Spatio-temporal classification for polyp diagnosis.

Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-cancerous polyps. Computer-aided polyp characterisation can determine which polyps need polypectomy and recent deep learning-based approaches have shown promising results as clinical decision support tools. Yet polyp appearance during a procedure can vary, making automatic predictions unstable. In this paper, we investigate the use of spatio-temporal information to improve the performance of lesions classification as adenoma or non-adenoma. Two methods are implemented showing an increase in performance and robustness during extensive experiments both on internal and openly available benchmark datasets.

Identifying key mechanisms leading to visual recognition errors for missed colorectal polyps using eye-tracking technology.

BACKGROUND AND AIM: Lack of visual recognition of colorectal polyps may lead to interval cancers. The mechanisms contributing to perceptual variation, particularly for subtle and advanced colorectal neoplasia, have scarcely been investigated. We aimed to evaluate visual recognition errors and provide novel mechanistic insights. METHODS: Eleven participants (seven trainees and four medical students) evaluated images from the UCL polyp perception dataset, containing 25 polyps, using eye-tracking equipment. Gaze errors were defined as those where the lesion was not observed according to eye-tracking technology. Cognitive errors occurred when lesions were observed but not recognized as polyps by participants. A video study was also performed including 39 subtle polyps, where polyp recognition performance was compared with a convolutional neural network. RESULTS: Cognitive errors occurred more frequently than gaze errors overall (65.6%), with a significantly higher proportion in trainees (P = 0.0264). In the video validation, the convolutional neural network detected significantly more polyps than trainees and medical students, with per-polyp sensitivities of 79.5%, 30.0%, and 15.4%, respectively. CONCLUSIONS: Cognitive errors were the most common reason for visual recognition errors. The impact of interventions such as artificial intelligence, particularly on different types of perceptual errors, needs further investigation including potential effects on learning curves. To facilitate future research, a publicly accessible visual perception colonoscopy polyp database was created.

Polyp characterization using deep learning and a publicly accessible polyp video database.

OBJECTIVES: Convolutional neural networks (CNN) for computer-aided diagnosis of polyps are often trained using high-quality still images in a single chromoendoscopy imaging modality with sessile serrated lesions (SSLs) often excluded. This study developed a CNN from videos to classify polyps as adenomatous or nonadenomatous using standard narrow-band imaging (NBI) and NBI-near focus (NBI-NF) and created a publicly accessible polyp video database. METHODS: We trained a CNN with 16,832 high and moderate quality frames from 229 polyp videos (56 SSLs). It was evaluated with 222 polyp videos (36 SSLs) across two test-sets. Test-set I consists of 14,320 frames (157 polyps, 111 diminutive). Test-set II, which is publicly accessible, 3317 video frames (65 polyps, 41 diminutive), which was benchmarked with three expert and three nonexpert endoscopists. RESULTS: Sensitivity for adenoma characterization was 91.6% in test-set I and 89.7% in test-set II. Specificity was 91.9% and 88.5%. Sensitivity for diminutive polyps was 89.9% and 87.5%; specificity 90.5% and 88.2%. In NBI-NF, sensitivity was 89.4% and 89.5%, with a specificity of 94.7% and 83.3%. In NBI, sensitivity was 85.3% and 91.7%, with a specificity of 87.5% and 90.0%, respectively. The CNN achieved preservation and incorporation of valuable endoscopic innovations (PIVI)-1 and PIVI-2 thresholds for each test-set. In the benchmarking of test-set II, the CNN was significantly more accurate than nonexperts (13.8% difference [95% confidence interval 3.2-23.6], P = 0.01) with no significant difference with experts. CONCLUSIONS: A single CNN can differentiate adenomas from SSLs and hyperplastic polyps in both NBI and NBI-NF. A publicly accessible NBI polyp video database was created and benchmarked.

Computer-aided characterization of early cancer in Barrett's esophagus on i-scan magnification imaging: a multicenter international study.

BACKGROUND AND AIMS: We aimed to develop a computer-aided characterization system that could support the diagnosis of dysplasia in Barrett's esophagus (BE) on magnification endoscopy. METHODS: Videos were collected in high-definition magnification white-light and virtual chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and nondysplastic BE (NDBE) from 4 centers. We trained a neural network with a Resnet101 architecture to classify frames as dysplastic or nondysplastic. The network was tested on 3 different scenarios: high-quality still images, all available video frames, and a selected sequence within each video. RESULTS: Fifty-seven patients, each with videos of magnification areas of BE (34 dysplasia, 23 NDBE), were included. Performance was evaluated by a leave-1-patient-out cross-validation method. In all, 60,174 (39,347 dysplasia, 20,827 NDBE) magnification video frames were used to train the network. The testing set included 49,726 i-scan-3/optical enhancement magnification frames. On 350 high-quality still images, the network achieved a sensitivity of 94%, specificity of 86%, and area under the receiver operator curve (AUROC) of 96%. On all 49,726 available video frames, the network achieved a sensitivity of 92%, specificity of 82%, and AUROC of 95%. On a selected sequence of frames per case (total of 11,471 frames), we used an exponentially weighted moving average of classifications on consecutive frames to characterize dysplasia. The network achieved a sensitivity of 92%, specificity of 84%, and AUROC of 96%. The mean assessment speed per frame was 0.0135 seconds (SD ± 0.006). CONCLUSION: Our network can characterize BE dysplasia with high accuracy and speed on high-quality magnification images and sequence of video frames, moving it toward real-time automated diagnosis.

Single-antiplatelet regimen in ruptured cerebral blood blister and dissecting aneurysms treated with flow-diverter stent reconstruction.

BACKGROUND: Flow diversion treatment of ruptured cerebral aneurysms remains challenging due to the need for double-antiplatelet therapy. We report our experience with flow-diverter stent (FDS) reconstruction with single-antiplatelet therapy of ruptured cerebral blood blister and dissecting aneurysms. METHODS: In this case series we performed a retrospective analysis of all patients with ruptured cerebral aneurysms who were treated with a phosphoryl-bonded FDS between 2019 and 2022 in a single center. Periprocedurally, all patients received weight-adapted eptifibatide IV and heparin IV. After 6-24 hours, eptifibatide was switched to oral prasugrel as monotherapy. We analyzed the rate of bleeding complications, thromboembolic events, occlusion rate and clinical outcome. RESULTS: Nine patients with subarachnoid hemorrhage were treated, eight within 24 hours of symptom onset. Seven patients were treated with one FDS and two patients received two FDS in a telescopic fashion. Two aneurysms were additionally coil embolized. Fatal re-rupture occurred in one case; eight patients survived and had no adverse events associated with the FDS. Six patients showed complete occlusion of the aneurysm after 3 months (n=2) and 1 year (n=4), respectively. Two patients showed subtotal occlusion of the aneurysm at the last follow-up after 3 months and 6 months, respectively. Favorable clinical outcome was achieved in five patients. CONCLUSIONS: Peri-interventional single-antiplatelet therapy with eptifibatide followed by prasugrel was sufficient to prevent thromboembolic events and reduce re-bleeding using an anti-thrombogenic FDS. FDS with single-antiplatelet therapy might be a viable option for ruptured blood blister and dissecting cerebral aneurysms.

Polyp detection on video colonoscopy using a hybrid 2D/3D CNN.

Colonoscopy is the gold standard for early diagnosis and pre-emptive treatment of colorectal cancer by detecting and removing colonic polyps. Deep learning approaches to polyp detection have shown potential for enhancing polyp detection rates. However, the majority of these systems are developed and evaluated on static images from colonoscopies, whilst in clinical practice the treatment is performed on a real-time video feed. Non-curated video data remains a challenge, as it contains low-quality frames when compared to still, selected images often obtained from diagnostic records. Nevertheless, it also embeds temporal information that can be exploited to increase predictions stability. A hybrid 2D/3D convolutional neural network architecture for polyp segmentation is presented in this paper. The network is used to improve polyp detection by encompassing spatial and temporal correlation of the predictions while preserving real-time detections. Extensive experiments show that the hybrid method outperforms a 2D baseline. The proposed architecture is validated on videos from 46 patients and on the publicly available SUN polyp database. A higher performance and increased generalisability indicate that real-world clinical implementations of automated polyp detection can benefit from the hybrid algorithm and the inclusion of temporal information.

A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks.

BACKGROUND AND AIMS: Seattle protocol biopsies for Barrett's Esophagus (BE) surveillance are labour intensive with low compliance. Dysplasia detection rates vary, leading to missed lesions. This can potentially be offset with computer aided detection. We have developed convolutional neural networks (CNNs) to identify areas of dysplasia and where to target biopsy. METHODS: 119 Videos were collected in high-definition white light and optical chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and non-dysplastic BE (NDBE). We trained an indirectly supervised CNN to classify images as dysplastic/non-dysplastic using whole video annotations to minimise selection bias and maximise accuracy. The CNN was trained using 148,936 video frames (31 dysplastic patients, 31 NDBE, two normal esophagus), validated on 25,161 images from 11 patient videos and tested on 264 iscan-1 images from 28 dysplastic and 16 NDBE patients which included expert delineations. To localise targeted biopsies/delineations, a second directly supervised CNN was generated based on expert delineations of 94 dysplastic images from 30 patients. This was tested on 86 i-scan one images from 28 dysplastic patients. FINDINGS: The indirectly supervised CNN achieved a per image sensitivity in the test set of 91%, specificity 79%, area under receiver operator curve of 93% to detect dysplasia. Per-lesion sensitivity was 100%. Mean assessment speed was 48 frames per second (fps). 97% of targeted biopsy predictions matched expert and histological assessment at 56 fps. The artificial intelligence system performed better than six endoscopists. INTERPRETATION: Our CNNs classify and localise dysplastic Barrett's Esophagus potentially supporting endoscopists during surveillance.

Arrhythmia Caused by a Giant Coronary Artery Aneurysm.

Giant coronary artery aneurysms (GCAAs) are rare cardiovascular malformations that necessitate a multimodal, interdisciplinary diagnostic and individualized interventional approach. This report describes the case of a young, healthy patient with an idiopathic GCAA with pseudoaneurysmal protrusion into the right atrium that caused syncope, cardiac decompensation, and arrhythmia, which subsided postoperatively.

Performance of artificial intelligence for detection of subtle and advanced colorectal neoplasia.

OBJECTIVES: There is uncertainty regarding the efficacy of artificial intelligence (AI) software to detect advanced subtle neoplasia, particularly flat lesions and sessile serrated lesions (SSLs), due to low prevalence in testing datasets and prospective trials. This has been highlighted as a top research priority for the field. METHODS: An AI algorithm was evaluated on four video test datasets containing 173 polyps (35,114 polyp-positive frames and 634,988 polyp-negative frames) specifically enriched with flat lesions and SSLs, including a challenging dataset containing subtle advanced neoplasia. The challenging dataset was also evaluated by eight endoscopists (four independent, four trainees, according to the Joint Advisory Group on gastrointestinal endoscopy [JAG] standards in the UK). RESULTS: In the first two video datasets, the algorithm achieved per-polyp sensitivities of 100% and 98.9%. Per-frame sensitivities were 84.1% and 85.2%. In the subtle dataset, the algorithm detected a significantly higher number of polyps (P < 0.0001), compared to JAG-independent and trainee endoscopists, achieving per-polyp sensitivities of 79.5%, 37.2% and 11.5%, respectively. Furthermore, when considering subtle polyps detected by both the algorithm and at least one endoscopist, the AI detected polyps significantly faster on average. CONCLUSIONS: The AI based algorithm achieved high per-polyp sensitivities for advanced colorectal neoplasia, including flat lesions and SSLs, outperforming both JAG independent and trainees on a very challenging dataset containing subtle lesions that could have been overlooked easily and contribute to interval colorectal cancer. Further prospective trials should evaluate AI to detect subtle advanced neoplasia in higher risk populations for colorectal cancer.

Insights into the substrate binding mechanism of SULT1A1 through molecular dynamics with excited normal modes simulations.

Sulfotransferases (SULTs) are phase II drug-metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3'-phosphoadenosine 5'-phosphosulfate (PAPS) to a substrate. It has been previously suggested that a considerable shift of SULT structure caused by PAPS binding could control the capability of SULT to bind large substrates. We employed molecular dynamics (MD) simulations and the recently developed approach of MD with excited normal modes (MDeNM) to elucidate molecular mechanisms guiding the recognition of diverse substrates and inhibitors by SULT1A1. MDeNM allowed exploring an extended conformational space of PAPS-bound SULT1A1, which has not been achieved up to now by using classical MD. The generated ensembles combined with docking of 132 SULT1A1 ligands shed new light on substrate and inhibitor binding mechanisms. Unexpectedly, our simulations and analyses on binding of the substrates estradiol and fulvestrant demonstrated that large conformational changes of the PAPS-bound SULT1A1 could occur independently of the co-factor movements that could be sufficient to accommodate large substrates as fulvestrant. Such structural displacements detected by the MDeNM simulations in the presence of the co-factor suggest that a wider range of drugs could be recognized by PAPS-bound SULT1A1 and highlight the utility of including MDeNM in protein-ligand interactions studies where major rearrangements are expected.

Interdependence of VA-ECMO output, pulmonary congestion and outcome after cardiac surgery.

BACKGROUND: Venoarterial-extracorporeal membrane oxygenation (VA-ECMO) is a life-saving method for patients with low-output failure after cardiac surgery. However, VA-ECMO therapy may increase left ventricular afterload due to retrograde blood flow in the aorta, which may lead to progression of pulmonary congestion. We examined the predictive value of pulmonary congestion in patients that need VA-ECMO support after cardiovascular surgery. METHODS: We enrolled a total of 266 adult patients undergoing VA-ECMO support following cardiovascular surgery at a university-affiliated tertiary care centre into our single-center registry. Pulmonary edema was assessed on bedside chest X rays at day 0, 3, 5 after VA-ECMO implantation. RESULTS: Median age was 65 (57-72) years, 69% of patients were male and 30-day survival was 63%. At ICU-admission 20% of patients had mild, 54% had moderate and 26% showed severe pulmonary congestion. Pulmonary congestion at day 0 was not associated with outcome (adjusted HR 1.31; 95%-CI 0.89-1.93;P = 0.18), whereas pulmonary congestion at day 3 (adj. HR 2.81; 95%-CI 1.76-4.46;P<0.001) and day 5 (adj. HR 3.01;95%-CI 1.84-4.93;P<0.001) was significantly associated with survival. Linear regression revealed that out of left ventricular function, cardiac output, central venous saturation, maximum dobutamine and norepinephrine dose as well as fluid balance solely ECMO rotation was associated with the evolution of pulmonary congestion (P = 0.007). CONCLUSIONS: Pulmonary edema three and five days after ECMO implantation are associated with poor survival. Interestingly, a high VA-ECMO output was the most important determinant of worsening pulmonary congestion within the first five days.

Regulation of autoimmune arthritis by the SHP-1 tyrosine phosphatase.

BACKGROUND: The Src homology region 2 domain-containing phosphatase-1 (SHP-1) is known to exert negative regulatory effects on immune cell signaling. Mice with mutations in the Shp1 gene develop inflammatory skin disease and autoimmunity, but no arthritis. We sought to explore the role of SHP-1 in arthritis using an autoimmune mouse model of rheumatoid arthritis. We generated Shp1 transgenic (Shp1-Tg) mice to study the impact of SHP-1 overexpression on arthritis susceptibility and adaptive immune responses. METHODS: SHP-1 gene and protein expression as well as tyrosine phosphatase activity were evaluated in spleen cells of transgenic and wild type (WT) mice. WT and Shp1-Tg (homozygous or heterozygous for the transgene) mice were immunized with human cartilage proteoglycan (PG) in adjuvant, and arthritis symptoms were monitored. Protein tyrosine phosphorylation level, net cytokine secretion, and serum anti-human PG antibody titers were measured in immune cells from WT and Shp1-Tg mice. WT mice were treated with regorafenib orally to activate SHP-1 either before PG-induced arthritis (PGIA) symptoms developed (preventive treatment) or starting at an early stage of disease (therapeutic treatment). Data were statistically analyzed and graphs created using GraphPad Prism 8.0.2 software. RESULTS: SHP-1 expression and tyrosine phosphatase activity were elevated in both transgenic lines compared to WT mice. While all WT mice developed arthritis after immunization, none of the homozygous Shp1-Tg mice developed the disease. Heterozygous transgenic mice, which showed intermediate PGIA incidence, were selected for further investigation. We observed differences in interleukin-4 and interleukin-10 production in vitro, but serum anti-PG antibody levels were not different between the genotypes. We also found decreased tyrosine phosphorylation of several proteins of the JAK/STAT pathway in T cells from PG-immunized Shp1-Tg mice. Regorafenib administration to WT mice prevented the development of severe PGIA or reduced disease severity when started after disease onset. CONCLUSIONS: Resistance to arthritis in the presence of SHP-1 overexpression likely results from the impairment of tyrosine phosphorylation (deactivation) of key immune cell signaling proteins in the JAK/STAT pathway, due to the overwhelming tyrosine phosphatase activity of the enzyme in Shp1-Tg mice. Our study is the first to investigate the role of SHP-1 in autoimmune arthritis using animals overexpressing this phosphatase. Pharmacological activation of SHP-1 might be considered as a new approach to the treatment of autoimmune arthritis.

Protocol analysis of dual-energy CT for optimization of kidney stone detection in virtual non-contrast reconstructions.

OBJECTIVES: Previous studies have shown that split-bolus protocols in virtual non-contrast (VNC) reconstructions of dual-energy computed tomography (DE-CT) significantly decrease radiation dose in patients with urinary stone disease. To evaluate the impact on kidney stone detection rate of stone composition, size, tube voltage, and iodine concentration for VNC reconstructions of DE-CT. METHODS: In this prospective study, 16 kidney stones of different sizes (1.2-4.5 mm) and compositions (struvite, cystine, whewellite, brushite) were placed within a kidney phantom. Seventy-two scans with nine different iodine contrast agents/saline solutions with increasing attenuation (0-1400 HU) and different kilovoltage settings (70 kV/150 kV; 80 kV/150 kV; 90 kV/150 kV; 100 kV/150 kV) were performed. Two experienced radiologists independently rated the images for the presence and absence of stones. Multivariate classification tree analysis and descriptive statistics were used to evaluate the diagnostic performance. RESULTS: Classification tree analysis revealed a higher detection rate of renal calculi > 2 mm in size compared with that of renal calculi < 2 mm (84.7%; 12.7%; p < 0.001). For stones with a diameter > 2 mm, the best results were found at 70 kV/Sn 150 kV and 80 kV/Sn 150 kV in scans with contrast media attenuation of 600 HU or less, with sensitivity of 99.6% and 96.0%, respectively. A higher luminal attenuation (> 600 HU) resulted in a significantly decreased detection rate (91.8%, 0-600 HU; 70.7%, 900-1400 HU; p < 0.001). In our study setup, the detection rates were best for cystine stones. CONCLUSION: Scan protocols in DE-CT with lower tube current and lower contrast medium attenuation show excellent results in VNC for stones larger than 2 mm but have limitations for small stones. KEY POINTS: • The detection rate of virtual non-contrast reconstructions is highly dependent on the surrounding contrast medium attenuation at the renal pelvis and should be kept as low as possible, as at an attenuation higher than 600 HU the VNC reconstructions are susceptible to masking ureteral stones. • Protocols with lower tube voltages (70 kV/Sn 150 kV and 80 kV/Sn 150 kV) improve the detection rate of kidney stones in VNC reconstructions. • The visibility of renal stones in virtual non-contrast of dual-energy CT is highly associated with the size, and results in a significantly lower detection rate in stones below 2 mm.

Defining the subcellular distribution and metabolic channeling of phosphatidylinositol.

Phosphatidylinositol (PI) is an essential structural component of eukaryotic membranes that also serves as the common precursor for polyphosphoinositide (PPIn) lipids. Despite the recognized importance of PPIn species for signal transduction and membrane homeostasis, there is still a limited understanding of the relationship between PI availability and the turnover of subcellular PPIn pools. To address these shortcomings, we established a molecular toolbox for investigations of PI distribution within intact cells by exploiting the properties of a bacterial enzyme, PI-specific PLC (PI-PLC). Using these tools, we find a minor presence of PI in membranes of the ER, as well as a general enrichment within the cytosolic leaflets of the Golgi complex, peroxisomes, and outer mitochondrial membrane, but only detect very low steady-state levels of PI within the plasma membrane (PM) and endosomes. Kinetic studies also demonstrate the requirement for sustained PI supply from the ER for the maintenance of monophosphorylated PPIn species within the PM, Golgi complex, and endosomal compartments.

Monitoring Non-vesicular Transport of Phosphatidylserine and Phosphatidylinositol 4-Phosphate in Intact Cells by BRET Analysis.

Non-vesicular lipid transport via lipid transfer proteins (LTPs) at membrane contact sites (MCSs) is critical for the maintenance of the lipid composition of biological membranes. The ability to measure lipid transfer activity of diverse LTPs in live cells without interrupting the fine structural organization is essential to better understand the contribution of non-vesicular lipid transport to membrane organization. Here, we describe a semiquantitative method to analyze phosphatidylinositol 4-phosphate (PI4P) and phosphatidylserine (PS) changes at the plasma membrane (PM) as they relate to LTP functions. This live cell method is based on bioluminescence resonance energy transfer (BRET) measurements between a luciferase-tagged lipid-recognizing module and a PM-targeted fluorescent acceptor. Oxysterol-binding protein-related protein (ORP) 5 is a PI4P/PS lipid transfer protein which is stably tethered to the ER and also dynamically interacts with PM PI4P/PI(4,5)P2 via its pleckstrin homology (PH) domain. We show that this method is able to detect PI4P and PS changes in the PM upon acute recruitment of an ORP5 construct to the PM. This method is convenient and robust, utilizing population of cells in 96-well plates analyzed in a plate reader. We will also highlight potential further applications extending the method for other LTPs and other lipid cargoes.

3D/2D model-to-image registration by imitation learning for cardiac procedures.

PURPOSE: In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application. METHODS: This paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images. RESULTS: Accuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was [Formula: see text] on 1000 test cases, superior to that of manual ([Formula: see text]) and gradient-based ([Formula: see text]) registration. High robustness is shown in 19 clinical CRT cases. CONCLUSION: Besides the proposed methods feasibility in a clinical environment, evaluation has shown good accuracy and high robustness indicating that it could be applied in image-guided interventions.

Real-Time X-MRI-Guided Left Ventricular Lead Implantation for Targeted Delivery of Cardiac Resynchronization Therapy.

OBJECTIVES: This study sought to test the feasibility of a purpose-built, integrated software platform to process, analyze, and overlay cardiac magnetic resonance (CMR) data in real time within a combined cardiac catheter laboratory and magnetic resonance imaging scanner suite (X-MRI) to guide left ventricular (LV) lead implantation. BACKGROUND: Suboptimal LV lead position is a major determinant of poor cardiac resynchronization therapy (CRT) response, and the optimal site is highly patient specific. Pacing myocardial scar is associated with poorer outcomes; conversely, targeting latest mechanical activation (LMA) may improve them. METHODS: Fourteen patients (age 74 ± 5.1 years; New York Heart Association functional class: 2.7 ± 0.4; 86% ischemic with ejection fraction 27 ± 7.6%; QRSd: 157 ± 19 ms) underwent CMR followed by immediate CRT implantation using derived scar and dyssynchrony data, overlaid onto fluoroscopy in an X-MRI suite. Rapid LV segmentation enabled detailed scar quantification, identification of LMA segments, and selection of myocardial targets. At coronary venography, the CMR-derived 3-dimensional shell was fused, enabling identification of viable venous targets subtended by target segments for LV lead placement. RESULTS: The platform was successful in all 14 patients, of whom 10 (71%) were paced in pre-procedurally defined target segments. Pacing in CMR-defined target segments (out of scar) showed a significant decrease in the LV capture threshold (mean difference: 2.4 [1.5 to 3.2]; p < 0.001) and shorter paced QRS duration (mean difference: 25 [15 to 34]; p < 0.001) compared with pacing in areas of CMR determined scar. In 5 (36%) patients with extensive scar in the posterolateral wall, CMR guidance enabled successful lead delivery in an alternative anatomically favorable site. Radiation dose and implant times were similar to historical controls (p = NS). CONCLUSIONS: Real-time CMR-guided LV lead placement is feasible and achievable in a single clinical setting and may prove helpful to preferentially select sites for LV lead placement.

Lenz-Majewski mutations in PTDSS1 affect phosphatidylinositol 4-phosphate metabolism at ER-PM and ER-Golgi junctions.

Lenz-Majewski syndrome (LMS) is a rare disease characterized by complex craniofacial, dental, cutaneous, and limb abnormalities combined with intellectual disability. Mutations in thePTDSS1gene coding one of the phosphatidylserine (PS) synthase enzymes, PSS1, were described as causative in LMS patients. Such mutations render PSS1 insensitive to feedback inhibition by PS levels. Here we show that expression of mutant PSS1 enzymes decreased phosphatidylinositol 4-phosphate (PI4P) levels both in the Golgi and the plasma membrane (PM) by activating the Sac1 phosphatase and altered PI4P cycling at the PM. Conversely, inhibitors of PI4KA, the enzyme that makes PI4P in the PM, blocked PS synthesis and reduced PS levels by 50% in normal cells. However, mutant PSS1 enzymes alleviated the PI4P dependence of PS synthesis. Oxysterol-binding protein-related protein 8, which was recently identified as a PI4P-PS exchanger between the ER and PM, showed PI4P-dependent membrane association that was significantly decreased by expression of PSS1 mutant enzymes. Our studies reveal that PS synthesis is tightly coupled to PI4P-dependent PS transport from the ER. Consequently, PSS1 mutations not only affect cellular PS levels and distribution but also lead to a more complex imbalance in lipid homeostasis by disturbing PI4P metabolism.

A differential gene expression study: Ptpn6 (SHP-1)-insufficiency leads to neutrophilic dermatosis-like disease (NDLD) in mice.

BACKGROUND: Irradiated syngeneic wild-type mice developed the same neutrophilic dermatosis-like disease (NDLD) after adoptive transfer of bone marrow cells from Ptpn6(meb2/meb2) mutant mice. OBJECTIVE: To analyze differentially expressed genes in the bone marrow of mice with NDLD to gain insight into the role of Ptpn6 in myelopoietic bone marrow pathology, and the mechanisms by which Ptpn6 insufficiency in the hematopoietic cells can lead to the development of skin lesions. METHODS: As Ptpn6 is involved in a myriad of signaling pathways, we used a global approach with microarray technology for the first time to characterize changes in the bone marrow and skin of motheaten-type mice. RESULTS: A total number of 1,511 probe sets in the bone marrow showed at least two-fold changes with FDR <0.05, of which 256 probe sets had over four-fold changes. A group of 63 genes in the bone marrow of NDLD mice had more than a 4-fold change with FDR <0.0001. From 503 genes encoding proteins with ITIM motif that binds to Ptpn6, 109 were up-regulated and 83 were down-regulated. We found that genes encoding hematopoietic receptors, neutrophil chemoattractants, Toll-like receptors (Tlr1, Tlr2 and Tlr4) and C-type lectin innate immunity receptors (Clec4e, Clec4d, Clec4n, Clec4a2 and Clec4a3) were significantly up-regulated in both NDLD bone marrow and skin. The Il1b gene was also significantly overexpressed in skin samples, confirming the importance of the IL-1/TLR pathway in the development of early skin inflammation in NDLD mice. CONCLUSION: Our results suggest that innate immunity genes play a major role in development of neutrophilic dermatosis-like disease in mice.

Structural insights and in vitro reconstitution of membrane targeting and activation of human PI4KB by the ACBD3 protein.

Phosphatidylinositol 4-kinase beta (PI4KB) is one of four human PI4K enzymes that generate phosphatidylinositol 4-phosphate (PI4P), a minor but essential regulatory lipid found in all eukaryotic cells. To convert their lipid substrates, PI4Ks must be recruited to the correct membrane compartment. PI4KB is critical for the maintenance of the Golgi and trans Golgi network (TGN) PI4P pools, however, the actual targeting mechanism of PI4KB to the Golgi and TGN membranes is unknown. Here, we present an NMR structure of the complex of PI4KB and its interacting partner, Golgi adaptor protein acyl-coenzyme A binding domain containing protein 3 (ACBD3). We show that ACBD3 is capable of recruiting PI4KB to membranes both in vitro and in vivo, and that membrane recruitment of PI4KB by ACBD3 increases its enzymatic activity and that the ACBD3:PI4KB complex formation is essential for proper function of the Golgi.