Search Results (777)

(1) Background: Sepsis is characterized by profound heterogeneity of immune responses, complicating biomarker-based prediction of clinical outcomes. Latent human cytomegalovirus (HCMV) infection is one of the strongest modulators of the human immune system and may influence cytokine-mediated signaling during sepsis. (2) Methods: In this post hoc analysis of 331 patients from the prospective multicenter SepsisDataNet.NRW cohort (German Clinical Trial Registry No. DRKS00018871), we quantified 13 serum cytokines on day 1 after sepsis diagnosis and determined HCMV IgG serostatus via ELISA. Using nested cross-validated logistic regression with exhaustive feature selection, we identified cytokine panels predictive of 30-day survival in the total cohort and in subgroups stratified by HCMV serostatus. (3) Results: In the total cohort, a four-cytokine panel (IL-6, IL-10, TNF-α, IL-12p70) predicted 30-day survival with a cross-validated area under the curve (AUC) of 0.66 [95% CI: 0.59–0.72]. Stratification by HCMV serostatus revealed distinct predictive profiles: in HCMV-seropositive patients, a two-cytokine model (IL-10, IL-23) achieved an AUC of 0.69 [95% CI: 0.61–0.77], whereas in seronegative patients, a model based on IL-8 and IL-17A failed to generalize (AUC = 0.47 [95% CI: 0.33–0.61]). Kaplan–Meier analysis confirmed a significant separation of survival curves for the HCMV-seropositive group (p < 0.001) but not for seronegative patients (p = 0.282). (4) Conclusions: HCMV serostatus defines an immunological context in which cytokine-based prediction of sepsis outcome becomes feasible. These data suggest that viral serostatus should be systematically incorporated into biomarker discovery and immunophenotyping approaches to improve the reproducibility and biological interpretability of sepsis endotyping. Full article

Background/Objectives: Despite the long-standing history of liver transplantation (LT) in Spain, no multicenter study has reviewed national outcomes for LT in metastatic neuroendocrine tumors (NETs). In the current era of transplant oncology, auditing these results is essential to refine patient selection and improve long-term outcomes. Methods: This retrospective observational study analyzed data from 13 centers, including 91 patients who underwent LT for NET between 1995 and 2024. Patients were stratified into two groups: Milan IN (those meeting the Milan criteria) and Milan OUT (the remainder). Results: Recurrence occurred in 57.1% of cases, and overall mortality was 51.6%. Of the 91 patients, 71 (78.0%) were Milan IN and 20 (22.0%) were Milan OUT. Five-year overall survival was 71.0% in Milan IN and 58.0% in Milan OUT, with a statistically significant difference. The 5-year disease-free survival (DFS) rate was 58.8% in Milan IN and 36.3% in Milan OUT; this difference was not statistically significant. Conclusions: In conclusion, strict adherence to Milan criteria and incorporation of modern prognostic factors are critical to optimize long-term survival in LT for NET. While the overall outcomes in this historical cohort are modest, future improvements are expected through more rigorous selection and the potential use of bridging or downstaging therapies. Full article

Background and Objectives: Traumatic injuries are a major public health issue, being the leading cause of death in the U.S. Advancements in medical care, injury prevention, and regional trauma systems have improved survival rates, but there is limited information on outcomes for survivors. Blunt, sharp, and firearm injuries are the primary mechanisms in trauma forensics. This study examines patient outcomes for blunt, sharp, and firearm injuries over 20 years. Materials and Methods: De-identified data were collected from the TriNetX Research network in June 2024. Patients aged 18–90 were categorized by injury type (blunt, sharp, firearm) from 2004 to 2023. Trends were analyzed by stratifying the data into 20 consecutive one-year intervals. Mortality, blood transfusions, traumatic shock, hypovolemic shock, and acute post-hemorrhagic anemia were recorded annually. Statistical analysis was performed using One Way Repeated ANOVA and post hoc Tukey testing, with significance defined as p < 0.05. Results: The study included 1,205,350 blunt, 710,875 sharp, and 144,562 firearm injuries. Firearm injuries predominantly affected males (83%) and African Americans (51%), while blunt and sharp injuries showed more demographic variability. Looking at the 20-year trends, the average age of firearm and sharp injury patients decreased by 21% (48 ± 13 to 38 ± 15, p ≤ 0.0001) and 14% (49 ± 16 to 42 ± 18, p ≤ 0.0001), respectively, while blunt injury patient age did not change significantly. Mortality rates significantly decreased from 12% for firearm, 7% for sharp, and 6% for blunt injuries in 2004 to less than 1% in 2023 for all three injury mechanisms. Blood transfusions increased 450% (2% to 11%) for firearm injuries and increased 100% for sharp and blunt injuries (1% to 2%). Traumatic shock and hypovolemic shock incidences also increased by 100% for firearm injuries (3% to 6% and 1% to 2%, respectively), while sharp and blunt injuries did not change significantly. Acute post-hemorrhagic anemia increased from 3% to 19% for firearm injuries (533% relative increase), while sharp and blunt injuries remained around 3% for the past 20 years. Conclusions: The study reveals that with improved survival rates over the last 20 years, there has been a significant increase in shock-related morbidities and blood transfusion rates, particularly for firearm injuries. These findings can inform trauma care to enhance resuscitation efforts, optimize resource allocation, and improve mortality and outcomes for these injury mechanisms. Full article

Background/Objectives: Peptide receptor radionuclide therapy (PRRT) is an established treatment for neuroendocrine tumors (NETs), enabling targeted radiation delivery via radiolabeled peptides. Small cell lung cancer (SCLC) remains a major therapeutic challenge due to its aggressive nature and poor prognosis. Despite advances, relapse rates are high and effective therapies are limited. We previously demonstrated the diagnostic potential of the cholecystokinin-2 receptor (CCK2R)-targeting minigastrin analog [⁶⁸Ga]Ga-DOTA-MGS5 in PET/CT imaging of different NETs. Building on this, we developed and evaluated [¹⁷⁷Lu]Lu-DOTA-MGS5 as a therapeutic PRRT agent. Methods: Preclinical studies investigating the receptor-mediated cellular internalization and intracellular distribution over time in A431 cells with and without CCK2R expression were performed using the fluorescent tracer ATTO-488-MGS5. Short- and long-term cytotoxic effects of [¹⁷⁷Lu]Lu-DOTA-MGS5 were evaluated on the same cell line using trypan blue exclusion and clonogenic survival assays. CCK2R expression was assessed by immunohistochemistry in 42 SCLC tissue specimens. In addition, the first PRRT with [¹⁷⁷Lu]Lu-DOTA-MGS5 was conducted in a patient with extensive disease SCLC (ED-SCLC) after confirming CCK2R-positive uptake in [⁶⁸Ga]Ga-DOTA-MGS5 PET/CT. Results: Rapid binding and internalization into A431-CCK2R cells, with progressive accumulation in intracellular compartments, was observed for ATTO-488-MGS5. Short-term irradiation effects of [¹⁷⁷Lu]Lu-DOTA-MGS5 were comparable for 4 h and 24 h incubation and were between the effects obtained with 2 and 4 Gy of external beam radiotherapy (EBRT). Clonogenic survival of A431-CCK2R cells incubated with increasing activity of [¹⁷⁷Lu]Lu-DOTA-MGS5 decreased in a dose-dependent manner. Immunohistochemistry on SCLC specimens confirmed moderate to high CCK2R expression in 16 out of 42 SCLC samples. In the first patient with SCLC treated with four cycles of [¹⁷⁷Lu]Lu-DOTA-MGS5 with a total activity of 17.2 GBq, an improvement in clinical symptoms was observed. Conclusions: The preclinical and clinical results confirm the feasibility of [¹⁷⁷Lu]Lu-DOTA-MGS5 PRRT in patients with SCLC and support further clinical studies investigating the therapeutic value and clinical applicability of this new CCK2R-targeted theranostic approach in larger patient cohorts. Full article

In precision oncology research, achieving joint modeling of tumor grading and treatment response, together with interpretable mechanism analysis, based on multimodal medical imaging and clinical data remains a challenging and critical problem. From a sensing perspective, these imaging and clinical data can be regarded as heterogeneous sensor-derived signals acquired by medical imaging sensors and clinical monitoring systems, providing continuous and structured observations of tumor characteristics and patient states. Existing approaches typically rely on invasive pathological grading, while grading prediction and treatment response modeling are often conducted independently. Moreover, multimodal fusion procedures generally lack explicit structural constraints, which limits their practical utility in clinical decision-making. To address these issues, a grade-guided multimodal collaborative modeling framework was proposed. Built upon mature deep learning models, including 3D ResNet-18, MLP, and CNN–Transformer, tumor grading was incorporated as a weakly supervised prior into the processes of multimodal feature fusion and treatment response modeling, thereby enabling an integrated solution for non-invasive grading prediction, treatment response subtype discovery, and intrinsic mechanism interpretation. Through a grade-guided feature fusion mechanism, discriminative information that is highly correlated with tumor malignancy and treatment sensitivity is emphasized in the multimodal joint representation, while irrelevant features are suppressed to prevent interference with model learning. Within a unified framework, grading prediction and grade-conditioned treatment response modeling are jointly realized. Experimental results on real-world clinical datasets demonstrate that the proposed method achieved an accuracy of $84.6\%$ and a kappa coefficient of $0.81$ in the tumor-grading prediction task, indicating a high level of consistency with pathological grading. In the treatment response prediction task, the proposed model attained an AUC of $0.85$, a precision of $0.81$, and a recall of $0.79$, significantly outperforming single-modality models, conventional early-fusion models, and multimodal CNN–Transformer models without grading constraints. In addition, treatment-sensitive and treatment-resistant subtypes identified under grading conditions exhibited stable and significant stratification differences in clustering consistency and survival analysis, validating the potential value of the proposed approach for clinical risk assessment and individualized treatment decision-making. Full article

Background and Objectives: Autosomal dominant polycystic kidney disease (ADPKD) is a leading cause of end-stage renal disease (ESRD). Progressive renal cyst growth in ADPKD can exert mass effects, including compression of the inferior vena cava (IVC), a rare but clinically significant complication with implications for hemodynamic stability and renal outcomes. This study evaluated the prevalence of IVC compression in ADPKD and its impact on progression to ESRD, mortality, and overall survival. We aimed to provide quantitative measures to elucidate its prognostic significance. Materials and Methods: Using the TriNetX database, we conducted a retrospective cohort study of 658 ADPKD patients with IVC compression, comparing them to unmatched controls without compression. Outcomes included ESRD incidence, mortality, and survival. Kaplan–Meier curves and hazard ratios (HRs) with 95% confidence intervals (CIs) were used for analysis. Results: ESRD Risk: IVC compression was associated with a higher risk of ESRD (77.4% vs. 29.7%, RR: 2.61, 95% CI: 2.49–2.73, p < 0.001). Survival Probability: 5-year Survival was significantly reduced in patients with IVC compression (42.6%) compared to controls (61.7%) (HR: 4.00, 95% CI: 3.45–4.63, p = 0.002). Mortality: Mortality was higher in the compression group (29.2% vs. 9.1%). Combined Impact: ESRD patients with IVC compression had a lower survival rate (11.9%) than ESRD patients without compression (28.5%) (HR: 5.60, 95% CI: 5.12–6.13, p < 0.001). Conclusions: IVC compression in ADPKD is associated with significantly worse outcomes, including increased ESRD risk, higher mortality, and reduced survival. These findings underscore the importance of early diagnosis and targeted management strategies. Full article

With the industry development of sea cucumber Apostichopus japonicus aquaculture, the indoor high cost and low survival rate have become serious problems. Therefore, it is necessary to optimize substrate selection for seedling protection in outdoor pond net cages. This study explores the succession of periphyton on the different substrate surface types, including a curvimurate net (CU), nylon mesh (NM), and ground cages (including a ground cage net (CN) and ground cage plate (CP)), and their effects on the seedling protection of sea cucumbers. In addition, we monitored the substrates’ dry weight, chlorophyll-a, and the community composition of substrates, alongside seedling growth, yield, and survival rate. The results show that a total of 7 phyla, 23 genera, and 31 species were detected on the substrates, with diatoms dominating (19 species) and Chlorophyta (4 species) being the main species. The CU had the highest total number of alga species attached, significantly higher than the other substrates in week 13 (p < 0.05). In week 9, the diatom density dropped to its lowest point, and, after September, it rose with the decrease in water temperature. In terms of dry weight with and without ash, CP increased rapidly in the early stage, with NM, CU, and CP being significantly higher than CN in week 13 (p < 0.05). The chlorophyll-a content showed a decreasing–increasing–decreasing trend, with CU reaching 3.62 ± 0.48 μg/cm² in the 13th week, significantly higher than other substrates (p < 0.05). Finally, the A. japonicus survival rate and yield in the CU group at week 12 were significantly higher than those in the NM and ground cage groups (p < 0.05). At week 17, the average weight, yield, and survival rate in the CU group were still optimal, with the yield 5.76 times that in the initial dosage. These results suggest that the CU has a suitable mesh size, has good permeability, and may stably support sediment, which is conducive to the growth of benthic diatoms. In addition, it can provide sufficient natural feed and a good habitat environment and is the preferred substrate for A. japonicus seedling protection in outdoor pond net cages. Full article

Drought stress represents a major constraint on global apple production, with the widely used semi-dwarfing rootstock ‘M.26’ being particularly vulnerable to water deficit. Although the osmolyte trimethylamine N-oxide (TMAO) has been shown to improve abiotic stress tolerance in the model plant Arabidopsis, its potential role in enhancing drought resilience in woody fruit trees remains largely unexplored. Under prolonged moderate drought stress, exogenous TMAO application significantly promoted plant growth, mitigating the drought-induced suppression of plant height by 5.3–12.2% compared to untreated drought-stressed controls and alleviating the decline in above-ground biomass. This improvement was underpinned by a substantial alleviation of root growth inhibition, with TMAO restoring total root length and biomass from 37% in the control to only 6.1–9.5%. TMAO also fine-tuned the root-to-shoot ratio to favor resource allocation to roots. Consequently, TMAO-treated plants maintained superior leaf water status, exhibiting higher relative water content (drought-induced reduction limited to ~17.5% with TMAO versus 26.3% in the control). Physiologically, TMAO alleviated the drought-induced stomatal limitation of photosynthesis, sustaining higher net photosynthetic rate, stomatal conductance, and transpiration rate. Crucially, under severe drought stress, TMAO pretreatment markedly enhanced ‘M.26’ survival rates from approximately 39% in the untreated control to 60–68%, representing a relative increase of approximately 74%. Collectively, this study demonstrates that exogenous application TMAO significantly enhances drought tolerance in apple rootstock ‘M.26’, highlighting its potential as an effective and environmentally safe plant growth regulator for more sustainable cultivation of fruit trees under irregular/erratic irrigation conditions. Full article

The use of deep learning radiomics to predict whether advanced prostate cancer (PCa) will progress within two years after treatment has been validated, yet there remains a lack of research on estimating time to progression. Patients were enrolled from October 2017 to March 2024. One hundred and eighty-two patients with advanced PCa diagnosed through ultrasound-guided systematic prostate biopsy were enrolled. A deep learning-based radiomics model for predicting progression was firstly developed using pretreatment MR apparent diffusion coefficient (ADC) maps, and the performance of manual (ROIref) versus AI-derived (ROIai) tumor segmentations was compared. Then, survival analysis was performed to compare ROIref-based and ROIai-based radiomics-predicted probabilities in the risk stratification. The area under the receiver operating characteristics curve (AUC) was used to estimate the model efficacy. The model achieved high AUC values for progression prediction in test sets (ROIref: 0.840, ROIai: 0.852). No significant difference was observed between ROIai-based and ROIref-based approaches (ΔAUC = 0.012, p = 0.870) in the test set. Both ROIref-predicted and ROIai-predicted probabilities independently predicted progression in multivariate Cox proportional hazard regression models (p < 0.001) and stratified patients into distinct survival groups (log-rank p < 0.001). Decision curve analysis confirmed equivalent clinical utility across thresholds (0.1–0.6), with net benefit exceeding the “treat all” and “treat none” strategies. In conclusion, deep learning-based radiomics models could effectively predict advanced PCa progression, with AI-derived tumor annotations performing equally to manual expert ones. Full article

Lung cancer ranks among the world’s most prevalent and deadly diseases. Early detection is crucial for improving patient survival rates. Computed tomography (CT) is a common method for lung cancer screening and diagnosis. With the advancement of computer-aided diagnosis (CAD) systems, deep learning (DL) technologies have been extensively explored to aid in interpreting CT images for lung cancer identification. Therefore, this review aims to comprehensively examine DL techniques developed for lung cancer screening and diagnosis. It explores various datasets that play a crucial role in lung cancer CT image segmentation and classification tasks, analyzing their differences in aspects such as scale. Next, various evaluation metrics for measuring model performance are discussed. The segmentation section details convolutional neural network-based (CNN-based) segmentation methods, segmentation approaches using U-shaped network (U-Net) architectures, and the application and improvements of Transformer models in this domain. The classification section covers CNN-based classification methods, classification methods incorporating attention mechanisms, Transformer-based classification methods, and ensemble learning approaches. Finally, the paper summarizes the development of segmentation and classification techniques for lung cancer CT images, identifies current challenges, and outlines future research directions in areas such as dataset annotation, multimodal dataset construction, multi-model fusion, and model interpretability. Full article

Quantum anomalies are traditionally understood as classical symmetries that fail to survive quantization, while experimental “anomalies” denote deviations between theoretical predictions and measured values. In this work, we develop a unified framework in which both phenomena can be interpreted through the lens of algebraic quantum field theory (AQFT). Building on the renormalization group viewed as an extension problem, we show that renormalization ambiguities correspond to nontrivial elements of Hochschild cohomology, giving rise to a deformation of the observable algebra $A\ast B=AB+\epsilon \omega (A,B)$, where $\omega$ is a Hochschild 2-cocycle. We interpret $\omega$ as an intrinsic algebraic curvature of the net of local algebras, namely the (local) Hochschild class that measures the obstruction to trivializing infinitesimal scheme changes by inner redefinitions under locality and covariance constraints. The transported product is associative; its first-order expansion is associative up to $\mathcal{O}\left({\epsilon }^2\right)$ while preserving the ∗-structure and Ward identities to the first order. We prove the existence of nontrivial cocycles in the perturbative AQFT setting, derive the conditions under which the deformed product respects positivity and locality, and establish the compatibility with current conservation. The construction provides a direct algebraic bridge to standard cohomological anomalies (chiral, trace, and gravitational) and yields correlated deformations of physical amplitudes. Fixing the small deformation parameter $\epsilon$ from the muon $(g-2)$ discrepancy, we propagate the framework to predictions for the electron $(g-2)$, charged lepton EDMs, and other low-energy observables. This approach reduces reliance on ad hoc form-factor parametrizations by organizing first-order scheme-induced deformations into correlation laws among low-energy observables. We argue that interpreting quantum anomalies as manifestations of algebraic curvature opens a pathway to a unified, testable account of renormalization ambiguities and their phenomenological consequences. We emphasize that the framework does not eliminate renormalization or quantum anomalies; rather, it repackages the finite renormalization freedom of pAQFT into cohomological data and relates it functorially to standard anomaly classes. Full article

Background: Early risk stratification in acute decompensated heart failure with reduced ejection fraction (ADHF-rEF) remains challenging. The Endothelial Activation and Stress Index (EASIX)—a composite of lactate dehydrogenase, creatinine, and platelet count—reflects endothelial dysfunction, a pathophysiological contributor to early deterioration in ADHF-rEF. This study evaluated the prognostic utility of admission-based EASIX for in-hospital mortality. Methods: In this retrospective single-center cohort, 850 consecutive patients hospitalized with ADHF-rEF between January 2022 and June 2025 were analyzed. EASIX was calculated from first-day laboratory values. Logistic regression, ROC analysis, restricted cubic splines, and Kaplan–Meier survival methods were used to assess the association between EASIX and in-hospital mortality, and to evaluate its incremental value beyond established clinical and laboratory predictors. Results: In-hospital mortality was 12.4%. Higher EASIX values were significantly associated with mortality in both univariable and multivariable models (adjusted OR 1.273; p < 0.001). EASIX demonstrated moderate discriminative performance among evaluated biomarkers (AUC 0.751) and showed a clear dose–response risk gradient, with mortality rising from 1.4% in the lowest tertile to 26.2% in the highest. Incorporating EASIX into clinical and laboratory prediction models yielded substantial continuous net reclassification improvement (0.59 and 0.38, respectively). Survival curves diverged early and remained distinctly separated across EASIX strata. Conclusions: Admission EASIX is an independent predictor of in-hospital mortality in ADHF-rEF and provides complementary prognostic information beyond conventional models. This is the first study to demonstrate the prognostic value of EASIX in the ADHF-rEF setting, supporting its potential utility as an accessible endothelial stress biomarker for early risk stratification. Full article

In this paper, we propose a literature review regarding two deep learning architectures, namely Convolutional Neural Networks (CNNs) and Capsule Networks (CapsNets), applied to medical images, in order to analyze them to help in medical decision support. CNNs demonstrate their capacity in the medical diagnostic field; however, their reliability decreases when there is slight spatial variability, which can affect diagnosis, especially since the anatomical structure of the human body can differ from one patient to another. In contrast, CapsNets encode not only feature activation but also spatial relationships, hence improving the reliability and stability of model generalization. This paper proposes a structured comparison by reviewing studies published from 2018 to 2025 across major databases, including IEEE Xplore, ScienceDirect, SpringerLink, and MDPI. The applications in the reviewed papers are based on the benchmark datasets BraTS, INbreast, ISIC, and COVIDx. This paper review compares the core architectural principles, performance, and interpretability of both architectures. To conclude the paper, we underline the complementary roles of these two architectures in medical decision-making and propose future directions toward hybrid, explainable, and computationally efficient deep learning systems for real clinical environments, thereby increasing survival rates by helping prevent diseases at an early stage. Full article

The South American tomato leaf miner, Tuta absoluta (Meyrick, 1917) (Syn.: Phthorimaea absoluta), is a pest of great economic importance worldwide. Although T. absoluta shows a strong preference for tomato, it can also attack potato, eggplant, and various wild solanaceous plants, thereby posing new challenges for pest control. To assess the adaptability of this pest to different potato varieties, an age-stage, two-sex life table method was used to determine the development, survival, reproduction, and key population parameters of the pest on six common potato varieties (Hezuo No. 88, Lishu No. 6, Weiyu No. 3, Zhongshu No. 5, Qingshu No. 9, and Qingshu No. 10) in China. The results showed that T. absoluta could complete its entire life cycle on all cultivars. However, key life history parameters varied significantly. On cultivars Qingshu No. 9 and Qingshu No. 10, the pest exhibited significantly prolonged preadult duration and total pre-oviposition period (TPOP), as well as reduced adult fecundity. In contrast, Hezuo No. 88 supported the highest intrinsic rate of increase (r) and net reproductive rate (R₀). The 60-day population projections further highlighted this contrast, showing that the T. absoluta population on Hezuo No. 88 increased by a factor of 4.26 and 3.52 times compared to that on Qingshu No. 9 and Qingshu No. 10, respectively. We conclude that cultivars Qingshu No. 9 and Qingshu No. 10 exhibit antibiosis resistance against T. absoluta. This study not only provides a theoretical foundation and candidate materials for breeding pest-resistant potato varieties, but also establishes a basis for IPM strategies against T. absoluta that are founded on host resistance. Full article

Background/Objectives: Post-thrombotic syndrome (PTS) following unprovoked deep vein thrombosis (DVT) lacks readily available, calibrated risk estimates at defined follow-up horizons. Building on signals that thrombus burden, care processes, and a form of metabolic–inflammatory tone influence outcomes, we prospectively evaluated survival machine-learning models, explicitly including hyperuricemia while excluding what we consider major inflammatory confounders. Methods: Adults with first-episode unprovoked lower-extremity DVT were enrolled at two centers (July 2024–September 2025). PTS (Villalta) was assessed at 3, 6, 9, and 12 months. The cohort was split 70/30 into training and test sets. Eight learners (RSF, GBM, LASSO + Cox, CoxBoost, survivalsvm, XGBoost-Cox, superpc, and plsRcox) were tuned using 10-fold cross-validation in training and once evaluated in the independent test set. Performance metrics included all time-dependent AUCs, fixed-time ROC AUCs with bootstrap 95% CIs, C-index, various forms of calibration, decision-curve analysis, and simple Kaplan–Meier risk group separation. Results: 193 patients were analyzed (PTS in 64%). High 9-month AUCs were seen in training: GBM (0.992) and RSF (0.982) being the strongest; by 12 months, both remained near constant. Test set performance followed a similar pattern, with RSF again favored (AUC 0.948) and XGBoost/GBM close behind. Calibration was satisfactory, net benefit from decision curves positive, and to a large extent, risk groups were separated as expected. Conclusions: Survival machine-learning models, at least in this dual-center prospective cohort, produced a clinically useful risk of PTS. Hyperuricemia, or any metabolically based signal, is a valuable addition to the “anatomy and care” of DVT. External validation is still required. Full article