Abstract: Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are devastating and currently untreatable neurodegenerative diseases, whose genetic and molecular etiologies remain largely unclear. The histopathological hallmark of both diseases is the cytoplasmic deposition of TDP-43 in neurons, which is attributed to both intrinsic (e.g., mutations, aberrant cleavage) and extrinsic factors (e.g., prolonged oxidative stress, impaired clearance pathways). Mutations and certain PTMs (e.g., cysteine oxidation) destabilize RNA binding, promoting monomer misfolding and increasing its half-life. Disruptions to core ubiquitin-proteasome system (UPS) subunits impede efficient processing, contributing to the clearance failure of misfolded TDP-43 monomers. The accumulation of monomers drives phase separation within stress granules, creating nucleation hotspots that eventually bypass the thermodynamic barrier, resulting in exponential growth. This rapid growth then culminates in the failure of the autophagy-lysosome pathway (ALP) to contain the aggregation, resulting in a self-sustaining feed-forward loop. Here, we synthesize these factors into a unified kinetic cascade model. Therapeutic strategies must therefore move beyond simple clearance and focus on targeting these kinetic inflection points (e.g., oligomer seeding, PTM modulation).

Abstract: Recently, the need for unified orchestration frameworks that can manage extremely heterogeneous, distributed, and resource-constrained environments has arisen due to the rapid development of cloud, edge, and IoT computing. Kubernetes and other traditional cloud-native orchestration systems are not built to facilitate autonomous, decentralized decision-making across the computing continuum or to seamlessly integrate non-container-native devices. This paper presents the Distributed Adaptive Cloud Continuum Architecture (DACCA), a Kubernetes-native architecture that extends orchestration beyond the data center to encompass edge and Internet of Things infrastructures. Decentralized self-awareness and swarm formation are supported for adaptive and resilient operation, a resource and application abstraction layer is established for uniform resource representation, and a Distributed and Adaptive Resource Optimization (DARO) framework based on multi-agent reinforcement learning is integrated for intelligent scheduling in the proposed architecture. Verifiable identity, access control, and tamper-proof data exchange across heterogeneous domains are further guaranteed by a distributed-ledger-technology-based zero-trust security framework. When combined, these elements enable completely autonomous workload orchestration with enhanced interoperability, scalability, and trust. Thus, the proposed architecture enables self-managing and context-aware orchestration systems that support next-generation AI-driven distributed applications across the entire computing continuum.

Abstract: Background/Objectives: Capric acid–based therapeutic deep eutectic systems (THEDES) 18 are emerging as a distinct class of biofunctional matrices capable of reshaping drug solubilization, permeability, and bioactivity. Methods: Relevant studies on capric acid–based therapeutic deep eutectic systems (THEDES) were identified through targeted database searches and screened for evidence on their design, mechanisms, and pharmaceutical performance. Results: This review synthesizes current evidence on their structural design, mechanistic behavior, and pharmaceutical performance, revealing several unifying principles. Across multiple drug classes, capric acid consistently drives strong, directional hydrogen bonding and drug amorphization, enabling exceptional solubility enhancements and stabilized supersaturation. Its amphiphilic C10 chain further contributes to membrane fluidization, which explains the improved transdermal and transmucosal permeation repeatedly observed in capric acid-based THEDES. Additionally, synergistic antimicrobial and anticancer effects reported in several systems confirm that capric acid acts not only as a solvent component but as a bioactive co-therapeutic. Collectively, the reviewed data show that capric acid serves as a structurally determinant element whose dual hydrogen-bonding and membrane-interacting roles underpin the high pharmaceutical performance of these systems. However, gaps remain in long-term stability, toxicological profiling, and regulatory classification. Emerging Artificial Intelligence (AI) and Machine Learning (ML)-guided predictive approaches offer promising solutions by enabling rational selection of eutectic partners, optimal ratios, and property optimization through computational screening. Conclusion: Overall, capric acid-based THEDES represent a rationally designable platform for next-generation drug delivery, where solvent functionality and therapeutic activity converge within a single, green formulation system.

Abstract: Ningbo (NGB), one of the world's most important port cities located on the East Coast of China, contains more than 100 rivers and streams across three major catchments, the Yong, Yuyao and Fenghua Rivers. During the 1970s – 2000s, extensive river engineering, including channelisation, conversion of natural rivers into artificial canals, and construction of sluice gates and embankments were undertaken to cope with rapid urbanisation and development. Since the 2010s, the Ningbo Government and Water Bureau have initiated smart river and fluvial flood management strategies to enhance digital twins and smart flood management technologies, such as 3D flood mapping and real-time water level and discharge monitoring, significantly improving precision. In this study, we demonstrate that smart technology has performed effectively in Ningbo, with applications in the recent climate extreme events such as Typhoon In-Fa and Muihua in 2021 and 2022, during which the Municipal Bureau has safeguarded public safety and welfare. This further strengthening both municipal and national commitment to enhance climate resilience. Nevertheless, further advancement of the DT platform remains necessary. Key areas for improvement include faster computational capacity, enhanced coordination across departments and open data sharing mechanisms, and integration of artificial intelligence (AI) to support more effective decision-making processes in response to the climate extremes and adverse water hazards conditions.

Abstract: Primary cutaneous lymphomas (PCLs) are a heterogeneous group of extranodal non-Hodgkin lymphomas presenting in the skin without evidence of extracutaneous disease at diagnosis. They encompass a broad clinicopathologic spectrum dominated by cutaneous T-cell lymphomas (CTCL), primarily mycosis fungoides (MF) and Sézary syndrome (SS), and by distinct entities of primary cutaneous B-cell lymphomas (PCBCL). Recent updates of the WHO–EORTC classification have refined disease definitions and introduced new entities and lymphoproliferative disorders, with direct consequences for prognosis and therapeutic decision-making. Parallel advances in genomics and im-munobiology have revealed recurrent alterations in T-cell receptor (TCR) signalling, JAK–STAT and NF-κB pathways, as well as hallmarks of immune evasion in the tumour microenvironment, providing a rationale for targeted and immune-based therapies. This narrative review, written from a dermatologic perspective, summarises current concepts in the classification, epidemiology and clinicopathologic features of the major PCL subtypes. We discuss key molecular drivers of CTCL and PCBCL, practical aspects of diagnosis and staging at the interface between dermatology, pathology and haematology, and the role of non-invasive imaging. We then review the contemporary therapeutic armamentarium, including skin-directed therapies, systemic biologic agents and chemotherapy, and emphasise pivotal trials of antibody-drug conjugates and immune therapies such as brentuximab vedotin and mogamulizumab. Finally, we highlight unmet needs, including diagnostic delay, real-world prognostic stratification, manage-ment of advanced and relapsed disease, and the integration of biomarkers into person-alised care. Dermatologists occupy a central role in early recognition, longitudinal monitoring and multidisciplinary management of PCLs, and ongoing collaboration between specialties is essential to translate molecular insights into improved patient outcomes.

Abstract: In this field, several erroneous theories had long been accepted as fundamental laws and formulas. Recent corrections to these misconceptions were made possible through the application of Exploratory Data Analysis (EDA). This article outlines how EDA contributed to these breakthroughs and offers a brief guide for those wishing to begin using it themselves.

Abstract: Despite decades of investigation, Aldose Reductase (AR; AKR1B1) -an enzyme that plays a key role in the metabolism of glucose and other carbonyl compounds and whose hy-peractivity contributes to oxidative stress and vascular dysfunction- inhibitors have failed to translate into clinical application for Diabetic Retinopathy (DR). We argue that these failures might arise from non-selective inhibition, which does not consider AR’s dual roles in pathology but also in retinal health, as AR is also an important detoxifying enzyme for aldehydes produced during oxidative stress, and discuss the missing structural infor-mation, despite the over one hundred crystal structures of AR in complex with inhibitors. Our review bridges this gap by proposing how recent advances in structural biology, namely, fragment-based drug discovery and MicroED, provide novel ways of selectively modulating AR functions, offering advantages in the detection of weak, allosteric, or conformation-dependent binding events. Despite past challenges, we suggest that therapeutic targeting or finding new-generation inhibitors for AR will become more effective once we have a clearer understanding of AR’s requirements for selective inhi-bition of its pathological and physiological functions. By integrating fragment screening and structural biology, we outline a strategy to reinvigorate AR modulation as a viable retina-specific approach for managing DR first, although potentially relevant across multiple diabetic microvascular complications later.

Abstract: Optic neuritis (ON) has been recognized since antiquity, but its modern clinical identity emerged only in the late 19th century and was definitively shaped by the Optic Neuritis Treatment Trial (ONTT). The ONTT established the natural history, visual prognosis, association with multiple sclerosis (MS), and therapeutic response to corticosteroids, building the foundation for contemporary ON management. Over the past two decades, ON has evolved from a seemingly uniform demyelinating syndrome into a group of biologically distinct disorders. The identification of aquaporin-4-IgG ON (AQP4-ON), myelin oligodendrocyte glycoprotein antibody–associated ON (MOG-ON), and double-negative ON has transformed diagnostic and therapeutic strategies. These subtypes differ in immunopathology, clinical course, MRI features, retinal injury patterns, CSF profiles, and long-term outcomes, making early and accurate differentiation essential. MRI provides key distinctions in lesion length, orbital tissue inflammation, bilateral involvement, and chiasmal or optic tract extension. Optical coherence tomography (OCT) offers complementary structural biomarkers, including severe early ganglion cell loss in AQP4-ON, relative preservation in MOG-ON, and variable patterns in double-negative ON. CSF analysis further refines diagnosis, with oligoclonal bands strongly supporting MS-ON. Together, these modalities enable precise early stratification and timely initiation of targeted immunotherapy, which is critical for preventing irreversible visual disability. Despite major advances, significant unmet needs persist. Access to high-resolution MRI, OCT, cell-based antibody assays, and evidence-based treatments remains limited in many regions, contributing to global disparities in outcomes. The pathogenesis of double-negative ON, reliable biomarkers of relapse and visual recovery, and standardized multimodal diagnostic thresholds remain unresolved. Future research must expand biomarker discovery, refine imaging criteria, and ensure equitable global access to cutting-edge diagnostic platforms and therapeutic innovations. Four decades after the ONTT, ON remains a dynamic field of investigation, with ongoing advances holding the potential to transform care for patients worldwide.

Abstract: This study investigates hydro-meteorological trends in five Alpine catchments within the Upper Po River basin, spanning Northwestern Italy and Southern Switzerland. We ana-lyzed climatic variables from 25 weather stations (1950–2022) alongside streamflow data from 14 river sections (1911–2022). Trends were assessed using the Mann-Kendall test to detect monotonic changes and the Theil-Sen estimator to quantify magnitude, ensuring robustness against outliers. Results reveal pronounced warming, particularly in spring maximum temperatures (+0.95 °C per decade). Conversely, average and minimum daily temperatures show lower rates (+0.50 and +0.39 °C per decade). Consequently, potential evapotranspiration increased significantly (+15.1 mm per decade), contributing to a marked decline in summer streamflow in 8 out of 14 sections. Correlation analysis con-firms that snow dynamics modulate the hydrological response: while precipitation drives discharge annually and in autumn, winter exhibits a weaker coupling, as winter precipi-tation is partially stored in the basin as snow, contributing to discharge during spring and summer. By focusing on this strategic region for European agriculture and industry, the study provides essential insights to support effective adaptation strategies.

Abstract: Building Information Modeling (BIM) represents a paradigmatic transformation in architecture and engineering, facilitating the transition from two-dimensional documentation to integrated multidimensional collaborative environments. This article synthesizes a systematic literature review (2014-2024) combining meta-analyses, case studies, and quantitative-qualitative research on the adoption of BIM in the AEC sector. The results document significant benefits: reductions of 25-30% in design errors, 20% in execution time and 15% in costs. However, adoption reveals geographic fragmentation (US 60%, UK 80%, Latin America <25%) and multidimensional barriers: lack of specialized training, cultural resistance, absence of specific legal frameworks in developing countries, and limited interoperability. The analysis identifies that successful integration requires deep organizational transformation, coordinated public policies, and educational curricular adaptation. Recommendations include micro-regional contextual strategies, contractual standardization (ISO 19650) and applied research in BIM-Facility Management integration and emerging technologies (XR, digital twins). BIM integrates geometric (3D), temporal (4D-schedule), economic (5D-costs) and operational (6D-facility management) information into collaborative parameterized models. Beyond visualization, the methodology calls for clarity on specific Development Levels (LODs) for each phase of the asset lifecycle, from LOD 100 (conceptual) to LOD 500 (as-built). Interoperability using IFC (ISO 16739) and ISO 19650 standards requires robust model validation and accurate definition of model views (MVDs), areas where 74% of projects in developing countries still have critical gaps. This article emphasizes that BIM is not only a software tool, but a comprehensive information management protocol that permeates processes from conceptual design to sustainable operation and demolition.

Abstract: Microwave photonics has recently come to the forefront as a valuable approach to generating, processing, and measuring signals in high-performance domains such as communication, radar, and timing systems. Recent studies have introduced a range of photonics-based phase-noise analyzers (PNAs) that utilize a variety of architectures, including phase detection, frequency discrimination, and hybrid mechanisms that combine optical with electronic processing. This review delves into the microwave photonics methodologies developed with the specific purpose of measuring phase noise, by exploring their fundamental principles, system design frameworks, and performance indicators. Through the integration of insights garnered from recent publications, our objective is to deliver a comprehensive understanding of the strengths and limitations associated with PNAs and to pinpoint new and promising areas for advancing research in the field of oscillator metrology.

Abstract: In complex traffic environments, image degradation caused by haze, low illumination, and occlusion significantly undermines the reliability of vehicle and pedestrian detection. To address these challenges, this paper proposes an aerial vision framework that tightly couples multi-level image enhancement with a lightweight detection architecture. At the image preprocessing stage, a cascaded “dehazing + illumination” module is constructed. Specifically, a learning-based dehazing method, Learning Hazing to Dehazing, is employed to restore long-range details affected by scattering artifacts. Additionally, HVI-CIDNet is introduced to decouple luminance and chrominance in the Horizontal/Vertical Intensity (HVI) color space, thereby simultaneously enhancing structural fidelity in low-light regions and achieving global brightness consistency. On the detection side, a lightweight yet robust detection architecture, termed GDEIM-SF, is designed. It adopts GoldYOLO as the lightweight backbone and integrates D-FINE as an anchor-free decoder. Furthermore, two key modules, CAPR and ASF, are incorporated to enhance high-frequency edge modeling and multi-scale semantic alignment, respectively. Evaluated on the VisDrone dataset, the proposed method achieves improvements of approximately 2.5–2.7 percentage points in core metrics such as mAP@50–90 compared to similar lightweight models (e.g., the DEIM baseline and YOLOv12s), while maintaining low parameter count and computational overhead. This ensures a balanced trade-off among detection accuracy, inference efficiency, and deployment adaptability, providing a practical and efficient solution for UAV-based visual perception tasks under challenging imaging conditions.

Abstract: Large language models and related generative AI systems increasingly operate in safety-critical and high-impact settings, where reliability, alignment, and robustness under distribution shift are central concerns. While retrieval-augmented generation (RAG) has emerged as a practical mechanism for grounding model outputs in external knowledge, it does not by itself provide guarantees against system-level failure modes such as hallucination, mis-grounding, or deceptively stable unsafe behavior. This work introduces SORT-AI, a structural safety and reliability framework that models advanced AI systems as chains of operators acting on representational states under global consistency constraints. Rather than proposing new architectures or empirical benchmarks, SORT-AI provides a theoretical and diagnostic perspective for analyzing alignment-relevant failure modes, structural misgeneralization, and stability breakdowns that arise from the interaction of retrieval, augmentation, and generation components. Retrieval-augmented generation is treated as a representative and practically relevant testbed, not as the primary contribution. By analyzing RAG systems through operator geometry, non-local coupling kernels, and global projection operators, the framework exposes failure modes that persist across dense retrieval, long-context prompting, graph-constrained retrieval, and agentic interaction loops. The resulting diagnostics are architecture-agnostic and remain meaningful across datasets, implementations, and deployment contexts. SORT-AI connects reliability assessment, explainability, and AI safety by shifting evaluation from local token-level behavior to global structural properties such as fixed points, drift trajectories, and deceptive stability. While illustrated using RAG, the framework generalizes to embodied agents and quantum-inspired operator systems, offering a unifying foundation for safety-oriented analysis of advanced AI systems.

Abstract: Integration of deep learning in healthcare has revolutionized the analysis of complex, high-dimensional, and heterogeneous data. However, traditional single-modal approaches often fail to grasp the multi-faceted nature of human health, in which genetic, environmental, lifestyle, and physiological factors interact in complex ways. The rapid development of multimodal machine learning (MML) has been a transformational paradigm that allows seamless integration of these heterogeneous data sources toward a better understanding of health and disease. This review goes in-depth with the methodologies of MML, with special emphasis on the main strategies of fusion and advanced techniques. We also discuss the wide applications of MML in different health domains, such as brain disorders, cancer prediction, chest-related conditions, skin diseases, and other medical challenges. We illustrate, through detailed case studies, how MML provides better diagnostic accuracy, and personalized treatment strategies. While it has seen huge progress, MML is confronted with a few major challenges around data heterogeneity, alignment complexities, and the subtleties of effective fusion strategies. The review concludes with a discussion on the future directions calling for robust data integration techniques, efficient and scalable architectures, and fairness and bias mitigation. MML is still an evolving field, and it has the potential to revolutionize healthcare delivery and drive innovations in the direction of more personalized, equitable, and effective patient care globally.

Abstract: The MMA–DMF framework connects cosmological “dark sector” phenomenology with quantum-foundational phenomena by treating a single screened scalar field as both a mediator of large-scale modified gravity and a stochastic vacuum bath responsible for gravitational decoherence. This paper consolidates the full, dated MMA–DMF validation record contained in the project materials (with an audited, frozen parameter set) and reports the complete test suite relevant to uncertainty and decoherence: (i) a strict Fluctuation–Dissipation Theorem (FDT) stability test for the Generalized Langevin Equation (GLE) memory kernel, which passes an energy-drift criterion of |slope| < 10−5 in long integrations; (ii) a dynamic contextuality roll-off test in which the CHSH Bell parameter transitions from the Tsirelson value S ≈ 2.828 at quasi-static settings to the classical bound S → 2 under fast modulation, quantified by explicit frequency-dependent suppression formulas; and (iii) a T-MAGIS atom-interferometry campaign prediction in which a density-modulated environment produces a detectable contrast loss ∆V ≈ 3.4 × 10−3 to 4 × 10−3 under representative configurations, with a tabulated scaling versus distance and interrogation time and a shot-noise sensitivity forecast yielding high signal-to-noise for hour-scale integration. We also summarize MMA–DMF-linked phenomenology across scales, including a joint cosmological likelihood structure with cross-covariance correction and representative reported values (H0, S8) ≈ (72.1 km s−1 Mpc−1, 0.761), plus a gravitational-wave echo delay estimate of ∆techo ≈ 32 ms for stellar-mass systems. The combined record constrains MMA–DMF by demanding simultaneous thermodynamic consistency of the stochastic sector, a controlled transition from contextual to classical correlations under finite response time, and a falsifiable laboratory decoherence signature under controlled density modulation.

Abstract: It is of great significance to prepare carbon supported non-noble metal catalysts for hydrogen evolution reaction (HER) via a sustainable method. Meanwhile, the enhanced metal-support interaction (MSI) is vital for promoting the catalytic activity of metal/carbon catalysts. Herein, we prepare biomass-derived porous carbon supported metal Ni catalyst (Ni/APC) with the enhanced MSI via atomic Ni-N4 sites utilizing agaric as a precursor. The highly dispersed Ni-N4 species preferentially adsorb dye molecules and reactant H2O, beneficial to efficient electron transfer and promoting H2O dissociation. Meanwhile, Ni nanoparticles undertake the active sites for H2 desorption. In virtue of the synergistic effect of metal Ni nanoparticles and atomic Ni-N4 for different roles of active sites, Ni/APC catalysts show more effective dye-sensitized photocatalytic HER activities, compared with pure Ni and pure APC. The Ni/APC catalyst with an optimal Ni loading amount exhibits a high AQY of 41.0 % with an excellent long-term stability in terms of both HER activity and structure. It is the first report of an application for biomass-derived carbon catalysts in dye-sensitization hydrogen production, and the synergistic effect of atomic Ni and particled Ni on the dye-sensitized photocatalytic HER is deeply investigated. This work provides new deep insight into the design of new non-noble metal/carbon materials by taking advantages of biomass materials.

Abstract: Sulfate-reducing bacteria (SRB)-induced corrosion presents a considerable challenge to the integrity of shale gas pipelines. Conventional reliance on chemical biocides is limited by the potential for microbial resistance and environmental impact. As an alternative, the bio-competitive exclusion approach, utilizing microbes such as denitrifying bacteria (DNB), offers a promising strategy. This study investigates an integrated control method, combining the biocide glutaraldehyde with DNB to synergistically inhibit SRB activity and corrosion. The efficacy and mechanisms were systematically evaluated through electrochemical measurements, weight-loss analysis, surface characterization, and microbial community profiling. Following synergistic treatment with glutaraldehyde and DNB, the average corrosion rate was reduced by 44.2% and the maximum corrosion depth decreased by 84.3% compared to the SRB-inoculated system. Microbial community analysis revealed a substantial decline in SRB abundance from 62.7% on day 1 to 11.9% by day 14 under the synergistic treatment. The combined approach proves economically and environmentally viable, offering the advantages of reduced chemical dosage and the avoidance of additional corrosion typically associated with DNB. These results provide a novel strategy for developing microbial-influenced corrosion control measures in shale gas infrastructure.

Abstract: Acidic cannabinoids (e.g., THCA, CBDA) are the dominant phytoconstituents in Cannabis Sativa and serve as precursors to neutral forms (THC, CBD) via decarboxylation. This is the third work in an integrated series exploring how dietary cannabis inputs interact with the Endocannabinoid System (ECS) pathways. This paper examines the role of physiological environments—specifically stomach acidity, blood pH, and hepatic metabolism—in determining the fate and bioavailability of ingested acidic cannabinoids.[Approach & Findings] Integrating organic chemistry and pharmacokinetics, the study confirms that gastric conditions (pH 1.5–3.5, 37°C) induce a minor, diet-dependent partial decarboxylation ($\leq 5-10\%$) due to the low activation energy at physiological temperature and poor solubility. Upon absorption, systemic blood pH (7.35–7.45) stabilizes the acidic cannabinoids, which exist primarily as non-decarboxylating carboxylate anions (>99% ionized). The hepatic first-pass metabolism then primarily processes the compounds through CYP and UGT enzymes, leading to conjugated metabolites (e.g., THCA-glucuronide) rather than extensive decarboxylation.[Microbiome & Implication] Crucially, the gut microbiome is identified as a secondary modulator, utilizing microbial decarboxylases and $\beta$-glucuronidases to potentially recycle cannabinoids via enterohepatic circulation, thus impacting systemic exposure and therapeutic effects. This comprehensive analysis integrates chemical kinetics and physiological variables, showing that acidic cannabinoids are delivered largely intact to modulate the ECS directly (e.g., THCA activating TRPA1; CBDA inhibiting FAAH).[Conclusion] The minor in vivo conversion rate means the therapeutic potential of ingested acidic cannabinoids is shaped more by direct ECS interaction and microbial/metabolic processing than by thermal decarboxylation.

Abstract: The precise and automated classification of histopathology images is essential for early detection of cancer, particularly for widespread cancers like Colorectal Cancer (CRC) and Lung Cancer (LC). However, traditional deep learning models frequently encounter challenges due to significant intra-class variability, similarities between different classes, and inconsistent image quality. To overcome these limitations, a detailed multi-layer diagnostic framework is proposed. This method begins with a robust preprocessing pipeline that includes gamma correction, bilateral filtering, and adaptive CLAHE, leading to substantial improvements in quantitative metrics of image quality. A deep learning architecture based on hybrid attention mechanisms has been introduced, which integrates an Xception backbone, a Convolutional Block Attention Module (CBAM), a Transformer block, and an MLP classifier to effectively merge local features with global context. When evaluated on three publicly accessible datasets, the proposed model attained exemplary results, reaching classification accuracies of 99.98% on LC-2500, 99.58% on CRC-VAL-HE-7K, and 99.29% on NCT-CRC-HE-100K. To improve transparency, thorough explain ability analyses are performed utilizing layer-wise feature visualization and Grad-CAM. Lastly, the practical application of this framework is showcased through its implementation on a web-based platform, offering a valuable and user-friendly tool to assist in pathological diagnosis.

Abstract: Purpose: We investigated the long-term effects of cataract surgery by the eight-chop technique on intraocular pressure (IOP) in cataract patients.Methods: The patients were classified into three groups (Grade II, III, and IV) according to the lens hardness. The operative time, phaco time, aspiration time, cumulative dissipated energy, and volume of fluid used were measured intraoperatively. The best-corrected visual acuity and corneal endothelial cell density were measured. The IOP was monitored for 5 years. Based on the preoperative IOP, eyes were classified into two groups for analysis: IOP > 15 mmHg and < 15 mmHg.Results: The operative time in Grades II, III, and IV were 4.63 ± 0.88 min, 5.48 ± 1.52 min, and 7.77 ± 1.47 min, respectively. The rate of corneal endothelial cell density loss was 1.9 ± 8.3% at 19 weeks. Postoperatively, the IOPs at 1 year were 12.6 ± 2.4 mmHg, 13.2 ± 2.3 mmHg, and 11.7 ± 2.2 mmHg, and at 5 years were 13.1 ± 2.5 mmHg, 12.0 ± 2.0 mmHg, and 12.0 ± 0.6 mmHg, in Grades II, III, and IV, respectively. In patients with a preoperative IOP < 15 mmHg, the IOP remained significantly lower even after 5 years of surgery.Conclusions: The eight-chop technique can lower the IOP and this effect persists for 5 years. This procedure is short and is associated with a minimal reduction in corneal endothelial cell density. Thus, this technique is very effective in lowering IOP in patients with cataracts.