This article examines a comprehensive model for managing refractive errors, with a specific focus on myopia. It investigates the epidemiological context of refractive errors and their socio-economic implications. It underscores the importance of early detection and management, especially for severe ocular conditions like retinal lesions and glaucoma. The article critiques existing refractive error management models' limitations and highlights challenges in managing asymptomatic myopic patients. It proposes a “Myopia Chronic Disease Management(MCDM)” model as an innovative comprehensive management approach. The model establishes a data-driven closed-loop management pathway that encompasses screening, diagnosis, intervention, follow-up, and feedback. Through a comparative analysis with the chronic care model(CCM)and the World Health Organization's(WHO)Integrated Patient-Centered Eye Care(IPCEC), it highlights its innovative strengths in integrating digital technologies with multi-tiered healthcare networks. This model encompasses the entire refractive correction process and incorporates strategies for public education via the internet and new media. In terms of strategy implementation, the article discusses the necessity of establishing eye health records and long-term follow-up plans, as well as the potential applications of medical consortium models and family contract-based services in management. Moreover, the article emphasizes the importance of intelligent software systems in chronic ocular condition health management. It provides an overview of the benefits and challenges associated with this novel management model and proposes directions for future research and potential enhancements. Through this thorough examination and analysis, the article highlights the critical importance and effectiveness of implementing comprehensive, multifaceted, and sustained strategies in the management of refractive errors.

Cancer remains a leading cause of global mortality, necessitating the development of advanced therapeutic strategies with enhanced efficacy and reduced systemic toxicity. Among promising bioactive agents, lactoferrin (LF)—a multifunctional iron-binding glycoprotein abundantly found in mammalian milk and exocrine secretions—has garnered significant interest for its potent and multifaceted anti-cancer properties. This review provides a comprehensive analysis of the current understanding of LF’s role in oncology, encompassing its structural biology, diverse mechanisms of action, and groundbreaking advancements in its application through nano-engineering. LF exerts anti-tumor effects through multiple pathways, including extracellular action, intracellular action, and immune regulation. It demonstrates a remarkable affinity for cancer cell membranes, binding to overexpressed anionic components such as glycosaminoglycans and sialic acids, as well as to specific receptors including the low-density lipoprotein receptor-related protein-1 (LRP-1). This selective binding facilitates targeted uptake. Upon internalization, LF orchestrates a direct assault by inducing cell-cycle arrest in phases such as G0/G1 or S phase through the modulation of key regulators including cyclins, CDKs, and p53. Furthermore, it promotes programmed cell death via apoptotic pathways, involving caspase activation and downregulation of anti-apoptotic proteins such as survivin. A more recently elucidated mechanism is the induction of ferroptosis, an iron-dependent form of cell death characterized by overwhelming lipid peroxidation. Beyond direct cytotoxicity, LF acts as a potent immunomodulator. It enhances natural killer (NK) cell activity, modulates T-lymphocyte populations, and crucially reprograms tumor-associated macrophages (TAMs) from a pro-tumor M2 state to an anti-tumor M1 state, thereby reversing the immunosuppressive tumor microenvironment (TME). The translation of LF’s potential has been significantly accelerated by nanotechnology. The inherent biocompatibility and natural tumor-targeting capabilities of LF make it an ideal platform for sophisticated drug-delivery systems. This review details various fabrication strategies for LF-based nanoparticles (NPs), including self-assembly, sol-in-oil emulsion, and electrostatic nanocomplexes, among others. Research demonstrates that nano-formulations not only protect LF from degradation but also enhance its bioactivity and anti-cancer potency. More importantly, LF NPs serve as versatile carriers for a wide array of therapeutic agents, including conventional chemotherapeutics, natural compounds, and imaging agents. These engineered systems enable synergistic therapy and facilitate site-specific delivery. Notably, the ability of LF to bind to receptors on the blood-brain barrier (BBB) has been leveraged to develop nano-systems for glioblastoma treatment. Other innovative designs utilize LF to modulate the TME—for instance, by alleviating tumor hypoxia to sensitize cells to radiotherapy and chemotherapy. Despite compelling pre-clinical evidence, the clinical translation of LF and its nano-formulations remains nascent. While early-phase trials have established a favorable safety profile for recombinant human LF, larger Phase III studies have yielded mixed results, underscoring the complexity of its action in humans. Key challenges include enhancing drug targeting, optimizing loading efficiency, ensuring batch-to-batch reproducibility, and achieving deep tumor penetration. Future research must focus on the rational design of next-generation LF-NPs. This entails developing standardized manufacturing protocols, engineering “smart” stimuli-responsive systems for targeted drug release in the TME, and constructing multi-targeting platforms. A concerted interdisciplinary effort is paramount to bridge the gap between bench and bedside. In conclusion, LF, particularly in its nano-engineered forms, represents a highly promising and versatile agent in the oncological arsenal, holding immense potential for precise and effective cancer therapy.

Cancer remains a leading cause of global mortality, necessitating the development of advanced therapeutic strategies with enhanced efficacy and reduced systemic toxicity. Among promising bioactive agents, lactoferrin (LF)—a multifunctional iron-binding glycoprotein abundantly found in mammalian milk and exocrine secretions—has garnered significant interest for its potent and multifaceted anti-cancer properties. This review provides a comprehensive analysis of the current understanding of LF’s role in oncology, encompassing its structural biology, diverse mechanisms of action, and groundbreaking advancements in its application through nano-engineering. LF exerts anti-tumor effects through multiple pathways, including extracellular action, intracellular action, and immune regulation. It demonstrates a remarkable affinity for cancer cell membranes, binding to overexpressed anionic components such as glycosaminoglycans and sialic acids, as well as to specific receptors including the low-density lipoprotein receptor-related protein-1 (LRP-1). This selective binding facilitates targeted uptake. Upon internalization, LF orchestrates a direct assault by inducing cell-cycle arrest in phases such as G0/G1 or S phase through the modulation of key regulators including cyclins, CDKs, and p53. Furthermore, it promotes programmed cell death via apoptotic pathways, involving caspase activation and downregulation of anti-apoptotic proteins such as survivin. A more recently elucidated mechanism is the induction of ferroptosis, an iron-dependent form of cell death characterized by overwhelming lipid peroxidation. Beyond direct cytotoxicity, LF acts as a potent immunomodulator. It enhances natural killer (NK) cell activity, modulates T-lymphocyte populations, and crucially reprograms tumor-associated macrophages (TAMs) from a pro-tumor M2 state to an anti-tumor M1 state, thereby reversing the immunosuppressive tumor microenvironment (TME). The translation of LF’s potential has been significantly accelerated by nanotechnology. The inherent biocompatibility and natural tumor-targeting capabilities of LF make it an ideal platform for sophisticated drug-delivery systems. This review details various fabrication strategies for LF-based nanoparticles (NPs), including self-assembly, sol-in-oil emulsion, and electrostatic nanocomplexes, among others. Research demonstrates that nano-formulations not only protect LF from degradation but also enhance its bioactivity and anti-cancer potency. More importantly, LF NPs serve as versatile carriers for a wide array of therapeutic agents, including conventional chemotherapeutics, natural compounds, and imaging agents. These engineered systems enable synergistic therapy and facilitate site-specific delivery. Notably, the ability of LF to bind to receptors on the blood-brain barrier (BBB) has been leveraged to develop nano-systems for glioblastoma treatment. Other innovative designs utilize LF to modulate the TME—for instance, by alleviating tumor hypoxia to sensitize cells to radiotherapy and chemotherapy. Despite compelling pre-clinical evidence, the clinical translation of LF and its nano-formulations remains nascent. While early-phase trials have established a favorable safety profile for recombinant human LF, larger Phase III studies have yielded mixed results, underscoring the complexity of its action in humans. Key challenges include enhancing drug targeting, optimizing loading efficiency, ensuring batch-to-batch reproducibility, and achieving deep tumor penetration. Future research must focus on the rational design of next-generation LF-NPs. This entails developing standardized manufacturing protocols, engineering “smart” stimuli-responsive systems for targeted drug release in the TME, and constructing multi-targeting platforms. A concerted interdisciplinary effort is paramount to bridge the gap between bench and bedside. In conclusion, LF, particularly in its nano-engineered forms, represents a highly promising and versatile agent in the oncological arsenal, holding immense potential for precise and effective cancer therapy.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

ObjectiveTo analyze the data structure and standards of rehabilitation outpatient medical records, to provide data support for improving the quality of rehabilitation outpatient care and developing medical insurance payment policies. MethodsBased on the normative documents issued by the National Health Commission, Basic Standards for Medical Record Writing and Standards for Electronic Medical Record Sharing Documents, in accordance with the Quality Management Regulations for Outpatient (Emergency) Diagnosis and Treatment Information Pages (Trial), reference to the framework of the World Health Organization Family of International Classifications (WHO-FICs), the data framework and content of rehabilitation outpatient medical records were determined, and the data standards were discussed. ResultsThis study constructed a data framework for rehabilitation outpatient medical records, including four main components: patient basic information, visit process information, diagnosis and treatment information, and cost information. Three major reference classifications of WHO-FICs, International Classification of Diseases, International Classification of Functioning, Disability and Health, and International Classification of Health Interventions,were used to establish diagnostic standards and standardized terminology, as well as coding disease diagnosis, functional description, functional assessment, and rehabilitation interventions, to improve the quality of data reporting, and level of quality control in rehabilitation. ConclusionThe structuring and standardization of rehabilitation outpatient medical records are the foundation for sharing of rehabilitation data. The using of the three major classifications of WHO-FICs is valuable for the terminology and coding of disease diagnosis, functional description and assessment, and intervention in rehabilitation outpatient medical records, which is significant for sharing and interconnectivity of rehabilitation outpatient data, as well as for optimizing the quality and safety of rehabilitation medical services.

ObjectiveTo explore the standardization of inpatient rehabilitation medical record summary sheet, encompassing its structure, content and data standards, to enhance the standardization level of inpatient rehabilitation medical record summary sheet, improve data reporting quality, and provide accurate data support for medical insurance payment, hospital performance evaluation, and rehabilitation discipline evaluation. MethodsBased on the relevant specifications of the National Health Commission's Basic Norms for Medical Record Writing, Specifications for Sharing Documents of Electronic Medical Records, and Quality Management and Control Indicators for Inpatient Medical Record Summary Sheet (2016 Edition), this study analyzed the structure and content of the inpatient rehabilitation medical record summary sheet. The study systematically applied the three major reference classifications of the World Health Organization Family of International Classifications, International Classification of Diseases (ICD-10/ICD-11, ICD-9-CM-3), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), for disease diagnosis, functional description and assessment, and rehabilitation intervention, forming a standardized terminology system and coding methods. ResultsThe inpatient rehabilitation medical record summary sheet covered four major sections: inpatient information, hospitalization information, diagnosis and treatment information, and cost information. ICD-10/ICD-11 were the standards and coding tools for admission and discharge diagnoses in the inpatient rehabilitation medical record summary sheet. The three functional assessment tools recommended by ICD-11, the 36-item version of World Health Organization Disability Assessment Schedule 2.0, Brief Model Disability Survey and Generic Functioning domains, as well as ICF, were used for rehabilitation functioning assessment and the coding of outcomes. ICHI Beta-3 and ICD-9-CM-3 were used for coding surgical procedures and operations in the medical record summary sheet, and also for coding rehabilitation intervention items. ConclusionThe inpatient rehabilitation medical record summary sheet is a summary of the relevant content of the rehabilitation medical record and a tool for reporting inpatient rehabilitation data. It needs to be refined and optimized according to the characteristics of rehabilitation, with necessary data supplemented. The application of ICD-11/ICD-10, ICF and ICHI Beta-3/ICD-9-CM-3 classification standards would comprehensively promote the accuracy of inpatient diagnosis of diseases and functions. Based on ICD-11 and ICF, relevant functional assessment result data would be added, and ICHI Beta-3/ICD-9-CM-3 should be used to code rehabilitation interventions. Improving the quality of rehabilitation medical records and inpatient rehabilitation medical record summary sheet is an important part of rehabilitation quality control, and also lays an evidence-based data foundation for the analysis and application of inpatient rehabilitation medical record summary sheet.

OBJECTIVES: To explore the underlying pharmacological mechanisms and its potential effects of Chinese medicine herbal formula Sini Powder (SNP) on hepatocellular carcinoma (HCC). METHODS: The active components of SNP and their in vivo distribution were identified using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Construction of component-target-disease networks, protein-protein interaction network, Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and molecular docking were employed to analyze the active components and anti-HCC mechanisms of SNP. Cell viability assay and wound healing assay were utilized to confirm the effect of SNP-containing serum (2.5%, 5.0%, 10%, 20%, and 40%), isoprenaline or propranolol (both 10, 100, and 1,000 µ mol/L) on proliferation and migration of HepG 2 or Huh7 cells. Meanwhile, the effect of isoprenaline or propranolol on the β 2 adrenergic receptor (ADRB2) mRNA expression on HepG2 cells were measured by real-time quantitative reverse transcription (RT-qPCR). Mice with subcutaneous tumors were either subjected to chronic restraint stress (CRS) followed by SNP administration (364 mg/mL) or directly treated with SNP (364 mg/mL). These two parallel experiments were performed to validate the effects of SNP on stress responses. Stress-related proteins and hormones were quantified using RT-qPCR, enzyme-linked immunosorbent assay, and immunohistochemistry. Metagenomic sequencing was performed to confirm the influence of SNP on the gut microbiota in the tumor-bearing CRS mice. RESULTS: The distribution of the 12 active components of SNP was confirmed in various tissues and feces. Network pharmacology analysis confirmed the anti-HCC effects of the 5 active components. The potential anti-HCC mechanisms of SNP may involve the epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase Src (SRC) and signal transducer and activator of transcription 3 (STAT3) pathways. SNP-containing serum inhibited the proliferation of HepG2 and Huh7 cells at concentrations of 2.5% and 5.0%, respectively, after 24 h of treatment. Furthermore, SNP suppressed tumor progression in tumor-bearing mice exposed to CRS. SNP treatment also downregulated the expressions of stress-related proteins and pro-inflammatory cytokines, primarily by modulating the gut microbiota. Specifically, the abundance of Alistipes and Prevotella, which belong to the phylum Bacteroidetes, increased in the SNP-treated group, whereas Lachnospira, in the phylum Firmicutes, decreased. CONCLUSION SNP can combat HCC by alleviating stress responses through the regulation of gut microbiota.
Animals ; Gastrointestinal Microbiome/drug effects* ; Liver Neoplasms/microbiology* ; Carcinoma, Hepatocellular/microbiology* ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Powders ; Cell Proliferation/drug effects* ; Mice ; Molecular Docking Simulation ; Cell Line, Tumor ; Hep G2 Cells ; Receptors, Adrenergic, beta-2/genetics* ; Stress, Physiological/drug effects* ; Cell Movement/drug effects* ; Male ; Protein Interaction Maps/drug effects* ; Cell Survival/drug effects* ; Proto-Oncogene Mas

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Brain organoids are three-dimensional (3D) neural cultures that self-organize from pluripotent stem cells (PSCs) cultured in vitro. Compared with traditional two-dimensional (2D) neural cell culture systems, brain organoids demonstrate a significantly enhanced capacity to faithfully replicate key aspects of the human brain, including cellular diversity, 3D tissue architecture, and functional neural network activity. Importantly, they also overcome the inherent limitations of animal models, which often differ from human biology in terms of genetic background and brain structure. Owing to these advantages, brain organoids have emerged as a powerful tool for recapitulating human-specific developmental processes, disease mechanisms, and pharmacological responses, thereby providing an indispensable model for advancing our understanding of human brain development and neurological disorders. Despite their considerable potential, conventional brain organoids face a critical limitation: the absence of a functional vascular system. This deficiency results in inadequate oxygen and nutrient delivery to the core regions of the organoid, ultimately constraining long-term viability and functional maturation. Moreover, the lack of early neurovascular interactions prevents these models from fully recapitulating the human brain microenvironment. In recent years, the introduction of vascularization strategies has significantly enhanced the physiological relevance of brain organoid models. Researchers have successfully developed various vascularized brain organoid models through multiple innovative approaches. Biological methods, for example, involve co-culturing brain organoids with endothelial cells to induce the formation of static vascular networks. Alternatively, co-differentiation strategies direct both mesodermal and ectodermal lineages to generate vascularized tissues, while fusion techniques combine pre-formed vascular organoids with brain organoids. Beyond biological approaches, tissue engineering techniques have played a pivotal role in promoting vascularization. Microfluidic systems enable the creation of dynamic, perfusable vascular networks that mimic blood flow, while 3D printing technologies allow for the precise fabrication of artificial vascular scaffolds tailored to the organoid’s architecture. Additionally, in vivo transplantation strategies facilitate the formation of functional, blood-perfused vascular networks through host-derived vascular infiltration. The incorporation of vascularization has yielded multiple benefits for brain organoid models. It alleviates hypoxia within the organoid core, thereby improving cell survival and supporting long-term culture and maturation. Furthermore, vascularized organoids recapitulate critical features of the neurovascular unit, including the early structural and functional characteristics of the blood-brain barrier. These advancements have established vascularized brain organoids as a highly relevant platform for studying neurovascular disorders, drug screening, and other applications. However, achieving sustained, long-term functional perfusion while preserving vascular structural integrity and promoting vascular maturation remains a major challenge in the field. In this review, we systematically outline the key stages of human neurovascular development and provide a comprehensive analysis of the various strategies employed to construct vascularized brain organoids. We further present a detailed comparative assessment of different vascularization techniques, highlighting their respective strengths and limitations. Additionally, we summarize the principal challenges currently faced in brain organoid vascularization and discuss the specific technical obstacles that persist. Finally, in the outlook section, we elaborate on the promising applications of vascularized brain organoids in disease modeling and drug testing, address the main controversies and unresolved questions in the field, and propose potential directions for future research.

ObjectiveIn nature, objects cast shadows due to illumination, forming the basis for stereoscopic perception. Birds need to adapt to changes in lighting (meaning they can recognize stereoscopic shapes even when shadows look different) to accurately perceive different three-dimensional forms. However, how neurons in the key visual brain area in birds handle these lighting changes remains largely unreported. In this study, pigeons (Columba livia) were used as subjects to investigate how neurons in pigeon’s ventrolateral mesopallium (MVL) represent stereoscopic shapes consistently, regardless of changes in lighting. MethodsVisual cognitive training combined with neuronal recording was employed. Pigeons were first trained to discriminate different stereoscopic shapes (concave/convex). We then tested whether and how light luminance angle and surface appearance of the stereoscopic shapes affect their recognition accuracy, and further verify whether the results rely on specify luminance color. Simultaneously, neuronal firing activity of neurons was recorded with multiple electrode array implanted from the MVL during the presentation of difference shapes. The response was finally analyzed how selectively they responded to different stereoscopic shapes and whether their selectivity was affected by the changes of luminance condition (like lighting angle) or surface look. Support vector machine (SVM) models were trained on neuronal population responses recorded under one condition (light luminance angle of 45°) and used to decode responses under other conditions (light luminance angle of 135°, 225°, 315°) to verify the invariance of responses to different luminance conditions. ResultsBehavioral results from 6 pigeons consistently showed that the pigeons could reliably identify the core 3D shape (over 80% accuracy), and this ability wasn’t affected by changes in light angle or surface appearance. Statistical analysis of 88 recorded neurons from 6 pigeons revealed that 83% (73/88) showed strong selectivity for specific 3D shapes (selectivity index>0.3), and responses to convex shapes were consistently stronger than to concave shapes. These shape-selective responses remained stable across changes in light angle and surface appearance. Neural patterns were consistent under both blue and orange lighting. The decoding accuracy achieves above 70%, suggesting stable responses under different conditions (e.g., different lighting angles or surface appearance). ConclusionNeurons in the pigeon MVL maintain a consistent neural encoding pattern for different stereoscopic shapes, unaffected by illumination or surface appearance. This ensures stable object recognition by pigeons in changing visual environments. Our findings provide new physiological evidence for understanding how birds achieve stable perception (“invariant neural representations”) while coping with variations in the visual field.