Search Results (155)

Aging is a complex biological process marked by a progressive decline in cellular function, leading to age-related diseases such as neurodegenerative disorders, cancer, and cardiovascular diseases. Despite significant advancements in aging research, finding effective interventions to decelerate aging remains a challenge. This review explores microgravity as a novel therapeutic approach to combat aging and promote healthy longevity. The hallmarks of aging, including genomic instability, telomere shortening, and cellular senescence, form the basis for understanding the molecular mechanisms behind aging. Interestingly, microgravity has been shown to accelerate aging-like processes in model organisms and human tissues, making it an ideal environment for studying aging mechanisms in an accelerated manner. Spaceflight studies, such as NASA’s Twins Study and experiments aboard the International Space Station (ISS), reveal striking parallels between the physiological changes induced by microgravity and those observed in aging populations, including muscle atrophy, bone density loss, cardiovascular deconditioning, and immune system decline in a microgravity environment. However, upon microgravity recovery, cellular behavior, gene expression, and tissue regeneration were seen, providing vital insights into aging mechanisms and prospective therapeutic approaches. This review examines the potential of microgravity-based technologies to pioneer novel strategies for decelerating aging and enhancing healthspan under natural gravity, paving the way for breakthroughs in longevity therapies. Full article

Sustainable Forest Management (SFM) requires the building of relationships among diameter increment, shape, and size (ISS), and increment–age variables to identify critical changes in forest structure and dynamics. This understanding is essential for maintaining forest productivity, structural and species diversity, stability, and sustainability. This study focused on measuring, reporting, and modeling these relationships for Araucaria angustifolia (Bertol.) Kuntze, across various diameters and three stands, located at different rural properties in southern Brazil. A random sample of 186 individual trees was acquired; the trees were measured for multiple dendrometric variables, and several morphometric indices were calculated. Additionally, two cores were extracted from each tree using an increment borer, enabling the measurement of growth rings and annual diameter increments. These were modeled using generalized linear models to assess the relationships among them and to quantify changes in forest structure and dynamics. The results revealed the dominance of A. angustifolia and a decline in the increment rate with increasing age, shape, and size in both old and young trees, indicating potential risks to the structure and dynamics of these unmanaged forests. Therefore, the models constructed in this study can guide conservation-by-use efforts and ensure the long-term continuity and productivity of forest remnants at selected rural properties, where A. angustifolia trees are predominant. Full article

Background and Objectives: Major trauma ranks among the leading causes of mortality and handicap in both developing and developed countries, consuming substantial healthcare resources. Its unpredictable nature and diverse clinical presentations often lead to rapid and challenging-to-predict changes in patient conditions. An increasing number of models have been developed to address this challenge. Given our access to extensive and relatively comprehensive data, we seek assistance in making a meaningful contribution to this topic. This study aims to leverage artificial intelligence (AI)/machine learning (ML) to forecast potential adverse effects in major trauma patients. Methods: This retrospective analysis considered major trauma patient admitted to Chi Mei Medical Center from 1 January 2010 to 31 December 2019. Results: A total of 5521 major trauma patients were analyzed. Among five AI models tested, XGBoost showed the best performance (AUC 0.748), outperforming traditional clinical scores such as ISS and GCS. The model was deployed as a web-based application integrated into the hospital information system. Preliminary clinical use demonstrated improved efficiency, interpretability through SHAP analysis, and positive user feedback from healthcare professionals. Conclusions: This study presents a predictive model for estimating recovery probabilities in severe burn patients, effectively integrated into the hospital information system (HIS) without complex computations. Clinical use has shown improved efficiency and quality. Future efforts will expand predictions to include complications and treatment outcomes, aiming for broader applications as technology advances. Full article

Background/Objectives: Trauma scoring systems are essential tools for predicting clinical outcomes in patients with multiple injuries. This study aimed to compare the performance of various anatomical and physiological scoring systems in predicting mortality among patients admitted to the emergency department following traffic accidents. Methods: In this prospective observational study, trauma patients presenting with traffic-related injuries were evaluated using seven scoring systems: ISS, NISS, AIS, GCS, RTS, TRISS, and APACHE II. Demographic data, clinical findings, and laboratory values were recorded. The prognostic performance of each score was assessed using ROC curve analysis, and diagnostic metrics including sensitivity, specificity, and likelihood ratios were calculated. Results: Among 554 patients included in the study, the overall mortality rate was 2%. The TRISS and GCS scores demonstrated the highest predictive performance, each with an AUC of 0.98, sensitivity of 100%, and specificity exceeding 93%. APACHE II followed closely with an AUC of 0.97, also achieving 100% sensitivity. NISS (AUC = 0.92) and ISS (AUC = 0.91) were effective anatomical scores, while RTS showed moderate predictive value (AUC = 0.90). Strong correlations were noted between ISS, NISS, and AIS (Rho > 0.85), while RTS was negatively correlated with these anatomical scores. All scoring systems showed statistically significant associations with mortality. Conclusions: TRISS, GCS, and APACHE II were the most effective trauma scoring systems in predicting mortality among emergency department patients. While complex models offer higher accuracy, simpler scores such as RTS and GCS remain valuable for rapid triage. The integration of both anatomical and physiological parameters may enhance early risk stratification and support timely decision-making in trauma care. Full article

Fused filament fabrication (FFF) is a 3D printing technology that has been successfully demonstrated aboard the International Space Station (ISS), proving its suitability for space applications. In this study, we aimed to apply FFF to the 3D printing of recycled space beverage packaging, made of LDPE and a PET-Aluminum-LDPE (PAL) trilaminate. To minimize material waste and optimize the experimental process, we first conducted numerical simulations of additive manufacturing. Using Digimat-AM 2021.1 software, we analyzed residual stresses and warpage in an LDPE/PAL composite with a 10 wt% filler content, processed through the FFF technique. Three key printing parameters, including printing speed and infill pattern, were varied across different levels to assess their impact. Once the optimal combination of parameters for minimizing residual stresses and warpage was identified, we proceeded with the experimental phase, printing objects of increasing complexity to validate the correlation between numerical predictions and the 3D-printed models. The successful fabrication of all geometries under optimized conditions confirmed the numerical predictions, particularly the reduction in warpage and residual stress, validating the material’s viability for additive manufacturing. These findings support the potential application of the LDPE/PAL composite for in situ resource utilization strategies in long-term space missions. Full article

In recent years, finite-control-set model predictive control (FCS-MPC) has attracted significant attention in power electronic converter control, resulting in substantial research advancements. However, no formal method currently exists to prove the stability of FCS-MPC systems. Additionally, many application studies have yet to adequately address the relationship between the selection of design parameters and system performance. To address the lack of stability and performance guarantees in FCS-MPC system design, this paper investigates a class of single-step FCS-MPC systems. The analysis is based on regional input-to-state stability (ISS) theory. Sufficient conditions for ensuring regional stability are derived, and a method for estimating the system’s domain of attraction and ultimate bounded region is developed. Simulation experiments validated the analytical results and revealed the relationships between the domain of attraction and system stability, as well as between the ultimate bounded region and steady-state performance. The results indicate that appropriate parameter design can ensure system stability. Furthermore, the proposed method elucidates how changes in design parameters affect system stability and steady-state performance, providing a theoretical foundation for designing a class of FCS-MPC systems. Full article

Urban landscape lakes (ULLs) in water-scarce cities face significant water quality challenges due to limited resources and intense human activity. This study identifies the main factors affecting transparency (SD) in these water bodies and proposes targeted management strategies. Machine learning techniques, including Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANNs), were applied to analyze SD drivers under various water supply conditions. Results show that, for surface water-supplied lakes, the GBDT model was most effective, identifying chlorophyll-a (Chl-a), inorganic suspended solids (ISS), and hydraulic retention time (HRT) as primary factors. For tap water-supplied lakes, ISS and dissolved oxygen (DO) were critical while, for rainwater retention bodies, the XGBoost model highlighted chemical oxygen demand (COD_Mn) and HRT as key factors. Further analysis with ANN models provided optimal learning rates and hidden layer configurations, enhancing SD predictions through contour mapping. The findings indicate that, under low suspended solid conditions, the interaction between HRT and ISS notably affects SD in surface water-supplied lakes. For tap water-supplied lakes, SD is predominantly influenced by ISS at low levels, while HRT gains significance as concentrations increase. In rainwater retention lakes, COD_Mn emerges as the primary factor under low concentrations, with HRT interactions becoming prominent as COD_Mn rises. This study offers a scientific foundation for effective strategies in ULL water quality management and aesthetic enhancement. Full article

Background/Objectives: Multiple myeloma (MM) is a plasma cell neoplasm predominantly diagnosed in older adults. However, the significance of defining patient frailty, as well as identifying the most suitable and reliable tools for its assessment, remains to be firmly established. Methods: This retrospective observational study investigated 36 patients aged 65 or older who underwent Comprehensive Geriatric Assessment (CGA). The average patient age was 76 (SD 6.22), with 33.3% being female. Patients were evaluated using the International Myeloma Working Group Frailty Index (IMWG-FI) and the 40-item Rockwood’s Frailty Index (FI) at the Oncogeriatrics clinic of the IRCCS Polyclinic San Martino Hospital, Genoa, Italy between December 2017 and August 2021. Laboratory, cancer-specific, demographic, and clinical variables were collected. Survival analysis and frailty comparison were conducted using Stata version 17.0. Results: Stepwise multivariate analysis identified the Numerical Rating Scale (NRS) (HR 1.40, 95% CI 1.09–1.78, p = 0.008) and Rockwood’s Frailty Index (FI) (HR 2.23, 95% CI 1.29–3.87, p = 0.004) as significant prognostic predictors, adjusted for sex, ISS stage, and multimorbility. Comparison between Rockwood’s FI and IMWG-FI using Spearman correlation coefficient showed no statistically significant correlation (r = 0.268, p = 0.114). Multivariate Cox model, adjusting for sex, International Staging System (ISS) stage, and Cumulative Illness Rating Scale (CIRS) comorbidity index demonstrated the superior predictive ability of Rockwood’s FI over IMWG-FI (C-index 0.775 vs. 0.749). Conclusions: The 40-item Rockwood FI emerges as a valuable tool for prognostication in old MM patients, demonstrating non-inferiority to the traditional IMWG-FI in predictive accuracy, emphasizing the importance of a comprehensive approach considering both disease-specific and patient-related factors. Full article

Background: Nonalcoholic fatty liver disease (NAFLD) is a subset of fatty liver disease that is not caused by alcohol or viruses, and its increasing incidence presents a major global health concern. As few pharmacotherapies are available for NAFLD, lifestyle modifications, including diet and exercise, serve as the foundation for treatment. Therefore, NAFLD prevention is more important than cure, emphasizing the need for drugs with excellent safety and long-term efficacy. Fig leaf tea contains rutin and isoschaftoside (ISS), which may possess anti-inflammatory properties. Therefore, the aim of this murine-model-based study was to investigate the potential benefits of fig leaf tea in alleviating NAFLD and to determine the underlying mechanism by gene expression analysis. Results: We found that in mice with NAFLD induced by a high-fat diet, the administration of high concentration fig leaf tea or 50 µM ISS significantly ameliorated lobule inflammation. In contrast, low concentration fig leaf tea containing 75 µM ISS did not improve inflammation. The balance between the NAFLD-promoting component of fig leaf tea and the inhibitory effect of ISS was thought to be affected. Gene expression analysis of the liver showed that high concentration fig leaf tea or ISS significantly suppressed the expression of M1 macrophage markers such as CD antigens, toll-like receptors (TLR), chemokines, and cytokines. Further, ISS suppressed the amount of TNF-α released during the M1 polarization of macrophage cells upon lipopolysaccharide (LPS) stimulation. Conclusions: Overall, these results suggest that controlling macrophage polarization may improve NAFLD. Furthermore, these findings highlight the potential clinical applicability of ISS. Full article

The reflectance-based vicarious calibration approach uses measurements at well-understood test sites to provide top-of-atmosphere reference reflectance values suitable for inter-calibration approaches and does not require coincident views. The challenge is that results from such data may suffer from high variability from day to day. Data from high-quality sensors, such as the imaging spectrometers on the International Space Station (ISS) platform, provide an opportunity to use improved fine spectral information about the test sites with various sun/sensor geometries and site surface and atmospheric conditions to improve the test sites’ characterization. The results here are based on data from the DLR Earth Sensing Imaging Spectrometer (DESIS) instrument installed on the ISS since 2018 combined with output from the Radiometric Calibration Network (RadCalNet) site at Railroad Valley Playa (RRV) to decouple the effects of sun/sensor geometry from the RadCalNet predictions. The approach here uses the precessing orbit of the ISS to allow similar sensor view zenith angles at varying sun angles over short periods that limit the impact of any sensor changes and highlight the bi-directional effects of the surface reflectance and atmospheric conditions. DESIS data collected at (i) similar solar angles but varying view angles, (ii) similar sensor angles and varying solar angles, and (iii) similar scatter angles are compared. The DESIS results indicate that the top-of-atmosphere reflectance spectra for RRV at similar solar zenith angles but with varying sensor viewing angles provide more consistent data than those with varying solar zenith but with similar sensor viewing angles. In addition, comparisons of reflectance spectra of the site performed in terms of the sensor view scatter angle show good agreement, indicating that a directional reflectance correction would be straightforward and could offer a significant improvement in the use of RadCalNet data. The work shows that observations from imaging spectroscopy data from DESIS, and eventually Earth Surface Mineral Dust Source Investigation (EMIT), Surface Biology and Geology (SBG), and the climate-quality sensor CLARREO Pathfinder (CPF), provide the opportunity for the development of a model-based, SI-traceable prediction of at-sensor radiance over the RRV site that would serve as the basis for similar site characterizations with error budgets valid for arbitrary view and illumination angles. Full article

Background: Atrial fibrillation (AF) and ischemic stroke (IS) are intricately linked to chronic obstructive pulmonary disease (COPD). Patients who suffer from both COPD and AF demonstrate a 2.85-fold greater risk of IS. However, the long-term risk remains insufficiently explored. Methods: This study utilized data from the Taiwanese National Health Insurance dataset spanning 2000 to 2015. Patients who were newly diagnosed with COPD, identified using the International Classification of Disease, Ninth Revision, Clinical Modification [ICD-9-CM] codes of 491, 492, and 496 and diagnosed with AF (ICD-9-CM code 427.3), were included in the study. The measured events included ISs (ICD-9-CM codes 433–437). Multivariate Cox proportional hazard models were employed to evaluate IS risk factors in this longitudinal analysis. Results: The combined presence of COPD and AF increased the risk of IS, with an adjusted hazard ratio of 5.722 (95% CI: 2.737–8.856, p < 0.001), AF without COPD with an adjusted HR of 3.506 (95% CI: 1.459–5.977, p < 0.001), and COPD with AF with an adjusted HR of 2.215 (95% CI: 1.099–3.538, p < 0.001) compared with patients without COPD and AF. Elderly patients exhibited a greater burden of cardiovascular comorbidities, including obstructive sleep apnea, thus further compounding the risk of IS. Conclusions: The coexistence of COPD and AF was associated with a markedly elevated risk of IS. The result highlights the additive and synergistic contributions of COPD and AF to the risk for IS. Aggressive treatment may mitigate the risk of IS. Full article

Background and Objectives: Two major classification systems exist for rib fracture (RFX) displacement. One system uses a 50% displacement threshold: Grade I (<50%), Grade II (≥50% to <100%), and Grade III (completely dislocated). Another proposes a 10% threshold: Undisplaced (<10%), Offset (≥10% to <100%), and Displaced (completely dislocated). We analyzed risk factors for adverse outcomes for pulmonary complications and mortality according to both classification criteria. Materials and Methods: We retrospectively reviewed trauma registry and medical records from January 2019 to December 2023. All radiographic parameters were recorded based on initial computed tomography. Primary outcomes were pneumonia and other pulmonary complications requiring surgery. Least absolute shrinkage and selection operator (LASSO) regression was conducted to select risk factors and minimize overfitting. Multivariable logistic regression (MLR) was performed after LASSO. Results: Among the 621 patients, 61 (9.8%) had one or more adverse outcomes. In MLR, regardless of both classifications, the age (p < 0.001), ISS (p < 0.001), and number of completely displaced RFX (p = 0.001) were statistically significant. After excluding 280 patients with completely displaced RFX, we conducted a subgroup analysis with the remaining 341 patients. In this analysis, 22 (6.5%) patients experienced one or more adverse outcomes. Regardless of both classifications, the AIS head (p = 0.006), AIS extremities (p = 0.012), and number of segmental RFX (p < 0.001) were statistically significant in MLR. The area under the receiver operating curve for both MLR models was 0.757 in the total patient group and 0.823 in the subgroup that excluded patients with completely displaced RFX. Conclusions: Completely displaced RFX is the most crucial factor, regardless of the classification criteria. Unless ribs are completely displaced, the degree of displacement may not be crucial, and the number of segmental RFX was a significant risk factor. Full article

The incorporation of machine learning (ML) into healthcare information systems (IS) has transformed multi-objective healthcare management by improving patient monitoring, diagnostic accuracy, and treatment optimization. Notwithstanding its revolutionizing capacity, the area lacks a systematic understanding of how these models are divided and analyzed, leaving gaps in normalization and benchmarking. The present research usually overlooks holistic models for comparing ML-enabled ISs, significantly considering pivotal function criteria like accuracy, precision, sensitivity, and specificity. To address these gaps, we conducted a broad exploration of 306 state-of-the-art papers to present a novel taxonomy of ML-enabled IS for multi-objective healthcare management. We categorized these studies into six key areas, namely diagnostic systems, treatment-planning systems, patient monitoring systems, resource allocation systems, preventive healthcare systems, and hybrid systems. Each category was analyzed depending on significant variables, uncovering that adaptability is the most effective parameter throughout all models. In addition, the majority of papers were published in 2022 and 2023, with MDPI as the leading publisher and Python as the most prevalent programming language. This extensive synthesis not only bridges the present gaps but also proposes actionable insights for improving ML-powered IS in healthcare management. Full article

Background: Lactate is a byproduct of glycolysis, often linked to oxygen deprivation. This study aimed to examine how lactate levels (LLs) affect clinical outcomes in patients with severe TBI, hypothesizing that higher LLs would correlate with worse outcomes. Methods: This is a level 1 single-center, retrospective study of patients with severe TBI between 1 January 2020 and 31 December 2023, inclusive. Results: Single-factor ANOVA indicated a significant decrease in LLs with increasing age. Linear regression models showed the same for hospital admission, Intensive Care Unit (ICU) admission LLs, and death LLs. Prognostic scores such as Injury Severity Scores (ISS) and Glasgow Coma Score (GCS) showed a strong correlation with both Hospital admission and ICU admission LLs. ANOVA indicated higher LLs with increasing ISS and increasing LLs with decreasing GCS. Linear regressions revealed a strong positive correlation between ISS and LLs. On linear regression, the LL measured at hospital admission and ICU admission was positively associated with the length of stay (LOS) in the hospital, LOS in the ICU, ventilator days, and mortality. Linear regression models showed that a decreased delta LL during ICU admission led to an increased LOS at the hospital and the ICU, as well as a higher number of days on a ventilator. Discussion: We discovered that high LLs were linked to higher AIS and GCS scores, longer stays in the hospital and ICU, more days requiring a ventilator, and higher mortality rates in patients with severe TBI. Conclusions: LLs can be considered a strong predictor of poor clinical outcomes in patients with severe TBI. Full article

Background/Objectives: Adverse pregnancy outcomes correlate with blood glucose levels in women with type 1 diabetes (T1DM). There is a gap between the glycaemic targets and the blood glucose control achieved in pregnancy. This study aimed to investigate the impact of an intensive weekly service on glycaemic control compared with our previous care model in pregnancies affected by T1DM. Materials and Methods: This is a retrospective cross-sectional pre/post study comparing measures of glycaemic control in women with T1DM in each trimester of pregnancy in the 12 months before and the 8 months after the commencement of an intensive weekly insulin stabilisation service (ISS). Results: This study utilised data from Dexcom continuous glucose monitoring (CGM) reports to analyse pregnancy-specific glycaemic data (incorporating time in the range of 3.5–7.8 mmol/L). In total, 16 women provided data for 35 trimesters pre-ISS and 17 women provided data for 38 trimesters post-ISS. There was an improvement in pregnancy-specific time in range in trimester 3 following the commencement of the intensive weekly insulin stabilisation service (pre-ISS mean: 49.6%, post-ISS mean: 61.4%, p = 0.042). Similar results were seen when women using hybrid closed-loop technology were excluded, although statistical significance was not reached. It was not possible to assess the effect of the intervention during the first trimester. There were no statistically significant changes in glycaemia in trimester 2. Conclusions: In a small group of pregnant women with T1DM, a clinically significant improvement in pregnancy-specific time in range occurred in trimester 3, but not in trimester 1 or 2, following the introduction of intensive weekly clinical support. Full article