Search Results (1,353)

Periodontal ligament stem cells (PDLSCs) represent a promising cell source for true periodontal regeneration due to their ability to form bone, cementum, and functional ligament. This review critically synthesised twelve in vivo studies (rats = 5, pigs = 2, dogs = 2, sheep = 2, one human trial) evaluating PDLSC transplantation for periodontal defects. A comprehensive search of PubMed, Web of Science, Embase, and the Cochrane Library (to May 2025) identified 358 records, of which 12 met predefined inclusion criteria. Data extraction encompassed cell source, scaffold, dose, follow-up, and quantitative regenerative outcomes. Nine studies reported cell doses (5 × 10⁵–2 × 10⁷ cells) and six PDLSC regeneration rates (33–100%). After normalisation for host mass, effective delivery ranged from 10⁵ to 10⁶ cells·kg⁻¹, with optimal outcomes typically above 10⁵ cells·kg⁻¹. PDLSC transplantation consistently enhanced alveolar bone, cementum, and periodontal-ligament regeneration compared with scaffold-alone or untreated controls, with the highest outcomes obtained using biocompatible scaffolds such as Hydroxyapatite/Tricalcium Phosphate (HA/TCP), Gelfoam, or amniotic membrane. Both autologous and allogeneic PDLSC achieved equivalent performance and excellent safety, while xenogeneic models confirmed immune tolerance. Despite encouraging results, the evidence remains preliminary—most studies were short-term and small-scale, and only one randomised human trial has been published. Standardisation of cell preparation, scaffold selection, dosing (absolute and mass-normalised), and follow-up is urgently needed. Future research should include Good Manufacturing Practice (GMP)-compliant clinical trials and mechanistic studies on PDLSC differentiation, paracrine signalling, and exosome-mediated effects to consolidate their translational potential for predictable periodontal regeneration. Full article

In this paper, a hybrid fault-tolerant control (FTC) system for quadcopter unmanned aerial vehicles (UAVs) is proposed to counteract the deterioration of the performance of the quadcopter due to motor faults. A robust and adaptive approach to controlling fault conditions is simulated by combining an integral back-stepping controller for translational motion and a nonlinear observer-based sliding-mode controller for rotational motion, and then implemented on an FPGA. Finally, motor faults are treated as disturbances and are successfully compensated by the controller to ensure safe and high-performance flight. Simulations were taken at 0%, 10%, 30%, and 50% motor faults to test how effective the proposed FTC system is. After simulations, the controller’s real-time performance and reliability were validated through hardware-in-the-loop (HIL) experiments. The results validated that the proposed hybrid controller can guarantee stable flight and precision tracking of the desired trajectory when any single motor fails up to the order of 50%. It shows that the controller is of high fault tolerance and robustness, which will be a potential solution for improving the reliability of UAVs in fault-prone conditions. Full article

Retinal laser therapy has been a mainstay for treating proliferative diabetic retinopathy, retinal vascular disease, and retinal breaks since 1961. However, conventional millisecond photocoagulation can cause permanent scarring and procedure discomfort, motivating the development of damage-sparing approaches that preserve the neurosensory retina. Clinically, panretinal photocoagulation remains effective for proliferative disease but trades off peripheral visual field and night vision. This review synthesizes development, mechanisms, and clinical evidence for laser modalities, including short-pulse selective retinal therapy (SRT), subthreshold diode micropulse (SDM), and pattern-scanning photocoagulation. We conducted a targeted narrative search of PubMed/MEDLINE, Embase, Web of Science, and trial registries (1960–September 2025), supplemented by reference list screening. We prioritized randomized/prospective studies, large cohorts, systematic reviews, mechanistic modeling, and relevant preclinical work. Pulse duration is the primary determinant of laser–tissue interaction. In the microsecond regime, SRT yields retinal pigment epithelium (RPE)-selective photodisruption via microcavitation and uses real-time optoacoustic or OCT feedback. SDM 100–300 µs delivers nondamaging thermal stress with low duty cycles and titration-based dosing. Pattern-scanning platforms improve throughput and tolerance yet remain destructive photocoagulation. Feedback-controlled SRT shows anatomic/functional benefit in chronic central serous chorioretinopathy and feasibility in diabetic macular edema. SDM can match threshold macular laser for selected DME and may reduce anti-VEGF injection burden. Sub-nanosecond “rejuvenation” lasers show no overall benefit in intermediate AMD and may be harmful in specific phenotypes. Advances in delivery, dosimetry, and closed-loop feedback aim to minimize collateral damage while retaining therapeutic effect. Key gaps include head-to-head trials (SRT vs. PDT/SDM), standardized feedback thresholds across pigmentation and devices, and long-term macular safety to guide broader clinical adoption. Full article

The demand for organ transplantation continues to rise worldwide, intensifying the gap between supply and demand and driving research in tissue engineering (TE). Bioprinting, particularly light-based vat photopolymerization (VP) methods such as digital light processing (DLP), has emerged as a promising strategy to fabricate complex, cell-compatible tissue constructs with high precision. In this study, we developed an open-source, bottom-up DLP bioprinter designed to provide a cost-effective and modular alternative to commercial systems. The device was built from commercially available components and custom-fabricated parts, with tolerance allocation and deviation analyses applied to ensure structural reliability. Mechanical and optical subsystems were modeled and validated, and the control architecture was implemented on the Arduino platform with a custom Python-based graphical interface. The system achieved a theoretical Z-axis resolution of 1 $\mathsf{\mu }$m and a vertical travel range of 50 mm, with accuracy and repeatability comparable to research-grade bioprinters. Initial printing trials using polyethylene glycol diacrylate (PEGDA) hydrogels demonstrated high-fidelity microfluidic constructs with adequate dimensional precision. Collectively, these results validate the functionality of the proposed system and highlight its potential as a flexible, precise, and cost-effective platform that is also easy to customize to advance the democratization of biofabrication in TE. Full article

Cyclic di-GMP (c-di-GMP), a universal second messenger in bacteria, orchestrates a wide array of essential life processes. Its intracellular dynamics are meticulously regulated by diguanylate cyclases (DGCs) and phosphodiesterases (PDEs), ensuring precise spatiotemporal control. The functional output of c-di-GMP signaling hinges on effector proteins—molecular decoders that translate c-di-GMP signals into specific cellular responses. This review systematically examines diverse classes of c-di-GMP effectors, using several representative bacterial species as model systems, to dissect their structural and mechanistic diversity. Particular emphasis is placed on their pivotal roles in bacterial pathogenicity, antibiotic tolerance, and host–pathogen interactions, offering fresh insights into the regulatory mechanisms underlying c-di-GMP signaling. Full article

Background/Objectives: The classification of patients with diabetes into phenotypes with distinct risks and therapeutic needs is crucial for individualized care. We recently introduced a clustering model for gestational diabetes mellitus (GDM). This study aims to further characterize the proposed clusters and to identify cluster-specific differences in glucometabolic parameters during early pregnancy in an independent cohort. The metabolic profiles and dietary habits of GDM clusters will be compared with those of a normal glucose-tolerant (NGT) control group. Methods: 1088 women (195 who developed GDM and 893 who remained NGT) underwent a broad risk evaluation at early pregnancy. GDM patients were further categorized into the three proposed GDM subtypes (CL1 to CL3). Results: Among GDM patients, 7.7% were classified as CL1, 35.9% as CL2, and 56.4% as CL3. CL1 showed higher age, pregestational BMI, and increased glucose concentrations both at fasting and during the diagnostic oral glucose tolerance test. CL2 was characterized by elevated BMI and fasting glucose, while CL3 showed higher glucose concentrations after the oral glucose load, with BMI levels comparable to NGT mothers. Women in the CL1 group exhibited impaired insulin sensitivity and β-cell function at early pregnancy and showed elevated lipid levels. Compared to NGT women, a positive family history of diabetes was more prevalent in CL1 and CL3, but not in CL2. Dietary patterns were similar across all groups. Conclusions: Our study showed distinct alterations in glucometabolic parameters already at early pregnancy among GDM subtypes. Patients in CL1 exhibited the most unfavorable risk constellation and could benefit from lifestyle changes and nutrition therapy in early pregnancy, despite showing similar dietary patterns as the NGT group. Full article

Carbon dioxide (CO₂) has been widely applied in gas flooding for reservoir development due to its remarkable oil recovery potential. However, because its viscosity is lower than that of water and most crude oils, severe channeling often occurs during the flooding process, resulting in a significant reduction in the sweep efficiency. To address this issue, foam flooding has attracted considerable attention as an effective method for controlling CO₂ mobility. In this study, a compound foam system was developed with alpha-olefin sulfonate (AOS) as the primary foaming agent, alcohol ethoxylate (AEO) and cetyltrimethylammonium bromide (CTAB) as co-surfactants, and partially hydrolyzed polyacrylamide (HPAM) as the stabilizer. The optimal system was screened through evaluations of comprehensive foam index, salt tolerance, oil resistance, and shear resistance. Results indicate that the AOS+AEO formulation exhibits superior foaming ability, salt tolerance, and foam stability compared with the AOS+CTAB system, with the best performance achieved at a mass ratio of 2:1 (AOS:AEO), balancing both adaptability and economic feasibility. A heterogeneous reservoir model was constructed using parallel core flooding to investigate the displacement performance and blocking capability of the system. Nuclear magnetic resonance (NMR) imaging was employed to monitor in situ oil phase migration and clarify the recovery mechanisms. Experimental results show that the compound foam system demonstrates excellent conformance control performance, achieving a blocking efficiency of 84.5% and improving the overall oil recovery by 4.6%. NMR imaging further reveals that the system effectively mobilizes low-permeability zones, with T₂ spectrum analysis indicating a 4.5% incremental recovery in low-permeability layers. Moreover, in reservoirs with larger permeability ratio, the system exhibits enhanced blocking efficiency (up to 86.5%), though the incremental recovery is not strictly proportional to the blocking effect. Compared with previous AOS-based CO₂ foam studies that primarily relied on pressure drop and effluent analyses, this work introduces NMR imaging and T₂ spectrum diagnostics to directly visualize pore-scale fluid redistribution and quantify sweep efficiency within heterogeneous cores. The NMR data provide mechanistic evidence that the enhanced recovery originates from selective foam propagation and the mobilization of residual oil in low-permeability channels, rather than merely from increased flow resistance. This integration of advanced pore-scale imaging with macroscopic displacement analysis represents a mechanistic advancement over conventional CO₂ foam evaluations, offering new insights into the conformance control behavior of AOS-based foam systems in heterogeneous reservoirs. Full article

This study introduces a novel approach to optimizing geometric tolerances on freeform surfaces, specifically turbine blades, by leveraging a global tolerance framework. Unlike traditional methods that rely on multiple local tolerances, this research proposes a unified model to streamline design complexity while maintaining functional integrity and cost efficiency. A turbine blade, reconstructed from 3D-scanned point cloud data, serves as the basis for this investigation. The reconstructed geometry was analyzed to define deviation distributions, followed by the application of a global tolerance model. Using genetic algorithms, the tolerances were optimized to balance manufacturing costs and performance penalties. Results demonstrate a substantial simplification in quality control processes, with a reduction in manufacturing costs by up to 20%, while preserving aerodynamic and structural performance. The study highlights the potential of global tolerance strategies to transform tolerance allocation in industries such as aerospace and energy, where freeform surfaces are prevalent. The integration of optimization techniques and advanced surface analysis offers a forward-looking perspective on enhancing manufacturing precision and efficiency. Full article

Background: Regorafenib is a recognised treatment for refractory metastatic colorectal cancer (mCRC). The phase II ReDOS trial indicated that a stepwise dose escalation approach could enhance tolerability and persistence while maintaining efficacy. The ReTrITA study, a significant multicentre real-world cohort in Italy, served as the foundation for this sub-analysis concentrating solely on patients treated with regorafenib. Methods: This retrospective analysis encompassed 713 patients treated at 17 Italian centres from 2012 to 2023. Patients were categorised into two groups: ReDOS-like escalation (n = 313) and fixed dosing (no-ReDOS) (n = 400). The endpoints assessed were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), duration of response (DoR), and safety. Survival was assessed using Kaplan–Meier and Cox models, accompanied by exploratory subgroup analyses. Results: The median overall survival (OS) was comparable between the escalation and fixed dosing groups, recorded at 7.4 months and 6.7 months, respectively (HR 1.00, 95% CI 0.85–1.18, p = 0.93). Progression-free survival (PFS) demonstrated a significant improvement with escalation, recording 3.1 months compared to 3.9 months (HR 0.76, 95% CI 0.65–0.89, p = 0.0007). Subgroup analyses demonstrated a consistent progression-free survival (PFS) benefit in patients aged ≥70 years (HR 0.71, p = 0.015), with an Eastern Cooperative Oncology Group performance status (ECOG PS) of 0–1 (HR 0.76, p = 0.022), RAS wild-type tumours (HR 0.69, p = 0.026), and rectal primaries (HR 0.72, p = 0.043). The disease control rate (DCR) and objective response rate (ORR) were comparable, at 23.2% versus 25.3% and 2.0% compared 2.6%, respectively. Although not statistically significant, the fixed dose group’s duration of response (DoR) was numerically longer (15.4 months) than that of the variable dosing group (8.9 months). A lower percentage of patients experienced grade 3/4 adverse events with escalation (35.4% compared to 39.5%, p = 0.0042). Conclusions: This sub-analysis of the ReTrITA cohort demonstrates that regorafenib dose escalation is achievable in real-world settings, resulting in notable improvements in progression-free survival and enhanced tolerability, while not adversely affecting overall survival. These results support and improve the findings of the ReDOS study, showing that dosage escalation is possible and helpful in a diverse, unselected group of people, which is what is performed in routine oncology treatment. The findings are consistent with both randomised and observational studies, endorsing individualised dosing as a practical strategy in refractory mCRC. Full article

Elephant grass (Pennisetum purpureum Schumach, EG) is a promising biomass energy crop due to its high productivity and adaptability to harsh environments. In the transition to renewable energy, varietal evaluation is essential to identify cultivars that maximize biomass and energy yield. This study assessed three varieties (VS-19, VA-06, and VDP as control) across three harvest cycles (new planting, first regrowth, and second regrowth) between 2022 and 2024 at the Cotton and Agricultural Development Research Institute, Ninh Thuan Province, Vietnam. The site was characterized by mean temperatures of 25–36 °C, relative humidity of 65–82%, and average precipitation of 75.7 mm per month. Agronomic traits, energy potential (heating oil equivalent per hectare, HOE/ha), forage quality, and soil amendment value of the EG were examined to address the research question whether EG can be integrated into a three-cycle utilization model (energy, forage, soil amendment) to support a circular bioeconomy in Vietnam. All cultivars showed good growth, strong drought tolerance, and resistance to pests and diseases. VS-19 showed superior tillering, strong lodging resistance, and the highest biomass yield (63.8 t/ha) with an energy output of 32,636 HOE/ha, while VA-06 (56.1 t/ha; 28,699 HOE/ha) and VDP (54.7 t/ha; 27,952 HOE/ha) produced slightly lower but comparable outputs. Forage evaluation indicated moderate nutritional quality, while residues from the third cycle showed favorable carbon and nutrients content, making EG suitable as a soil amendment. EG thus demonstrates high biomass and energy yields, forage potential, and soil improvement capacity, reinforcing its role in integrated bioenergy and agricultural systems. Full article

In a global industrial landscape where the digital economy accounts for over 40% of total output, cloud computing technology is reshaping business models at a compound annual growth rate of 19%. This trend has led to an increasing number of cloud computing tasks requiring timely processing. However, most computational tasks are latency-sensitive and cannot tolerate significant delays. This has led to the urgent need for researchers to address the challenge of effectively scheduling cloud computing tasks. This paper proposes a hybrid SAO and RIME optimizer (HSAO) for global optimization and cloud task scheduling problems. First, population initialization based on ecological niche differentiation is proposed to enhance the initial population quality of SAO, enabling it to better explore the solution space. Then, the introduction of the soft frost search strategy and hard frost piercing mechanism from the RIME optimization algorithm enables the algorithm to better escape local optima and accelerate its convergence. Additionally, a population-based collaborative boundary control method is proposed to handle outlier individuals, preventing them from clustering at the boundary and enabling more effective exploration of the solution space. To evaluate the effectiveness of the proposed algorithm, we compared it with 11 other algorithms using the IEEE CEC2017 test set and assessed the differences through statistical analysis. Experimental data demonstrate that the HSAO algorithm exhibits significant advantages. Furthermore, to validate its practical applicability, we applied HSAO to real-world cloud computing task scheduling problems, achieving excellent results and successfully completing the scheduling planning of cloud computing tasks. Full article

This study investigated the effects of asparagus powder supplementation on blood glucose regulation, insulin, lipid profile, and oxidative stress in overweight and obese individuals. Forty-four adults aged 18–59 years participated in a 12-week randomized controlled trial and were randomly assigned to receive either asparagus powder (40 mg/kg/day) or a placebo (maltodextrin, 40 mg/kg/day). Assessments included an oral glucose tolerance test (OGTT), fasting blood glucose (FBG), insulin, homeostasis model assessment of insulin resistance (HOMA-IR) and β-cell function (HOMA-B), lipid profile, and oxidative stress markers (malondialdehyde [MDA], protein carbonyl, and superoxide dismutase [SOD]). In the asparagus group, OGTT at 30 min and low-density lipoprotein cholesterol (LDL-C) significantly decreased, while SOD activity significantly increased (all p < 0.05). In contrast, the placebo group showed significant increases in OGTT at 30 min, insulin, HOMA-IR, HOMA-B, triglycerides (TG), the TG/high-density lipoprotein cholesterol (HDL-C) ratio, and the total cholesterol (TC)/HDL-C ratio (all p < 0.05). Between-group comparisons indicated that FBG, area under the BG curve at 30–120 min, TG, TG/HDL-C, and MDA levels were significantly lower in the asparagus group than in the placebo group (all p < 0.05), whereas OGTT, LDL-C, SOD activity, insulin, HOMA-IR, HOMA-B, and TC/HDL-C did not differ significantly. Other indices, including TC, HDL-C, and protein carbonyl, showed no significant within- or between-group differences. In conclusion, 12 weeks of asparagus powder supplementation partially improved glycemic control, lipid profile, and oxidative stress in overweight and obese individuals. These findings suggest a potential role of asparagus as a complementary nutritional strategy to reduce the risk of diabetes and cardiovascular disease in this population. Full article

Dongxiang wild rice (DXWR, Oryza rufipogon Griff.), the northernmost known wild rice species, exhibits exceptional tolerance to combined low-temperature and anaerobic stress during seed germination, providing a unique model for understanding plant adaptation to complex environmental constraints. Here, we employed an integrated multi-omics approach combining genomic, transcriptomic, and metabolomic analyses to unravel the synergistic regulatory mechanisms underlying this tolerance. Genomic comparative analysis categorized DXWR genes into three evolutionary groups: 18,480 core genes, 15,880 accessory genes, and 6822 unique genes. Transcriptomic profiling identified 10,593 differentially expressed genes (DEGs) relative to the control, with combined stress triggering the most profound changes, specifically inducing the upregulation of 5573 genes and downregulation of 5809 genes. Functional characterization revealed that core genes, including DREB transcription factors, coordinate energy metabolism and antioxidant pathways; accessory genes, such as glycoside hydrolase GH18 family members, optimize energy supply via adaptive evolution; and unique genes, including specific UDP-glycosyltransferases (UDPGTs), confer specialized stress resilience. Widely targeted metabolomics identified 889 differentially accumulated metabolites (DAMs), highlighting significant accumulations of oligosaccharides (e.g., raffinose) to support glycolytic energy production and a marked increase in flavonoids (153 compounds identified, e.g., procyanidins) enhancing antioxidant defense. Hormonal signals, including jasmonic acid and auxin, were reconfigured to balance growth and defense responses. We propose a multi-level regulatory network based on a “core-unique-adaptive” genetic framework, centered on ERF family transcriptional hubs and coordinated through a metabolic adaptation strategy of “energy optimization, redox homeostasis, and growth inhibition relief”. These findings offer innovative strategies for improving rice stress tolerance, particularly for enhancing germination of direct-seeded rice under early spring low-temperature and anaerobic conditions, by utilizing key genes such as GH18s and UDPGTs, thereby providing crucial theoretical and technological support for addressing food security challenges under climate change. Full article

Salinity is a major abiotic stress that limits plant growth and survival. Colobanthus quitensis, the only native dicotyledon in the Antarctic Peninsula and southern South America, naturally inhabits environments with contrasting salinity regimes. This study compared the salt stress responses of three geographically distinct populations—Antarctic (pA), Magellanic coastal (pPA), and Andean inland (pC)—exposed to 0, 50, and 150 mM NaCl under controlled conditions. Morpho-physiological traits, photosynthetic parameters, osmolyte accumulation, oxidative damage markers, and antioxidant responses were evaluated. Population-specific strategies were observed. In pA, salinity reduced shoot biomass by 58% and doubled lipid peroxidation levels at 50 mM, indicating high oxidative stress. In pPA, shoot growth was maintained even at 150 mM, although chlorophyll and carotenoid contents decreased by approximately 20%, along with a reduction in total antioxidant capacity. In contrast, pC showed a coordinated tolerance response, maintaining biomass while accumulating the highest proline levels (742 µmol g⁻¹ FW at 150 mM) and enhancing total antioxidant capacity by 35% compared to the control. Multivariate analyses supported the contrasting strategies among populations. These results provide novel evidence of local adaptation and ecological plasticity in C. quitensis, particularly highlighting the hidden resilience of non-coastal populations. The findings support the potential of this extremophile species as a model system for investigating salinity tolerance and as a promising genetic resource for developing biotechnological strategies aimed at improving crop resilience under saline conditions. Full article

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua. Full article