The U.S. North Pacific harbors some of the densest and most diverse cold-water coral and sponge communities globally, yet quantitative data for depths below 900 m in the Gulf of Alaska and Aleutian Islands remain scarce. Most records originate from <300 m, despite ∼80% of Alaska’s seafloor exceeding 200 m depth. Using Remotely Operated Vehicle video imagery from two NOAA Ocean Exploration expeditions in 2023 (Seascape Alaska 3 and 5), we conducted a quantitative, image-based assessment of deep-sea coral and sponge communities across 15 previously unvisited sites spanning 380-3,200 m depth. From 15,531 observations, we documented 164 distinct morphotypes - 90 Porifera and 74 Cnidaria - substantially extending known distributions for multiple taxa, including five sponge genera new to Alaska and the northernmost Pacific record of the coral genus Umbellapathes . Density and diversity peaked along the margins of the Oxygen Minimum Zone (∼500-1,800 m; O 2 ≤1.43 mg L -1 ). Additionally, eight high-density aggregations were identified using quantitative spatial criteria, reaching densities up to 20.68 individuals m -2 . Community composition was structured by a hierarchy of drivers, with oceanographic setting explaining most variation. These findings highlight the importance of oceanographic context in structuring deep-sea biodiversity and provide a baseline for future ecological, taxonomic, and conservation research in North Pacific deep-sea ecosystems. Understanding the distribution and drivers of cold-water coral and sponge communities is vital for anticipating ecosystem responses to future ocean scenarios and for designing effective management strategies to safeguard these vulnerable deep-sea habitats. • Quantitative assessment of deep-sea corals and sponges across 380-3,200 m in Alaska • Fifteen previously unvisited sites surveyed using NOAA Ocean Exploration data • Eight high-density VME habitats identified using quantitative spatial criteria • OMZ drives abundance peaks while oceanographic regimes structure communities

Improved health risk management of heavy metals in cigarette smoke from middle Chinese best-selling cigarettes using respiratory tract deposition modeling and simulated lung fluid extraction.

Global bifurcation for a predator-prey system with nonlinear boundary-mediated dispersal

[¹⁷⁷Lu]Lu-PSMA RLT represents an effective option for advanced mCRPC. SPECT/CT allows the assessment of biodistribution and lesion-level tracer accumulation. We aimed to determine whether semiquantitative RLT SPECT/CT parameters could predict response on a lesion level. We retrospectively considered consecutive mCRPC patients who, between February 2022 and January 2026, received minimum two [¹⁷⁷Lu]Lu-PSMA-617 cycles at Messina and Genova Universities, with SPECT/CT at the 1st RLT, including whole PSMA-positive disease. Each [¹⁷⁷Lu]Lu-PSMA-617-positive lesion was semiautomatically segmented using MIM through a 40% threshold to extract SUVmax, SUVmean, Total Lesion Volume (TLV), and Total Lesion Activity (TLA=SUVmean×TLV). Each lesion's semiquantitative parameter was correlated with the single-lesion response at the last RLT cycle, classified as progressive or non-progressive (complete/partial response, stable disease) according to cut-offs from qualitative RECIP 1.0 and quantitative PPP criteria. ROC curves were used to determine the best cutoffs. We included 23 mCRPC patients for a total of 290 lesions: 249 osteomedullary, 34 lymph nodal, and 7 visceral. At the last cycle, 60 out of 290 lesions progressed, while 230 out of 290 remained stable/responded to RLT. 1st cycle [¹⁷⁷Lu]Lu-PSMA-617 SPECT/CT semiquantitative parameters were significantly higher in non-PD than in PD lesions (p < 0.001 for TLA, SUVmax, SUVmean; p = 0.008 for TLV). On ROC analysis, TLA reached an AUC of 0.854 (best cut-off = 61.7), SUVmax 0.843 (best cut-off = 11.8), SUVmean 0.838 (best cut-off = 6), and TLV 0.612 (best cut-off = 7.05mL), respectively. Our preliminary findings suggest that 1st cycle [¹⁷⁷Lu]Lu-PSMA-617 SPECT/CT semiquantitative parameters, especially when reflecting PSMA-expression, may serve as promising early predictors of the single-lesion response to RLT.

Decision-making (DM) problems in real-world environments are frequently described by ambiguity, expert hesitation, linguistic assessments, and incomplete information, which limit the effectiveness of traditional fuzzy set (FS) and intuitionistic fuzzy set (IFS) frameworks. To deal with such problems, this paper demonstrates an innovative and more expressive model, called linguistic cubic interval-valued intuitionistic fuzzy sets (LCuIVIFSs), which combines interval-valued intuitionistic fuzzy uncertainty, linguistic information, and cubic structures into a single framework. Some traditional operations of the newly defined LCuIVIFS model, such as union, intersection, and complement, are systematically introduced to ensure operational consistency and mathematical soundness. Within the framework of LCuIVIFSs, several aggregation operators (AOs), including arithmetic AO, geometric AO, weighted arithmetic AO, and weighted geometric AO, is presented to significantly combine complex and uncertain information. The key features of the proposed AOs are investigated. Moreover, a novel multi-criteria decision-making (MCDM) technique is developed using the newly defined AOs. To discuss the significance of the proposed approach, it is implemented to a case study of supplier selection problem in smart manufacturing, where both quantitative and qualitative criteria under uncertainty are considered. The final results ensure that the newly defined approach contributes reliable, flexible, and robust decision outcomes compared with existing FS-based models. The proposed study thus provides a valuable decision-support framework for complex DM problems under linguistic and cubic uncertainty.

Social media platforms increasingly shape public opinion through algorithmic content curation, yet the precise mathematical conditions under which such algorithms induce societal polarization remain poorly understood. This study extends classical bounded confidence opinion dynamics models by incorporating an algorithmic amplification term capturing the tendency of engagement-maximizing recommendation systems to promote extreme content. We analyze a two-group mean-field reduction using dynamical systems theory and derive exact analytical results for equilibrium structure, stability, and bifurcation behavior. The central finding is a supercritical pitchfork bifurcation at critical algorithmic strength α_c^*=2β , where β denotes the social interaction rate: below this threshold, only extreme consensus states are stable; above it, polarized equilibria emerge continuously with opinion gap δ^*=√(1-2β\/α). We establish a complete phase diagram comprising three regimes: extreme consensus (radicalization), partial polarization with cross-group interaction, and echo chambers with communication breakdown, with boundaries determined by algorithmic strength, interaction rate, and confidence threshold. Notably, within this model class, the centrist equilibrium is unconditionally linearly unstable for any positive algorithmic amplification, suggesting that engagement-driven algorithms may tend to destabilize moderate discourse. Agent-based simulations validate all analytical predictions. These results provide quantitative criteria for platform design and policy interventions aimed at mitigating algorithmic polarization.

Lower respiratory tract infections (LRTIs) represent a significant global health burden. While targeted next-generation sequencing (tNGS) offers potential advantages for pathogen detection, its clinical implementation is hindered by the absence of validated quantitative interpretation criteria for pathogen discrimination. We conducted a multicenter prospective study of 631 patients with suspected LRTIs across five intensive care units in eastern China from January 2022 to March 2025. Bronchoalveolar lavage fluid specimens underwent concurrent tNGS and conventional microbiological testing (CMT). Expert group A established the reference standard by classifying patients into LRTI/non-LRTI categories and identifying clinically significant pathogens based on comprehensive clinical criteria. Expert group B, blinded to tNGS quantitative data, provided qualitative interpretation based solely on detected microorganisms to eliminate any influence from quantitative parameters. Expert group C, blinded to all tNGS data, provided interpretation based on conventional microbiological testing combined with clinical manifestations. Quantitative diagnostic models incorporating reads per kilobase per million mapped reads (RPKM) and pathogen copy numbers were developed using a training cohort (n = 420) and validated in an independent cohort (n = 211). Of 631 patients, 358 (56.7%) met the diagnostic criteria for LRTI. Polymicrobial infections were identified in 77 patients, with the majority co-infected with Acinetobacter baumannii and Pseudomonas aeruginosa. tNGS demonstrated enhanced detection of Gram-negative bacteria, Candida species and Pneumocystis jirovecii, while CMT showed better detection for Aspergillus species. The quantitative models demonstrated excellent discriminatory performance for bacterial pathogens. The sensitivity and specificity for conventional microbiological testing alone were 58.7% and 74.7%. Adding clinical manifestations to CMT resulted in a sensitivity of 68.8% and specificity of 72.0%. In comparison, qualitative tNGS achieved a sensitivity of 78.5% and a specificity of 76.6%, while the model-based algorithm demonstrated the highest diagnostic accuracy with a sensitivity of 82.4% and a specificity of 85.0%. For antimicrobial resistance prediction, tNGS achieved moderate accuracy (AUC 0.715) with high concordance for key antimicrobial resistance markers including KPC, NDM, OXA-48 and mecA. We developed and validated quantitative models for tNGS-based pathogen detection in LRTIs, enabling precise discrimination between pathogenic and background organisms. These models represent a significant step forward in the clinical application of tNGS for LRTI diagnosis and antimicrobial resistance detection.

Language-driven speech output in individuals with aphasia shows considerable variability, including phonological errors and pauses during word searches. This makes it difficult to use traditional keyword classification systems and further reduces trust in deep neural models, complicating their application in clinical settings. This paper introduces AQFormer, a severity-aware transformer architecture designed to classify spoken keywords in aphasic speech, and A-CAM, a dual-stream attribute framework aimed at assisting individuals with aphasic impairments. AQFormer generates acoustic representations that are severity-adaptive by integrating patient-level Aphasia Quotient (AQ) scores through Feature-wise Linear Modulation (FiLM) and A-CAM. A-CAM consists of two main components: (i) a branch that influences WavLM convolutional features, a prediction-focused one, and (ii) a multimodal aphasia filter that captures pauses, phoneme variations, and interruptions at word boundaries, an impairment-focused branch. We introduce an adaptive perturbation and dual-filtering gradient scheme that enforces non-negative, mask-consistent attributions over time-frequency regions. Experiments utilizing a subset of AphasiaBank keywords (93 speakers, 960 recordings; training set expanded to 5,138) with rigorous speaker-disjoint evaluation indicate that AQFormer achieves approximately 96.61% accuracy (F1 = 96.8%) on previously unseen speakers. A-CAM consistently outperforms several Grad-CAM variants when deletion/insertion AUPC and ADCC metrics are employed. This results in stable, sparse explanations that reflect how aphasia is usually caused: Discriminates correct from incorrect productions with Cohen’s d = 2.05 (a massive effect size) and spatial localization of error regions with Intersection over Union (IoU) of 0.461 against phoneme boundaries. Montreal Forced Aligner meets the quantitative validation criteria for the aphasia filter. The impairment-focused A-CAM maps achieve an IoU of 0.712 against detected error regions, with a severity correlation that doubles from rho = −0.374 (base) to rho = −0.754 (filter-gated). By tightly coupling severity-aware modelling with aphasia-informed attributes, the proposed framework advances explainable learning systems for aphasia-affected speech without needing clinician-labelled training targets.

The 2023 Bo Lindell Laureate Lecture: Assessing and managing radiological risk.

Obesity is commonly viewed as a reversible condition primarily driven by excess body weight. Increasing evidence, however, suggests that adipose tissue may undergo persistent immunometabolic and structural alterations that do not fully revert after weight loss, raising the hypothesis of a durable biological imprint that could hinder long-term remission. Whether such persistence is consistently reflected across the biomedical literature remains uncertain. We performed an AI-driven semantic analysis of a construct-anchored corpus of obesity-related publications retrieved from PubMed and Scopus using transformer-based biomedical embeddings (PubMedBERT). Unsupervised density-based clustering (HDBSCAN) identified coherent semantic regions, and Uniform Manifold Approximation and Projection (UMAP) enabled visualization. Core macro-domains were selected using predefined quantitative criteria (cluster stability, temporal persistence, and semantic coherence) and independently evaluated by two blinded experts. Interpretability was assessed through stratified human validation, quantifying inter-rater agreement (Cohen's κ) and AI label acceptance rates. An exportable curated core corpus of mapped publications was generated to support downstream focused screening and structured synthesis. Three mutually exclusive yet highly coherent macro-domains emerged: (1) inflammatory adipose biology, (2) adipose remodeling and chronic dysfunction, and (3) stress-triggered immune persistence. Despite document-level exclusivity, these domains showed exceptionally high semantic similarity (pairwise cosine similarity > 0.97), indicating a shared conceptual core. The semantic architecture of the corpus is consistent with obesogenic memory conceptualized as biological hysteresis in adipose tissue, although not constituting mechanistic proof. The curated corpus provides a structured foundation for subsequent conventional evidence synthesis.

Grain drying is a pivotal post-harvest process that safeguards the storage safety and quality of grain. Conventional drying control strategies, however, predominantly rely on empirical operations and single-parameter monitoring. Although the concept of accumulated temperature has been applied in grain drying, few studies have systematically investigated the dynamic characteristics of drying accumulated temperature windows, resulting in a lack of quantitative and stable control criteria for the drying process. This study first defines the drying accumulated temperature window and further classifies it into three types: the equivalent window, actual window, and good window. On this basis, the window characteristics during continuous grain drying are systematically analyzed, accurate calculation methods for equivalent and actual accumulated temperature are established, and a feasible judgment criterion for the good window is proposed. A MATLAB 2022-based simulation model for continuous corn drying is constructed to verify the proposed methods. Experimental results show that three types of windows exhibit distinct dynamic response characteristics: the equivalent accumulated temperature responds instantaneously to changes in drying conditions, while the actual accumulated temperature has a time lag of one complete drying cycle. After the drying process stabilizes, the absolute difference between equivalent and actual accumulated temperature is controlled within 1500 °C·min. A drying process is identified to enter the good window state when the outlet moisture content stably maintains at 14.5 ± 0.5% for more than 3 h. The established simulation model demonstrates high prediction accuracy, with the mean relative errors of key indicators maintained at approximately 5%. This study clarifies the dynamic mechanism of accumulated temperature windows in continuous grain drying and provides a practical quantitative basis for the intelligent control and efficiency improvement of the grain-drying process.

The UK Medical Research Council's Guidance on Developing and Evaluating Complex Interventions (MRC GDECI) outlines a 4-phase framework for structuring research programs on interventions: development, feasibility, evaluation, and implementation. However, it provides limited practical direction on how researchers should select which phases to conduct or determine when and whether to progress between phases. This gap is particularly challenging in the context of digital health interventions (DHIs), given their fast-paced and rapidly evolving nature. This scoping review examined the research phases conducted, how researchers progressed through them, and the intervention characteristics associated with overall program structure and duration in DHI research, to inform the design of future research programs. We searched PubMed, Embase, CINAHL, PsycINFO, and ClinicalTrials.gov to identify complex DHIs promoting health among adolescents and young adults, implemented between 2017 and 2026, for which at least 2 phases of the MRC GDECI were reported, including the evaluation phase. For each eligible intervention, all related protocols, preprints, and published articles were retrieved to reconstruct the full research program. For each program, we analyzed the presence of each research phase, its organization (ie, phase arrangements), and the mechanisms guiding progression between phases (ie, progression mechanisms). Phase-specific and overall program durations were recorded. A total of 31 research programs, covering 31 interventions and reported in 130 articles, were included. Development, feasibility, evaluation, and implementation phases were reported in 26, 23, 31, and 7 research programs, respectively. Three types of phase arrangements were identified: sequential, iterative, and overlapping. Progression mechanisms between phases included automatic progression, conditional progression based on researchers' appraisal of findings without prespecified criteria, and progression based on predefined quantitative criteria. Six main research program structures were observed, combining phase arrangements and progression mechanisms. Iterative arrangements were most common, observed in 22 research programs, followed by overlapping (n=10) and strictly sequential structures (n=7). Most progressions relied on researchers' appraisal of findings without prespecified criteria. Justifications for phase iteration, omission, or progression decisions were rarely reported. The median program duration was 5.8 (IQR 3.8-6.6) years (n=13). Based on these findings, a novel 4-step operational framework and visualization tools were developed to guide the design and planning of DHIs, highlighting key considerations for each step, as well as the strengths, limitations, and risks associated with each phase arrangement and progression mechanism. This scoping review is the first to systematically examine phase arrangements and progression mechanisms in DHI research programs. Beyond descriptive reporting, it provides a conceptualization of research program structures and offers a flexible operational framework to support the concrete implementation of the MRC GDECI. Greater explicitness in decisions about program structure may enhance methodological rigor, reduce research waste, and improve the integrity and reproducibility of interventions. PROSPERO CRD42023401979; https://tinyurl.com/mvc265y3.

This paper presents a unified, data-driven optimization framework for the planning and operation of an arbitrary number N of grid-connected microgrids connected to a distribution feeder. Each microgrid is represented as a controllable energy entity comprising local loads, battery energy storage systems (BESS) modeled through their State of Energy (SOE), and optional local generation. The microgrids are embedded explicitly in a radial distribution network subject to hosting-capacity and ramp-rate constraints at the point of common coupling (PCC). Unlike many existing studies that rely on synthetic or stylized demand profiles, this work employs real, open-access hourly load data from the Electricity Load Measurements and Analysis (ELMAS) dataset (France) to construct heterogeneous residential, commercial, and industrial microgrid instances. A plug-and-play integration rule is formulated at the planning level: the connection of an additional microgrid is admissible if and only if the enlarged optimization problem remains feasible and all reliability, network, and safety-oriented constraints are satisfied. The deterministic formulation is extended to handle uncertainty via scenario-based stochastic modeling of load variability. A comprehensive case study based on real French load profiles illustrates how feeder hosting capacity can be quantified in terms of the maximum number of microgrids that can be safely integrated. The results demonstrate that coordinated planning significantly improves PCC behavior, reduces operational stress, and provides a clear quantitative criterion for plug-and-play microgrid integration in distribution networks.

Wheat (Triticum aestivum L.) is a globally essential cereal crop whose productivity and processing efficiency are critically influenced by the morphological traits of the grain. While biotic and abiotic stresses reduce field yields, post-harvest milling losses further diminish flour output, underscoring the importance of optimizing grain morphology for processing efficiency. This study investigates the relationships between the wheat grain shape and size parameters and their impact on milling performance outcomes to identify optimal morphological characteristics that minimize yield losses. Using a Korean wheat core collection of 566 accessions, we applied image-based phenotyping to quantify key grain traits, in this case the width, length, area, perimeter, aspect ratio, circularity, roundness, and skewness. Multivariate analyses through k-means clustering and principal component analysis showed two distinct morphological groups and highlighted the kernel width and uniformity as potential indicators. Strong positive correlations between size traits and negative correlations between shape descriptors emphasize the trade-offs influencing milling quality. Optimal wheat grains for enhanced the milling yield exhibited large, plump, regular kernels with high circularity and low skewness. These findings provide quantitative criteria to guide wheat breeding programs with the goal of genetically optimizing the grain morphology to improve the milling yield and processing quality, thereby contributing to global food security.

Rejuvenating aging human skin is a major therapeutic goal, but objective, quantitative measures of intrinsic aging are limited. We performed a cross-sectional histological study of UV-protected buttock and abdominal skin in adults spanning multiple decades of life to identify features that reliably track age. Epidermal thickness measured between rete ridges was unchanged, but rete ridge size declined linearly with age: ridges became shorter and thinner in both sites, though rete ridge number decreased only in the abdomen. Consistent with these structural changes, proliferative cells (Ki67+) per ridge and expression of integrin β4 (ITGB4), a putative stem-cell marker, were reduced in aged skin. We combined these biomarkers into a predictive model that estimated skin age more accurately than any single marker. To test whether the model detects longitudinal change, we analyzed aged abdominal skin before and after xenografting onto young or aged mice, a procedure previously reported to rejuvenate human skin in young but not aged recipient mice. Both individual biomarkers and the imaging model indicated rejuvenation regardless of host age; however, notably, engraftment efficiency was lower in aged hosts, with surviving grafts showing younger histological phenotypes. These results provide quantitative criteria for assessing intrinsic skin aging and suggest that the process of engraftment itself is sufficient to induce rejuvenation-like changes.

The Internet of Things (IoT) has become a major issue that has gained significant attention in the research community. Advances in IoT technologies have resulted in the emergence of various security issues and raised concerns about potential privacy breaches of IoT data. Utilizing Blockchain (BC) is seen as a promising solution for addressing security issues in the IoT. This paper offers a clear overview of IoT security threats, including the related security characteristics and the challenges that come with integrating BC with IoT. A brief discussion of various consensus protocols and existing security techniques is presented. A comparative study of several Distributed Ledger Technology (DLT) platforms based on both qualitative and quantitative evaluation criteria is also presented. This paper explores the role of BC Technology in improving security in Intrusion Detection Systems (IDS) and other applications in the IoT environment. Additionally, the paper identifies open issues and highlights potential research opportunities that can benefit future studies.

Vesicoureteral reflux (VUR) can cause retrograde urine flow under voiding pressure, facilitating ascending bacterial colonisation and recurrent inflammatory responses. These processes trigger a cascade of cellular and molecular events-innate immune activation, pro-inflammatory cytokine release, oxidative stress, apoptosis and extracellular matrix deposition-thereby promoting tubulointerstitial remodelling and increasing the risk of renal parenchymal injury and scarring. Static renal 99mTc-DMSA scintigraphy primarily reflects tracer uptake by proximal tubular cells in the renal cortex and can serve as an integrated phenotypic readout of tubular dysfunction and focal cortical involvement. However, its clinical interpretation remains experience-dependent and lacks reproducible quantitative criteria, while voiding cystourethrography (VCUG), the diagnostic and grading gold standard for VUR, is limited by invasiveness and procedural burden. In this study, we collected DMSA data from 346 children with febrile urinary tract infection treated at the Second Affiliated Hospital of Wenzhou Medical University between January 2019 and January 2023 and developed a deep learning model (MedSwinNet) for VUR risk stratification. Built on a Swin Transformer backbone and enhanced with multi-scale representation fusion, a convolutional block attention module and a gated selection strategy, MedSwinNet was designed to sensitively capture phenotypic signals such as reduced proximal tubular uptake and focal cortical defects while improving robustness. On the test set, the model achieved accuracies of 0.8290 under the severe-side input setting and 0.7997 under the bilateral-side input setting, demonstrating stable discriminative performance and favourable generalisation. Quality control analyses indicated broadly consistent distributions of key image quality metrics across data splits, mitigating potential bias from dataset shift. Collectively, deep learning-based decoding of tubular dysfunction-related phenotypic readouts enables noninvasive quantification of VUR-associated renal involvement, supports decision-making on whether VCUG is warranted and may reduce unnecessary invasive procedures while improving clinical risk-stratified management.

Ongoing supervision of wind turbine blades and their frameworks is an essential element of Structural Health Monitoring (SHM) systems. Unmanned Aerial Vehicles (UAV) have become increasingly significant for automated inspections in contemporary studies because of the impracticality of constant human monitoring. The use of computer vision in conjunction with machine learning is essential one for identifying the damages from images captured by UAVs. The work herein presents a hybrid two-stage pipeline that integrates YOLOv8 with CNN (DarkNet53) and lightweight Vision Transformer models (Swin-Tiny and DeiT-Small) to evaluate the detection performance under different architectural configurations. Two variants of YOLO-based object detection frameworks were implemented using different backbone feature extractors, including the CNN(DarkNet53), the Swin-Tiny transformer, and the DeiT-Small transformer. This investigation evaluates the model’s capacity to distinguish among five essential types of defects in wind turbine blades: paint peeling, leading-edge erosion, open damage, missing vortex generator, and non-open cracks. The framework is assessed using both imbalanced and balanced datasets, with performance comparisons conducted for quantitative criteria including accuracy, mAP, F1-score, recall, and FPS. Qualitative measurements are derived from the visual evaluation of predictions as bounding boxes. The YOLOv8 + DeiT-Small configuration trained on a balanced dataset has achieved excellent performance among the evaluated configurations. Dataset balancing plays a critical role in maximizing model performance, thus yielding comparably better accuracy, improved mAP, and smoother convergence, particularly when combined with computationally efficient backbone architectures.

Network diffusion underpins diverse phenomena from social contagion to neural dynamics, yet real-world spreading processes often exhibit complex temporal heterogeneity that transcends Markovian assumptions. Here we present a general theoretical framework incorporating node-specific waiting-time distributions through renewal processes, enabling the integration of temporal heterogeneity with network topology. By formulating dynamics in the Laplace domain, we derive closed-form expressions linking local temporal statistics to the network's spectral properties, yielding analytical bounds on relaxation times, mixing behavior, and sensitivity to temporal perturbations. Our approach provides quantitative criteria predicting how local timing alterations propagate to global dynamics. We validate the framework through numerical experiments and empirical analysis of α-synuclein spreading in mouse brain networks, where Gamma-based temporal kernels significantly outperform memoryless models. This work establishes a unified foundation for studying non-Markovian diffusion, with implications for understanding spreading processes across biological and social systems.

Muscle recovery in high-performance sports requires diagnostic precision combined withinterventions guided by quantitative data. This article presents a clinical protocol proposal developedover nearly two decades of specialized practice that integrates isokinetic dynamometry and functionalbiomechanical analysis to identify hidden asymmetries and specific torque deficits. Themultidisciplinary approach, grounded in exercise physiology and continuous systemic monitoring,aims to reduce the incidence of recurrence and optimize return to sport. It is proposed that sportsclearance be based on objective quantitative criteria, individualized goal-driven progression, and upto-date scientific evidence. Prospective multicenter validation of this protocol constitutes the nextstep in the investigation.