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Since decades, surgery of pancreatic adenocarcinoma is confronted with two major challenges. First, the prognosis of pancreatic adenocarcinoma is the worst of all gastrointestinal malignancies, characterized by late diagnosis and aggressive tumor biology. Second, postoperative mortality and morbidity have been exceptionally high, giving pancreatic resection a reputation of being one of the most dangerous procedures. This narrative review concentrates on the progress in decreasing postoperative mortality by centralization and standardization, the exertion of extended resections for local tumor clearance and multimodal strategies to improve oncologic survival. Research regarding surgical techniques, especially robotic assistance, indicated further progress in minimizing the perioperative trauma. Multimodal treatment concepts have been implemented with adjuvant therapy in resectable and neoadjuvant regimen in borderline and locally advanced cancer. However, significant problems have to be solved. Postoperative morbidity remains high, hindering the administration of adjuvant therapy, and toxicity is an essential factor in neoadjuvant strategies. Despite neoadjuvant therapies, resection rates of locally advanced carcinoma are modest and tumor progression hinders resection of borderline and resectable carcinomas. Obviously, deficits in understanding tumor biology are a central obstacle in improving resection rates and overall survival. Hence, improvement in selection of patients for surgical resection apart from using preoperative anatomical findings is mandatory. The progression of current standards, future developments and the status of surgery in multimodal concepts to treat pancreatic adenocarcinoma are delineated using historical and recent reports, registry data, meta-analysis and randomized controlled trials.

This article establishes a comprehensive framework of stochastic practical fixed-time input-to-state stability (SPFT-ISS). Its contributions are characterized by two points: 1) we propose the concept of SPFT-ISS and stochastic practical fixed-time stability (SPFTS), and establish the relationship between SPFT-ISS and the SPFT-ISS Lyapunov function; and 2) as the application of this framework, an interesting problem on adaptive practical fixed-time control of stochastic nonlinear systems with unknown parameters, high-order powers, uncertainties of nonlinear functions, and stochastic inverse dynamics is solved thoroughly.

Microplastic monitoring needs methods that operate directly in water with minimal sample handling. Conventional techniques such as infrared and Raman spectroscopy and pyrolysis-GC/MS provide polymer-specific information but require sample preparation and delayed laboratory analysis. We propose an optical sensor concept for real-time, in situ microplastic assessment, based on multispectral pulsed transmission in the visible range using synchronized laser-diode lines and the directly transmitted signal through an active sensor volume. After calibration on particle-free water, each particle event reduces to a water-normalized transmission whose deficit is set by geometrical beam-particle overlap and the wavelength-dependent extinction efficiency. The weak polymer absorption is represented by the Urbach-tail formalism, the refractive-index-related redirection of light by a Fresnel-based, surface- and orientation-averaged probability of direct transmission, and particle size and shape are decoupled through an effective optical length. The coupled nonlinear system is solved for the bounds of the polymer absorption coefficient per candidate geometry. Because each polymer occupies a bounded region in multi-wavelength absorption space fixed by its band gap and structural state, the method can, in principle, separate structural modifications of identical composition, such as low- and high-density polyethylene. This is a sensor concept with a model-based proof of concept, not full environmental validation. Experimental verification on real reference particles is reported separately; the present article establishes the measurement model and inversion scheme that this verification builds on.

Paclitaxel (PTX) is a widely explored antimitotic drug for the treatment of various cancers; however, various issues like its hydrophilic nature, lack of targeting ability, and toxic side effects may cause a reduction in its therapeutic effectiveness. By enhancing solubility, extending circulation, allowing tumor-targeted distribution, and decreasing systemic side effects, protein-based nanocarriers have arisen as a potential substitute to traditional formulations. An extensive literature review was carried out in PubMed, ScienceDirect, and Google Scholar databases of articles published in 2015-2025 using search strings that were predefined and included the terms that were related to paclitaxel, protein nanoparticles, and cancer therapy. The studies were used according to pre-established inclusion criteria (original research, proteinbased nanocarriers to deliver PTX, etc.) and exclusion criteria (non-protein carriers, conference abstracts, non-English articles, etc.). Google Patents and Espacenet were used in carrying out patent searches. The most recent articles (n=158) and 11 patents were selected, and relevant data were obtained. Various types of protein nanocarriers greatly increase PTX solubility, encapsulation efficiency, and tumor accumulation through both passive and active targeting mechanisms. Important examples include Abraxane®, pH/redox-responsive gelatin systems, folate-targeted zein nanoparticles demonstrating a more than seven-fold increase in oral bioavailability, and multifunctional ferritin structures including chemo-photodynamic therapy. Clinical and preclinical trials have shown consistently good antitumor activity and reduced toxicity as compared to free paclitaxel. Protein nanocarriers provide a flexible way to change the surface and release drugs in response to stimuli. They also solve some of the problems with conventional PTX dosage forms. But there are still problems that need to be solved before we can get scale production, controlled release kinetics, long-term stability, and high drug loading. Next-generation protein-based nanocarriers are a unique way to deliver paclitaxel. They have a greater therapeutic index and lead to better patient outcomes. There is a lot of optimism for precision oncology as these biocompatible technologies continue to be improved and used in clinical settings.

In engineering and technology, the micropolar-Sutterby fluid serves as a favorable alternative to conventional non-Newtonian fluids due to its practical applicability, robustness, and computational efficiency. This analysis focuses on the thermophysical characteristics of the magnetized flow of a micropolar-Sutterby fluid bounded by an exponentially stretching surface, taking into account Cattaneo-Christov heat flux, thermal radiation, activation energy, and heat source, which have wide industrial applications. The surface is influenced by Darcy-Forchheimer effects and is situated within a porous medium. The impacts of mobile microorganisms with magnetic flux are also probed. Similarity transformations are used to convert the governing PDEs into coupled ODEs. The resulting ordinary differential equations are solved using the collocation-based MATLAB built-in solver bvp4c. Tables, graphs, and literature comparisons are used to demonstrate the impact of different parameters on the involved profiles. Quantitative results obtained from the numerical simulations reveal that increasing the thermal radiation parameter Rd from 0.5 to 0.9 enhances the local Nusselt number by 24.3%, while increasing the magnetic parameter Ha from 0.5 to 0.9 increases the local skin friction coefficients by 3.8% in the x-direction and 3.9% in the y-direction. Furthermore, increasing the micropolar parameter K from 0.1 to 0.5 enhances the local skin friction in the y-direction by 46.4%. It is evident from the results that the temperature field is significantly enhanced for increasing values of thermal radiation and thermophoresis.

Curved beams are widely used in engineering applications due to their unique geometry and mechanical advantages. In this study, the mechanical response of hyperelastic curved beams under cyclic loading is investigated, considering the Mullins stress-softening effect and the probabilistic variability of material parameters. A Neo-Hookean hyperelastic model integrated with the Ogden-Roxburgh damage formulation is employed to capture the cyclic stress-softening behavior. The governing partial differential equations are derived in cylindrical coordinates under plane-stress conditions and solved numerically. Unlike classical pure bending assumptions, the present formulation captures the coupled radial, circumferential, and shear stress components arising from the finite thickness and curvature of the beam. The model is validated through comparison with two-dimensional finite element solutions. The influence of material uncertainty is further examined via a probabilistic parametric analysis, and statistical measures including mean, standard deviation, skewness, and kurtosis are evaluated. The results demonstrate that Mullins-induced stress softening is concentrated near the inner curvature and beam root, and that inherent material variability significantly affects the damage distribution. The proposed framework provides a fast and accurate method for predicting the mechanical behavior of hyperelastic curved beams under cyclic loading.

Reconstructing missing image content is especially challenging when the damaged region extends to the image boundary, rendering a portion of the boundary data inaccessible and turning the underlying partial differential equation (PDE) into an ill-posed elliptic Cauchy problem. Classical inpainting methods, whether PDE-driven or data-driven, typically assume that complete boundary information is available and therefore degrade significantly in this setting. In this work we propose a novel hybrid framework that unites the mathematical rigor of iterative regularization with the mesh-free flexibility of Physics-Informed Neural Networks (PINNs). The core idea is threefold. First, we decompose the ill-posed Cauchy problem into a sequence of well-posed mixed boundary-value problems via a relaxed Kozlov-Maz'ya-Fomin (KMF) alternating iteration that switches between Dirichlet and Neumann conditions on the inaccessible boundary. Second, each sub-problem is solved by a dedicated lightweight PINN whose loss function encodes the nonlinear edge-enhancing diffusion (EED) operator, thereby enforcing physical consistency; including anisotropic gradient preservation; without mesh generation or matrix assembly. Third, a Bayesian hyperparameter optimization layer automatically selects the relaxation factor, loss-term weights, and network capacity, eliminating tedious manual tuning and ensuring stable convergence across diverse inpainting scenarios. The proposed method achieves considerable improvement in PSNR over conventional PDE solvers and state-of-the-art deep inpainting models, with the most pronounced gains observed for large or boundary-adjacent damaged regions. These results highlight the untapped potential of coupling physics-based modeling with deep learning for principled, reliable, and fully automated image restoration.

This paper designs a statistical channel state information-based pinching antenna system for short-packet communication (SPC). To maximize the average maximal achievable rate (MAR) under physical collision-avoidance constraints, we formulate a highly non-convex geometry optimization problem, which is solved by our proposed novel phase-domain proximal policy optimization (PPO) framework. Unlike conventional coordinate-based approaches, the agent operates in a dual-component trigonometric phase domain, and the generated phase actions are mapped to feasible antenna positions via a customized phase-domain action mapping, which fundamentally avoids the 0/2π phase discontinuity and ensures stable learning. To evaluate the reliability of SPC, we derive a tractable statistical characterization of the received signal-to-noise ratio based on a mixture Gamma approximation over spatially correlated Rician fading channels, leading to a closed-form approximation for the average block error rate (BLER). A bisection search algorithm is further developed to minimize the required blocklength under the target reliability constraint. Simulation results demonstrate that the proposed phase-domain PPO scheme significantly outperforms the conventional algorithms in terms of average MAR, average BLER, and blocklength efficiency, with the performance gain becoming more pronounced as the number of antennas per waveguide increases.

Near-field beam shaping for phased-array antennas operating in the Fresnel region is a challenging non-convex electromagnetic synthesis problem, requiring coherent control of the radiated fields while accounting for the distinct positions, radiation patterns, and polarization states of individual array elements. This paper presents a physics-informed optimization framework for near-field beam shaping based on a unified vector formulation that enables the direct coherent summation of the electromagnetic fields radiated by array elements despite their distinct local spherical coordinate systems. Unlike conventional formulations that rely on repeated transformations between local spherical and global Cartesian coordinate systems, the proposed representation preserves the physical polarization properties of the electromagnetic field while providing a rigorous framework for near-field beam synthesis. To optimize the electromagnetic energy distribution over finite target surfaces rather than a single focal point, an analytical near-field point-focusing solution is integrated into the optimization process through a physically informed initialization strategy. The resulting non-convex optimization problem is solved using a genetic algorithm (GA) to determine the element phase distribution that maximizes electromagnetic energy within the prescribed target region while minimizing undesired field leakage. The proposed methodology is validated through full-wave electromagnetic simulations and extensive experimental measurements using a dedicated phased-array platform, including the design, fabrication, characterization, and calibration of the antenna array and phase-control network. The results demonstrate flexible near-field beam shaping and controlled energy focusing over finite target regions. The proposed framework is applicable to biomedical radar sensing, near-field synthetic aperture radar (SAR) illumination, wireless power transfer (WPT), high-power microwave (HPM) systems, and near-field millimeter-wave communications.

Emergency dispatching and rescue during emergencies have become a hot research topic in the field of transportation planning. Therefore, to address the low efficiency of cross-regional police emergency dispatch, the study carries out relevant exploration on the basis of emergency event level analysis and transportation planning. The emergency rescue demand evaluation system is constructed using the superior and inferior solution distance method to carry out the emergency hierarchy analysis. The police emergency dispatch model is constructed and solved using the improved particle swarm algorithm. The validation revealed that the improved solution algorithm improved the iteration efficiency by 54.33% on average and reduced the iteration time overhead by 56.10% on average over the particle swarm algorithm and the beetle antennae search algorithm. In the scheduling of emergency 1, the material demand satisfaction rate of all demand points was above 80%, among which the vegetable material demand satisfaction rate was 100%. In the scheduling of emergency 2, the demand for supplies at all demand points was above 91.3%, with the demand satisfaction rate for tent supplies as high as 100%. The above results indicate that the police emergency dispatch method can leverage the full advantages of transportation networks. By integrating intelligent optimization algorithms with artificial intelligence technology, the method achieves material dispatch for cross-regional emergency rescue incidents. This method is both feasible and practical.

Visible light communication (VLC) is widely regarded as a key enabler for future vehicular networks, thanks to its extremely large unlicensed bandwidth and non-interference with existing radio frequency (RF) communication networks. With the goal of maximizing the benefits of both RF and VLC technologies, aggregated VLC-RF vehicular networks, in which any vehicle can be served by both RF and VLC access points (APs) concurrently, have recently become a more robust and promising approach for enhancing vehicle-to-everything (V2X) applications and improving the quality-of-service (QoS) of vehicular networks. This paper focuses on the joint spectrum reuse and power allocation problem in aggregated VLC-RF vehicular networks with delayed channel state information (CSI) feedback, where vehicle-to-vehicle (V2V) links opportunistically reuse the RF spectrum allocated to vehicle-to-infrastructure (V2I) links. Specifically, we focus on maximizing the total V2I achievable rate to support high-rate content delivery, and guaranteeing the required reliability of V2V links tasked with exchanging safety-critical information. Furthermore, the sum V2I achievable rate maximization problem is decomposed into four subproblems, which are iteratively solved through an efficient block coordinate descent (BCD)-based alternating optimization algorithm. Moreover, simulation results validate the convergence and efficiency of the proposed algorithm while highlighting the impact of critical parameters on system performance, providing valuable insights for resource allocation in aggregated VLC-RF vehicular networks.

Over a decade of research on media for cultured meat and seafood production has resulted in multiple highly efficient serum-free and chemically defined formulations for some species, but it has also identified challenges yet to be solved-especially for aquatic cell lines. Depending on the product and cell type, the approach to develop highly efficient, sustainable, and low-priced media can diverge greatly. In this review, we provide an in-depth overview of this complex research area to facilitate strategic decision-making for stakeholders. We evaluate the advantages and limitations of utilizing hydrolysates, growth factor mutants, growth factor alternatives, and stabilizers in serum-free media formulations published for cultured meat production, as well as ongoing research efforts on developing adequate media for cultured seafood. We critically analyze strategies aimed at reducing medium costs and enhancing sustainability of cultured meat and seafood production, including their food-compatibility assessment. We summarize topics that require further exploration, such as identification of species-specific growth factors-particularly for aquatic species; exploration of hydrolysates as a substitute for basal medium; waste medium recycling strategies; and the potential application of artificial intelligence (AI) and machine learning (ML) technologies to enhance these areas. Additionally, we consider possible emerging regulatory issues and their impact on media formulation development. Finally, key performance indicators for media formulations are proposed to guide future strategic and operational improvements regarding an economical and sustainable production process.

Unmanned aerial vehicle semantic communications are increasingly required in low-altitude sensing, intelligent inspection, and emergency response, where raw image transmission is difficult to sustain under limited onboard resources and time-varying air-to-ground links. Meanwhile, the simultaneous transmission of visual semantic features and object-centre location metadata under third-party eavesdropping creates a dual-privacy vulnerability: an attacker can exploit both to reconstruct sensitive content. In this paper, we propose a differential privacy-based collaborative protection framework that inserts dedicated perturbations into visual semantic and location descriptors before transmission. For visual data, we design a region-aware differential privacy mechanism that applies stronger noise to sensitive semantic regions while preserving utility for non-critical areas. For location data, a scenario-adaptive strategy is developed, comprising randomized differential privacy for discrete grid-based location information (coarse spatial awareness) and Laplace-based differential privacy for continuous coordinates (fine-grained protection). To balance privacy and utility, we formulate a joint optimization problem. It maximizes legitimate-side semantic task performance by coordinating the visual privacy budget, location privacy budget, and transmit power. A BCD-based algorithm is developed to solve this non-convex problem. Attacker-side recoverability is verified empirically at the optimized operating point. Simulation results demonstrate stable convergence within a small number of iterations. Compared with uniform differential privacy, the proposed framework achieves a superior task-level privacy-utility trade-off and provides selective sensitive-region protection, with the two mechanisms yielding comparable whole-image attack suppression.

Based on the elemental composition of 359 samples of dry wines (Riesling, Chardonnay, Muscat, Cabernet Sauvignon, and Merlot) produced in the Krasnodar Territory (Russia), the influence of data clustering on the generalizing properties of neural network models for solving classification problems was studied. Clustering as a property of predicted classes being compact and separate from each other was evaluated using scatterplots of canonical values from discriminant analysis and the Silhouette Score, Calinski-Harabasz, Davies-Bouldin, and Dunn metrics. With a decrease in data clustering, the generalization properties of neural network models decrease despite an increase in dataset size. Since the predictive properties of neural networks are primarily correlated with the clustering of data, it seems logical to say that their growth will occur when "quantity turns into quality", that is, clustering will increase with increasing dataset size. The validity of the assumption explains the polarity of trends observed in literature. With the growth of training datasets, some researchers record improvements in the predictive properties of models, while others record deterioration. The results obtained are of great practical importance, as they indicate the unsuitability of unlimited data accumulation and allow optimizing the costs of collecting it, which is important, especially for food quality control.

Microbial survival and function often depend on metabolic interactions within communities. Therefore, a central question in disentangling microbial organization is determining which minimal groups of strains are able to thrive in a given medium-referred to as "minimal communities." Answering this question is essential for understanding microbial distribution, enhancing laboratory cultivation, and designing synthetic communities (SynComs). Here, we introduce misosoup, a Python package for identifying minimal communities (minimal supplying community search). Through genome-scale constraint-based metabolic modeling, misosoup enables the systematic identification of communities that support microbial growth in environments where individual strains fail to survive alone. We validate misosoup against experimentally verified minimal communities, demonstrating its ability to predict known cooperative interactions, cocultures, and consortia with biotechnological potential. We further illustrate the use of misosoup to investigate broad microbial ecology questions by applying it to a set of 60 marine microbes, finding pervasive cross-feeding-driven niche expansion, and showing how the detailed outputs provided by misosoup facilitate research on hot topics such as the identification of functional groups. In summary, misosoup provides a powerful tool for microbial ecology and community design, with potential applications in both research and biotechnological innovation. Microbes often rely on each other to survive, especially in environments where they cannot live alone. Understanding which small groups of microbes can thrive together-called minimal communities-is key to improving laboratory research, designing synthetic ecosystems, and exploring how microbes spread in nature. To support this, we developed misosoup, a Python tool that identifies these communities using advanced metabolic modeling. misosoup helps scientists discover how microbes cooperate by sharing nutrients, a process known as metabolic cross-feeding. When tested on sets of species from different origins, the tool showed that species could thrive in more environments when part of a group. This finding highlights the importance of cooperation in microbial life. misosoup not only predicts these interactions but also provides detailed insights that can guide ecological studies and biotechnological innovation. By revealing how microbes support each other, misosoup contributes to a deeper understanding of life's interconnectedness and offers tools for solving real-world challenges.

In the field of instant delivery, the mismatch between delivery resources and customer demands has led to increasingly significant customer losses. To address this issue, this study introduces the customer loss mechanism and constructs an evaluation function to screen out resource-intensive customers, thereby clarifying the scope of delivery services. Based on this, this study establishes the vehicle routing optimization model under the customer loss mechanism with the objective of minimizing the sum of vehicle fixed costs, variable routing costs, and time window penalty costs. An improved genetic algorithm is employed to solve this model. Case study results demonstrate that the improved genetic algorithm outperforms traditional genetic algorithms and tabu search algorithms in convergence speed, optimization capabilities, and stability, reducing total delivery cost by 36.25% and 4.18%, respectively, with zero delivery violations. Regarding model performance, when proactively excluding 8.33% of customers, the total delivery cost is reduced by 17.18%, primarily driven by the reduction in fleet size. Furthermore, large-scale experiments reveal a pronounced leverage effect: excluding a mere 5% of marginal customers counter-intuitively reduces both fleet size and travel distance, while a 10% loss yields an 18.39% total delivery cost reduction with zero violations, proving that the mechanism precisely screens out inefficient nodes rather than arbitrarily rejecting them. Sensitivity analysis further confirms the model's robustness across varying resource tightness, demonstrating that proactive customer loss is a feasible and effective strategy for improving resource utilization through precise resource focusing.

Mentoring is among the strongest predictors of doctoral student success, yet nursing programs continue to grapple with inconsistent practices and insufficient preparation for the faculty mentoring role. Without a systematic diagnosis of root causes, efforts to improve mentoring risk addressing symptoms rather than underlying structural and communication deficits. A faculty task force applied the Define-Decompose-Analyze-Act (D2A2) framework over 6 months. The process included a facilitated reframing session with 51 faculty, systematic deconstruction of contributing factors from both faculty and student perspectives, stakeholder consultations, and a targeted literature review. The problem shifted from "low student satisfaction with advising" to "misaligned expectations and insufficient communication regarding mentoring roles." Three recommendations emerged: (1) develop a shared definition of PhD mentoring, (2) establish guidelines differentiating primary and secondary mentor roles, and (3) implement annual faculty mentorship training. The D2A2 process offers a replicable model for doctoral nursing programs seeking to strengthen mentoring quality.

Existing non-contact three-axis angle-measurement methods are unsuitable for measuring the relative three-axis angles between the inner and outer ring frames of fifth-generation airborne optoelectronic gimbal platforms. Our previous study proposed a compact three-axis angle-measurement method based on an optical wedge for this application. However, the fixed-coefficient angle-solving model used in that method does not account for variations in the measurement distance L, which can produce distance-dependent nonlinear errors when axial displacement of the inner ring frame occurs. To address this limitation, the present study proposes a distance-adaptive method for three-axis angle measurements. An analytical measurement-distance model is established using ABCD ray-transfer-matrix theory, and L is incorporated into the fixed-coefficient angle-solving model. The fixed coefficients in the polynomial error-compensation model are thereby expressed as functions of L and updated according to the estimated measurement distance. Experimental results demonstrate that the proposed method significantly improves the measurement accuracy under varying-distance conditions. Taking the measurement-distance condition with the largest error, L = -2 mm, as an example, the RMS values of the measurement errors for the pitch, yaw, and roll angles are reduced from 13.0″, 8.4″, and 31.1″ to 5.6″, 3.8″, and 18.6″, respectively.

Background/Objectives: Shoulder pain or instability have different causes and therefore different treatment approaches. The most common are surgical approaches, ranging from more conservative to more radical, such as arthroplasty. In the presence of irreparable rupture or tears of the supraspinatus and subscapularis muscles, particularly in younger patients without arthritis or osteoarthrosis, it becomes a surgical challenge to avoid early arthroplasty. Despite the development and proposal of various surgical techniques to restore stability and function, and eliminate pain of the glenohumeral joint, there remains a lack of consensus on the most effective approach in the absence of arthritis. This work aims to systematically review the available evidence on surgical procedures for treating shoulder joint instability and pain due to irreparable subscapularis and supraspinatus tendon tears, excluding cases with coexisting arthritis or arthrosis. Methods: This systematic review was conducted following PRISMA 2020 guidelines. Search was performed across the major databases PubMed, Scopus, and Web of Science, up to 2025. Inclusion criteria comprised clinical studies (randomised controlled trials, cohort studies, case series), reviews and systematic reviews reporting on surgical interventions for shoulder instability and pain caused by irreparable tears of the subscapularis and supraspinatus in patients without degenerative joint disease. As shoulder surgery techniques have been continually evolving since the early procedures, no lower limit was set for the time interval. The related literature that was not identified in the mentioned databases was added manually through parallel searches of associated themes and suggestions from the websites of those databases. To be eligible, papers had to describe shoulder instability surgery, surgical techniques, indications, and patients' recovery outcomes. The key tasks, such as title and abstract screening, were performed by V. M., L. R., and M. A. N. to ensure thoroughness and reduce bias. Results: From an initial search result of 98 works, a total of 66 titles and abstracts were analysed, resulting in a final selection of 24 studies that fulfilled the mentioned criteria and were therefore included in this review. The oldest paper mentioning a shoulder surgery in this context was published in 2003. The most frequently described procedures included total joint replacement, tendon transfers, Superior Capsule Reconstruction, partial rotator cuff repair and others. Across studies, improvements were noted in shoulder stability, range of motion, and functional scores. However, heterogeneity in surgical techniques and outcome measures limited direct comparison. Conclusions: The conducted systematic review reveals an important gap and highlights the need to evolve beyond traditional shoulder surgery techniques and, if possible, to provide a single solution for the different origins of shoulder instability. Several surgical options demonstrate promising outcomes in managing shoulder instability and pain due to irreparable subscapularis and supraspinatus tears in patients without glenohumeral arthritis. However, those procedures are associated with anatomical changes in the shoulder joint, compromising the joint's full function and prolonging recovery time. Nevertheless, this conclusion is based on a small sample size of the current literature and a lack of high-level evidence. Further comparative and long-term studies are needed to establish optimal treatment strategies.