Search Results (1,232)

(1) Background: Dioxins and dioxin-like polychlorinated biphenyls (dl-PCBs) are persistent organic pollutants (POPs), characterized by high toxicity and strong lipophilicity, which promote their bioaccumulation in human tissues. Their detection in breast milk raises concerns about early-life exposure during lactation. Although dietary intake is the primary route of maternal exposure, environmental pathways—including inhalation, dermal absorption, and residential proximity to contaminated sites—may also significantly contribute to the maternal body burden. (2) Methods: This narrative review examined peer-reviewed studies investigating maternal and environmental determinants of dioxin and dl-PCB concentrations in human breast milk. A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science (2000–2024), identifying a total of 325 records. Following eligibility screening and full-text assessment, 20 studies met the inclusion criteria. (3) Results: The included studies consistently identified key exposure determinants, such as high consumption of animal-based foods (e.g., meat, fish, dairy), living near industrial facilities or waste sites, and maternal characteristics including age, parity, and body mass index (BMI). Substantial geographic variability was observed, with higher concentrations reported in regions affected by industrial activity, military pollution, or inadequate waste management. One longitudinal study from Japan demonstrated a declining trend in dioxin levels in breast milk, suggesting the potential effectiveness of regulatory interventions. (4) Conclusions: These findings highlight that maternal exposure to dioxins is influenced by identifiable environmental and behavioral factors, which can be mitigated through public health policies, targeted dietary guidance, and environmental remediation. Breast milk remains a critical bioindicator of human exposure. Harmonized, long-term research is needed to clarify health implications and minimize contaminant transfer to infants, particularly among vulnerable populations. Full article

The southern conifer Agathis australis (D.Don) Lindl. is a large and long-lived species endemic to Aotearoa New Zealand. It is threatened due to past logging activities, pathogen attack and potentially climate change, with increasing severity and frequency of drought and heatwaves across its distribution. Like many large tree species, little is known about the carbon dynamics of this ecologically and culturally significant species. We explored seasonal variations in non-structural carbohydrates (NSCs) and growth in trees ranging from 20 to 175 cm diameter at breast height (DBH). NSCs were seasonally stable with no measurable pattern across seasons. However, we found growth rates standardised to basal area and sapwood area (growth efficiency) declined with tree age and stem NSC concentrations (including total NSCs, sugars and starch) all increased as trees aged. Total NSC concentrations were 0.3%–0.6% dry mass for small trees and 0.8%–1.8% dry mass for larger trees, with strong relationships between DBH and total NSC, sugar and starch in stems but not roots. Cumulative growth efficiency across the two-year study period declined as tree size increased. Furthermore, there was an inverse relationship between growth efficiency across the two-year study period and NSC concentrations of stems. This relationship was driven by differences in carbon dynamics in trees of different sizes, with trees progressing to a more conservative carbon strategy as they aged. Simultaneously declining growth efficiency and increasing NSC concentrations as trees age could be evidence for active NSC accumulation to buffer against carbon starvation in larger trees. Our study provides new insights into changing carbon dynamics as trees age and may be evidence for active carbon accumulation in older trees. This may provide the key for understanding the role of carbon processes in tree longevity. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Background/Objectives: Gastrin-releasing peptide receptor (GRPR) is overexpressed in breast cancer and might be used as a theranostics target. The expression of GRPR strongly correlates with estrogen receptor (ER) expression. Visualization of GRPR-expressing breast tumors might help to select the optimal treatment. Developing GRPR-specific probes for SPECT would permit imaging-guided therapy in regions with restricted access to PET facilities. In this first-in-human study, we evaluated the safety, biodistribution, and dosimetry of the [^99mTc]Tc-DB8 GRPR-antagonistic peptide. We also addressed the important issue of finding the optimal injected peptide mass. Methods: Fifteen female patients with ER-positive primary breast cancer were enrolled and divided into three cohorts receiving [^99mTc]Tc-DB8 (corresponding to three distinct doses of 40, 80, or 120 µg DB8) comprising five patients each. Additionally, four patients with ER-negative primary tumors were injected with 80 µg [^99mTc]Tc-DB8. The injected activity was 360 ± 70 MBq. Planar scintigraphy was performed after 2, 4, 6, and 24 h, and SPECT/CT scans followed planar imaging 2, 4, and 6 h after injection. Results: No adverse events were associated with [^99mTc]Tc-DB8 injections. The effective dose was 0.009–0.014 mSv/MBq. Primary tumors and all known lymph node metastases were visualized irrespective of injected peptide mass. The highest uptake in the ER-positive tumors was 2 h after injection of [^99mTc]Tc-DB8 at a 80 µg DB8 dose (SUV_max 5.3 ± 1.2). Injection of [^99mTc]Tc-DB8 with 80 µg DB8 provided significantly (p < 0.01) higher uptake in primary ER-positive breast cancer lesions than injection with 40 µg DB8 (SUV_max 2.0 ± 0.3) or 120 µg (SUV_max 3.2 ± 1.4). Tumor-to-contralateral breast ratio after injection of 80 μg was also significantly (p < 0.01, ANOVA test) higher than ratios after injection of other peptide masses. The uptake in ER-negative lesions was significantly lower (SUV_max 2.0 ± 0.3) than in ER-positive tumors. Conclusions: Imaging using [^99mTc]Tc-DB8 is safe, tolerable, and associated with low absorbed doses. The tumor uptake is dependent on the injected peptide mass. The injection of an optimal mass (80 µg) provides the highest uptake in ER-positive tumors. At optimal dosing, the uptake was significantly higher in ER-positive than in ER-negative lesions. Full article

Elevated extracellular potassium (K⁺) in the tumor microenvironment (TME) of breast and other cancers is increasingly recognized as a critical factor influencing tumor progression and immune suppression. Current methods for noninvasive mapping of the potassium distribution in tumors are limited. Here, we employed photoacoustic chemical imaging (PACI) with a solvatochromic dye-based, potassium-sensitive nanoprobe (SDKNP) to quantitatively visualize extracellular potassium levels in an orthotopic metaplastic breast cancer mouse model, Ccn6-KO. Tumors of three distinct sizes (5 mm, 10 mm, and 20 mm) were imaged using multi-wavelength photoacoustic imaging at five laser wavelengths (560, 576, 584, 605, and 625 nm). Potassium concentration maps derived from spectral unmixing of the photoacoustic images at the five laser wavelengths revealed significantly increased potassium levels in larger tumors, confirmed independently by inductively coupled plasma mass spectrometry (ICP-MS). The PACI results matched ICP-MS measurements, validating PACI as a robust, noninvasive imaging modality for potassium mapping in tumors in vivo. This work establishes PACI as a promising tool for studying the chemical properties of the TME and provides a foundation for future studies evaluating the immunotherapy response through ionic biomarker imaging. Full article

Although evidence suggests adiposity as a modifiable risk factor for postmenopausal breast cancer (BC), its association with premenopausal BC remains uncertain. This potential differential relationship for menopausal status has been insufficiently investigated in the Moroccan population due to limited data. This study aims to assess the relationship between various indicators of adiposity and the risk of BC among Moroccan women by menopausal status. A multicenter case-control study was conducted in Morocco between December 2019 and August 2023, including 1400 incident BC cases and 1400 matched controls. Detailed measures of adiposity and self-reported measures from different life stages were collected. Unconditional logistic regression analyses were conducted to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between body size indicators and the risk of BC, adjusting for a range of known risk factors for BC. Higher waist circumference (WC) and hip circumference (HC) were associated with an increased risk of BC in both pre- (p-trend < 0.001 for both WC and HC) and post-menopausal women (p-trend < 0.001 for WC, 0.002 for HC). Current body mass index (BMI) ≥30 kg/m² increased the risk of postmenopausal BC (p-trend = 0.012). Among postmenopausal women, higher weight at age 20 was positively associated with BC risk (p-trend < 0.001), while, weight at age 30 was significantly associated with increased BC risk in both pre- (p-trend = 0.008) and post-menopausal women (p-trend = 0.028). Interestingly, weight gain since age 20 was inversely associated with BC risk in postmenopausal women in the adjusted model (p-trend = 0.006). Young-adult BMI observed a significant increased trend with BC risk in both pre- (p-trend = 0.008) and post-menopausal women (p-trend < 0.001). In premenopausal women, larger body shape during childhood and early adulthood was positively associated with BC risk (p-trend = 0.01 and = 0.011, respectively). In postmenopausal women, larger childhood and adolescent body silhouettes were also associated with increased BC risk (p-trend = 0.045 and 0.047, respectively). These results suggest that anthropometric factors may have different associations with pre- and post-menopausal BC among Moroccan women. This underscores the importance of conducting large prospective studies to better understand these findings and explore their links to different molecular subtypes of BC. Full article

Background/Objectives: Since breast cancer (BC) survival rates have increased to 91% at 5 years and 80% at 15 years postdiagnosis, there is a growing awareness of the importance of addressing the long-term well-being of patients. Consequently, integrative oncology, which combines standard therapies with complementary approaches (nutrition, mind–body practices, and lifestyle modifications), has emerged as a patient-centred model aimed at improving symptom management, treatment adherence, and overall quality of life (QoL). This study aims to demonstrate how integrative therapies can benefit body composition, phase angle, and fluid and electrolyte balance through bioelectrical impedance analysis (BIA). Methods: This study considers a patient who underwent BC surgery and was enrolled in the AMICO clinic for anamnesis, as well as their oncological pathology data, assessment of QoL, and BIA. The breast surgeon specialising in integrative oncology therapies prescribed the patient curcumin and polydatin, moderate physical activity, a balanced diet, and Qigong sessions. The patient underwent monitoring through haematochemical analysis, BIA, and a QoL questionnaire, with follow-up every four months. Results: Between 4 and 12 months, fat mass (FM) and body mass index (BMI) markedly decreased, whereas fat-free mass (FFM), total body water (TBW), and skeletal muscle mass (SMM) increased progressively. Moreover, the improvements in the Na/K ratio and phase angle (PhA) suggest a shift toward better electrolyte and fluid balance and enhanced cellular integrity and membrane function. Equally outstanding were her psychological benefits in terms of mood, sleep, anxiety, and melancholy. Conclusions: Patient progress in body composition, metabolic function, pain management, and psychological status measured during the 12-month follow-up demonstrates the potential benefits of an integrative approach to supportive cancer care. Full article

Background/Objectives: After diagnosis, it is common for women with breast cancer to gain weight, which is associated with worse clinical outcomes. However, traditional measures such as body weight, BMI, and waist circumference do not detect key changes in body composition, such as fat redistribution or muscle loss. The objective of this exploratory study was to assess the evolution of body composition and muscle strength after one year of treatment, and their relationship with metabolic biomarkers. Methods: Prospective observational study in newly diagnosed breast cancer patients. Body composition was assessed using bioelectrical impedance analysis (BIA) and ultrasound (US); muscle strength was measured by handgrip dynamometry. Biomarkers analyzed included glucose, insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), glycosylated hemoglobin (HbA1c), total cholesterol (and its fractions), triglycerides, C-reactive protein (CRP), 6-interleukin (IL-6), vitamin D, myostatin, and fibroblast growth factor 21 (FGF-21). Results: Sixty-one women (mean age 58 years) were included. After one year, fat mass and related parameters significantly increased, while skeletal muscle mass and muscle strength decreased. Sarcopenic obesity prevalence rose from 1.16% to 4.9%. No significant changes were found in biomarkers, but positive correlations were observed between fat parameters and insulin, HOMA-IR, and triglycerides, and negative correlations with HDL-cholesterol. Conclusions: BIA and US can detect unfavorable changes in body composition that are not reflected in conventional measurements. At one year post-diagnosis, women showed increased fat accumulation, muscle loss, and reduced strength, even without significant metabolic biomarker changes. Further research is warranted to elucidate the long-term clinical implications of these findings and the external validity in larger cohorts. Full article

Purpose: This study aimed to develop a predictive nomogram integrating AI-based BI-RADS lexicons and lesion-to-nipple distance (LND) ultrasound features to differentiate mass-type ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) visible on ultrasound. Methods: The final study cohort consisted of 170 women with 175 pathologically confirmed malignant breast lesions, including 26 cases of DCIS and 149 cases of IDC. LND and AI-based features from the S-Detect system (BI-RADS lexicons) were analyzed. Rare features were consolidated into broader categories to enhance model stability. Data were split into training (70%) and validation (30%) sets. Logistic regression identified key predictors for an LND nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, 1000 bootstrap resamples, and calibration curves to assess discrimination and calibration. Results: Multivariate logistic regression identified smaller lesion size, irregular shape, LND ≤ 3 cm, and non-hypoechoic echogenicity as independent predictors of DCIS. These variables were integrated into the LND nomogram, which demonstrated strong discriminative performance (AUC = 0.851 training; AUC = 0.842 validation). Calibration was excellent, with non-significant Hosmer-Lemeshow tests (p = 0.127 training, p = 0.972 validation) and low mean absolute errors (MAE = 0.016 and 0.034, respectively), supporting the model’s accuracy and reliability. Conclusions: The AI-based comprehensive nomogram demonstrates strong reliability in distinguishing mass-type DCIS from IDC, offering a practical tool to enhance non-invasive breast cancer diagnosis and inform preoperative planning. Full article

Background/Objectives: Obesity is associated with various skin complications, yet its impact on dermal elastic fibers—key components maintaining skin elasticity—remains unclear, particularly in cases of mild obesity prevalent in East Asian populations. The aim of this study was to investigate whether mild obesity is associated with the early structural deterioration of dermal elastic fibers and alterations in elastin-related gene expression in Japanese individuals. Methods: Abdominal skin samples from 31 Japanese women (the mean body mass index [BMI] 23.9 ± 3.2 kg/m², mean age 49.5 ± 4.8) undergoing breast reconstruction surgery were analyzed. Gene expression levels of elastin-regenerative and -degradative molecules were assessed by quantitative polymerase chain reaction in the epidermis, dermis, and subcutaneous fat. Dermal elastic fiber content was evaluated histologically using Elastica van Gieson staining. Results: No statistically significant correlations between the BMI and elastin-degrading gene expression (NE, MMP2, MMP9, and NEP) were observed. ELN expression in the dermis showed a significant positive correlation with the BMI (ρ = 0.517, p = 0.003), potentially reflecting a compensatory response. Histological analysis revealed a significant inverse correlation between dermal elastic fiber content and the BMI (r = −0.572, p = 0.001), independent of age or smoking history. Conclusions: Even mild obesity is associated with the early degradation of dermal elastic fibers despite limited transcriptional alterations. These findings underscore the need for early skin care interventions to mitigate obesity-related skin fragility, especially in populations with predominantly mild obesity. Full article

The present study aimed to develop biodegradable films formulated with pectin/carboxymethyl cellulose (CMC) and cinnamon essential oil, investigating the effects of CP treatment time on the properties of the films. The developed films were used as packaging to evaluate the shelf life of chicken meat. Biodegradable films were produced from a film-forming solution containing pectin/CMC, glycerol (30%), and cinnamon essential oil (2%). All formulations included the essential oil, and the control group corresponded to the film that was not subjected to CP treatment. The CP treatments were applied at 22.5 L/min, 20 kV, and 80 kHz for 10, 20, and 30 min. The results showed that increasing CP treatment time led to a progressive reduction in apparent viscosity, indicating improved homogeneity of the polymer system. Hydrophobicity increased with treatment time, as shown by a higher contact angle (from 51.15° to 62.38°), resulting in lower water solubility. Mechanical properties were also enhanced, with tensile strength rising from 3.29 MPa to 6.74 MPa after 30 min of CP. Biodegradability improved with treatment time, reaching 99.51% mass loss after 15 days for the longest exposure. Films produced from the solution treated for 30 min (FCP30) were most effective in extending the shelf life of chicken breast fillets, reducing lipid oxidation (TBARS: 61.9%), peroxide content (58.7%), and microbial spoilage (TVB-N: 59.2%) compared to the untreated film. Overall, the results highlight the importance of CP treatment time as a key factor in enhancing film performance, supporting its application in sustainable active packaging. Full article

Background/Objective: Programmed cell death ligand-1 (PD-L1), which is overexpressed in certain tumors, inhibits the body’s natural immune response by providing an “off” signal that enables cancer cells to evade the immune system. It has been demonstrated that [¹⁷⁷Lu]Lu-DOTA-iPD-L1 (PD-L1 inhibitor cyclic peptide) promotes immune responses. This study aimed to synthesize and evaluate [¹⁸F]AlF-NOTA-iPD-L1 as a novel radiotracer for PD-L1 positron emission tomography (PET) imaging and as a potential theranostic pair for [¹⁷⁷Lu]Lu-DOTA-iPD-L1. Methods: The NOTA-iPD-L1 peptide conjugate was synthesized and characterized by U.V.-vis, I.R.-FT, and UPLC-mass spectroscopies. Radiolabeling was performed using [¹⁸F]AlF as the precursor, and the radiochemical purity (HPLC), partition coefficient, and serum stability were assessed. Cellular uptake and internalization (in 4T1 triple-negative breast cancer cells), binding competition, immunofluorescence, and Western blot assays were applied for the radiotracer in vitro characterization. Biodistribution in mice bearing 4T1 tumors was performed, and molecular imaging (Cerenkov images) of [¹⁸F]AlF-NOTA-iPD-L1 and [¹⁷⁷Lu]Lu-DOTA-iPD-L1 in the same mouse was obtained. Results: [¹⁸F]AlF-NOTA-iPD-L1 was prepared with a radiochemical purity greater than 97%, and it demonstrated high in vitro and in vivo stability, as well as specific recognition by the PD-L1 protein (IC₅₀ = 9.27 ± 2.69 nM). Biodistribution studies indicated a tumor uptake of 6.4% ± 0.9% ID/g at 1-hour post-administration, and Cerenkov images showed a high tumor uptake of both [¹⁸F]AlF-NOTA-iPD-L1 and ¹⁷⁷Lu-iPD-L1 in the same mouse. Conclusions: These results warrant further studies to evaluate the clinical usefulness of [¹⁸F]AlF-NOTA-iPD-L1/[¹⁷⁷Lu]Lu-DOTA-iPD-L1 as a radiotheranostic pair in combination with anti-PD-L1/PD1 immunotherapy. Full article

Emerging evidence suggests a bidirectional relationship between gut microbiota, melatonin synthesis, and breast cancer (BC) development in hormone receptor-positive patients (HR+HER2+ and HR+HER2-). This study investigated alterations in gut microbiota composition, the serum serotonin–N-acetylserotonin (NAS)–melatonin axis, fecal short-chain fatty acids (SCFAs) and beta-glucuronidase (βGD) activity, and serum zonulin in HR+ BC patients compared to healthy controls. Blood and fecal samples were analyzed using mass spectrometry for serotonin, NAS, melatonin, and SCFAs; ELISA for AANAT, ASMT, 14-3-3 protein, and zonulin; fluorometric assay for βGD activity; and 16S rRNA sequencing for gut microbiota composition. HR+ BC patients exhibited gut dysbiosis with reduced Bifidobacterium longum and increased Bacteroides eggerthii, alongside elevated fecal βGD activity, SCFA levels (e.g., isovaleric acid), and serum zonulin, indicating increased intestinal permeability. Serum serotonin and N-acetylserotonin (NAS) levels were elevated, while melatonin levels were reduced, with a higher NAS/melatonin ratio in BC patients. AANAT levels were increased, and ASMT levels were decreased, suggesting disrupted melatonin synthesis. Bifidobacterium longum positively correlated with melatonin and negatively with βGD activity, while Bacteroides eggerthii showed a positive correlation with βGD activity. These findings suggested that gut microbiota alterations, disrupted melatonin synthesis, microbial metabolism, and intestinal permeability may contribute to BC pathophysiology. The NAS/melatonin ratio could represent a potential biomarker, necessitating further mechanistic studies to confirm causality and explore therapeutic interventions. Full article

Background/Objectives: The tumor microenvironment (TME) plays a critical role in cancer progression by shaping immune responses and influencing patient outcomes. We hypothesized that the relative proximity of specific immune cell pairs to cancer cells within the TME could help predict their pro- or anti-tumor functions and reflect clinically relevant immune dynamics. Methods: We analyzed imaging mass cytometry (IMC) data from lung adenocarcinoma (LUAD) and triple-negative breast cancer (TNBC) cohorts. For each immune cell pair, we calculated a relative distance (RD) score, which quantifies the spatial difference in proximity to cancer cells. We assessed the prognostic and predictive significance of these RD-scores by comparing them with conventional features such as cell fractions, densities, and individual cell distances. To account for variations in cell abundance, we also derived normalized RD-scores (NRD-scores). Results: RD-scores were more strongly associated with overall patient survival than standard immunological metrics. Among all immune cell pairs, the RD-score comparing the proximity of B cells to that of intermediate monocytes showed the most significant association with improved survival. In TNBC, RD-scores also improved the distinction between responders and non-responders to immunochemotherapy and chemotherapy. Normalized RD-scores reinforced these findings by minimizing the influence of cell density and further highlighting the importance of immune cell spatial relationships. Conclusions: RD-scores offer a spatially informed biomarker that outperforms traditional metrics in predicting survival and treatment response. This approach provides a new perspective on immune cell behavior in the TME and has potential utility in guiding personalized cancer therapies and patient stratification. Full article

Breast cancer treatments, such as chemotherapy, radiation, and surgery, often face significant limitations, highlighting the need for more effective and targeted therapies. Here, we investigate the potential of 266 nm laser irradiation of chlorpromazine as a novel approach to develop new antitumoral compounds. We identify six chlorpromazine photocompounds with masses in the range of 178–334 u, along with several dimeric compounds with masses between 566 and 600 u, using an HPLC-MS. In silico approaches assess their pharmacokinetic and pharmacodynamic properties while comparing their toxicity with the parent compound. Molecular docking simulations indicate that some photoproducts have a low estimated free energy of binding to cancer-related targets, suggesting enhanced therapeutic potential compared to chlorpromazine. Additionally, ADME-Tox predictions indicate that these photoproducts may have pharmacokinetic and toxicity profiles similar to chlorpromazine. Overall, this study highlights that laser-generated chlorpromazine photoproducts exhibit enhanced biological activity to breast cancer-related targets compared to chlorpromazine while maintaining a similar ADME-Tox profile. Full article