Search Results (4,833)

Raman spectroscopy is a non-destructive, label-free analytical technique that can probe biochemical alterations in tissues and cells. Raman spectroscopy, being sensitive to biochemical perturbations, can potentially provide early and real-time identification of changes preceding morphological changes, allowing early diagnosis as well as disease monitoring. Recent research has demonstrated its broad utility across diverse clinical domains, including cancers, neurological conditions, and infections. Raman spectroscopy combined with machine learning algorithms allows rapid assessment and automated pipelines and can act as a clinical adjunct, enhanced by integrating tools like principal component analysis (PCA), linear discriminant analysis (LDA), random forests, and deep-learning architectures. These models allow interpretation of complex spectra, and decipher meaningful biomarkers in heterogeneous clinical samples. This review highlights the earliest and most recent progress in Raman-based non-destructive diagnosis, underscoring advances in cancer diagnosis and challenges faced in clinical settings. Full article

Feature importance analysis is essential for interpreting machine learning models in diabetes mellitus (DM) risk prediction; however, existing interpretability methods often produce inconsistent feature rankings across models. This study proposes a unified ODE-inspired interpretability framework and an algorithmic decision procedure for robust feature selection by integrating contribution-based (SHAP), perturbation-based (permutation importance), and sensitivity-based feature importance measures. Multiple supervised machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, Histogram Gradient Boosting, and Multilayer Perceptron, were trained and evaluated on a longitudinal biochemical and demographic dataset comprising 200 patients with three repeated visits (N = 600 observations). To preserve longitudinal integrity and avoid patient-level information leakage, grouped cross-validation was applied. A sensitivity-based feature importance formulation using finite-difference approximations enabled model-agnostic comparison across heterogeneous machine learning architectures. Stability, normalization, and cross-method agreement analyses were additionally introduced to evaluate consistency of feature rankings across models and interpretability methods. Experimental results consistently identified HbA1c as the dominant predictor, followed by lipid-related variables, age, and body mass index. Strong agreement was observed between ODE-inspired feature importance and SHAP analysis, whereas permutation importance demonstrated comparatively weaker agreement with sensitivity-based methods. The proposed framework further enabled systematic analysis of ranking stability, cross-method agreement, longitudinal sensitivity dynamics, and the introduction of an agreement-weighted Consensus Interpretability Score (CIS) for unified feature ranking across heterogeneous interpretability methods. The results demonstrate that integrating ODE-inspired sensitivity analysis with machine learning provides a robust, interpretable, and computationally scalable framework for feature importance assessment in diabetes risk prediction. The proposed approach offers a principled solution to inconsistent feature importance estimation and supports more reliable interpretation of biomedical machine learning models. Full article

Background: Immune checkpoint inhibitors (ICIs), particularly programmed death-ligand 1 (PD-L1) inhibitors such as atezolizumab, have significantly improved outcomes in cancer therapy. However, these agents may cause immune-related adverse effects, including nephrotoxicity associated with oxidative stress and cellular stress responses. This study aimed to investigate and comparatively evaluate the protective effects of adenosine triphosphate (ATP) and taxifolin against atezolizumab-induced renal tissue injury in rats. Methods: Animals were divided into four groups: healthy (HG), atezolizumab (ATZ), ATP + atezolizumab (ATAZ), and taxifolin + atezolizumab (TXAZ). ATP (4 mg/kg, i.p.) and taxifolin (50 mg/kg, oral) were administered for six days, while atezolizumab (10 mg/kg, i.p.) was given on days 1 and 4. On day 7, renal tissues were collected for biochemical, histopathological, and double immunofluorescence analyses. Results: Atezolizumab significantly increased malondialdehyde (MDA) levels and decreased total glutathione (tGSH), superoxide dismutase (SOD), and catalase (CAT) levels, indicating enhanced oxidative stress and impaired antioxidant defense. These changes were accompanied by tubular degeneration and increased expression of apoptotic markers. Both ATP and taxifolin significantly ameliorated these alterations; however, ATP demonstrated a more pronounced protective effect. Conclusions: In conclusion, ATP and taxifolin attenuated the biochemical, histopathological, and immunofluorescence alterations associated with atezolizumab administration. ATP exhibited a more pronounced protective effect than taxifolin under the conditions of this experimental model. Nevertheless, further experimental studies are required to elucidate the mechanisms underlying these effects. Full article

Diabetes mellitus is strongly associated with cardiovascular dysfunction and remains one of the leading contributors to morbidity and mortality worldwide. Early identification of diabetes-related cardiovascular alterations is essential for timely risk stratification and personalized clinical management. In the present study, an interpretable machine learning framework for diabetes classification in patients with cardiovascular disease was developed using routinely available clinical, biochemical, renal, and echocardiographic parameters. A retrospective dataset consisting of 131 cardiovascular patients was included in the final analysis, comprising 65 patients with diabetes mellitus and 66 patients without diabetes. Demographic, metabolic, renal, and cardiovascular variables, including age, body mass index (BMI), glycated hemoglobin (HbA1c), glucose concentration, estimated glomerular filtration rate (eGFR), troponin level, heart rate, and left ventricular ejection fraction (EF), were included in the analysis. Multiple supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, and Random Forest, were implemented and compared using repeated stratified cross-validation. Among the evaluated models, Random Forest demonstrated the highest classification performance, achieving a mean ROC AUC of 0.880 ± 0.050. Statistical analysis revealed significantly elevated HbA1c, glucose, and troponin levels together with reduced ejection fraction values in diabetic patients. Explainable artificial intelligence analysis using SHAP and partial dependence plots identified glucose concentration, HbA1c, age, and renal function as the dominant contributors to diabetes classification. Nonlinear relationships between metabolic and cardiovascular variables were additionally observed. The obtained findings demonstrate that interpretable machine learning approaches can provide effective discrimination between diabetic and non-diabetic cardiovascular patients while maintaining clinically meaningful interpretability. The proposed framework may contribute to future intelligent clinical decision-support systems and personalized cardiovascular risk assessment strategies. Full article

Background: Metastatic lymph node (MLN) detection remains a major clinical challenge in head and neck cancer, as nodal involvement is strongly associated with poor prognosis and directly affects treatment planning. Previous approaches typically rely on cropped lymph node (LN) regions or tumor contours for MLN identification, requiring substantial expert annotation during preprocessing and relying solely on imaging information. As a result, small or low-contrast metastatic nodes may be missed, while benign lymph nodes may be incorrectly identified as metastatic due to overlapping imaging characteristics. To address these limitations, we propose a multimodal learning framework that integrates anatomical and metabolic features from head and neck PET/CT images with biochemical features derived from FLIm for patient-level MLN prediction, without requiring manual lymph node cropping or tumor contouring during inference. Methods: To enable robust imaging representation learning, a region-aware PET/CT network based on a merging-diverging architecture was first pretrained on the HECKTOR 2022 dataset and then fine-tuned on the institutional cohort. In parallel, FLIm point-wise measurements with clinical variables were encoded using a multilayer perceptron (MLP) and aggregated into subject-level representations. To effectively combine these modalities, two multimodal fusion strategies were evaluated at the decoder stage, including cube-based fusion and squeeze-and-excitation (SE)-based fusion. The proposed strategies were evaluated on a cohort of 53 patients. Results: Compared with the single-modality baselines, both multimodal fusion strategies achieved better patient-level MLN prediction. The PET/CT-only segmentation-driven model and FLIm-only model reached balanced accuracies of 0.815 and 0.665, with AUCs of 0.828 and 0.614, respectively. Cube-based fusion improved balanced accuracy and AUC to 0.827 and 0.850, respectively, while channel-wise SE-based fusion achieved the best overall performance, with a balanced accuracy of 0.839 and an AUC of 0.872. Conclusions: These results suggest that multimodal integration may improve patient-level MLN prediction compared with single-modality approaches. Given the limited sample size, these findings should be interpreted as hypothesis-generating and require validation in larger, independent patient cohorts. Full article

This study presents a critical historical, bibliometric, and technological overview of the evolution of postharvest science, emphasizing the transition from classical physiology-based approaches to emerging predictive and technology-driven systems. Scientific production related to postharvest research was analyzed using the Scopus and Web of Science databases, while bibliometric mapping and co-occurrence networks were generated using VOSviewer to identify thematic trends, emerging research areas, and structural scientific clusters. In parallel, a technological foresight analysis was conducted through the Lens.org platform to investigate the temporal evolution of patent deposits, the geographical distribution of innovation, the leading institutional applicants, and the predominant technological domains according to the Cooperative Patent Classification (CPC). The results revealed a substantial global expansion of postharvest research over recent decades. This growth was accompanied by increasing technological diversification and stronger integration between scientific knowledge and intellectual property protection. The analysis also highlighted the progressive incorporation of advanced methodologies into postharvest science, including biochemical approaches, non-destructive technologies, artificial intelligence, predictive modeling, and digital tools for quality assessment and shelf-life management. Overall, the study demonstrates that postharvest science is undergoing a paradigmatic transition toward integrated, multidisciplinary, and data-driven systems aligned with current demands for sustainability, food security, innovation, and reduction of postharvest losses. Full article

Accurate determination of endodormancy release is essential for grapevine dormancy management. However, most phenological models are validated only against macroscopic budbreak dates, without physiological verification of predicted release dates. Here, we integrated phenological modeling with biochemical profiling to characterize endodormancy release in two table grape cultivars with contrasting chilling requirements: ‘Muscat Hamburg’ (Vitis vinifera L.) and ‘Shine Muscat’ (Vitis labrusca × V. vinifera). Endodormancy release dates were determined by forced budbreak assays, and chilling and heat requirements were estimated from 5 min temperature records using the Dynamic Model and Growing Degree Hours. ‘Muscat Hamburg’ released endodormancy on December 16 (10.95 Chill Portions), whereas ‘Shine Muscat’ released on January 6 (22.78 CP). Around these dates, coordinated biochemical changes occurred in buds, including starch depletion, hexose accumulation, ABA decline, GA₃ increase, and redox-related changes in H₂O₂ content and CAT activity. These changes were more pronounced in buds than in canes and were not identical across all biochemical indicators. Hydrogen cyanamide treatment induced biochemical changes similar to those observed during natural dormancy release, with cultivar-specific responses consistent across both conditions. These results indicate that experimentally determined endodormancy release dates are associated with population-level physiological changes, supporting the integration of phenological modeling with biochemical characterization in table grape production. Full article

Climate change may intensify the deterioration of river water quality by altering streamflow regimes, precipitation patterns, and organic matter transport pathways. In this study, a Hydrological Simulation Program-FORTRAN (HSPF)-based streamflow and total organic carbon (TOC) water quality model for the Tamjin River Basin, Korea, was developed, and future TOC pollution was evaluated under quantile delta mapping (QDM) bias-corrected Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5) climate scenarios. Unlike previous studies that generally applied climate bias correction, watershed modeling, or pollutant-load assessment as separate procedures, this study links QDM-preserved climate change signals, process-based HSPF simulations, and TOC-specific discharge-load, delivered-load, exceedance-frequency, and load-reduction indicators within a single management framework. The model showed acceptable performance, with Nash–Sutcliffe efficiency (NSE) values of 0.67 and 0.68 for streamflow at Jangheung Dam and Gamcheon Bridge, respectively, and a TOC deviation of volume (DV) of 0.6% at Tamjin5. Under the SSP5-8.5 no-action scenario for the 2040s, the mean streamflow decreased by 33.1%, whereas the mean TOC concentration increased by 76.8% relative to the baseline. The number of days exceeding 4 mg/L TOC increased from 41 to 216 days yr⁻¹, and the Korean TOC-based water quality class deteriorated from Ib to III. In contrast, the 20% and 30% load reduction scenarios offset approximately 33.8% and 67.9% of the climate-driven increase in TOC, respectively, with the 30% reduction scenario showing greater effectiveness during low-flow seasons. Elevated TOC levels may have implications for downstream water treatment because organic matter can increase chemical demand and disinfection-byproduct formation potential. However, these treatment-related effects were not directly evaluated in this study. These results suggest that TOC should be considered as a complementary indicator to conventional biochemical oxygen demand (BOD)-based management when developing climate-resilient water-quality strategies for the Tamjin River Basin. Full article

Background/Objectives: Estrogen deficiency is associated with metabolic disturbances and impaired glucose homeostasis. Morin, a natural flavonol, has shown promising hypoglycemic and antioxidant properties, but its effects under hypoestrogenic diabetic conditions remain poorly understood. The aim of this study was to evaluate the effects of morin on body weight, fasting blood glucose, glucose tolerance, and selected serum biochemical markers in an experimental model of diabetes under estrogen-deficient conditions (ovariectomized diabetic female mice). Methods: Female CD1 mice underwent sham surgery or ovariectomy (OVX), and each surgical condition was further divided into non-diabetic and diabetic subgroups treated with vehicle, glibenclamide (10 mg/kg), or morin (30 mg/kg). Body weight and fasting blood glucose were monitored over a 15-day treatment period. Oral glucose tolerance was assessed on day 15, and serum biochemical markers, including glucose, cholesterol, triglycerides, uric acid, blood urea nitrogen, creatinine, ALT, and AST, were measured thereafter. Results: Ovariectomy aggravated diabetes-associated hyperglycemia, impaired glucose tolerance, and triglyceride elevation. Morin treatment reduced fasting blood glucose and improved glucose tolerance in diabetic mice, including ovariectomized animals. Morin also attenuated the increase in serum triglycerides and blood urea nitrogen in ovariectomized diabetic mice, although it did not significantly improve cholesterol, uric acid, creatinine, ALT, or AST levels. Compared with glibenclamide, morin showed relevant glucose-lowering activity but had a more limited effect on the overall biochemical profile. Conclusions: These findings suggest that morin may partially improve glycemic control and selected metabolic alterations in experimental diabetes associated with estrogen deficiency. Further studies are required to clarify its mechanisms of action, long-term efficacy, and translational relevance. Full article

Flowering plants are universally adorned with conical epidermal cells on their petals, which play a pivotal role in their function. They modulate the petal microenvironment by regulating wetting and temperature homeostasis and enhance pollinator attraction through tactile signaling. Despite their ecological and physiological significance, the molecular mechanisms that regulate the development of their distinct conical shape remain largely unknown. This review synthesizes recent advances in Arabidopsis thaliana (A. thaliana) research to elucidate the regulatory network governing conical cell morphogenesis. We summarize the recently established live confocal imaging approach for investigating conical cell morphogenesis and the core regulatory pathways elucidated thus far: the spatiotemporal orchestration of the cortical microtubule arrays, governed by the PP2A-KATANIN and ANGUSTIFOLIA-ROS modules. Furthermore, auxin-mediated cell wall acidification also plays a critical role in conical cell morphogenesis. Building upon these established regulatory modules, integrating computational modeling and uncovering new regulatory components in future research will profoundly enhance the value of conical cells as a system for studying plant cell morphogenesis. This will enable researchers to decipher the intricate biochemical signaling mechanisms that act in concert to orchestrate plant cell morphogenesis. Full article

MR-guided adaptive radiotherapy (ART) enables daily plan optimization for prostate cancer but is resource-intensive. This study evaluated dosimetric and clinical outcomes following transition from radiation oncologist (RO)-led to radiation therapist (RTT)-led MR-guided ART. All prostate cancer patients treated with MR-guided ART on a 1.5T MR-linac were retrospectively reviewed. Consecutive RO-led (September 2019–November 2021) and RTT-led (April 2022–October 2023) cohorts were compared, excluding the actual transition period. Toxicities (CTCAE v5.0), dose–volume metrics from daily adapted plans, target volume variation, and biochemical recurrence-free survival (BRFS) were analyzed. A total of 166 patients were included (78 RO-led, 88 RTT-led; median follow-up 40 and 35 months). Dosimetric differences between the cohorts were statistically small (<1%). Rates of G2+ GI adverse events were similar across all timepoints. An increase in on-treatment GU events was observed in the RTT-led cohort (G2+ 27% vs. 9%, G3 incidence n = 2 vs. n = 0), likely reflecting higher baseline urinary dysfunction; no post-treatment differences persisted. Early biochemical outcomes were comparable, with 36-month BRFS of 93.5% (RO-led) and 95.0% (RTT-led). RTT-led MR-guided ART achieved comparable dosimetric quality and early biochemical outcomes to RO-led workflows with adverse advents that resolved in the long term. With structured training and a mature practice setting, RTT-led ART represents a scalable model to support future adaptive radiotherapy practice. Full article

Diabetes mellitus (DM) markedly increases the risk of cardiovascular complications through mechanisms involving hyperglycemia-induced oxidative stress, inflammation, and apoptosis. Humanin (HN), a mitochondria-derived peptide with established cytoprotective properties, has been reported to exert antioxidant and anti-apoptotic effects in several experimental models. However, its role in diabetic cardiac injury remains insufficiently understood. The present study investigated the protective effects of repeated HN treatment against diabetes-induced cardiac injury in a streptozotocin (STZ)-induced mouse model. Mice were divided into four groups: control, HN-treated, STZ-induced diabetic, and STZ + HN-treated groups (n = 10/group). HN (4 mg/kg) was administered daily for 15 consecutive days. Biochemical analyses were performed to evaluate oxidative stress, inflammatory cytokines, and apoptotic markers. STZ-induced diabetes significantly increased oxidative stress markers, pro-inflammatory cytokines, and apoptotic activity while reducing antioxidant defenses and anti-inflammatory cytokines compared with controls. Repeated HN treatment markedly attenuated these alterations and restored redox and inflammatory balance in diabetic cardiac tissue. These findings demonstrate that repeated HN treatment attenuates oxidative stress, inflammation, and apoptosis in the hearts of diabetic mice. The results further suggest that HN may represent a promising therapeutic candidate for limiting diabetes-associated cardiac complications. Full article

To mitigate the environmental footprint of the aviation sector, this study proposes an integrated cascading biorefinery scheme to produce Sustainable Aviation Fuel (SAF) precursor bloodstock via enzymatic transesterification of coconut oil. Utilizing a synergistic binary alcohol system (ethanol-butanol) and the liquid lipase Eversa Transform 2.0, a strategic molecular reconfiguration of fatty acid esters was achieved. Optimization through Response Surface Methodology (RSM) identified critical parameters—5% catalyst loading, total binary alcohol-to-oil molar ratio of 7:1 (specifically comprised of a 2.5:4.5:1 ethanol/butanol/coconut oil matrix), and an operation temperature of 57.5 °C—yielding a 97% conversion efficiency. A sequential vacuum fractional distillation process was implemented to partition the ethyl-butyl esters into high-value streams. Notably, the light distillate fraction, characterized by a specific carbon chain distribution (C6: 27.2%, C8: 52.5%, C10: 6%, and C12: 13.6%), perfectly aligns with the molecular window of aviation kerosene. This fraction exhibits excellent cold-flow properties, viscosity, and volatility profiles, positioning it as an ideal high-performance SAF precursor blendstock to increase the renewable content of current aviation fuels. Simultaneously, the remaining C16–C18 residue serves as a high-density energy source for internal refinery processes, while C8–C14 species are recovered as high-purity chemical feedstocks. This circular model maximizes carbon atom economy and economic viability by cogenerating high added-value biochemicals alongside jet-grade blendstocks. These findings provide a scalable, enzymatic framework for the next generation of decarbonized aviation fuels. Full article

Background: Cancer cachexia and sarcopenia are highly prevalent complications affecting up to 50% of patients with cancer and are associated with increased treatment toxicity, poorer functional outcomes, and reduced survival. Early identification of muscle deterioration during hospitalization remains challenging. Objective: To evaluate the change in dominant-hand handgrip strength between hospital admission and discharge in hospitalized oncology patients. Methods: This prospective longitudinal study will evaluate hospitalized adults with confirmed malignancy and an expected hospital stay of ≥5 days. Daily handgrip strength and sEMG assessments will be performed as exploratory secondary measures to characterize temporal patterns of muscle function during hospitalization. Baseline and discharge evaluations will additionally include bioelectrical impedance analysis, validated patient-reported outcome measures (SARC-F, EORTC QLQ-C30, PSQI), and serum biomarkers related to inflammatory and nutritional status. Linear mixed models will be used to evaluate longitudinal changes and associations between functional, electrophysiological, and biochemical parameters. Expected results: The study aims to characterize trajectories of muscle function decline during hospitalization, identify candidate biomarker signatures for cachexia detection, and evaluate neuromuscular fatigue patterns using sEMG. Conclusions: This protocol proposes a feasible multimodal framework for monitoring skeletal muscle deterioration during acute oncology hospitalization and may inform future interventional strategies targeting cancer-related cachexia and sarcopenia. Full article

Cholestatic liver injury (CLI) is a complex condition for which current treatment options remain limited. Lysimachiae Herba (LH), a traditional Chinese medicine with hepatoprotective properties, has not yet been fully characterized in terms of its active constituents and underlying mechanisms in CLI. This study was designed to systematically determine the chemical composition of LH, characterize its absorbed constituents in vivo, and elucidate its therapeutic mechanisms against CLI. UPLC-Q-TOF-MS/MS was employed to analyze the chemical composition of LH and its absorbed components in rat serum. Key targets and signaling pathways were predicted using network pharmacology and molecular docking, followed by experimental validation in an ANIT-induced CLI mouse model and LCA-treated HepG₂ cells through biochemical assays, histological examination, transcriptomic analysis, qRT-PCR, Western blotting, and immunofluorescence analysis. A total of 129 compounds were tentatively identified in LH, among which 26 were detected in the bloodstream. Network analysis and molecular docking suggested that LH regulates bile acid homeostasis predominantly by the FXR signaling pathway. Both in vivo and in vitro experiments provided convergent evidence that LH modulates the FXR-related bile acid regulatory network, enhances bile acid efflux transporter expression, and alleviates CLI. In conclusion, this study systematically elucidates the chemical composition, absorbed constituents, and pharmacological mechanisms of LH in CLI, highlighting the involvement of FXR-related bile acid regulation as an important mechanism and providing a scientific basis for the potential development of LH for cholestatic liver injury. Full article