Protease inhibitor S (PiS) and protease inhibitor Z (PiZ) variants in the SERPINA1 gene are the main genetics factors associated with COPD; however, investigations about other polymorphisms are scanty. The aim of this study was to evaluate two missense single nucleotide polymorphisms (SNPs) (rs709932 and rs1303) in the SERPINA1 gene in Mexican mestizo patients with chronic obstructive pulmonary disease (COPD) related to tobacco smoking and biomass-burning exposure. 1700 subjects were genotyped and divided into four groups: COPD related to tobacco smoking (COPD-S, n = 297), COPD related to biomass-burning exposure (COPD-BB, n = 178), smokers without COPD (SWOC, n = 674), and biomass-burning exposed subjects (BBES, n = 551) by real-time PCR. Moreover, the patients’ groups were divided according to their exacerbations’ frequency. We carried out a haplotype analysis. We did not find differences in allele and genotype frequencies between groups in unadjusted and adjusted analyses, neither with these SNPs and lung function decline. Exacerbations’ frequency is not associated with these SNPs. However, we found a haplotype with major alleles (CT) associated with reduced risk for COPD (p < 0.05). Our analysis reveals that SNPs different from PiS and PiZ (rs709932 and rs1303) in the SERPINA1 gene are not associated with COPD and lung function decline in a Mexican mestizo population. However, a haplotype shaped by both major alleles (CT haplotype) is associated with reduced risk for COPD.

Background/Objectives: Ovarian adnexal masses present diagnostic challenges due to their heterogeneous etiologies. Accurately differentiating these conditions is critical for timely and effective clinical intervention. This study evaluated circulating molecules and serum-induced apoptosis as complementary tools to conventional diagnostic methods (CA125, HE4, and the ROMA index) for distinguishing benign masses from malignant masses. Methods: A cohort of 136 participants (9 healthy controls, 87 women with benign ovarian adnexal masses and 40 with malignant ovarian adnexal masses) was analyzed. The induction of apoptosis in Jurkat cells by patient serum was assessed using flow cytometry. Serum concentrations of sFas/CD95, HE4, CA125, and additional molecules were measured by ELISA and LEGENDplex™. Clinical, ultrasonographic, and histopathological data were correlated with tumor malignancy. To improve diagnostic performance beyond individual biomarkers, we developed two multiparametric classifiers that integrate the dominant parameters identified through group divergence analysis and ROC evaluation across multiple clinical comparisons. Results: Malignant tumors were associated with older age (51.45 ± 8.35 years, p = 0.0002), postmenopausal status (61.1%, p = 0.0013), and larger tumor size (>10 cm). Ultrasonographic features of complexity were observed exclusively in malignant masses. Functional assays revealed reduced apoptosis in Jurkat cells exposed to malignant sera, suggesting tumor-mediated immune evasion. Although higher sFas levels were observed in tumors, no significant differences were identified between the groups. Among the circulating biomarkers, CA125, HE4, MRP8/14, OPN, and SAA levels were significantly higher in malignant tumors than in benign tumors and controls. Conclusions: The evaluation of CA125, HE4, MRP8/14, and apoptosis (Classifier 1) and, more prominently, the measurement of additional molecules: OPN, SAA, IL-6, IL-8, and IGFBP-4 (Classifier 2), systematically outperformed the ROMA. Both achieved superior specificity and balanced accuracy (Youden’s J index) across all clinical comparisons by capturing the biological diversity of malignancies.

Acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease where current clinical practice guidelines remain focused on traditional cytogenetic markers. Despite recent advances demonstrating excellent diagnostic accuracy for gene expression signatures, a discontinuity exists between biomarker validation and clinical implementation. This study aimed to develop and validate a multiparametric gene expression signature using digital PCR (dPCR) to accurately diagnose pediatric ALL, with potential utility for monitoring measurable residual disease (MRD). We analyzed 130 bone marrow aspirates from pediatric patients from four clinical groups: non-leukemia, MRD-negative, MRD-positive and leukemia characterized by immunophenotype. Gene expression of an 8-gene panel (JUP, MYC, NT5C3B, GATA3, PTK7, CNP, ICOSLG, and SNAI1) was quantified by dPCR. The diagnostic performance of individual markers was assessed, and a Random Forest machine learning model was trained to classify active disease. The model was validated using a 5-fold stratified cross-validation approach. Individual markers, particularly JUP, MYC, and NT5C3B, showed good diagnostic accuracy for distinguishing leukemia from non-leukemia. However, integrating all eight markers into a multivariate Random Forest model significantly enhanced performance. The model achieved a mean cross-validated area under the curve (AUC) of 0.908 (±0.041) on receiver operator characteristic (ROC) analysis and 0.961 (±0.019) on Precision–Recall (PR) analysis, demonstrating high reliability and a favorable balance between sensitivity and precision. The integrated model achieved high sensitivity (88.9%) for detecting active disease, particularly at initial diagnosis. Although specificity was moderate (65.0%), the high positive predictive value (PPV 85.1%) and accuracy (81.5%) confirm the clinical utility of a positive result. While the panel showed promising performance for distinguishing MRD-positive from MRD-negative samples, the limited MRD-positive cohort size (n = 11) indicates that validation in larger MRD-focused studies is required before clinical implementation for treatment monitoring. This dPCR-based platform provides accessible, quantitative detection without requiring knowledge of clonal shifts or specific genomic landscape, offering potential advantages for resource-limited settings such as those represented in our Mexican pediatric cohort.

Background/Objectives: Differentiating between benign and malignant ovarian adnexal masses before surgery is a significant diagnostic challenge. This study examined a wide range of circulating plasma molecules (immune and inflammatory) to identify potential markers to classify tumors as benign or malignant. The goal was to develop a proof-of-concept tool for preoperative risk stratification. Methods: Twenty-three plasma molecules were evaluated in women with benign lesions (n = 68) and women with malignant ovarian tumors (n = 25). To evaluate the behavior of each marker in a normal population, women without adnexal pathology were also included (n = 11). The results were compared using the Kruskal–Wallis test with Dunn post hoc pairwise comparisons, the Benjamini–Hochberg correction for multiple comparisons, and ROC analysis. TGF-β isoforms and CA125 were measured in the complete cohort (n = 104), while the remaining molecules were assessed in a reduced cohort (n = 51). Two classification approaches were developed using TGF-β3 and IL-13: a sequential two-step algorithm and an ordinal logistic regression model. Apparent performance was corrected for optimism using Harrell’s bootstrap procedure. Results: Ten molecules reached statistical significance. Among the significant molecules, TGF-β3 identified ovarian lesions regardless of malignant status (AUC = 0.983), while IL-13 showed perfect malignancy-specific separation from benign cases (AUC = 1.000). Their combination in an ordinal logistic regression model produced three non-overlapping diagnostic strata across all three groups, significantly outperforming CA125, HE4, and ROMA at the malignancy-separation boundary (DeLong p < 0.05) in our exploratory cohort. Conclusions: TGF-β3 and IL-13 represent a biologically complementary marker pair whose combined ordinal regression model shows promising performance and warrants prospective validation in larger, multicenter independent cohorts as a preoperative risk-stratification tool for ovarian adnexal masses.

The use of essential oils as complementary therapies in oncology has gained interest due to their potential synergistic effects with conventional chemotherapy, although their specific efficacy against acute lymphoblastic leukemia (ALL) remains poorly characterized [1]. This study focuses on identifying potential complementary effects essential oils, aiming to enhance treatment efficacy of doxorubicin or vincristine, while potentially reducing toxicity. We evaluated the effects of several essential oils including Ginger (Zingiber officinale), Cinnamon (Cinnamomum zeylanicum), Thyme (Thymus vulgaris), Lavender (Lavandula angustifolia), Arborvitae (Thuja plicata), Oregano (Origanum vulgare), Tea tree (Melaleuca alternifolia), and Marjoram (Origanum majorana) on RS4;11 and SUP-B15 ALL cell lines. Cell viability was evaluated by Annexin V/Zombie Aqua staining and flow cytometry, while metabolic activity awas measured using WST-1 assays, to determine the half-maximal inhibitory concentrations (IC₅₀). Selected oils were then combined with vincristine (11 μg/mL) or doxorubicin (1 µM), concentrations previously established to achieve IC₅₀. Notably, combinations of vincristine with tea tree, marjoram, or ginger exhibited enhanced cytotoxicity in both cell lines. Similarly, doxorubicin combined with oregano, thyme, ginger, or cinnamon demonstrated significant additive effects. Additionally, oregano, cinnamon, ginger, and sandalwood displayed high cytotoxicity as single agents. These findings suggest that certain essential oils may hold therapeutic potential as complementary agents in ALL treatment. Further studies are needed to evaluate their selectivity and potential cross-toxicity. This preliminary research opens new avenues for developing more effective and potentially less toxic combination therapies for leukemia. Article published online: 05 March 2026 © 2026. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Background/Objectives: Whether the COVID-19 pandemic uniformly increased antimicrobial resistance (AMR) is contested, and aggregate analyses may mask organism-specific patterns. The objective of the study was to characterize changes in the level and slope of monthly multidrug-resistant (MDR), possible extensively drug-resistant (pXDR), possible pandrug-resistant (pPDR), vancomycin-resistant enterococci (VRE), and methicillin-resistant Staphylococcus aureus (MRSA) positivity at a Mexican tertiary-care hospital, and to assess heterogeneity by organism, antimicrobial class, specimen type, and hospital service. Methods: Retrospective interrupted time-series of deduplicated bacterial isolates (October 2017 to January 2026), analyzed by segmented regression across pre-pandemic, pandemic, and post-peak phases. Results: Cohort-wide MDR positivity had been declining before 2020 and rose abruptly at pandemic onset (level shift +6.81 percentage points, p = 0.0092) with a simultaneous slope reversal, reaching 24.27 percentage points above the extrapolated pre-pandemic trend by February 2022 (95% CI 14.26–34.28, p < 0.0001). The decline resumed after the pandemic peak, but the two slope changes did not offset one another (joint p = 0.0064). pXDR and pPDR moved in the same direction (+8.93, p = 0.0233; +15.40, p = 0.0113). VRE showed no significant departure, whereas MRSA declined in every phase yet departed significantly from its extrapolated trajectory (+23.68, p = 0.0040). Among species, Escherichia coli showed the largest departure from its counterfactual (+48.82 percentage points, p < 0.0001) and Klebsiella pneumoniae had the largest abrupt level rise (+14.46 percentage points, p = 0.0055), while S. aureus declined in every phase despite an abrupt rise in level at pandemic onset. At the class level, carbapenem and third-/fourth-generation cephalosporin non-susceptibility rose at pandemic onset and returned to baseline afterwards (19.1% to 28.0% to 18.7%, and 51.7% to 59.4% to 52.5%; all q < 0.001), whereas fluoroquinolone non-susceptibility rose and did not revert, climbing further in E. coli (61.5% to 65.2% to 72.7%, q = 0.0012) and plateauing at an elevated level in K. pneumoniae (37.6% to 46.5% to 49.3%, q = 0.1919 post-peak). Conclusions: Resistance trajectories across pandemic-related phases were divergent and, for the Gram-negative organisms, incompletely reversed, with the highest burden of advanced resistance in high-intensity settings. Organism-resolved surveillance and sustained stewardship are needed.