulation in the aortic tissue and induced organs damage. The iron metabolism-related factors were significantly changed during iron overload. Moreover, we found that iron overload leads to calcium deposition in aorta, playing a key role in the pathological process of VC by mediating osteoblast differentiation factors.

Uniformity Correction of CMOS Image Sensor Modules for Machine Vision Cameras.

Flat-field correction (FFC) is commonly used in image signal processing (ISP) to improve the uniformity of image sensor pixels. Image sensor nonuniformity and lens system characteristics have been known to be temperature-dependent. Some machine vision applications, such as visual odometry and single-pixel airborne object tracking, are extremely sensitive to pixel-to-pixel sensitivity variations. Numerous cameras, especially in the fields of infrared imaging and staring cameras, use multiple calibration images to correct for nonuniformities. This paper characterizes the temperature and analog gain dependence of the dark signal nonuniformity (DSNU) and photoresponse nonuniformity (PRNU) of two contemporary global shutter CMOS image sensors for machine vision applications. An optimized hardware architecture is proposed to compensate for nonuniformities, with optional parametric lens shading correction (LSC). Three different performance configurations are outlined for different application areas, costs, and power requirements. For most commercial applications, the correction of LSC suffices. For both DSNU and PRNU, compensation with one or multiple calibration images, captured at different gain and temperature settings are considered. For more demanding applications, the effectiveness, external memory bandwidth, power consumption, implementation, and calibration complexity, as well as the camera manufacturability of different nonuniformity correction approaches were compared.

Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD).

Defects are the primary problem affecting steel product quality in the steel industry. The specific challenges in developing detect defectors involve the vagueness and tiny size of defects. To solve these problems, we propose incorporating super-resolution technique, sequential feature pyramid network, and boundary localization. Initially, the ensemble of enhanced super-resolution generative adversarial networks (ESRGAN) was proposed for the preprocessing stage to generate a more detailed contour of the original steel image. Next, in the detector section, the latest state-of-the-art feature pyramid network, known as De-tectoRS, utilized the recursive feature pyramid network technique to extract deeper multi-scale steel features by learning the feedback from the sequential feature pyramid network. Finally, Side-Aware Boundary Localization was used to precisely generate the output prediction of the defect detectors. We named our approach EnsGAN-SDD. Extensive experimental studies showed that the proposed methods improved the defect detector's performance, which also surpassed the accuracy of state-of-the-art methods. Moreover, the proposed EnsGAN achieved better performance and effectiveness in processing time compared with the original ESRGAN. We believe our innovation could significantly contribute to improved production quality in the steel industry.

New Insights into the Physiology of Iron Transport: An Interdisciplinary Approach.

The TransCure project entitled 'Iron Transporters DMT1 and FPN1' took an interdisciplinary approach combining structural biology, chemistry and physiology to gain new insights into iron transport. Proteins studied included Divalent Metal Transporter 1 (DMT1, SLC11A2), enabling the import of Fe2+ into the cytoplasm, and the iron efflux transporter Ferroportin (FPN1, SLC40A1). The physiology and pathophysiology, and the mechanisms underlying iron transport in the gut, across the placenta and in bone were investigated. Small molecule high-throughput screening was used to identify improved modulators of DMT1. The characterization of DMT1 inhibitors have provided first detailed insights into the pharmacology of a human iron transport protein. In placental physiology, the identification of the expressional and functional alterations and underlying mechanisms in trophoblast cells clarified the association between placental iron transport by DMT1/FPN1 and gestational diabetes mellitus. In bone, iron metabolism was found to differ between cells of the monocyte/ macrophage lineages, including osteoclasts. Osteoclast development and activity depended on exogenous iron, the expression of high levels of the transferrin receptor (TFR) and low levels of FPN1 suggesting the expression of an "iron storage" phenotype by these cells. The principles and main findings of the TransCure studies on transmembrane iron  transport physiology are summarized in this review.