Search Results (322)

Obstructive Sleep Apnea Syndrome (OSAS) is associated with systemic inflammation and immune dysfunction, yet the specific metabolic alterations within immune cells remain poorly understood. In this exploratory study, we characterized the metabolome of peripheral mononuclear cells (PMNCs) from patients with OSAS and healthy controls to investigate potential immunometabolic signatures associated with the disease. Initial analyses identified 21 nominally differentially abundant metabolites between OSAS patients and controls, suggesting trends such as the depletion of carnitines and alterations in fatty acid and neurotransmitter metabolism. However, following correction for multiple testing (False Discovery Rate, FDR), only putatively annotated cis,cis-muconic acid remained statistically significant. Furthermore, pathway enrichment analysis highlighted exploratory trends primarily associated with SLC-mediated transmembrane transport and energy metabolism. Our findings indicate that while OSAS PMNCs exhibit metabolic shifts suggestive of cellular stress, most of these alterations represent nominal trends that require validation in larger cohorts. The robust identification of putatively annotated cis,cis-muconic acid highlights a potential target of interest. In this exploratory pilot study, given the absence of polysomnographic and objective hypoxia parameters in most participants, observed metabolic alterations are described as associated with, rather than directly caused by, intermittent hypoxia. Overall, this study provides a hypothesis-generating framework for future research on the immunometabolic consequences of OSAS. Full article

Background: The first-trimester ultrasound (11 + 0 to 13 + 6 weeks) is primarily used for aneuploidy screening, yet its potential for early identification of major fetal structural anomalies in an unselected population continues to be explored. This study evaluated the clinical yield and feasibility of a systematic first-trimester fetal anatomy assessment in a regional setting. Methods: This retrospective single-center study included 500 consecutive singleton pregnancies undergoing routine first-trimester ultrasound between 11 + 0 and 13 + 6 weeks of gestation. All examinations were performed by a single certified sonographer using an extended anatomical protocol according to ISUOG and FMF guidelines. The study center serves a regional population of approximately 400,000 inhabitants in eastern Mazovia, Poland. Cases with suspected anomalies underwent a detailed diagnostic work-up, including a targeted anomaly scan, early fetal echocardiography, and invasive genetic testing when clinically indicated. Results: Major fetal structural anomalies were suspected in 17 out of 500 fetuses (3.4%) during the first-trimester scan. Of the 17 suspected cases, 14 were confirmed on subsequent evaluation (14/17, 82.4%; 95% CI 56.6–96.2%), two were false-positives (11.8%), and one patient was lost to follow-up. Four fetuses (23.5%) presented with multiple anomalies involving different organ systems, with congenital heart defects being the most prevalent. A notable observational cluster of three cases of acrania/exencephaly was identified. Remarkably, only three of the confirmed affected cases (21.4%) presented with a nuchal translucency measurement above the 95th percentile. Conclusions: Integration of a systematic first-trimester fetal anatomy assessment allowed the identification of suspected major structural anomalies in 3.4% of fetuses in an unselected regional population. With a confirmation rate of 82.4% among screen-positive cases, these findings demonstrate the clinical feasibility of detailed first-trimester anatomical assessment in a regional setting. Full article

Objectives: Emotional tears are generated in a distinct neurophysiological context from reflex tears, but their metabolic composition remains poorly understood. Methods: This exploratory paired targeted-metabolomics study compared emotional and reflex tears collected from 22 healthy volunteers using a 600-multiple-reaction-monitoring platform. Among 412 detected metabolites, 344 were retained after data preprocessing. Paired statistical analysis prioritized 23 candidate metabolites based on the combined criteria of unadjusted p-value, fold change, and consistency of within-subject change. Results: Seven candidates were lower, and 16 were higher in emotional tears. Salicylic acid was retained in the descriptive candidate set but excluded from the primary machine-learning analysis because a contribution from the reflex-tear induction procedure could not be ruled out. Among the remaining 22 candidates, 4-hydroxy-3-methylbenzoic acid, vanillic acid, cytidine-5′-monophosphate, and hydroxyphenyllactic acid were consistently ranked among the leading features. A fixed four-metabolite combination achieved an area under the receiver operating characteristic curve of 0.864 (95% CI, 0.756–0.957) under ordinary leave-one-subject-out cross-validation. When candidate screening, feature selection, and model fitting were repeated within each training fold, the best nested pipeline achieved an area under the curve of 0.725 (95% CI, 0.603–0.843). Pathway mapping further linked the candidate metabolites to histidine, tyrosine, fatty-acid, ether-lipid, and ubiquinone-related metabolism. Conclusions: These findings demonstrate measurable within-subject metabolic differences between emotional and reflex tears and identify a focused set of candidate metabolites for future validation. Because no individual metabolite remained significant after false discovery rate correction and no independent validation cohort was available, the candidate signals and discrimination models should be confirmed in larger, independently collected cohorts. Full article

Since heavy clutter seriously restricts the ability of radar to detect targets, it is significant to build the target detector under heavy clutter. For practical situations without the secondary data or prior knowledge of target and clutter, this paper proposes a polarization-space-time detector. First, a general radar model is constructed for multiple pulses, multiple arrays, and multiple polarizations. Based on the theory of ternary hypothesis, the secondary data free (SDF) GLRT detector is proposed, which can maintain the constant false alarm probability (CFAR) in inhomogeneous clutter. Then, this paper proposes a matrix transform operator and an adaptive detection method using sliding window. These two approaches do not need to know the steering vector of radar and the noncentral parameter of clutter in advance, so the SDF-GLRT detector can adapt to different application scenarios. In addition, this paper optimizes the polarization waveform of the radar system by constructing a projection matrix. This method yields closed-form solutions of the optimal polarization and worst polarization, rather than relying on numerical solution. Finally, the performances of the SDF-GLRT detector and three other detectors are compared by simulated and real data. The proposed SDF-GLRT maintains $P_{\mathrm{FA}}$ of $5.4\times {10}^{-3}$ and $2.6\times {10}^{-3}$ on two IPIX datasets (#54 and #310) at a design $P_{\mathrm{FA}}={10}^{-3}$, whereas the other detectors deviate to $0.0249$–$0.7405$. The optimal polarization yields a detection-probability gain of more than $0.22$ over the worst polarization at $\mathrm{SCR}=0$ dB. Full article

Medical treatment decision making is a complex process that involves integrating multivariate time-oriented data from multiple sources and is often influenced by factors such as patient load. In this study, we propose the Recurring Target Prediction (RTP) Pipeline to support treatment decision making by predicting the next medical action most likely to be administered, based on the historical data from patients in similar contexts. The method transforms raw time-stamped data into symbolic time intervals, incorporating domain knowledge. Each of the patient’s data are segmented by pre-defined trigger conditions (e.g., hypoglycemia), with each segment containing a feature window (historical data as symbolic time intervals); a prediction window (e.g., treatment dosage); and an optional prediction gap between the feature and prediction windows, enabling a future treatment alert. A frequent pattern-mining method is applied to the feature windows, and features generated from the mined patterns (e.g., count within each record and mean duration) are used as input to a Two-Step prediction model. First, a binary classifier predicts whether treatment is necessary, followed by a regression model to predict dosage. Finally, SHapley Additive exPlanations (SHAP) provide insights into the model’s decision making. We have evaluated the pipeline on an Intensive Care Unit (ICU) dataset, across three domains: hypoglycemia, hypokalemia, and hypotension. Key contributions include leveraging the recurrence of medical conditions and events to enrich the dataset, reducing false positives through a Two-Step prediction model, allowing prediction gaps for advance treatment notice, and incorporating SHAP, and introducing a two-level SHAP-based method for aggregating the relative weights of temporal patterns and components, to enhance the model’s interpretability. Full article

Retinitis pigmentosa (RP) is a rare inherited retinal disorder in which fundus changes may be subtle and heterogeneous, limiting detection from color fundus images. This study evaluated multiple machine learning architectures for binary-RP versus healthy-control classification, and developed a reproducible pipeline for research-oriented screening support. Three publicly available fundus datasets were combined, including 248 RP images and 1045 healthy controls. An 80/20 train–test split was used, with targeted data augmentation applied only to RP images in the training set to address class imbalance. ConvNeXt-Tiny, ResNet101V2, EfficientNet-B0, a baseline classifier, and custom shallow convolutional neural networks were compared using accuracy, precision, recall, F1-score, confusion matrices, and ROC/precision–recall analyses. A compact ShallowCNN provided the best sensitivity–performance trade-off. On the fixed image-level test set, Adam with a learning rate of 0.0005 reached 96.51% accuracy, while SGD with a learning rate of 0.001 achieved 98% RP recall, minimizing false negatives. The trained models were exported to ONNX and integrated into a Windows inference tool. The proposed framework provides an open, reproducible benchmark for technical evaluation, although external validation is required before clinical use. Full article

A compact 24 GHz FMCW radar board was evaluated for low-speed bicycle and small-vehicle sensing under strict memory and latency constraints. The hardware uses only 30 MHz modulation bandwidth, giving a nominal range resolution of about 5.0 m and a Doppler-bin spacing of about 2.57 m/s. Its small, incompletely calibrated antenna path also prevents any claim of high-angular-resolution imaging-radar performance. Within this constrained platform, the measured sequences reveal four coupled failure modes: static reflectors remain prominent in the range–Doppler map, useful low-Doppler responses are easily lost near the processed spectral boundary, weak plots do not always initiate a track, and short echo gaps can break otherwise continuous trajectories. To address these limitations, we combine frame-differential range–Doppler enhancement, quadrant-aware boundary-adaptive CA-CFAR, physically gated seed-growing initiation, and finite-frame retained Kalman tracking with SNR-weighted updates. In addition to natural bicycle and small-vehicle measurements, a labeled synthetic 64 by 32 range–Doppler benchmark is used to report Precision, Recall, F1-score, ROC/AUC, detection probability, and false alarms per frame for multiple CFAR variants. Public-radar tracking metrics are also reported on RadarScenes, a public RADIATE foggy sample, and nuScenes mini radar-only sequences with a bounded-approximation JPDA baseline. These public-radar results evaluate tracker-lifecycle and data-association behavior under public target-center observations; they are not presented as full validation of the board-specific RD-to-track pipeline. The evidence supports a bounded embedded-processing claim for this low-resolution board, not general applicability to high-resolution imaging radar systems. Full article

Background and objectives: Artificial intelligence (AI) is increasingly embedded in public health workflows, yet adoption among practitioners remains uneven and is shaped by knowledge, legal awareness, and operational barriers. This cross-sectional study characterised determinants of AI adoption among healthcare professionals and examined how legal concern moderates the translation of technical knowledge into practical use, at a single Romanian tertiary academic centre. Methods: We surveyed 93 healthcare professionals (physicians, nurses, public health specialists, residents) at the “Pius Brînzeu” Clinical Emergency County Hospital and “Victor Babeș” University of Medicine and Pharmacy Timișoara. Participants were classified as AI adopters or non-adopters. Likert-derived composite scores (0–100; Cronbach’s α 0.79–0.88) quantified knowledge, trust, legal concern, privacy concern, and workflow confidence. Group comparisons used independent-samples t-tests and χ² tests; associations used Pearson correlation; predictors of adoption and usage intensity were modelled with logistic and multiple linear regression; a two-way ANOVA tested profession-by-training effects. Significance was set at p < 0.05. Benjamini–Hochberg false-discovery-rate correction was applied across the 18 bivariate tests reported in this study, and adjusted q-values are reported alongside unadjusted p-values. Results: Adopters (n = 51) were younger (34.7 ± 7.5 vs. 43.2 ± 8.5 years; p < 0.001) and reported higher knowledge (67.3 vs. 48.6; p < 0.001) and workflow confidence (64.2 vs. 41.9; p < 0.001) but lower legal concern (58.4 vs. 71.2; p < 0.001). Knowledge correlated positively with usage intensity (r = 0.536; p < 0.001), whereas legal concern correlated negatively (r = −0.426; p < 0.001). In multivariable models, younger age (OR = 0.91; p = 0.004), knowledge (OR = 1.06; p = 0.005), and trust (OR = 1.07; p = 0.005) independently predicted adoption. The linear model explained 46.1% of usage variance. Stratified analysis suggested legal concern attenuated the knowledge–usage slope (β: 0.51→0.18); however, the formal knowledge-by-concern interaction term was not statistically significant (p = 0.191), and this pattern is therefore exploratory. Conclusions: In this modest, single-centre sample, AI adoption was independently associated with knowledge and trust, and legal concern was independently and negatively associated with usage intensity; the apparent dampening of the knowledge–usage relationship by legal concern was suggestive but not statistically confirmed. Targeted legal-regulatory literacy and structured training may support practical AI uptake in public health settings, pending confirmation in larger, multicentre studies. Full article

Wild arrowhead (Sagittaria trifolia L.) often occurs as isolated plants or compact clusters in paddy fields, yet these states are difficult to distinguish in unmanned aerial vehicle (UAV) imagery because they share similar color, texture, and leaf morphology. This study presents a two-stage framework in which an unchanged YOLOv8n detector localizes candidate targets and a dedicated Patch-cls network refines the single- or multiple-plant label. The classifier combines multi-level features, local multi-scale enhancement, and channel attention; a GhostConv variant is also evaluated to examine the efficiency trade-off. Annotation-box and detector-generated-box results are reported separately, followed by a complete-system evaluation that retains missed targets, false positives, duplicate detections, localization errors, and classification errors. Across three random seeds, the proposed Patch-cls obtained 94.00 ± 0.34% accuracy, 83.81 ± 1.19% Macro-F1, and 73.18 ± 3.91% multiple-class Recall. In the complete test pipeline, Macro-F1 increased from 0.5459 to 0.5539 and multiple-class F1 from 0.4224 to 0.4384, while mean average precision at an intersection over union (IoU) of 0.50 (mAP50) decreased slightly from 0.7023 to 0.7016. The optimized pipeline achieved 88.89 frames per second (FPS) on an NVIDIA RTX A4000 with approximately 1.62 GB peak allocated graphics processing unit (GPU) memory. The results indicate that Patch-cls can improve category balance under detector-generated crops, although the overall gain is modest and does not replace the need for stronger localization and dense-target separation. Full article

Background: Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system, affecting more than 2.8 million individuals worldwide. Automated segmentation of white matter lesions on fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) is essential for reproducible diagnosis and treatment monitoring, yet remains challenging due to extreme class imbalance, high lesion load variability, and poor contrast at lesion boundaries. Method: We propose a 2D U-Net with a ResNet50 encoder that exploits ImageNet-pretrained representations through a novel pseudo-RGB input strategy: three consecutive FLAIR slices centred on the target slice are stacked channel-wise to form a three-channel input, recovering inter-slice spatial context while enabling direct reuse of pretrained convolutional weights without modality-specific pretraining. A two-phase transfer-learning protocol first optimises only the decoder with the encoder frozen, then fine-tunes the upper encoder blocks at a reduced learning rate. Test-time augmentation (TTA) averaging over horizontal-flip and vertical-flip transformations further improves prediction robustness. Results: Evaluation on the held-out test set of the MSLesSeg2024 benchmark (12 patients, approximately 1650 axial slices) shows that the proposed model achieves a Dice similarity coefficient (DSC) of 0.714 (95% confidence interval (CI): 0.6845–0.7194), intersection-over-union (IoU) of 0.6571 (95% CI: 0.6272–0.6632), and area under the receiver operating characteristic (ROC) curve (AUC) of 0.9628 (95% CI: 0.9422–0.9793). Critically, the model records the lowest false-negative pixel count per slice (FNV = 33.6 px/slice) across all ablation conditions, indicating superior sensitivity to lesion tissue; this is a property of direct clinical relevance for MS monitoring, where missed lesions carry the greatest diagnostic risk. A patient-matched comparison against a 3D nnU-Net baseline shows statistically comparable DSC (0.714 vs. 0.726; paired Wilcoxon $p=0.68$–$0.79$, not significant) alongside a substantially higher pixel-level AUC for the proposed model (0.963 vs. 0.773) and a true volumetric Hausdorff distance gap smaller than an earlier estimate (12.5 mm vs. 10.7 mm), showing an honest mixed-strengths result rather than an outright improvement. A supplementary ablation further shows that replicating a single FLAIR slice across all three channels significantly outperforms the pseudo-RGB adjacent-slice encoding ($p<0.001$), indicating that the anticipated inter-slice-context benefit did not materialise here (see Discussion section). Cross-dataset evaluation on the independent MSSEG 2016 benchmark confirms generalisability: zero-shot transfer achieves DSC = 0.6562, recovering to DSC = 0.7046 after brief fine-tuning (30 epochs), within 1.5 percentage points of in-domain performance. Conclusions: The present study reveals that an optimized, lightweight 2D pipeline can rival the segmentation overlap of context-aware 3D baselines on specific datasets, doing so with a significantly reduced computational footprint. Given its strong pixel-wise discrimination, this methodology offers an effective and practical tool for routine automated MS lesion assessment in clinical settings. Full article

Detecting ships near the coast in Synthetic Aperture Radar (SAR) images is an important task. However, achieving accurate detection in these scenarios remains a significant challenge. The complex coastal topologies and multiple scattering effects frequently induce severe shore–sea feature aliasing, which conventional end-to-end detectors struggle to untangle due to their inherent architectural conflicts between background suppression and fine-grained localization. To address this issue, we propose a two-stage generative framework named Segmentation Guidance and Enhanced Diffusion (SGED). In the first stage, an Enhanced Attention U-Net (EAU-Net) is specifically tailored for robust shore–sea separation. By integrating adaptive Signal-to-Noise Ratio (SNR) masking, lightweight Transformer bottlenecks, and an Edge-Aware Composite Loss, EAU-Net isolates the maritime search space, achieving a Dice Similarity Coefficient (DSC) of 0.9047 and an 85.30% Near-Shore Coverage Accuracy (NSCA). Building upon this refined prior, the second stage introduces a SAR-Enhanced Diffusion Detector (SAR-DDet). It constructs a structural–statistical dual-verification mechanism by embedding Pixel Difference Convolution (PDC) and Constant False Alarm Rate (CFAR) soft attention into the multi-scale features. Coupled with a Normalized Wasserstein Distance (NWD) loss and a four-step DDIM iterative denoising process, SAR-DDet effectively mitigates small-target gradient vanishing and corrects bounding box coordinate quantization errors. Experiments on a near-shore subset of the HRSID benchmark demonstrate that SGED achieves competitive performance. It achieves an mAP of 62.35%, an $A{P}_{75}$ of 74.67%, and a small-target $A{P}_S$ of 60.14%, with consistent improvements over monolithic baseline architectures on this dataset. Full article

In complex UAV aerial scenes, small object detection tasks face challenges such as extremely low pixel occupancy, strong background interference, and sparse effective features, which are further compounded by environmental factors like low illumination. Consequently, single-modality detection algorithms are prone to severe target feature loss and missed detections. Multi-modal image fusion, which complements the texture details of visible light with the thermal radiation characteristics of infrared, is considered an effective approach to overcome the limitations of single physical imaging. However, conventional fusion mechanisms often suffer from semantic gaps when processing heterogeneous data, easily introducing redundant noise and background false alarms. To further improve the accuracy and robustness of small object detection in UAV aerial scenes, this paper proposes a multi-modal detection network that integrates direction-aware multi-granularity and asymmetric context guidance, termed DMAC-Net. Specifically, a Direction-Aware Granularity Enhancement (DAGE) module is first constructed for unified backbone feature extraction, which captures local directions and contour edges of small objects in UAV aerial images with high sensitivity, and expands the receptive field through a multi-granularity mechanism, effectively suppressing false positives induced by complex backgrounds while enhancing the recall of occluded and weakly featured targets. Additionally, the Asymmetric Context Guided Fusion (ACGF) module builds a spatial mechanism via asymmetric receptive fields and performs semantic soft alignment of cross-modal features with dynamic weight assignment, effectively filtering out artifacts and clutter from cross-modal interaction. Experimental results on multiple aerial datasets, including RGBTDronePerson, AVMS and LLVIP demonstrate that the proposed method outperforms existing mainstream models in terms of overall detection accuracy and missed-detection suppression, while exhibiting strong generalization capability and stability under complex lighting transitions and multi-scale variations in UAV monitoring environments. Full article

Illegal angling activities pose significant threats to aquatic ecosystem conservation and sustainable water resource management by disrupting ecological balance and aquatic resource protection, emphasizing the need for effective intelligent monitoring approaches. Illegal angling detection in complex aquatic environments remains challenging due to small target sizes, diverse human postures, and severe interference from shoreline vegetation, water reflections, and other complex backgrounds. To address these issues, this paper proposes an improved YOLOv8-based illegal angling detection framework, termed ERFA–YOLO. To enhance the discriminative representation capability of slender targets in complex scenes, an Enhanced Receptive Field Attention mechanism (ERFAConv) is introduced. By leveraging adaptive contextual perception and spatial geometric feature modeling, the proposed mechanism effectively enhances fishing-related target features while suppressing false activations from background noise. Furthermore, a temporal consistency-based post-processing strategy is introduced to reduce false positives caused by transient prediction noise and improve detection stability in dynamic aquatic environments. In addition, a dedicated illegal angling dataset covering multiple time periods, weather conditions, and complex shoreline environments is constructed to improve the generalization capability of the model in real-world natural scenarios. Experimental results demonstrate that, compared with the original YOLOv8 baseline, ERFA–YOLO achieves a Precision of 93.19% (+4.67%), a Recall of 86.24% (+1.34%), an ${\mathrm{mAP}}_{50}$ of 93.49% (+3.47%), and an ${\mathrm{mAP}}_{50:95}$ of 60.53%, while achieving real-time inference performance of 75.34 FPS on an NVIDIA RTX 4090 GPU. Compared with several mainstream object detection algorithms, the proposed method exhibits superior robustness and detection stability in complex natural environments, demonstrating the potential of ERFA–YOLO for intelligent illegal angling monitoring in sustainable aquatic resource management scenarios. Full article

When marine chillers operate under complex marine conditions, they suffer from severe cross-equipment feature distribution shifts, scarce labeled fault samples in the target domain, industrial vibration noise mixed in sensor signals, and difficulties in accurately identifying subtle faults with varying severity levels. To tackle these issues, this paper improves upon the domain difference perception network (DDPN) and proposes a dual-hybrid attention feature discriminant domain-Adversarial network (DAFDAN) to realize intelligent fault diagnosis across different equipment and working conditions under few-shot scenarios. The proposed method constructs a dual-branch feature encoder consisting of a source domain compressor and a target domain extender to accommodate the distinct sensor dimensions of two heterogeneous chiller types. A hybrid attention module is formed by integrating squeeze-and-excitation efficient channel attention (SE-ECA, a module for screening channel-wise features) and spatial attention, which adaptively amplifies time-series features sensitive to faults and suppresses irrelevant noise. Residual connections (shortcut paths in deep neural networks to mitigate the vanishing gradient problem during deep-layer training) are introduced to optimize feature transmission. A dual-layer domain alignment framework is built with gradient reversal layers and maximum mean discrepancy (MMD). Combined with adversarial training (a training paradigm that learns domain-agnostic features through a game between a feature extractor and a domain discriminator), the framework achieves joint optimization of implicit feature confusion and explicit distance constraints. Meanwhile, a five-stage progressive training strategy is designed, which activates multiple loss functions, including weighted cross-entropy, mean square error (MSE), binary cross-entropy (BCE), and Kullback–Leibler (KL) divergence stage by stage. Class weighting and early stopping strategies are adopted to alleviate sample imbalance and model overfitting. In this paper, the public ASHRAE RP-1043 centrifugal chiller dataset is used as the source domain, and time-series measurement data collected from a self-developed laboratory marine screw chiller serves as the target domain. Verification experiments are carried out covering one normal steady-state operating condition and 15 gradient faults falling into five major categories with different severity degrees. Results from ablation experiments (controlled-variable comparative experiments that quantify the independent contribution of each component by comparing model performance with or without a specific module/loss), multi-algorithm comparisons, and confusion matrix visualization demonstrate that the cross-domain fault diagnosis accuracy of the proposed DAFDAN approaches is 100%, outperforming mainstream transfer learning algorithms such as support vector machine (SVM), deep neural network (DNN), MMD, correlation alignment (CORAL), and domain-adversarial neural network (DANN). Multiple ablation experiments verify that the three core components—hybrid attention, adversarial training, and semi-supervised learning—jointly boost the model’s diagnosis accuracy and operational stability. The loss curves of the complete five-stage training process converge smoothly. The confusion matrix reveals zero misjudgments and zero false alarms across all 16 refined operating states, enabling precise identification of subtle incipient faults of all severity levels. This study proves that DAFDAN can effectively address the pain points of few-shot cross-equipment fault diagnosis for marine chillers and provides a reliable algorithmic reference for the intelligent operation and maintenance of ship refrigeration equipment. Full article

Proteasome inhibitors are approved to treat multiple myeloma and mantle cell lymphoma. Recent reports suggest sarcomas also display proteasome addiction. Mechanistic explanations cite proteotoxic stress. In sarcoma patients, we analyzed the impacts of 377 human E3 ubiquitin ligases on sarcoma patient overall survival (OS) and recurrence-free survival (RFS), identified substrates of E3 ligases with the most significant and robust effects, and performed enrichment analyses. High expression of 102 E3 ligases was associated with shortened OS. Thirteen of these shortened OS by >40 months, six with false-discovery rates (FDR) ≤ 5%. Nineteen showed correlation between increased expression and shortened RFS, two with FDR ≤ 5%. Overexpression of 73 E3 ligases significantly extended OS, with 18 extending OS by >40 months; six with FDR ≤ 5%. Elevated expression of 21 significantly extended RFS, one with FDR ≤ 5%. Enrichment analyses of substrates unique to the E3 ligases whose elevated expression most reliably shortened or extended OS by >40 months revealed non-overlapping functions: the E3 ligases associated with shortened OS uniquely targeted cell cycle, cell–cell communication, cellular responses to stimuli, chromatin organization, DNA repair, DNA replication, hemostasis, reproduction, and vesicle-mediated transport functions. Both OS-impacting E3 ligase sets targeted developmental biology, gene expression, immune system, metabolism of proteins, and signal transduction functions. Three specific functions were targeted by both groups. Functions uniquely targeted by each set of ligases could reveal therapeutic targets with a greater therapeutic index than the proteasome. Full article