The concept of anticoagulant-related nephropathy (ARN), first proposed in 2009, manifests as acute kidney injury or the rapid deterioration of chronic kidney disease secondary to anticoagulant therapy. Large numbers of tubular RBCs and/or tubular RBC casts and ATN, without active glomerulonephritis are the main pathological features. The history of anticoagulant drug use and pathological manifestations are the most important characteristics that distinguish it from other renal injuries. ARN is considered rare, with just over 100 cases documented globally, yet its incidence may be underestimated due to limited awareness. This article reviews the epidemiology, risk factors, etiology and pathogenesis, diagnosis and treatment of ARN. New advances in recent years are highlighted, including the relationship between ARN and other kidney diseases, the characteristics of nonvitamin K oral anticoagulants and their effects on renal function, and the complexities of peri-biopsy and therapeutic anticoagulation. To address the critical peri-biopsy management impasse, we evaluated anticoagulation strategies from published case reports. Integrating these empirical insights with established perioperative antithrombotic principles, we propose a synthesized strategy for ARN management during the peri-biopsy and treatment phases, accompanied by a diagnostic flowchart. Given the current reliance on case-level evidence, this review does not intend to issue definitive clinical guidelines. Rather, it aims to build a comprehensive cognitive framework and a structured consideration system, facilitating nuanced assessments to ultimately improve patient outcomes in this challenging clinical domain.

Probiotics offer diverse health benefits, but their efficacy is compromised by gastrointestinal stress and burst release in conventional encapsulation systems, limiting the efficacy of probiotics in functional foods. Herein, an alginate-coated cellulose roll capsule with anisotropic porous structures and sustained release property was developed via a combined strategy of directional freeze casting and ethanol coagulation bath, which avoids the need for energy-intensive freeze-drying. The pH-responsive alginate shell and layered cellulose roll core synergistically blocked acidic penetration and enhanced probiotic survival in the gastric environment, while the gradual dissolution of alginate shell and unfolding of cellulose roll led to controlled release of probiotics from the aligned microchannels in the intestinal environment. The design endowed the capsule with excellent survival rate (91.03%), high release count (1.82 × 10[8] CFU), prolonged release duration (9 h) and zero-order release kinetics. This study is based on in vitro simulated digestion using a single probiotic strain, and future work will address in vivo validation with additional probiotic species. This bioinspired system provides a strategy for functional food delivery by addressing the burst release challenge of probiotic encapsulation.

Exact gaussian process (GP) inference scales cubically in data size, posing a severe bottleneck for applications that require repeated GP kernel inversions, such as time series models like gaussian process factor analysis (GPFA), where data size can vary across trials. To address this, we introduce an algorithmic variant of GPFA that shares computations across trials of different lengths. It does so by leveraging block-matrix identities to incrementally reuse inverses and determinants from smaller GP kernel matrices to accelerate computations on larger ones without sacrificing numerical precision. By casting successive trial-length extensions as Schur-complement updates, our method reduces the cost of each new inversion to depend only on the size difference among matrices. We demonstrate that this approach yields more than two times speed-ups over direct and persymmetric inversion baselines on both synthetic data sets and data from neural recordings. In an additional comparison to sparse inducing-point GPFA, sparse inference reduced inference time for small inducing sets at the expense of worse model fits, while optimizing inducing locations was slower than our exact GPFA variant when the optimization cost was included. We also provide a scikit-learn-compatible Python GPFA package, BlockInverseGPFA, and introduce a variance-explained metric for model evaluation.

Meniscal tears have limited spontaneous healing due to poor vascularization and intrinsic repair capacity, often leading to post-traumatic osteoarthritis. Current clinical treatments fail to restore native meniscal structure and function, necessitating advanced regenerative strategies. Tissue-engineered scaffolds offer promise, yet conventional designs lack the anisotropic architecture and cannot mitigate the oxidative injury microenvironment. Herein, we developed a meniscus-inspired anisotropic hydrogel scaffold incorporating succinate for bioenergetic regulation and loaded with synovial mesenchymal stem cells (SMSCs) for repair of red-red and red-white transitional zones. The anisotropic structure was fabricated via freeze-casting to mimic native meniscal alignment, while succinate modification enabled bioenergetic regulation by reducing intracellular ROS levels, preserving mitochondrial homeostasis, and maintaining cellular energy status, thereby enhancing SMSC proliferation and migration. The scaffold improved SMSC behavior in vitro and promoted fibrocartilage-like meniscal repair while attenuating cartilage degeneration in a rabbit tear model. This bioenergetic, biomimetic scaffold integrates structural anisotropy, stem cell therapy, and metabolic regulation, representing a promising strategy for fibrocartilage-like meniscal repair and attenuation of secondary articular cartilage degeneration.

Radiation oncology has proven an early adopter of machine learning (ML) and deep learning (DL) owing to its image-centered workflow and data richness. DL auto-segmentation of normal structures is already guideline-endorsed, with self-configuring nnU-Net delivering near-state-of-the-art accuracy and widely adopted. In this review, we explore advances in ML algorithms in processing and interpreting imaging-based data beyond normal tissue auto-segmentation with applications upstream and downstream of this task, highlighting training data volume and best-in-class architectures. Applications discussed range from diagnostics with "virtual biopsies" and automated staging to radiotherapy planning with gross tumor volume and clinical target volume (CTV) prediction. We illustrate the developments of cutting-edge transformer-based designs and foundation models, but also the important role of lightweight, task-specific classical ML models. Data scarcity may be eased by developments in few-shot learning (FSL) and transfer learning strategies. We also consider how end-to-end automated CTV delineation may require an agentic approach to mimic the reasoning of a radiation oncologist, capable of processing multi-modal data with clinical context. Together, these advancements point toward a future where ML will become increasingly central to radiation oncology practice. However, implementation of these tools beyond normal tissue auto-segmentation remains limited and will require careful assessment and validation in the clinic.

Orthodontic treatment is performed to treat malocclusions by improving oral functions, tooth alignment, and facial esthetics that impact the quality of life of patients significantly. Nevertheless, various factors like complexity of malocclusions, patient compliance, and dental hygiene can influence treatment outcomes. Traditional diagnostic methods such as clinical evaluation, radiography techniques, and dental casts have played a major role in orthodontics and have remained a standard diagnostic method in dentistry to date. On the other hand, these methods are associated with major disadvantages like radiation exposure and invasive procedures. To address these limitations, recent diagnostic methods have identified various biomarkers in gingival crevicular fluid and saliva as non-invasive diagnostic methods. These biomarkers can help clinicians in planning orthodontic treatment at the optimal time and determine the appropriate initiation of the treatment. Point-of-care diagnostic tools such as lateral flow assays, Enzyme-Linked Immunosorbent Assay, Clustered Regularly Interspaced Short Palindromic Repeats and microfluidic devices, and wearable technologies are emerging as promising alternatives for orthodontic treatment monitoring by offering rapid, cost-effective, and non-invasive alternatives to traditional methods. Recent developments such as incorporation of artificial intelligence and machine learning into orthodontic software attempt to improve the overall monitoring of orthodontic treatment. This review aims to present various traditional and innovative methods that have shown promising outcomes in research, along with insights into emerging areas that serve as potential game changers in orthodontic treatment monitoring.

The integration of broadband microwave absorption, infrared modulation, and mechanical flexibility within a single material remains a formidable challenge. Herein, a gradient multilayered composite is constructed by sequentially casting condensation-type room-temperature-vulcanized silicone rubber (RTV) layers containing carbonyl iron powder (CIP), conductive carbon black (CB), and zinc sulfide (ZnS) at progressively increasing filler loadings. This architecture spatially decouples impedance matching, electromagnetic attenuation, conductive reflection, and infrared modulation. The gradient composite achieves an absorption coefficient exceeding 0.9 across 8-18 GHz, corresponding to a 10 GHz effective absorption bandwidth. This broadband performance originates from a gradient impedance matching mechanism, whereby the top low-permittivity layer admits microwaves with minimal reflection and the underlying CIP layers progressively attenuate the transmitted energy through magnetic and dielectric losses, while the conductive CB bottom layer redirects residual waves back into the lossy region for a second attenuation pass. The mechanism is verified by transmission matrix method calculations and full-wave electromagnetic simulations. The ZnS-containing layer imparts tunable infrared emissivity, decreasing from 0.781 to 0.695 (3-5 µm) as the ZnS content increases from 10 to 30 wt%, with a steady-state radiation temperature reduction of 22.6 °C relative to Neat RTV. Mechanically, the gradient composite retains an elongation at break of 73.8%, approximately 2.6 times that of the individual brittle functional layers, owing to the protective effect of the ductile outer layers. This gradient multilayered design overcomes the inherent trade-offs among microwave absorption, infrared modulation, and mechanical flexibility, offering a versatile platform for flexible multifunctional composites.

The creep resistance of γ'-strengthened CoNi-based superalloys is governed by the interactions of mechanical fields and localized chemical compositions, while the coupled chemo-mechanical origin and kinetics evolution under the creep stress at high temperature are still unclear. A phase-field crystal-plasticity model is proposed to investigate the morphology-dependent creep of the L12-γ'-(Co, Ni)3Al phase in a newly designed CoNi-based superalloy. The non-equilibrium precipitate is incorporated to capture the microstructural asymmetry. Under 750 MPa stress, the Y-axis loading exhibits an extended creep life 5.3 times that of the X-axis loading, while the X-axis loading accelerates creep rafting instability. The X-axis stress exacerbates the local concentration of equivalent stress at γ matrix channels more than that of the Y-axis; the stress gradient triggers a chemo-mechanical coupling effect, driving the uphill diffusion and resulting in Al depletion and reduced γ' volume fraction. The localized γ' phase dissolution induces the equivalent plastic strain peaks, surpassing the critical threshold for topological inversion and structural degradation. This work quantitatively links localized chemo-mechanical degradation to macroscopic morphology-dependent creep failure, providing critical insights for the microstructural design of next-generation superalloys.