Search Results (1,775)

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related mortality, with colorectal liver metastasis (CRLM) being the major determinant of poor prognosis. Tumor metabolic reprogramming and spatial heterogeneity complicate biomarker discovery and clinical management. This study aimed to characterize the spatial metabolomic landscape of CRC and identify progression-associated metabolic alterations and potential metabolic signatures for liver metastasis. Methods: A total of 23 tissue samples were collected from patients with CRC, with and without liver metastasis. Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was used to map the spatial metabolite distributions. Region-of-interest analysis guided by histopathology enabled comparative metabolomic profiling across different tissue types. Multivariate statistical analysis, pathway enrichment, and receiver operating characteristic (ROC) curve analyses were performed to identify key metabolic alterations and evaluate potential biomarker performance. Results: Distinct spatial metabolomic profiles were observed across normal mucosa, primary tumors, liver metastases, and normal liver tissues. In primary colorectal tumors, amino acid, purine, and choline metabolism were significantly upregulated, whereas liver metastases were characterized by elevated levels of triglycerides, diglycerides, cholesteryl esters, and acylcarnitines, indicating enhanced lipid synthesis, incomplete fatty acid oxidation, and/or mitochondrial dysfunction. Progression-associated analyses across tissue types revealed consistently increasing trends in glycerides and acylcarnitines, along with heterogeneous alterations in amino acids and phospholipids. Furthermore, 122 differential metabolites were identified between metastatic and non-metastatic CRC, and a four-lipid panel demonstrated strong discriminatory performance. Conclusions: This study provides a spatially resolved characterization of metabolic reprogramming during CRC progression and liver metastasis, highlighting lipid and amino acid metabolism as key features and revealing the metabolic signatures of CRLM. Full article

For studies utilizing methods such as water color parameter inversion and algal bloom classification, abundant spectral bands and high spectral resolution are of great significance. However, for multispectral satellite sensors that are not designed for water color studies (e.g., Sentinel-2 MSI), the number of bands in the visible–near-infrared range is limited, and lacks specific spectral bands with rich spectral information. Hyperspectral reconstruction of multispectral data based on hyperspectral remote sensing reflectance ($R_{rs}$) databases and machine learning algorithms have been proven to be a feasible solution. Based on the in situ measured $R_{rs}$ data, this study constructed a large-sample hyperspectral $R_{rs}$ database covering various optical water types using two Chinese hyperspectral satellites, and compared the spectral reconstruction accuracy of six machine learning algorithms. The results show that expanding the $R_{rs}$ database for model training by integrating hyperspectral satellite data can effectively improve the reconstruction accuracy in waters of different optical types. Comparisons with in situ measured hyperspectral $R_{rs}$ indicate that the reconstructed Sentinel-2 hyperspectral data achieve high accuracy, with the Spectral Angle Mapper (SAM) less than 5° and the correlation coefficient ($r$) higher than 0.7. Furthermore, the reconstructed data can effectively restore spectral information not captured by the original multispectral data, such as the suspended sediment $R_{rs}$ peak at 580 nm and the chlorophyll $R_{rs}$ valley at 680 nm. Through spectral reconstruction, the spectral resolution of Sentinel-2 can be maximized while retaining its advantages of fast revisit capability and high spatial resolution, thereby expanding its application potential in water color remote sensing. Full article

Accurate detection of chlorophyll-a (Chl-a) is critical for monitoring water quality in inland waters, where high concentrations of coloured dissolved organic matter (CDOM) complicate retrieval process. Reliable Chl-a estimation depends on the precise determination of the phytoplankton absorption coefficient (a_ph). This study evaluates Chl-a detection from in situ a_ph measurements and assesses the accuracy of phytoplankton absorption retrieval from Sentinel-2/MSI (S2) and Sentinel-3/OLCI (S3) using the Case-2-Regional-Coast-Colour (C2RCC) processor across diverse optical water types (OWTs) in boreal lakes. OWTs were classified based on remote sensing reflectance features, representing Clear, Moderate, Turbid, Very Turbid, and Brown conditions. CDOM absorption strongly influenced the underwater light field, particularly in Brown and Turbid waters. Linear relationships between a_ph and Chl-a were generally strong across OWTs, with improved relationships in the red spectral region (670 nm). Satellite-derived a_pig estimates showed a weak relationship with in situ data (R² = 0.26–0.45). Both sensors overestimated small a_ph values, while S3 underestimated larger ones. S2 underestimated a_ph in Clear and Brown OWTs, with median absolute percentage differences near 100% for all OWTs. These findings emphasize the challenges posed by bio-optical complexity in boreal lakes and highlight the need for OWT-specific algorithms to improve satellite-based absorption and Chl-a retrieval accuracy. Full article

Colorectal cancer (CRC) shows consistent sex-related differences in incidence, anatomic distribution, molecular subtype, immune context, and clinical outcome. However, these differences are often discussed through broad parallel themes such as hormones, genetics, or the microbiome, rather than through the biological settings in which sex meaningfully modifies tumor behavior. This review argues that sex is most informative in CRC when treated as a contextual modifier whose relevance emerges only after integrating tumor sidedness, mismatch repair status, oncogenic background, immune ecology, and age at onset. The clearest signals arise from interaction-based contexts, particularly when sex is interpreted together with tumor sidedness and dMMR/MSI-H or BRAF-linked disease states. Current evidence indicates that women are enriched for proximal or right-sided, microsatellite instability-high, mismatch repair-deficient, CpG island methylator phenotype-high, and BRAF-associated CRC, whereas men more often present with distal disease and a higher overall burden. Mechanistic studies further show that sex-related differences extend beyond hormone exposure to include KRAS–STAT4–KDM5D signaling, site-specific immune-checkpoint programs, metabolic phenotypes, epigenetic biomarker variation, and microbiota–hormone crosstalk. These effects are most evident in defined clinical niches, particularly right-sided CRC, mismatch repair-deficient disease, BRAF-mutated metastatic CRC, and early-onset CRC. A sex-aware, subtype-aware, and location-aware framework therefore offers a more clinically useful interpretation of CRC heterogeneity than descriptive male-versus-female comparisons alone. Full article

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article

Endometrial cancer (EC) is the sixth most common cancer in women. Its overall incidence has increased by 132% over the past 30 years, reflecting an increase in the prevalence of risk factors. The mortality rate decreased by 15% in the last 30 years, despite the high number of endometrial cancer-related deaths occurring world-wide. An inverse relationship has been observed between the incidence of EC, mortality and socio-economic status: more patients living in low-income countries die from EC because they do not have access to timely and effective treatment. About 80% of EC cases are diagnosed in an early stage and have a good prognosis. However, about 20% of cases present in advanced stages and are characterized by a poor prognosis. The molecular classification proposed by The Cancer Genome Atlas (TGCA) and its surrogate for clinical use allowed the adoption of personalized treatments. The assessment of the status of the MMR has revolutionized the treatment of advanced ECs, leading to significant results both in terms of PFS and OS. In this review we will focus on MMR deficiency (dMMR)/microsatellite instability-hypermutated (MSI-H) tumors, which globally account for 20–30% of ECs. The dMMR group encompasses multiple etiologies, including sporadic defects in MMR genes, germline mutations, and hypermethylation of the MLH1 promoter. Currently, the combination of immunotherapy (I-O) with standard chemotherapy has become the new standard first-line treatment for dMMR advanced or recurrent ECs. Although the main clinical trials involving patients with MMRd/MSI-H ECs treated with I-O and chemotherapy have demonstrated efficacy and long-term control of the disease, a significant number of patients do not respond to treatment (intrinsic or primary resistance) and others develop progression during treatment (acquired or secondary resistance). In this narrative approach the biological and molecular bases of these tumors have been integrated with recent advances involving diagnostic techniques, therapeutic opportunities and the genomic and phenotypical alterations underpinning the mechanisms of resistance. Special attention was given to the need for robust, clinically affordable biomarkers to promptly identify responders and non-responders to the current treatment regimens. Full article

Background: We aimed to investigate the prevalence and clinical significance of pontine microtubular signal intensity (MSI), presumed dilated perivascular or perineural spaces, in patients with hemifacial spasm (HFS) using high-resolution MRI using proton density-weighted imaging (HR-PDI). Methods: We retrospectively analyzed 438 patients with unilateral HFS who underwent microvascular decompression (MVD) and preoperative HR-PDI. MSI was defined as a linear or curvilinear hyperintense lesion along the presumed course of the intraparenchymal facial nerve fascicles within the pons on HR-PDI. The presence and laterality of MSI were evaluated by consensus between two reviewers and classified according to their relationship to the symptomatic side of HFS as ipsilateral (same side as the facial spasm), contralateral (opposite side), or bilateral. Clinical characteristics, surgical findings, and postoperative outcomes were compared according to the presence of ipsilateral MSI. A control group of 307 subjects who underwent HR-PDI for non-central neurologic symptoms was included to assess the prevalence of MSI. Multivariable logistic regression analysis was performed to identify factors associated with immediate postoperative improvement after MVD. Results: MSI was more frequently observed in patients with HFS than in controls after adjusting age and sex (OR, 3.78; 95% CI, 2.747–5.197; p < 0.001). Ipsilateral MSI was identified in 267 of 438 patients (61.0%). Patients with ipsilateral MSI showed a significantly higher frequency of contralateral MSI (p < 0.001) and vertebralartery-related compression (p = 0.002). Immediate postoperative improvement after MVD was less frequent in patients with ipsilateral MSI than in those without MSI (77.5% vs. 86.5%, p = 0.019). Multivariable logistic regression analysis demonstrated that ipsilateral MSI was independently associated with a lower likelihood of immediate postoperative improvement (OR, 0.411; 95% CI, 0.222–0.759; p = 0.005). However, long-term surgical outcomes were not significantly different according to the presence of MSI. Conclusions: Pontine MSI on HR-PDI is more frequently observed in patients with HFS and is associated with a lower likelihood of immediate postoperative improvement and a tendency toward delayed recovery after MVD but not with poorer long-term outcomes. These findings suggest that MSI may represent microstructural or neurofluidic alterations along the pontine facial nerve pathway and may serve as an imaging marker of delayed recovery dynamics. Full article

Salinity is a major constraint for lettuce production, affecting plant growth, physiological status, and market quality. This study evaluated the combined effects of increasing salinity levels (S₀: non-saline control; S₃₀, S₆₀, and S₁₂₀ mM NaCl) and Trichoderma harzianum inoculation on morphological, physiological, and quality-related traits of lettuce. Increasing salinity levels resulted in significant reductions in growth-related parameters, particularly leaf area, shoot biomass, root volume, and cutting resistance (CR), with the most pronounced decreases observed at S₁₂₀. In contrast, several physiological and quality-related parameters showed different response patterns. Membrane stability index (MSI) and chlorophyll index remained relatively stable across salinity treatments, while total soluble solids (C) increased with increasing salinity, indicating osmotic adjustment under stress conditions. Leaf color parameters showed reductions in lightness and chroma at higher salinity levels, suggesting structural and optical changes in leaves rather than severe pigment degradation. The effects of Trichoderma on plant growth were limited and did not consistently mitigate growth reductions under salinity. However, inoculation influenced several physiological and quality-related traits, including MSI and TSS, indicating a role in physiological regulation and stress adaptation rather than direct growth promotion. Multivariate analyses indicated that salinity was the main factor contributing to treatment separation, whereas Trichoderma application influenced the overall trait profile without consistently increasing growth parameters. Overall, the results suggest that under saline conditions, Trichoderma may contribute to stress tolerance and physiological stability rather than directly increasing plant growth, and its effectiveness depends on stress severity. Full article

KNOTTED1-LIKE HOMEOBOX (KNOX) genes are conserved transcription factors that play crucial roles in plant growth, development, and stress responses. However, systematic characterization of the KNOX family in Malus sieversii, a valuable germplasm resource with outstanding stress tolerance and flavonoid accumulation, remains lacking. In this study, we performed a genome-wide identification of the KNOX gene family in M. sieversii and identified 21 MsiKNOX genes. Phylogenetic analysis classified these genes into three subfamilies (Class I, II, and M), with structural features and motif compositions consistent with those of their orthologs in Arabidopsis thaliana and cultivated apple. Chromosomal localization revealed an uneven distribution across 13 chromosomes, and synteny analysis indicated both conserved evolution and lineage-specific expansion of the KNOX family in M. sieversii. Promoter cis-element analysis suggested that MsiKNOX genes are potentially involved in responses to multiple abiotic stresses and hormone signaling. Expression profiling under ABA and GA treatments showed that most MsiKNOX genes responded differentially to these phytohormones. Notably, MsiKNOX09 was significantly upregulated by ABA and downregulated by GA, and was further shown to physically interact with the anthocyanin-associated MsiMYB1 in yeast two-hybrid and split-luciferase assays. These findings provide a comprehensive overview of the KNOX gene family in M. sieversii and suggest that MsiKNOX09 acts as a hormone-responsive regulator and may participate in MsiMYB1-mediated regulatory pathways. Full article

Angiogenesis is a spatially regulated hallmark of colorectal cancer (CRC) progression, yet current analytical frameworks fail to resolve how nutraceutical bioactive compounds interact with angiogenic signalling within the heterogeneous tumour microenvironment. This review advances a central hypothesis: that the spatial localisation of palm oil mill effluent (POME)-derived bioactive compounds within CRC tumour tissues is predictive of their functional anti-angiogenic activity. POME—the largest waste stream of palm oil processing—contains a chemically diverse array of bioactives, including tocotrienols, phenolics, carotenoids, and fatty acids, with reported antioxidant, anti-inflammatory, and anti-angiogenic properties. However, the existing evidence is predominantly derived from bulk in vitro analyses, limiting mechanistic conclusions about compound behaviour within spatially organised tumour architectures. To address this gap, we propose an integrated framework positioning mass spectrometry imaging (MSI)—across matrix-assisted laser desorption/ionisation (MALDI), desorption electrospray ionisation (DESI), and secondary ion mass spectrometry (SIMS) platforms—as the analytical bridge between compound localisation and angiogenic function. By enabling the label-free, spatially resolved co-localisation of POME-derived compounds with key angiogenic mediators, including VEGF, HIF-1α, and NF-κB, within intact CRC tissues, MSI provides a mechanistic platform that transcends the limitations of conventional molecular analyses. A four-component translational roadmap is outlined, encompassing POME bioactive profiling, spatial compound mapping, angiogenic co-localisation analysis, and functional validation. Critically, the existing evidence on oil palm-derived bioactives is appraised with respect to study quality, mechanistic depth, and translational limitations, identifying the most analytically tractable candidate compounds for spatial investigation. Collectively, this framework positions POME valorisation within a precision nutraceutical oncology paradigm, offering a spatially informed strategy for anti-angiogenic intervention in CRC while simultaneously addressing the environmental burden of palm oil processing waste. Full article

The increasing integration of generative artificial intelligence (GenAI) into scientific research raises the question of whether such systems can be evaluated not only through external benchmarks but also through structured analysis of their own meta-evaluative responses. This study introduces a conceptual framework for simulated self-assessment of GenAI models, formalized through a multidimensional self-assessment profile and a metacognitive self-assessment index (MSI). The proposed framework integrates quantitative criteria capturing hallucination propensity, knowledge currency, formal-structure handling, source validity, and terminological precision. To evaluate the reliability of model-generated self-assessments, psychometric instruments traditionally used in human metacognition research—MAI, SRIS, and SDQ—are adapted for large language models. Experimental results across multiple GPT models indicate that, despite the absence of genuine introspective mechanisms, GenAI systems can produce internally consistent and moderately calibrated meta-evaluative responses. These findings suggest that simulated self-assessment, when interpreted within a rigorous methodological framework and combined with external validation, can serve as a complementary quantitative tool for trust analysis and reliability assessment of generative models. Full article

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers—e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This study aims to investigate the existence of mutation clusters (co-mutations) and evaluate the correlation of these clusters with various clinical and laboratory parameters. Methods: A retrospective study was conducted utilizing pathological samples from lung cancer patients harboring mutations in EGFR, KRAS, ALK, BRAF, MET, HER2, ROS1, NTRK, and NRG1. Data were collected from the Institute of Pathology at Carmel Medical Center between the years 2022 and 2024. Patients were stratified using a Two-Step Cluster Analysis algorithm based on actionable mutations and co-mutations. Heatmaps and dendrograms were generated to assess the correlation between these genomic clusters, clinical metrics, and predictive biomarkers. Results: The study cohort included 129 patients with actionable mutations. Five distinct clusters were identified: Clusters 1, 2, and 3 exhibited a high expression of STK11 and TP53 co-mutations alongside KRAS drivers (n = 38, n = 12, and n = 23, respectively). Clusters 4 and 5 demonstrated high expression of ALK alterations and tumor suppressor gene mutations (n = 31 and n = 25, respectively). Cluster comparisons demonstrated statistically significant differences between clusters regarding age, gender, PD-L1 expression, and tumor mutational burden. No significant associations were found regarding ethnicity or microsatellite instability status. Conclusions: By constructing clusters based on the aggregate of genomic alterations in patients with actionable mutations, it is possible to predict associations with distinct demographic and clinical characteristics. Future research should apply this analytical approach to larger cohorts to further characterize these subgroups and investigate potential correlations with therapeutic efficacy. Full article

Colorectal cancer (CRC) remains a leading cause of cancer morbidity and mortality worldwide, with nearly one quarter of patients presenting with metastatic disease at diagnosis. The epidermal growth factor receptor (EGFR) plays a central role in CRC pathogenesis through activation of downstream RAS/RAF/MAPK and PI3K/AKT/mTOR signaling pathways, and has become a major therapeutic target. Anti-EGFR monoclonal antibodies, cetuximab and panitumumab, have demonstrated survival benefit in selected patients, particularly those with left-sided, RAS wild-type tumors. However, primary and acquired resistance limit their efficacy, underscoring the need for predictive biomarkers and novel strategies. This review synthesizes current knowledge of EGFR biology, therapeutic integration, and biomarker development, including RAS and BRAF mutations, MSI status, HER2 amplification, EGFR ligands (AREG/EREG), consensus molecular subtypes, and liquid biopsy applications. We also discuss mechanisms of resistance such as pathway reactivation, receptor mutations, and epithelial-to-mesenchymal transition, alongside emerging approaches, including combination regimens, ctDNA-guided rechallenge, and genotype-specific inhibitors. Collectively, these insights highlight the evolving landscape of precision oncology in CRC and the importance of molecular stratification to optimize EGFR-targeted therapy and overcome resistance. Full article

Background: The rising prevalence of childhood overweight/obesity (OWOB) and elevated blood pressure (EBP) parallels a global decline in muscular fitness. However, evidence linking whole-body muscular strength to the comorbidity of these cardiometabolic risks remains scarce. Methods: Data were obtained from five nationally representative waves of the Chinese National Survey on Students’ Constitution and Health (CNSSCH, 2000–2019), including 1,072,404 children and adolescents aged 7–18 years. A novel Muscle Strength Index (MSI) was developed by integrating handgrip strength (HGS) and standing broad jump (SBJ), standardized for body weight and height, respectively. Generalized linear mixed-effects models (GLMMs) with restricted cubic splines (RCS) were first applied to characterize dose–response associations. Subsequently, categorical analyses and forest plots were conducted to quantify risks of OWOB, EBP, and their comorbidity across five waves and subgroups. Sex-specific normative reference curves were established using the LMS method, and population-attributable fractions (PAFs) were estimated to assess the potential public health benefits of improving muscular strength. Results: Between 2000 and 2019, the prevalence of OWOB, EBP, and comorbidity increased markedly, reaching 25.80%, 12.23%, and 4.83% in 2019, and are projected to rise further to 37.88%, 20.16%, and 10.01% by 2030. Over the same period, mean MSI increased from 2000, peaked in 2005, and subsequently declined by 2019 with the values for boys and girls, being 1.73, 1.75, 1.63 and 1.46, 1.49, 1.41, respectively. Dose–response analyses revealed consistent L-shaped associations, with the greatest risk reductions observed when moving from low to moderate MSI levels. In 2019, participants with low MSI had higher odds of OWOB (OR 4.81, 95% CI 4.65–4.97), EBP (OR 1.42, 95% CI 1.36–1.49), and comorbidity (OR 3.49 95% CI 3.26–3.73) compared with those at middle levels. PAF analyses indicated that improving MSI to at least the 40th percentile could potentially avert 43.5% of OWOB cases, 12.3% of EBP cases, and 48.2% of comorbidity cases. The highest potential benefits were observed in northern and northeastern provinces, particularly Tianjin and Heilongjiang. Conclusions: Chinese children and adolescents face a dual burden of rising cardiometabolic comorbidity and declining muscular strength. Muscular strength demonstrates a strong nonlinear protective association with OWOB, EBP, and their co-occurrence. Targeted improvement among those with low muscular strength may substantially reduce future cardiometabolic burden. Full article

Microsatellite instability (MSI) testing is frequently used to screen patients for the early detection of Lynch syndrome, the most common hereditary colorectal cancer syndrome. MSI testing compares microsatellite repeat lengths in tumor DNA with those in matched normal tissue from the same patient. Therefore, precise sample identification is critical for obtaining reliable test results. The Penta-C and Penta-D pentanucleotide markers are widely used for sample identification in MSI testing. We investigated instability, defined as allelic mismatches or shifts, discordant fragment sizes, or the appearance of alleles in tumor DNA that were absent in the corresponding normal DNA, in the Penta-C and Penta-D loci across 2609 paired colorectal tumor and matched normal tissue or blood DNA samples. The allele sizes of both markers did not match in 0.3% of microsatellite-stable (MSS) and 12.3% of microsatellite instability-high (MSI-H) patients (p < 0.001, difference in proportions, 12.0% (95% CI, 8.9–15.1%)). Non-matching allele sizes in 12.3% of the MSI-H tumors suggest that other repeat markers may also be unstable and not suitable for sample identification in these tumors. Full article