Segmental Branches Research Articles

Advancements in the techniques for rescue and vegetative propagation of adult Mimosa caesalpiniifolia Benth. trees

ABSTRACT This study focused on the method known as air layering to facilitate the growth of new plants from Mimosa caesalpiniifolia Benth. a technique relatively unexplored for this species. The primary goal was to ascertain whether air layering could effectively produce new clonal (genetically identical) plants. Additionally, the study aimed to establish and enhance a small-scale nursery for clonal plants. To this end, trials were conducted on segments of branches from six adult trees in a special setup which stimulates root growth using various indole-3-butyric acid (IBA) concentrations. Root development was monitored biweekly. Based on these experiments, a clonal mini-garden was established to observe the growth and health of these new plants, assessing the number of new shoots and mini-cuttings generated and their survival rates. The findings indicate that root development occurred under all conditions, although the IBA concentration did not significantly affect the outcome. The success of plant growth within the clonal mini-garden appeared to be linked to the quality of root development during the air layering process. Therefore, employing air layering to cultivate new Mimosa caesalpiniifolia plants is viable and can support the creation of a clonal mini-garden for subsequent propagation.

A Multi-Level Adaptive Lightweight Net for Damaged Road Marking Detection Based on Knowledge Distillation

To tackle the complexity and limited applicability of high-precision segmentation models for damaged road markings, this study proposes a Multi-level Adaptive Lightweight Network (MALNet) based on knowledge distillation. By incorporating multi-scale dilated convolution and adaptive spatial channel attention fusion modules, the MALNet model significantly enhances the precision, integrity, and robustness of its segmentation branch. Furthermore, it employs an intricate knowledge distillation strategy, channeling rich, layered insights from a teacher model to a student model, thus elevating the latter’s segmentation ability. Concurrently, it streamlines the student model by markedly reducing its parameter count and computational demands, culminating in a segmentation network that is both high-performing and pragmatic. Rigorous testing on three distinct data sets for damaged road marking detection—CDM_P (Collective Damaged road Marking—Public), CDM_H (Collective Damaged road Marking—Highways), and CDM_C (Collective Damaged road Marking—Cityroad)—underscores the MALNet model’s superior segmentation abilities across all damage types, outperforming competing models in accuracy and completeness. Notably, the MALNet model excels in parameter efficiency, computational economy, and throughput. After distillation, the student model’s parameters and computational load decrease to only 31.78% and 27.40% of the teacher model’s, respectively, while processing speeds increase to 1.9 times, demonstrating a significant improvement in lightweight design.

BiDiCOS: Camouflaged object segmentation via bilateral diffusion model

The task of camouflaged object segmentation (COS) is to extract objects with camouflage characteristics that are difficult to detect in an image. The current mainstream COS method mainly relies on the general object segmentation framework using the encoder-decoder structure to predict the objects. However, the current COS framework suffers from over-reliance on the labeled ground truth and the output results. To this end, we propose a bilateral diffusion model for camouflaged object segmentation (BiDiCOS). The main innovation of BiDiCOS is that we design a new bilateral diffusion paradigm, transforming the segmentation and depth estimation tasks into denoising tasks. At the same time, the depth estimation branch can provide semantic information to the segmentation branch. Specifically, we design the adaptive feature fusion module for two branches to provide the required features adaptively. We design the bilateral feature fusion module to fuse the depth information with the mask information to reduce the semantic and logical errors during segmentation. Finally, we develop the specialized training strategy and inference strategy for BiDiCOS, which provide a solid foundation for realizing rational training and accurate prediction. Comprehensive experiments on four challenging COS datasets attest to the superior performance of BiDiCOS compared to the existing 20 COS methods. Our BiDiCOS achieves state-of-the-art with different sizes of backbones, where the MAE is reduced by 4.3 % on average. We have also performed comparative experiments on depth estimation. The experiments demonstrate that BiDiCOS can achieve a win–win situation for both tasks. The code will be available at https://github.com/jiangxinhao2020/BiDiCOS.

Inter-observer and inter-modality concordance of non-contrast MR angiography and CT angiography for preoperative assessment of potential renal donors

BackgroundMagnetic resonance angiography (MRA) is rapidly being employed as an effective substitute for CTA, particularly in situations of poor kidney function. We aimed to examine the inter-observer and inter-modality reliability of non-contrast MR angiography (NC-MRA) and CTA as a non-invasive tool for assessing the anatomical findings of potential living kidney donors.ResultsAll potential donors were referred from specialized kidney transplantation center and underwent NC-MRA of the renal arteries using a respiratory-triggered magnetization prepared 3D balanced steady-state free precession (b-SSFP) with inversion recovery pulses and fat saturation (Inhance 3D Inflow Inversion Recovery (IFIR)). Two experienced radiologists reviewed NC-MRA images and were asked to evaluate both renal arteries anatomy and their branching pattern, presence of accessory or aberrant renal arteries, and identify any anatomical variant. Lin’s correlation test was performed to test MRA readings by each of the two observers against CTA findings which considered as the gold standard for assessment of renal arteries. Additionally, observers were asked to assess the image quality. The study included 60 potential kidney donors (43 males and 17 females) with mean age ± SD of 31.3 ± 5.6 years. Excellent to very good inter-observer agreement was found between both observers in the assessment of renal arteries by NC-MRA. There was perfect concordance between MRA and CTA findings in detecting early arterial division, caliber, and length of left extra-parenchymal segmental branches. Moderate concordance was found in the assessment of the supplied segments of extra-parenchymal segmental renal arterial branches and substantial concordance between both MRA observers’ findings in the remaining variables of the study. There was excellent agreement between both observers in the assessment of image quality parameters.ConclusionsNC-MRA for the renal arteries is an effective alternative for CTA without the risks of radiation or contrast media.

A Study on the Pattern of Arterial Segmentation in the Human Spleen

Abstract Background: Partial splenectomy is preferred after splenic injuries, which will help to preserve healthy splenic tissue, hence the immunity. During splenic surgery particularly in the resection of a segment of the spleen, ligation of branches of the splenic artery is necessary. A precise knowledge of the segmental arteries is prerequisite because the spleen is separated into avascular lobes and segments by splenic arterial branches. The aim of the present work was to study the pattern of arterial segmentation in the human spleen and variations of the splenic arterial pattern. Methodology: The present study was conducted on 31 spleens with intact splenic artery after getting the ethical clearance. Specimens were collected depending on the availability of cadaver or unclaimed autopsy body. Tracing from terminal branches, the segmental branches of the splenic artery were studied by piece-meal dissection. The terminal branches of the splenic artery and its segmental branches were cleaned and observed. The number of the segmental branches of the spleen and the presence of arterial anastomosis between adjacent segmental arteries were noted. Results: The splenic artery terminated as bifurcation in 66.73% and trifurcation in 33.27%. We also found two, three, four, five, six, and seven arterial segments in 2 (6.46%), 14 (45.16%), 7 (22.58%), 3 (9.67%), 3 (9.67%), and 2 (6.46%) specimens, respectively. These segments are separated by an avascular plane in all specimens except one where we found intersegmental arterial anastomosis. Conclusions: Knowledge of the splenic arterial branching pattern of the present study may help the general surgeon as well as surgical gastroenterologist in performing the surgery of the spleen, particularly partial splenectomy after splenic injuries. This may also help anatomists in learning and teaching arterial segmentation of the spleen.

Multi-task network with inter-task consistency learning for face parsing and facial expression recognition at real-time speed

In recent years, face parsing and facial expression recognition have attracted increasing interest. Even though there are relevant results about face parsing and face representation, these approaches seek accuracy at the expense of speed. In this paper, we design a novel multi-task learning network for face parsing and facial expression recognition (MPENet). Specifically, MPENet consists of shared encoders and three downstream branches. In the edge perceiving branch, we use category edge and binary edge to extract face boundary information and improve localization of face boundaries. In the segmentation branch, we use graph learning to fuse edge and semantic information of the image, analyze the relations between different feature regions, and capture more contextual relationships. Finally, we design a consistent learning loss function, forcing different branches to learn the same predictions. We have carried out experiments on face datasets, and found that it shows high precision and fast inference speed. Specifically, MPENet achieves F1 scores of 85.9 on the CelebAMask-HQ dataset and 92.9 on the Lapa dataset, with an inference speed of 92.9 FPS. Moreover, MPENet precisely delineates the semantic boundaries of facial regions and, through consistent multi-task learning, effectively facilitates synergy among various tasks.

YOLOv8n-DDA-SAM: Accurate Cutting-Point Estimation for Robotic Cherry-Tomato Harvesting

Accurately identifying cherry-tomato picking points and obtaining their coordinate locations is critical to the success of cherry-tomato picking robots. However, previous methods for semantic segmentation alone or combining object detection with traditional image processing have struggled to accurately determine the cherry-tomato picking point due to challenges such as leaves as well as targets that are too small. In this study, we propose a YOLOv8n-DDA-SAM model that adds a semantic segmentation branch to target detection to achieve the desired detection and compute the picking point. To be specific, YOLOv8n is used as the initial model, and a dynamic snake convolutional layer (DySnakeConv) that is more suitable for the detection of the stems of cherry-tomato is used in neck of the model. In addition, the dynamic large convolutional kernel attention mechanism adopted in backbone and the use of ADown convolution resulted in a better fusion of the stem features with the neck features and a certain decrease in the number of model parameters without loss of accuracy. Combined with semantic branch SAM, the mask of picking points is effectively obtained and then the accurate picking point is obtained by simple shape-centering calculation. As suggested by the experimental results, the proposed YOLOv8n-DDA-SAM model is significantly improved from previous models not only in detecting stems but also in obtaining stem’s masks. In the mAP@0.5 and F1-score, the YOLOv8n-DDA-SAM achieved 85.90% and 86.13% respectively. Compared with the original YOLOv8n, YOLOv7, RT-DETR-l and YOLOv9c, the mAP@0.5 has improved by 24.7%, 21.85%, 19.76%, 15.99% respectively. F1-score has increased by 16.34%, 12.11%, 10.09%, 8.07% respectively, and the number of parameters is only 6.37M. In the semantic segmentation branch, not only does it not need to produce relevant datasets, but also improved its mIOU by 11.43%, 6.94%, 5.53%, 4.22% and mAP@0.5 by 12.33%, 7.49%, 6.4%, 5.99% compared to Deeplabv3+, Mask2former, DDRNet and SAN respectively. In summary, the model can well satisfy the requirements of high-precision detection and provides a strategy for the detection system of the cherry-tomato.

Effects of different proportions of fruit tree branches on nicotine content and microbial diversity during composting of tobacco waste

Adding fruit tree branches to the compost pile in appropriate proportions is one of the methods used to address the challenge of tobacco waste recycling. However, the effects of different proportions of fruit tree branches on nicotine concentration and microbial diversity during tobacco waste composting have not been reported. In this study, a composting system with tobacco waste, cow dung, and fruit tree branches was established in a laboratory fermenter to assess the impact of adding 10%, 20%, and 30% fruit tree branches on quantity changes. In addition, the relationships between nicotine degradation, compost properties, enzyme activities, and microbial diversities were determined using biochemical assay methods and high-throughput sequencing. The results showed that adding appropriate proportions of fruit branch segments affected changes in physical and chemical properties during composting and promoted tobacco waste compost maturity. Aerobic composting effectively degraded nicotine in tobacco waste. Increased proportions of fruit branch segments led to elevations in nicotine degradation rates and enzyme activities related to lignocellulose degradation. The addition of fruit branches influenced the relative abundance and species of dominant bacteria and fungi at the phylum and genus levels. However, it did not significantly affect the relative abundance of the main bacterial genera involved in nicotine degradation. Nevertheless, it reduced the sensitivity of enzyme activity to nicotine content within heaps, increasing reliance on total nitrogen changes. The results of this study provide a theoretical basis for the utilization of tobacco waste in composting systems and indicate that fruit tree branches can enhance nicotine degradation efficiency during tobacco waste composting.

Integrated Circuit Bonding Distance Inspection via Hierarchical Measurement Structure.

Bonding distance is defined by the projected distance on a substrate plane between two solder points of a bonding wire, which can directly affect the morphology of the bonding wire and the performance between internal components of the chip. For the inspection of the bonding distance, it is necessary to accurately recognize gold wires and solder points within the complex imagery of the chip. However, bonding wires at arbitrary angles and small-sized solder points are densely distributed across the complex background of bonding images. These characteristics pose challenges for conventional image detection and deep learning methods to effectively recognize and measure the bonding distances. In this paper, we present a novel method to measure bonding distance using a hierarchical measurement structure. First, we employ an image acquisition device to capture surface images of integrated circuits and use multi-layer convolution to coarsely locate the bonding region and remove redundant background. Second, we apply a multi-branch wire bonding inspection network for detecting bonding spots and segmenting gold wire. This network includes a fine location branch that utilizes low-level features to enhance detection accuracy for small bonding spots and a gold wire segmentation branch that incorporates an edge branch to effectively extract edge information. Finally, we use the bonding distance measurement module to develop four types of gold wire distribution models for bonding spot matching. Together, these modules create a fully automated method for measuring bonding distances in integrated circuits. The effectiveness of the proposed modules and overall framework has been validated through comprehensive experiments.

TAF15 downregulation contributes to the benefits of physical training on dendritic spines and working memory in aged mice.

Moderate physical training has been shown to hinder age-related memory decline. While the benefits of physical training on hippocampal memory function are well-documented, little is known about its impact on working memory, which is linked to the prelimbic cortex (PrL), one major subdivision of the prefrontal cortex. Here, we examined the effects of physical training on spatial working memory in a well-established animal model of physical training, starting at 16 months of age and continuing for 5 months (running wheel 1 h/day and 5 days/week). This training strategy improved spatial working memory in aged mice (22-month-old), which was accompanied by an increased spine density and a lower TAF15 expression in the PrL. Specifically, physical training affected both thin and mushroom-type spines on PrL pyramidal cells, and prevented age-related loss of spines on selective segments of apical dendritic branches. Correlation analysis revealed that increased TAF15-expression was detrimental to the dendritic spines. However, physical training downregulated TAF15 expression in the PrL, preserving the dendritic spines on PrL pyramidal cells and improving working memory in trained aged mice. When TAF15 was overexpressed in the PrL via a viral approach, the benefits of physical training on the dendritic spines and working memory were abolished. These data suggest that physical training at a moderate pace might downregulate TAF15 expression in the PrL, which favors the dendritic spines on PrL pyramidal cells, thereby improving spatial working memory.

DeepSEM-Net: Enhancing SEM defect analysis in semiconductor manufacturing with a dual-branch CNN-Transformer architecture

Due to the increasing complexity of modern semiconductor integrated circuit fabrication processes, various defects may occur at each process step, leading to a semiconductor integrated circuit yield loss as well as reliability problems. This study introduces DeepSEM-Net, a novel dual-branch architecture that harmoniously integrates Convolutional Neural Networks (CNN) with Transformers. This innovative approach significantly enhances defect classification and precise segmentation in Scanning Electron Microscope (SEM) defect images, crucial for semiconductor manufacturing processes. DeepSEM-Net consists of a classification branch, which synergizes CNN and Transformers for robust global feature extraction, and a segmentation branch, which incorporates a Pyramid Attention module to meticulously recalibrate and exploit the feature map, thereby enriching the segmentation quality. A semi-supervised defect analysis system based on this model markedly reduces manual inspection efforts and is adept at operating in both simultaneous and sequential classification-segmentation modes. Rigorously tested on a diverse dataset from a real 12-inch wafer fab, DeepSEM-Net not only demonstrated a commendable classification accuracy of 97.25% on a 5-class dataset and a segmentation IoU of 84.40% but also exhibited remarkable resilience against the introduction of new defect classes in an unbalanced dataset. The dual-branch strategy of DeepSEM-Net, thus, significantly elevates the efficiency and reliability of SEM defect image analysis, offering a substantial leap forward in addressing yield loss and ensuring the reliability of semiconductor products.

Boundary-enhanced dual-stream network for semantic segmentation of high-resolution remote sensing images

ABSTRACT Deep convolutional neural networks (DCNNs) have been successfully used in semantic segmentation of high-resolution remote sensing images (HRSIs). However, this task still suffers from intra-class inconsistency and boundary blur due to high intra-class heterogeneity and inter-class homogeneity, considerable scale variance, and spatial information loss in conventional DCNN-based methods. Therefore, a novel boundary-enhanced dual-stream network (BEDSN) is proposed, in which an edge detection branch stream (EDBS) with a composite loss function is introduced to compensate for boundary loss in semantic segmentation branch stream (SSBS). EDBS and SSBS are integrated by highly coupled encoder and feature extractor. A lightweight multilevel information fusion module guided by channel attention mechanism is designed to reuse intermediate boundary information effectively. For aggregating multiscale contextual information, SSBS is enhanced by multiscale feature extraction module and hybrid atrous convolution module. Extensive experiments have been tested on ISPRS Vaihingen and Potsdam datasets. Results show that BEDSN can achieve significant improvements in intra-class consistency and boundary refinement. Compared with 11 state-of-the-art methods, BEDSN exhibits higher-level performance in both quantitative and visual assessments with low model complexity. The code will be available at https://github.com/lixinghua5540/BEDSN.

Fibromuscular Dysplasia: Renal Artery Dissection and Infarction in a Young Male

Fibromuscular dysplasia is an abnormal cell growth of the arterial wall that usually affects the renal and carotid arteries. It may lead to stenosis, tortuosity, aneurysm, or dissection of the artery and can potentially compromise the circulation from ensuing total occlusion and infarction of the recipient area. We report a case of a young male who presented with severe acute abdominal pain in the right side of the abdomen associated with nausea, vomiting, and constipation. Computed tomography of the abdomen with intravenous contrast showed acute renal infarction of a focal area at the lower pole of the right kidney, subsequent computed angiography showed dissection of a segmental branch of the right renal artery associated with fibromuscular dysplasia and beading appearance of the right renal artery. The renal function test and urinalysis were both normal. The patient was treated with unfractionated heparin infusion, with the resolution of abdominal pain, he was subsequently discharged home uneventfully on oral aspirin.

WSM-MIL: a weakly supervised segmentation method with multiple instance learning for C elegans image

Recently, image analysis techniques have been introduced to automate nematode information assessment. In image analysis-based nematode information assessment, the initial step involves detecting and segmenting C. elegans from microscopic images and network-based methods have been investigated. However, training a network for C. elegans image segmentation is typically associated with the labor-intensive process of pixel-level mask labeling. To address this challenge, we introduced a weakly supervised segmentation method using multiple instance learning (WSM-MIL). The proposed multi-instance weakly supervised segmentation method comprises three key components: a backbone network, a detection branch, and a segmentation branch. In contrast to fully supervised pixel-level annotation, we opted for weakly supervised bounding box-level annotation. This approach reduces the labour cost of annotation to some extent. The approach offers several advantages, such as simplicity, an end-to-end architecture, and good scalability. We conducted experiments comparing the proposed network with benchmark methods, and the results showed that the network exhibits competitive performance in the image segmentation task of C. elegans. The results of this study provide an effective method in the field of biological image analysis, as well as new ideas for solving complex segmentation tasks. The method is not only applicable to the study of C. elegans but also has wide applicability in biological image segmentation problems in other fields.

UAV imagery-based railroad station building inspection using hybrid learning architecture

The apparent defects of railroad station building such as the broken glass, missing screw, and rust corrosion need to be inspected regularly and timely to ensure the safety of passenger. Unmanned aerial vehicle (UAV) imagery-based inspections have the potential to revolutionize current manual visual inspections by providing a better overhead view and mitigating safety concerns. This paper proposes a hybrid learning architecture called YOLOS (you only look once station scene) to simultaneously detect and segment station building surface defects of UAV images. First, a novel squeeze-and-excitation (SE) attention block is integrated into the detection branch to adaptively learn the weights of the feature channels, thereby promoting the network to pay attention to the critical deep features of the objects. And then, a new semantic segmentation branch parallel to the detection branch is designed and assembled in YOLOS for pixel-level defect recognition. Finally, extensive experiments on railroad station dataset established with drone imagery prove the effectiveness and robustness of the SE-based network on station surface defect detection. This method can quickly convert UAV imagery into useful information with a high detection rate.

JOINEDTrans: Prior guided multi-task transformer for joint optic disc/cup segmentation and fovea detection

Deep learning-based image segmentation and detection models have largely improved the efficiency of analyzing retinal landmarks such as optic disc (OD), optic cup (OC), and fovea. However, factors including ophthalmic disease-related lesions and low image quality issues may severely complicate automatic OD/OC segmentation and fovea detection. Most existing works treat the identification of each landmark as a single task, and take into account no prior information. To address these issues, we propose a prior guided multi-task transformer framework for joint OD/OC segmentation and fovea detection, named JOINEDTrans. JOINEDTrans effectively combines various spatial features of the fundus images, relieving the structural distortions induced by lesions and other imaging issues. It contains a segmentation branch and a detection branch. To be noted, we employ an encoder with prior-learning in a vessel segmentation task to effectively exploit the positional relationship among vessel, OD/OC, and fovea, successfully incorporating spatial prior into the proposed JOINEDTrans framework. There are a coarse stage and a fine stage in JOINEDTrans. In the coarse stage, OD/OC coarse segmentation and fovea heatmap localization are obtained through a joint segmentation and detection module. In the fine stage, we crop regions of interest for subsequent refinement and use predictions obtained in the coarse stage to provide additional information for better performance and faster convergence. Experimental results demonstrate that JOINEDTrans outperforms existing state-of-the-art methods on the publicly available GAMMA, REFUGE, and PALM fundus image datasets. We make our code available at https://github.com/HuaqingHe/JOINEDTrans.

Is it safe to discontinue anticoagulant after surgical treatment of varicose vein disease, which caused pulmonary embolism?

Superficial vein thrombosis, which is mostly caused by lower extremity varicose vein disease, can be complicated by pulmonary embolism. At the same time, the optimal duration of anticoagulant therapy for pulmonary embolism originated from varicose vein thrombosis is still under debate. On the one hand, the presence of varicose veins is considered a small risk factor for the development of venous thromboembolic events, which persistence determines an increased risk of relapse and requires prolonged anticoagulant therapy. On the other hand, elimination of varicose veins is associated with reduced risk of subsequent venous thromboembolic events. The article describes a clinical case of recurrent pulmonary embolism after surgical treatment of varicose vein disease, which caused primary pulmonary embolism. A 45-year-old patient suffering from left lower extremity varicose vein disease for 5 years was admitted to the intensive care unit with suspected pulmonary embolism. The further examination revealed signs of thrombotic occlusion of the segmental and subsegmental branches of the pulmonary arteries bilaterally and the middle lobe branch of the right pulmonary artery, as well as signs of the right-sided heart overload. In this case, pulmonary embolism was found to originate from thrombosis of the trunk of the great saphenous vein ofthe left lower extremity with a proximal border at the level of the lower leg. No signs of deep vein involvement were detected. The parenteral anticoagulant therapy initiated in the hospital was followed by switching to therapeutic doses of rivaroxaban. The signs of recanalization of involved veins were identified after 6 months of treatment, and it was decided to perform endovenous laser coagulation of the trunk of the great saphenous vein combined with mini-phlebectomy of varicose tributaries on continuous oral anticoagulant therapy. The rivaroxaban therapy was completed a month after intervention. However, 7 days later the patient was diagnosed with repeated symptomatic pulmonary embolism, which originated from thrombosis of the left popliteal vein. It was recommended to resume anticoagulant therapy of indefinite duration. The article discusses the issues of optimal duration of treatment for pulmonary embolism originated from thrombosis of superficial varicose veins, as well as the possible timing of completion of anticoagulant therapy after the intervention.

Pest Detection and Classification in Peanut Crops

Recent advancements in image processing have significantly improved pest detection and classification in peanut crops. Our study introduces an innovative approach that optimizes image features for accurate pest identification. Leveraging insights from successful image analysis methodologies, our model employs a tailored architecture for pest detection, segmentation, and classification tasks. By integrating dual branch segment representations and a dual-layer transformer encoder, we aim to enhance image representations and consolidate pest image segments of varying sizes. We evaluate our approach using three distinct pest datasets—Aphids, Wireworm, and Gram Caterpillar—ensuring comprehensive analysis and model validation. Prior to training, we preprocess the datasets extensively, employing feature extraction techniques and addressing image quality issues. We then apply normalization procedures to standardize the data for seamless integration into our model architecture. Our methodology focuses on extracting key features through self-attention mechanisms and standardized scaling processes to enhance predictive capabilities. Comprehensive experimentation demonstrates the superiority of our approach, outperforming established benchmarks in pest detection and classification with high accuracy rates. In summary, our study presents a novel framework that optimizes feature extraction and enhances predictive accuracy in pest detection and classification for peanut crops, addressing the unique challenges of agricultural pest identification.

A CASE OF RIGHT HEART FAILURE: WHEN IT IS NOT PULMONARY EMBOLISM

Abstract A 66–year–old patient, showing exertional dyspnea and atypical chest pain, came to our attention with instrumental findings at echocardiogram of right ventricular failure and was hospitalized due to worsening signs and symptoms of right heart failure. Signs of right ventricular strain and the McGinn–White sign (S1Q3T3 pattern) were seen on the ECG, leading to the suspicion of pulmonary thromboembolism (PE). Pulmonary CT angiography confirmed bilateral PE involving the main pulmonary artery. Oral anticoagulant therapy was initiated with initial partial benefit and relief of symptoms. However, after four weeks, the patient was readmitted for severe heart failure and signs of low cardiac output. Echocardiography showed the presence of a conspicuous, mobile, isoechoic mass occupying most of the main pulmonary artery, suggesting thrombosis. The patient underwent urgent surgical thromboendoarterectomy extended to the third segmental left branches and to the second segmental right branches of the pulmonary artery; a severe pulmonary valve regurgitation was observed and therefore the valve was replaced with a Edwards–Magna pericardial bioprosthetic valve. ECMO (Extracorporeal Membrane Oxygenation) was initiated due to haemodinac instability. In the postoperative period, the severe heart failure worsened, unresponsive to pharmacological treatments and mechanical support, leading to the patient passing away in the following days. Unexpectedly, histological analysis of the surgical specimen revealed neoplastic cells with vascular/endothelial differentiation; fluorescence in situ hybridization (FISH) detected the MDM2 gene (cfr.23–F–00275), involved in the p53 protein regulation mechanisms, which is consistent with a diagnosis of angiosarcoma. Pulmonary–artery angiosarcomas are extremely rare (few anecdotical cases are described in literature) and resemble the CT–signs and clinical features of PE, seriously delaying the diagnosis. They are usually associated with a worse prognosis than PE, one and a half months survival time when inoperable. Radical surgery, associated with thirty six and a half months survival time, is often unfeasible because of locally advanced disease or metastatic disease already present at diagnosis. The main differences with PE are the more proximal and almost–occluding involvement of the pulmonary artery, the absence of peripheral vein thrombosis and the absence of response to anticoagulant therapy.

MYOCARDIAL INFARCTION DUE TO LEFT CIRCUMFLEX ARTERY OCCLUSION: A SUBTLE TROJAN HORSE

Abstract A 47–year–old man presented to the emergency department complaining of recurrent chest pain (CP) since the previous day. His medical history included active smoking and dyslipidemia. The ECG revealed sinus bradycardia (56 bpm) with widespread ST elevation, consistent with benign early repolarization, and hyperacute and broad–based T waves in V2–V3 (Fig. A). The echocardiography showed normal left and right ventricular systolic function and no valvular abnormalities. A low but abnormal initial high–sensitivity cardiac troponin I value (40 pg/mL, normal range: 1.7– 34,2 pg/mL) and a slightly and early increase after two hours (52 pg/ml) were seen. Therefore, the patient was admitted to our intensive cardiac care unit (ICCU) with a working diagnosis of non–ST–segment elevation myocardial infarction (NSTEMI). No sign or symptoms of heart failure on physical examination were recorded. GraceScore 2.0 was calculated as 68 points and coronary angiography was organized within 24h. Two hours after admission to the ICCU, the patient experienced a new episode of CP. Consequently, ECG and echo were repeated. Compared to the previous results, ECG showed dynamic changes in V2–V3, with the resolution of hyperacute T waves (Fig B); bedside echo remained unchanged. Considering the unresponsiveness to nitrate therapy, the cath lab was activated for a time–dependent coronary angiography. The exam showed an acute total thrombotic occlusion of the mid–segment of the circumflex branch (LCx) in the setting left–sided dominant circulation. A drug eluting stent was deployed into the culprit lesion (Fig. C), and the coronary flow was restored completely (TIMI Flow 3). The subsequent hospital stay proceeded without complications. The peak troponin level was 60’000 pg/ml. The patient was discharged on the fifth day, asymptomatic and hemodynamically stable, with instructions to continue dual antiplatelet therapy. The case report focuses on the electrocardiographic and angiographic expression of LCx total occlusion. Despite acute myocardial infarction (AMI) size was large, as confirmed by the abnormal troponin I values and the angiographic total occlusion, the first and the repeated ECGs showed only vague repolarization changes without clear ST–segment modifications. LCx occlusion is often characterized by the absence of significant ST–elevation, therefore categorized as NSTEMI. Greater awareness of the presentation of LCx–related AMI can be crucial to avoid delays in reperfusion.