Search Results (27)

Though having been discovered in one third of sarcomas, gene fusions are less studied in their roles as potential therapeutic targets, making conventional modalities the mainstream treatment options for sarcoma patients. Recent decades have witnessed encouraging progress in basic research delving into mechanisms underlying how gene fusions drive sarcomas; nevertheless, further translation to clinical application fails to keep abreast with the advances achieved in basic science. In this review, we will focus on key chromosomal translocation-driven sarcomas defined by characteristic hallmark fusion oncoproteins, including Ewing sarcoma with EWSR1–FLI1/ERG fusion, epithelioid hemangioendothelioma with WWTR1–CAMTA1/YAP1–TFE1 fusion, and others, to discuss the potential of directly targeting these fusion proteins as therapeutic targets in preclinical and clinical contexts. Full article

Maize tassels are critical phenotypic organs in maize, and their quantity is essential for determining tasseling stages, estimating yield potential, monitoring growth status, and supporting crop breeding programs. However, tassel identification in complex field environments presents significant challenges due to occlusion, variable lighting conditions, multi-scale target complexities, and the asynchronous and irregular growth patterns characteristic of maize tassels. In response to these challenges, this paper presents a DMSF-YOLO model for maize tassel detection. In the network’s backbone front, conventional convolutions are replaced with conditional parameter convolutions (CondConv) to enhance feature extraction capabilities. A novel DMSF-P2 network architecture is designed, including a multi-scale fusion module (SSFF-D), a scale-splicing module (TFE), and a small object detection layer (P2), which further enhances the model’s feature fusion capabilities. By integrating a dynamic detection head (Dyhead), superior recognition accuracy for maize tassels across various scales is achieved. Additionally, the Wise-IoU loss function is used to improve localization precision and strengthen the model’s adaptability. Experimental results demonstrate that on our self-built maize tassel detection dataset, the proposed DMSF-YOLO model shows remarkable superiority compared with the baseline YOLOv8n model, with precision (P), recall (R), $mA{P}_{50}$, and $mA{P}_{50:95}$ increasing by 0.5%, 3.4%, 2.4%, and 3.9%, respectively. This approach enables accurate and reliable maize tassel detection in complex field environments, providing effective technical support for precision field management of maize crops. Full article

Aiming at the problem that the existing auxiliary diagnosis methods for fractures are mostly limited to specific body parts and lack generality and robustness when applied to multi-part diagnoses, this study proposes a two-stage upper limb fracture auxiliary diagnosis method based on deep learning and develops a corresponding auxiliary diagnosis system. In the first stage, this study employs an improved ResNet-50 model combined with transfer learning and a Squeeze-and-Excitation (SE) attention mechanism for fracture image localization. In the second stage, an improved You Only Look Once (YOLO) model based on Scale Sequence Feature Fusion (SSFF) and Triple Feature Encoder (TFE) modules is used for fracture diagnoses in different body parts. Contrary to the traditional methods that are tailored to specific body parts, the integrated design approach presented in this paper is better suited to meeting the diagnostic needs of multiple body parts, demonstrating better generality and clinical application potential. Full article

Lead (Pb) is one of the most common environmental pollutants that negatively impacts male reproductive health. Thus far, the underlying molecular mechanisms of Pb-induced reproductive toxicity are still not well understood. In this study, 64 male ICR mice were given drinking water with Pb (0, 100, 200, and 300 mg/L) for 90 days. We found that exposure to 300 mg/L Pb resulted in reduced sperm quality and elevated autophagy-related protein levels in the mouse testes. Our findings indicate that the Pb hindered the autophagic clearance by impairing the lysosomes’ function and then obstructing the fusion of lysosomes and autophagosomes. The autophagy cycle obstruction prevented the lipid droplets from breakdown and led to their accumulation in the Sertoli cells. In turn, the ccytotoxic effects that resulted from the interruption of the autophagy maturation stage, instead of the elongation phase, could be alleviated by either Chloroquine or Bafilomycin A1. Furthermore, exposure to 400 μM Pb initiated the TFE3 nuclear translocation and caused the increased expression of its target genes. Then, the knockdown of TFE3 reduced the formation of the autophagosome. In addition, the use of the antioxidant NAC notably enhanced the autophagic activity and reduced the occurrence of lipid droplets in the Sertoli cells. This study demonstrated that Pb disrupted the autophagic flow, which caused lipid droplet accumulation in the TM4 cells. Consequently, focusing on the maturation stage of autophagy might offer a potential therapeutic approach to alleviate male reproductive toxicity caused by Pb exposure. Full article

Introduction: Adolescent/young adult (AYA) patients with metastatic soft tissue sarcoma (STS) typically face a dismal prognosis. However, a subset of patients with incurable disease lives beyond two years. Due to the rarity of diagnoses and inherent heterogeneity within this population, a paucity of data exists regarding the experiences of AYAs with an indolent course (and how to best capture these experiences). With increasing biological insight and clinical experience, including the use of targeted or immune therapies, it is anticipated that more such patients will experience prolonged survival. Our pilot study aimed to describe the clinical characteristics and illness experiences of AYAs with incurable yet indolent metastatic STS who were living two years after their diagnoses. Our exploratory aim was to generate a conceptual framework that could subsequently be tested in a multi-center study with a larger cohort of patients. Materials and Methods: Patients with metastatic incurable STS, aged 15–39 years at diagnosis, and at least two years from diagnosis, were eligible. Patients were recruited over a two-year period at a quaternary children’s hospital with a comprehensive AYA oncology program. Participants completed a demographic form and PROMIS short form questionnaires for seven domains and answered an open-ended question. Responses to open-ended questions were coded independently by two authors and utilized to generate themes. Clinical variables were collected from medical records. Results: Five patients completed questionnaires. Mean age was 29.4 years (18.5–39.8 years) at diagnosis and 34 years (23.2–45.7 years) at study. Three patients were female; two were male; four were White; and one was Black/African American. Diagnoses included ASPSCR1::TFE3 alveolar soft part sarcoma; WWTR1::CAMTA1 epithelioid hemangioendothelioma; INI-1 deficient epithelioid sarcoma; EWSR1::NR4A3 extra-skeletal myxoid chondrosarcoma; and low-grade ARHGAP23::FER spindle cell malignancy, a novel fusion-driven sarcoma. Mean time since diagnosis was 4.5 years (2.6–6 years), and mean treatment duration was 4.2 years (1.5–6 years). On average, patients received 4.8 lines (range 2–8 lines) of antineoplastic therapy. All patients received at least one targeted therapy or immune checkpoint inhibitor. Patients reported increased fatigue and anxiety and decreased physical function compared to the standardized US reference population. Themes emerging from qualitative responses included managing physical symptoms, navigating feelings of guilt and inadequacy, self-reflection generating gratitude, and changing illness experiences over time. Conclusions: AYA patients living with incurable metastatic soft tissue sarcoma for more than two years were treated with multiple lines of antineoplastic therapy longitudinally. PROMIS data identified fatigue, anxiety, and decreased physical function within this population. Exploratory thematic analysis of qualitative responses generated concepts that could be further tested in an expanded cohort of patients. Full article

Background: The purpose of this article is to overview the clinical significance of left supraclavicular adenopathy and review the etiology of inferior vena cava (IVC) thrombosis, starting from a presentation of a rare case of renal cell carcinoma (RCCs) with Xp11.2 translocation involving TFE3 gene fusion. This article also aims to review the literature to understand the characteristics of this rare type of renal tumor. Renal cell carcinoma (RCC) associated with Xp11.2 translocation/gene fusion TFE3 is a rare subtype of kidney cancer that was classified in 2016 as belonging to the family of renal carcinomas with MiT gene translocation (microphthalmia-associated transcription factor). The prognosis for these kidney cancers is poorer compared to other types. Methods: We present a case of a 66-year-old man with Virchow–Troisier adenopathy during physical examination, which raises the suspicion of infra-diaphragmatic tumor. The echocardiography highlighted a heterogeneous mass in the right cardiac cavities, and the abdominal ultrasound exam revealed a solid mass at the upper pole of the left kidney. Results: Following computed tomography, magnetic resonance imaging, PET-CT, and histopathological and immunohistochemical examinations, the patient was diagnosed with renal carcinoma with Xp11.2 translocation and TFE3 gene fusion. Conclusions: IVC thrombosis is often associated with neoplastic disease due to the procoagulant state of these patients, the most common malignancies related to IVC thrombosis being represented by RCCs (38%), genitourinary cancers (25%), bronchus and lung cancers, retroperitoneal leiomyosarcoma, and adrenal cortical carcinoma. Imaging methods play a crucial role in differential diagnosis, allowing for the localization of the primary tumor and assessment of its characteristics. Full article

Weeds significantly impact crop yields and quality, necessitating strict control. Effective weed identification is essential to precision weeding in the field. Existing detection methods struggle with the inconsistent size scales of weed targets and the issue of small targets, making it difficult to achieve efficient detection, and they are unable to satisfy both the speed and accuracy requirements for detection at the same time. Therefore, this study, focusing on three common types of weeds in the field—Amaranthus retroflexus, Eleusine indica, and Chenopodium—proposes the HAD-YOLO model. With the purpose of improving the model’s capacity to extract features and making it more lightweight, this algorithm employs the HGNetV2 as its backbone network. The Scale Sequence Feature Fusion Module (SSFF) and Triple Feature Encoding Module (TFE) from the ASF-YOLO are introduced to improve the model’s capacity to extract features across various scales, and on this basis, to improve the model’s capacity to detect small targets, a P2 feature layer is included. Finally, a target detection head with an attention mechanism, Dynamic head (Dyhead), is utilized to improve the detection head’s capacity for representation. Experimental results show that on the dataset collected in the greenhouse, the mAP for weed detection is 94.2%; using this as the pre-trained weight, on the dataset collected in the field environment, the mAP for weed detection is 96.2%, and the detection FPS is 30.6. Overall, the HAD-YOLO model has effectively addressed the requirements for accurate weed identification, offering both theoretical and technical backing for automatic weed control. Future efforts will involve collecting more weed data from various agricultural field scenarios to validate and enhance the generalization capabilities of the HAD-YOLO model. Full article

TFE3-rearranged Renal Cell Carcinoma (TFE3-RCC) is an aggressive subtype of RCC characterized by Xp11.2 rearrangement, leading to TFE3 fusion proteins with oncogenic potential. Despite advances in understanding its molecular biology, effective therapies for advanced cases remain elusive. This study investigates the role of ARID2, a component of the SWI/SNF chromatin remodeling complex, in TFE3-RCC. Through a series of in vitro and in vivo experiments, we confirmed that ARID2 acts as a tumor suppressor in TFE3-RCC. ARID2 knockout (KO) enhanced TFE3-RCC cell migration, proliferation, and tumor growth. Transcriptomic analysis revealed ERBB3 as a key target gene regulated by both PRCC-TFE3 and ARID2. Chromatin immunoprecipitation (ChIP) assays demonstrated that PRCC-TFE3 directly binds to and upregulates ERBB3 expression, with ARID2 KO further enhancing this effect. TFE3-RCC ARID2 KO cells exhibited significant gene expression enrichment in MAPK and ERBB3 signaling pathways. These cells also showed increased activation of ERBB3, EGFR, and selective activation of SRC and MAPK. TFE3-RCC ARID2 KO cells demonstrated heightened sensitivity to the ERBB3 inhibitor AZD8931 compared to their wild-type counterparts, exhibiting significantly reduced migration and proliferation rates. These findings suggest that the PRCC-TFE3-ARID2-ERBB3 axis plays a critical role in TFE3-RCC pathogenesis and highlights the potential of targeting ERBB3 in ARID2-deficient TFE3-RCC as a therapeutic strategy. This study provides new insights into the molecular mechanisms of TFE3-RCC and suggests avenues for precision treatment of this aggressive cancer. Full article

Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, distant objects are often small, which increases the risk of detection failures. To address this challenge, the MST-YOLOv8 model, which incorporates the C2f-MLCA structure and the ST-P2Neck structure to enhance the model’s ability to detect small objects, is proposed. This paper introduces mixed local channel attention (MLCA) into the C2f structure, enabling the model to pay more attention to the region of small objects. A P2 detection layer is added to the neck part of the YOLOv8 model, and scale sequence feature fusion (SSFF) and triple feature encoding (TFE) modules are introduced to assist the model in better localizing small objects. Compared with the original YOLOv8 model, MST-YOLOv8 demonstrates a 3.43% improvement in precision (P), an 8.15% improvement in recall (R), an 8.42% increase in mAP_0.5, a reduction in missed detection rate by 18.47%, a 70.97% improvement in small object detection AP, and a 68.92% improvement in AR. Full article

In unmanned aerial vehicles (UAVs) detection, challenges such as occlusion, complex backgrounds, motion blur, and inference time often lead to false detections and missed detections. General object detection frameworks encounter difficulties in adequately tackling these challenges, leading to substantial information loss during network downsampling, inadequate feature fusion, and being unable to meet real-time requirements. In this paper, we propose a Real-Time Small Object Detection YOLO (RTSOD-YOLO) model to tackle the various challenges faced in UAVs detection. We further enhance the adaptive nature of the Adown module by incorporating an adaptive spatial attention mechanism. This mechanism processes the downsampled feature maps, enabling the model to better focus on key regions. Secondly, to address the issue of insufficient feature fusion, we employ combined serial and parallel triple feature encoding (TFE). This approach fuses scale-sequence features from both shallow features and twice-encoded features, resulting in a new small-scale object detection layer. While enhancing the global context awareness of the existing detection layers, this also enriches the small-scale object detection layer with detailed information. Since rich redundant features often ensure a comprehensive understanding of the input, which is a key characteristic of deep neural networks, we propose a more efficient redundant feature generation module. This module generates more feature maps with fewer parameters. Additionally, we introduce reparameterization techniques to compensate for potential feature loss while further improving the model’s inference speed. Experimental results demonstrate that our proposed RTSOD-YOLO achieves superior detection performance, with ${\mathrm{mAP}}_{50}$/${\mathrm{mAP}}_{50:95}$ reaching 97.3%/51.7%, which represents improvement of 3%/3.5% over YOLOv8, and 2.6%/0.1% higher than YOLOv10. Additionally, it has the lowest parameter count and FLOPs, making it highly efficient in terms of computational resources. Full article

Object detection technology holds significant promise in unmanned aerial vehicle (UAV) applications. However, traditional methods face challenges in detecting denser, smaller, and more complex targets within UAV aerial images. To address issues such as target occlusion and dense small objects, this paper proposes a multi-scale object detection algorithm based on YOLOv5s. A novel feature extraction module, DCNCSPELAN4, which combines CSPNet and ELAN, is introduced to enhance the receptive field of feature extraction while maintaining network efficiency. Additionally, a lightweight Vision Transformer module, the CloFormer Block, is integrated to provide the network with a global receptive field. Moreover, the algorithm incorporates a three-scale feature fusion (TFE) module and a scale sequence feature fusion (SSFF) module in the neck network to effectively leverage multi-scale spatial information across different feature maps. To address dense small objects, an additional small object detection head was added to the detection layer. The original large object detection head was removed to reduce computational load. The proposed algorithm has been evaluated through ablation experiments and compared with other state-of-the-art methods on the VisDrone2019 and AU-AIR datasets. The results demonstrate that our algorithm outperforms other baseline methods in terms of both accuracy and speed. Compared to the YOLOv5s baseline model, the enhanced algorithm achieves improvements of 12.4% and 8.4% in AP₅₀ and AP metrics, respectively, with only a marginal parameter increase of 0.3 M. These experiments validate the effectiveness of our algorithm for object detection in drone imagery. Full article

Accurate traffic flow prediction in road networks is essential for intelligent transportation systems (ITS). Since traffic data are collected from the road network with spatial topological and time series sequences, the traffic flow prediction is regarded as a spatial–temporal prediction task. With the powerful ability to model the non-Euclidean data, the graph convolutional network (GCN)-based models have become the mainstream framework for traffic forecasting. However, existing GCN-based models either use the manually predefined graph structure to capture the spatial features, ignoring the heterogeneity of road networks, or simply perform 1-D convolution with fixed kernel to capture the temporal dependencies of traffic data, resulting in insufficient long-term temporal feature extraction. To solve those issues, a spatial–temporal correlation constrained dynamic graph convolutional network (STC-DGCN) is proposed for traffic flow forecasting. In STC-DGCN, a spatial–temporal embedding encoder module (STEM) is first constructed to encode the dynamic spatial relationships for road networks at different time steps. Then, a temporal feature encoder module with heterogeneous time series correlation modeling (TFE-HCM) and a spatial feature encoder module with dynamic multi-graph modeling (SFE-DCM) are designed to generate dynamic graph structures for effectively capturing the dynamic spatial and temporal correlations. Finally, a spatial–temporal feature fusion module based on a gating fusion mechanism (STM-GM) is proposed to effectively learn and leverage the inherent spatial–temporal relationships for traffic flow forecasting. Experimental results from three real-world traffic flow datasets demonstrate the superior performance of the proposed STC-DGCN compared with state-of-the-art traffic flow forecasting models. Full article

Background: Identical translocations involving the TFE3 gene and various partners have been found in both renal and soft tissue tumors, like alveolar soft part sarcoma (ASPSCR1), ossifying fibromyxoid tumor (PHF1), epithelioid hemangioendothelioma, and the clear cell stromal tumor of the lung (YAP1). Methods: Herein, we review in detail the clinicopathologic and molecular data of TFE3-rearranged renal tumors and propose our perspective, which may shed light on this emerging conundrum. Results: Among the kidney tumors carrying TFE3 translocations, most are morphologically heterogeneous carcinomas labeling for the tubular marker PAX8. The others are mesenchymal neoplasms known as PEComas, characterized by epithelioid cells co-expressing smooth muscle actin, cathepsin-K, melanogenesis markers, and sometimes melanin pigment deposition. Over the past 30 years, numerous TFE3 fusion partners have been identified, with ASPL/ASPSCR1, PRCC, SFPQ/PSF, and NONO being the most frequent. Conclusions: It is not well understood why similar gene fusions can give rise to renal tumors with different morpho-immunophenotypes, which may contribute to the recent disagreement regarding their classification. However, as these two entities, respectively, epithelial and mesenchymal in nature, are widely recognized by the pathology community and their clinicopathologic features well established, we overall believe it is still better to retain the names TFE3-rearranged renal cell carcinoma and TFE3-rearranged PEComa. Full article

Background: Epithelioid haemangioendothelioma (EHE) is an ultra-rare malignant vascular tumour with a prevalence of 1 per 1,000,000. It is typically molecularly characterised by a WWTR1::CAMTA1 gene fusion in approximately 90% of cases, or a YAP1::TFE3 gene fusion in approximately 10% of cases. EHE cases are typically refractory to therapies, and no anticancer agents are reimbursed for EHE in Australia. Methods: We report a cohort of nine EHE cases with comprehensive histologic and molecular profiling from the Walter and Eliza Hall Institute of Medical Research Stafford Fox Rare Cancer Program (WEHI-SFRCP) collated via nation-wide referral to the Australian Rare Cancer (ARC) Portal. The diagnoses of EHE were confirmed by histopathological and immunohistochemical (IHC) examination. Molecular profiling was performed using the TruSight Oncology 500 assay, the TruSight RNA fusion panel, whole genome sequencing (WGS), or whole exome sequencing (WES). Results: Molecular analysis of RNA, DNA or both was possible in seven of nine cases. The WWTR1::CAMTA1 fusion was identified in five cases. The YAP1::TFE3 fusion was identified in one case, demonstrating unique morphology compared to cases with the more common WWTR1::CAMTA1 fusion. All tumours expressed typical endothelial markers CD31, ERG, and CD34 and were negative for pan-cytokeratin. Cases with a WWTR1::CAMTA1 fusion displayed high expression of CAMTA1 and the single case with a YAP1::TFE3 fusion displayed high expression of TFE3. Survival was highly variable and unrelated to molecular profile. Conclusions: This cohort of EHE cases provides molecular and histopathological characterisation and matching clinical information that emphasises the molecular patterns and variable clinical outcomes and adds to our knowledge of this ultra-rare cancer. Such information from multiple studies will advance our understanding, potentially improving treatment options. Full article

Telomeres play an essential role in protecting the ends of linear chromosomes and maintaining the integrity of the human genome. One of the key hallmarks of cancers is their replicative immortality. As many as 85–90% of cancers activate the expression of telomerase (TEL+) as the telomere maintenance mechanism (TMM), and 10–15% of cancers utilize the homology-dependent repair (HDR)-based Alternative Lengthening of Telomere (ALT+) pathway. Here, we performed statistical analysis of our previously reported telomere profiling results from Single Molecule Telomere Assay via Optical Mapping (SMTA-OM), which is capable of quantifying individual telomeres from single molecules across all chromosomes. By comparing the telomeric features from SMTA-OM in TEL+ and ALT+ cancer cells, we demonstrated that ALT+ cancer cells display certain unique telomeric profiles, including increased fusions/internal telomere-like sequence (ITS+), fusions/internal telomere-like sequence loss (ITS−), telomere-free ends (TFE), super-long telomeres, and telomere length heterogeneity, compared to TEL+ cancer cells. Therefore, we propose that ALT+ cancer cells can be differentiated from TEL+ cancer cells using the SMTA-OM readouts as biomarkers. In addition, we observed variations in SMTA-OM readouts between different ALT+ cell lines that may potentially be used as biomarkers for discerning subtypes of ALT+ cancer and monitoring the response to cancer therapy. Full article