Search Results (2,653)

Greenhouses play a vital role in modern agriculture by providing controlled environments for year-round crop production. However, climate regulation within these structures accounts for a significant portion of their energy consumption, often exceeding 50% of operational costs. Current greenhouse systems predominantly rely on reactive control strategies, leading to energy inefficiency and unstable internal conditions. Addressing this gap, the present study develops a machine learning-based framework that leverages time series forecasting models—specifically Long Short-Term Memory (LSTM)—that predict key climate parameters and generate optimal actuator control recommendations. The system utilizes multivariate environmental data to forecast temperature, humidity, and CO₂ levels and minimize a composite energy proxy through proactive adjustments to heating, ventilation, and lighting systems. Experimental results demonstrate high prediction accuracy (R² = 0.9835) and significant improvements in energy efficiency. By integrating predictive analytics with real-time sensor feedback, the proposed approach supports intelligent, energy-aware decision-making and advances the development of smart agriculture through proactive greenhouse climate management. Full article

The aviation industry operates as a complex, dynamic system generating vast volumes of data from aircraft sensors, flight schedules, and external sources. Managing this data is critical for mitigating disruptive and costly events such as mechanical failures and flight delays. This paper presents a comprehensive application of predictive analytics and machine learning to enhance aviation safety and operational efficiency. We address two core challenges: predictive maintenance of aircraft engines and forecasting flight delays. For maintenance, we utilise NASA’s C-MAPSS simulation dataset to develop and compare models, including one-dimensional convolutional neural networks (1D CNNs) and long short-term memory networks (LSTMs), for classifying engine health status and predicting the Remaining Useful Life (RUL), achieving classification accuracy up to 97%. For operational efficiency, we analyse historical flight data to build regression models for predicting departure delays, identifying key contributing factors such as airline, origin airport, and scheduled time. Our methodology highlights the critical role of Exploratory Data Analysis (EDA), feature selection, and data preprocessing in managing high-volume, heterogeneous data sources. The results demonstrate the significant potential of integrating these predictive models into aviation Business Intelligence (BI) systems to transition from reactive to proactive decision-making. The study concludes by discussing the integration challenges within existing data architectures and the future potential of these approaches for optimising complex, networked transportation systems. Full article

Residual inflammatory risk after acute coronary syndromes (ACSs) remains a critical contributor to atherosclerosis progression and plaque destabilization. Inflammatory biomarkers such as interleukin-1 receptor antagonist (IL-1ra), resistin, and C-reactive protein (CRP) may provide additional insights into coronary lesion complexity and vulnerability. The main aim of the study was to evaluate the association of interleukin-1 receptor antagonist (IL-1ra), resistin, and C-reactive protein (CRP) with coronary disease extent; functional significance of non-culprit lesions, assessed by fractional flow reserve (FFR); and plaque vulnerability, assessed by optical coherence tomography (OCT) in patients with acute coronary syndrome (ACS). This prospective study enrolled 93 ACS patients undergoing invasive coronary assessment for an ACS. Inflammatory biomarkers were measured at admission and 6 months post-event. Patients were stratified post hoc into tertiles by biomarker distribution. SYNTAX score, FFR, and OCT-defined thin-cap fibroatheroma (TCFA) were used to characterize lesion burden and morphology. Multivariate logistic regression was performed adjusting for conventional cardiovascular risk factors and ACS type. Higher tertiles of IL-1ra, resistin, and CRP were significantly associated with increased SYNTAX score (p < 0.05), FFR < 0.80 (68% in the highest tertile), and presence of TCFA (62% vs. 20%, p < 0.01). All biomarkers correlated with coronary disease severity. In multivariate logistic models, IL-1ra (OR 1.23 per 100 pg/mL, p = 0.03), resistin (OR 2.35 per 1 ng/mL, p = 0.001), and CRP (OR 1.11 per 0.001 ng/mL, p = 0.006) independently predicted high-risk coronary profiles. IL-1ra, resistin, and CRP are independently associated with lesion complexity, functional significance, and vulnerability in ACS. Inflammatory biomarker profiling may provide complementary anatomical and physiological assessment in future ACS risk stratification strategies. Full article

Non-small-cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide, demanding improved biomarkers and therapeutic approaches. This review synthesizes the extensive evidence positioning the miR-200 family as a master regulator of NSCLC progression. We detail the core molecular circuitry centered on the bistable, double-negative feedback loop between miR-200 and the ZEB1/ZEB2 transcription factors, which governs epithelial–mesenchymal transition (EMT). This review connects this central mechanism to critical clinical challenges, including the development of resistance to EGFR-targeted therapies and the regulation of immune evasion through PD-L1 expression and CD8+ T cell infiltration. We evaluate the strong clinical evidence for the miR-200 family’s utility as a diagnostic, prognostic, and predictive biomarker. Finally, we explore emerging therapeutic strategies that target this network, including miRNA replacement, epigenetic reactivation, and rational combinations with immunotherapy and targeted agents. We synthesize evidence positioning the miR-200/ZEB feedback circuit as a central regulatory node in NSCLC that links EMT with therapeutic resistance and immune evasion. Beyond summarizing associations, we interpret how this circuitry could inform biomarker development and rational combinations with targeted and immune therapies. Given heterogeneous study designs and non-standardized assays, translational claims remain provisional; we outline immediate priorities for assay harmonization and biomarker-stratified trials. Full article

We compared oxidative markers and their links to inflammation in diabetic nephropathy and hemodialysis to identify independent determinants. We studied 180 adults, 90 patients with type 2 diabetes and diabetic nephropathy and 90 patients on hemodialysis. We measured serum advanced oxidation protein products (AOPP) and thiobarbituric acid reactive substances (TBARS) by enzyme-linked immunosorbent assay (ELISA). Partial correlations were adjusted for age, sex, and albumin with false discovery rate (FDR) control. Principal component analysis (PCA) summarized inflammatory indices and linear models tested predictors of AOPP and TBARS. Oxidative damage was higher in hemodialysis, with AOPP median 25.80 versus 5.06 and TBARS 8.49 versus 1.89, p less than 0.0001. C reactive protein (CRP) and mean corpuscular volume-to-lymphocyte ratio (MCVL) were higher in patients ongoing hemodialysis; systemic immune-inflammation index (SII) was higher in diabetic nephropathy. PCA yielded a dominant inflammation axis in both cohorts, 74.73 percent in hemodialysis and 85.20 percent in diabetic nephropathy. In regression, creatinine (β = 2.47, p = 0.026) predicted AOPP in hemodialysis. Dialysis vintage inversely predicted TBARS, β = −0.2305, p = 0.0209. In diabetic nephropathy, the PCA inflammation score predicted AOPP, β = 1.134, p = 0.0003. Protein oxidation tracked systemic inflammation in diabetic nephropathy, but not in hemodialysis. AOPP outperformed TBARS as an inflammatory partner and a practical monitoring candidate in diabetic kidney disease. Prospective studies should test for prognostic value and therapy sensitivity. Full article

Hepatitis B virus (HBV) infection is a global health concern with an estimated 254 million people with chronic HBV infection. The utilization of immunosuppressive therapies (ISTs) is increasing and expanding continuously with new agents being implemented across multiple medical disciplines. The occurrence of HBV reactivation (HBVr) during or after IST varies from 15% to 50% in HBsAg-positive individuals and can be higher than 75% after stem cell transplantation. HBVr is gaining increasing significance in contemporary clinical practice. The American Gastroenterological Association (AGA) in 2025, the European Association for the Study of the Liver (EASL) in 2025, and the Asian Pacific Association for the Study of the Liver (APASL) in 2021, published their most recent clinical guidelines as major societies in the area, which enables us to better predict and manage HBVr. This narrative review focuses on comparing these three current guidelines, highlighting key similarities and differences to provide valuable guidance for practitioners navigating the complex, sometimes conflicting recommendations, thereby aiding clinicians in their decision-making. The risk of HBVr during IST has been stratified into three categories in all three guidelines: high (>10%), moderate (1–10%), and low (<1%). The effectiveness of prophylaxis scales with baseline risk for HBV reactivation. Prophylaxis is clearly cost-effective for high-risk patients, potentially beneficial for those at moderate risk, and generally may not be justified for low-risk individuals. Entecavir (ETV), tenofovir disoproxil fumarate (TDF), and tenofovir alafenamide (TAF) are all highly effective in preventing HBV reactivation during immunosuppression and all are considered to be economically viable options for HBVr high risk patients. When selecting among these agents, safety considerations—particularly renal and bone toxicity—and insurance coverage remain the primary factors directing clinical decision-making. Full article

Identifying effective indicators and developing predictive models for the early detection of severe adenoviral pneumonia (SAP) is critical to safeguarding patients’ lives. This study examined differences between 428 patients with SAP and those with non-severe adenoviral pneumonia (NSAP) from March 2022 to January 2023, focusing on variables such as age, sex, type of coinfection, and a range of clinical laboratory indicators. SAP was significantly more common in children aged 3–6 years (20/54 of all SAP cases, p = 0.0258) and among those with polymicrobial coinfections (p < 1.20 × 10⁻¹¹). Patients with SAP exhibited significantly higher prealbumin (PA) level, while C-reactive protein (CRP) level was significantly lower. Composite indicator, such as CRP -to- prealbumin ratio (CPAR), was also significantly elevated (p < 0.05). The random forest model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.699, with an accuracy of 84.5% and a precision of 91.5%. Analysis of the data revealed key predictive parameters for early-stage SAP. Indicators such as CPAR, PA, and CRP are valuable for assessing SAP risk. Moreover, commonly available clinical indicators can effectively construct a random forest-based predictive model for SAP. Full article

Nanoplastics are pervasive contaminants that adversely affect male reproductive function, yet the molecular basis of polystyrene nanoplastic (PS-NP) toxicity in immature testes and effective preventive strategies remain unclear. Here, male mice (postnatal days 22–35, PND 22–35) and TM3 Leydig cells were exposed to graded PS-NPs, followed by transcriptomic profiling to identify differentially expressed genes (DEGs). Candidate therapeutics were prioritized using Connectivity Map (CMap) analysis and molecular docking, and protein interactions were examined by co-immunoprecipitation (Co-IP). PS-NPs accumulated in immature testes, eliciting excessive reactive oxygen species (ROS) and activation of NF-κB. These events coincided with the downregulation of steroidogenic enzymes (CYP11A1 and StAR) and disruption of testicular microarchitecture. In TM3 cells, PS-NPs suppressed testosterone synthesis in a concentration-dependent manner; this effect was fully reversed by pretreatment with N-acetylcysteine (NAC) or Bay 11-7082. Co-IP demonstrated p65–steroidogenic factor-1 (SF-1) binding consistent with formation of a transcriptional repressor complex targeting steroidogenic genes. CMap and docking analyses nominated parthenolide (PTL) as a candidate inhibitor of NF-κB nuclear translocation (predicted binding affinity, −6.585 kcal/mol), and PTL mitigated PS-NP-induced impairment of testosterone synthesis in vitro. Collectively, these data indicate that PS-NPs disrupt testosterone biosynthesis in immature testes through the ROS/NF-κB/p65–SF-1 axis, while PTL emerges as a candidate small molecule to counter nanoplastic-associated reproductive toxicity. These findings underscore translational relevance and support future evaluation under chronic low-dose exposure conditions, including in vivo validation of PTL efficacy, pharmacokinetics, and safety. Full article

The high carbon emissions associated with cement-based materials in lightweight foamed soils have become a significant environmental concern. In addition to this applied problem, there is also a scientific challenge: current studies of carbon–magnesium reactions in lightweight carbonated soils (LCSS) lack multiparameter predictive models, which are essential for understanding the coupled effects of MgO dosage and CO₂ foam content on material performance. This study addresses this gap by systematically investigating the influence of MgO and CO₂ foam on the strength, resistivity, and carbonation behavior of LCSS. A multiparameter regression model was developed to predict these properties, and its statistical significance and predictive accuracy were verified. The results show that MgO dosage strongly promotes carbonation and strength development, while CO₂ foam content primarily regulates porosity and carbonation degree. The established model provides reliable predictions of LCSS performance and offers a scientific basis for optimizing carbon–magnesium reactions in sustainable soil stabilization. Full article

Geological Carbon Sequestration (GCS) plays a crucial role in addressing climate change, particularly in oil and gas development. Understanding the reaction of supercritical CO₂ under in situ conditions and its effects on minerals is essential for advancing GCS technology. This study investigates the reaction mechanisms of feldspar (potassium and sodium feldspar) and clay minerals (chlorite, illite, montmorillonite, kaolinite) in CO₂ environments. The impacts on mineral crystal structures, morphologies, and elemental compositions were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and ion concentration measurements (ICP-OES and ICP-MS). The results show that feldspar minerals exhibit lower reaction rates, with sodium feldspar dissolving faster than potassium feldspar, due to the higher solubility of sodium ions in acidic conditions. Chlorite showed significant crystal structure damage after 30 days, while montmorillonite underwent both dissolution and precipitation, influenced by interlayer cation dissociation. Kaolinite exhibited minimal reaction, primarily showing localized dissolution. Additionally, the formation of siderite (FeCO₃) was observed as Fe²⁺ substituted for Ca²⁺ in CaCO₃, highlighting the role of iron-bearing carbonates in CO₂ interactions. The study provides insights into the factors influencing mineral reactivity, including mineral structure, ion exchange capacity, and solubility, and suggests that chlorite, montmorillonite, and illite are more reactive under reservoir conditions, while kaolinite shows higher resistance to CO₂-induced reactions. These findings offer valuable data for optimizing GCS technologies and predicting long-term sequestration outcomes. Full article

Background/Objectives: Hashimoto’s thyroiditis-induced hypothyroidism (HT–HypoT) is frequently accompanied by ocular surface complaints, but the role of systemic inflammatory markers in dry eye disease (DED) among these patients remains unclear. This study aimed to evaluate the relationship between composite inflammatory indices and the presence and severity of DED in patients with HT–HypoT. Methods: This retrospective study included 86 HT–HypoT patients and 43 DED controls without systemic comorbidities. DED diagnosis and severity were assessed using the Ocular Surface Disease Index (OSDI) and objective ocular surface tests. Laboratory parameters and composite inflammatory indices—including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), C-reactive protein-to-albumin ratio (CAR), and prognostic nutritional index (PNI)—were compared between groups. Results: DED was present in 44% of HT–HypoT patients. SIRI and CAR were higher in HT–HypoT patients with DED and increased with severity. Both indices independently predicted the presence and severity of DED and exhibited higher diagnostic performance than other inflammatory indices. Conclusions: In patients with HT–HypoT, SIRI and CAR provide additional diagnostic value for identifying the presence and severity of DED beyond that offered by traditional markers. These findings highlight the potential utility of routine blood-derived indices as adjunctive biomarkers in thyroid-related DED. Full article

Background: Sepsis remains a major cause of morbidity and mortality in both developed and limited-resource countries. Despite over a century of research, accurate biomarkers for reliable diagnosis and prognosis in critically ill patients have yet to be established. Methods: This multicenter retrospective observational study aims to evaluate serum levels of Calprotectin, Azurocidin, cytokines, chemokines, procalcitonin (PCT) and C-Reactive Protein (CRP) in 15 healthy volunteers (controls), 15 non-infectious SIRS patients, 92 alive septic patients (Sepsis_A) and 29 dead septic patients (Sepsis_D). Results: Most biomarkers showed significantly higher serum concentrations in septic patients compared with controls, with IL-4 being increased only in the Sepsis_D group. In addition, several markers, including Calprotectin, Azurocidin, IL-6, IL-8, IL-10, TNF-α, and IL-35, were progressively elevated from SIRS to Sepsis_A and Sepsis_D cohorts, reflecting disease severity. All biomarkers showed good diagnostic performance for predicting Gram-negative bacteremia, although their accuracy in discriminating survivors from non-survivors was relatively low. Conclusions: In conclusion, calprotectin, azurocidin, IL-8, TNF-α, and IL-35 may assist clinicians in identifying Gram-negative bacteremia in septic patients; however, their prognostic value appears to be limited. Full article

This study investigated factors predicting cognitive flexibility in Chinese–English bilinguals, with a comprehensive focus on demographic and language-related variables. Cognitive flexibility was assessed using reaction times (RTs) and maximum absolute deviation (MAD) in a mouse-tracking nonverbal task-switching paradigm, capturing both mix and switch costs. Regression analyses revealed that bilingual experience explained a larger proportion of variance in mix costs than in switch costs, with stronger effects for MAD than RTs. Higher composite factor scores (CFS) were positively associated with mix costs, whereas balanced language use across life stages, activities, and interlocutors predicted smaller mix costs, suggesting a move to multi-dimensional, experience-based approaches. In contrast, switch costs were largely unrelated to CFS, but balanced language use across situational contexts, which predicted reduced switch costs in MAD, indicating enhanced reactive control. Moreover, bilingual experiences in the home environment appeared to be positively associated with cognitive flexibility. These findings highlight the multidimensional nature of bilingual experience and underscore the value of movement trajectory measures in capturing subtle effects on sustained and transient cognitive control. Full article

Background/Objectives: Empathy is essential for successful social functioning, mediating different aspects of social cognition in everyday life. An intriguing aspect is the involvement of empathy even in basic neural mechanisms of action perception, thanks to its association with the Mirror Neuron System (MNS). The present retrospective study explores whether individual differences in cognitive and affective empathy, measured by the Interpersonal Reactivity Index (IRI) questionnaire, can predict motor resonance—the enhancement of motor cortex reactivity during the observation of biological movements—during transitive and intransitive action observation. Methods: Data from 160 healthy subjects who participated in transcranial magnetic stimulation (TMS) experiments assessing corticospinal excitability during action observation were retrospectively analyzed using multiple linear regression models. Participants filled the IRI and observed intransitive single-digit finger movements (n = 80) or grasping actions directed at different targets (intransitive, object-directed, social-directed; n = 80) synchronized with TMS over the primary motor cortex, allowing the investigation of how action features modulate the relationship between participants’ empathic traits and motor resonance magnitude. Results: Results show that empathic traits do not affect motor resonance during intransitive movements, whereas they do when motor resonance is measured during the observation of transitive actions. Cognitive empathy, particularly the perspective-taking scale, significantly predicts motor resonance magnitude when observing goal-directed actions. Meanwhile, affective empathy, specifically the empathic concern scale, predicts motor resonance while observing social action. Conclusions: These findings highlight that different facets of empathy are significantly related to humans’ ability to understand others’ actions through inner simulation mechanisms, particularly concerning action goals and social relevance. Full article

Digital twins (DTs), virtual replicas that integrate mechanistic modeling with real-time clinical data, are emerging as powerful tools in healthcare with particular promise in pediatrics, where age-dependent physiology and ethical considerations complicate infectious disease management. This narrative review examines current and potential applications of DTs across antimicrobial stewardship (AMS), diagnostics, vaccine personalization, respiratory support, and system-level preparedness. Evidence indicates that DTs can optimize antimicrobial therapy by simulating pharmacokinetics and pharmacodynamics to support individualized dosing, enable Bayesian therapeutic drug monitoring, and facilitate timely de-escalation. They also help guide intravenous-to-oral switches and treatment durations by integrating host-response markers and microbiological data, reducing unnecessary antibiotic exposure. Diagnostic applications include simulating host–pathogen interactions to improve accuracy, forecasting clinical deterioration to aid in early sepsis recognition, and differentiating between viral and bacterial illness. Immune DTs hold potential for tailoring vaccination schedules and prophylaxis to a child’s unique immune profile, while hospital- and system-level DTs can simulate outbreaks, optimize patient flow, and strengthen surge preparedness. Despite these advances, implementation in routine pediatric care remains limited by challenges such as scarce pediatric datasets, fragmented data infrastructures, complex developmental physiology, ethical concerns, and uncertain regulatory frameworks. Addressing these barriers will require prospective validation, interoperable data systems, and equitable design to ensure fairness and inclusivity. If developed responsibly, DTs could redefine pediatric infectious disease management by shifting practice from reactive and population-based toward proactive, predictive, and personalized care, ultimately improving outcomes while supporting AMS and health system resilience. Full article