Search Results (1,027)

Background: Dupilumab, a monoclonal antibody targeting the IL-4/IL-13 receptor, has shown significant efficacy in improving asthma control and reducing exacerbations in patients with severe eosinophilic asthma. However, there is a lack of knowledge about real-world data on clinical remission rates and their predictors. Objective: This study aimed to evaluate clinical outcomes, remission rates, and predictive factors of remission in a real-life cohort of patients with severe eosinophilic asthma treated with dupilumab. Methods: We conducted a retrospective, bicentric, observational study including 52 patients with severe eosinophilic asthma treated with dupilumab. Clinical, functional, and biomarkers were assessed at baseline, 6 months, and 12 months. Statistical analyses included logistic regression to identify independent predictors of clinical remission. Results: After 12 months of treatment, 48.2% of patients achieved clinical remission. Dupilumab significantly improved asthma control and lung function (including FVC and FEF_25–75), reduced exacerbation rates, and maintenance therapy. High blood eosinophil levels (>490 cells/µL), high FeNO levels (>25 ppb), a history of CRSwNP, and better baseline FEV1 were associated with asthma remission. Conversely, obesity (BMI > 30) and related comorbidities (such as GERD, OSAS, and hypertension) and bronchiectasis were associated with a lower likelihood of remission. Multivariate analysis confirmed higher baseline FEV1 (OR 2.94; IC 1.13–7.6), positive history of CRSwNP (OR 8.03; IC 1.41–45.5), and higher baseline blood eosinophils (OR 1.003 IC 1.001–1.006) as independent predictors of clinical remission. Conclusions: These results are in line with the known effectiveness of dupilumab in severe eosinophilic asthma and identified a history of CRSwNP, higher baseline FEV1, and elevated blood eosinophils as key predictors of clinical remission. These findings may contribute to a more personalized approach to treatment selection, emphasizing the importance of comorbidity assessment together with type 2 inflammation biomarkers. Further prospective studies are needed to validate these results. Full article

Trichophyton indotineae is an emerging dermatophyte species responsible for recalcitrant and terbinafine-resistant dermatophytosis, raising concerns over diagnostic accuracy and treatment efficacy. This study aimed to improve the identification and resistance profiling of T. indotineae by integrating molecular methods with machine learning-assisted analysis of MALDI-TOF mass spectra. A total of 56 clinical isolates within the Trichophyton mentagrophytes complex were analyzed using ITS and ERG1 gene sequencing, antifungal susceptibility testing, and MALDI-TOF MS profiling. Terbinafine resistance was detected in 23 isolates and correlated with specific ERG1 mutations, including F397L, L393S, F415C, and A448T. While conventional MALDI-TOF MS failed to reliably distinguish T. indotineae from closely related species, unsupervised statistical methods (PCA and hierarchical clustering) revealed distinct spectral groupings. Supervised machine learning algorithms, particularly PLS-DA and SVM, achieved 100% balanced accuracy in species classification using 10-fold cross-validation. Biomarker analysis identified discriminatory spectral peaks for both T. indotineae and T. mentagrophytes (3417.29 m/z and 3423.53 m/z). These results demonstrate that combining MALDI-TOF MS with multivariate analysis and machine learning improves diagnostic resolution and may offer a practical alternative to sequencing in resource-limited settings. This approach could enhance the routine detection of terbinafine-resistant T. indotineae and support more targeted antifungal therapy. Full article

Cervical cancer (CC) represents a major public health concern, ranking as the fourth most frequently diagnosed cancer and one of the leading causes of cancer-related mortality among middle-aged women worldwide. CC is caused by persistent infection with high-risk human papillomaviruses (HR-HPVs), with HPV 16 being the cause of more than 50% of CC cases. In this study, the exometabolome of the HPV 16-positive cell lines SiHa and Ca Ski, as well as the HPV 16-negative control cell line C-33 A, was evaluated. The exometabolome was validated through molecular signatures using a transcriptomic approach to identify genes encoding cellular metabolic enzymes. The exometabolome was analyzed using ¹H nuclear magnetic resonance spectroscopy (¹H-NMR). Exometabolomic profiles were subsequently compared through both multivariate and univariate statistical analyses to identify significant differences between cell lines. Molecular signatures were analyzed from the GSE9750 dataset obtained from the GEO database. Exometabolic profiling of the HPV 16 positive cell lines showed higher concentrations of leucine, isoleucine, valine, lysine, methionine, glutamine, ornithine, choline, glucose, and tryptophan. An expression analysis showed increased expression of enzymes involved in amino acid synthesis, the tricarboxylic acid cycle, glycolysis, the pentose phosphate pathway, galactose metabolism, and HIF-1α. These data suggest metabolites and metabolism-associated genes that can be used as non-invasive, stable diagnostic and prognostic biomarkers, as well as therapeutic targets for CC in the presence of HPV 16. Full article

To elucidate the hydrochemical characteristics and evolution mechanisms of shallow groundwater in the alluvial–coastal transitional zone of the Tangshan Plain, 76 groundwater samples were collected in July 2022. An integrated approach combining Piper and Gibbs diagrams, ionic ratio analysis, multivariate statistical methods (including Pearson correlation, hierarchical cluster analysis, and principal component analysis), and PHREEQC inverse modeling was employed to identify hydrochemical facies, dominant controlling factors, and geochemical reaction pathways. Results show that groundwater in the upstream alluvial plain is predominantly of the HCO₃–Ca type with low mineralization, primarily controlled by carbonate weathering, water–rock interaction, and natural recharge. In contrast, groundwater in the downstream coastal plain is characterized by high-mineralized Cl–Na type water, mainly influenced by seawater intrusion, evaporation concentration, and dissolution of evaporite minerals. The spatial distribution of groundwater follows a pattern of “freshwater in the north and inland, saline water in the south and coastal,” reflecting the transitional nature from freshwater to saline water. Ionic ratio analysis reveals a concurrent increase in Na⁺, Cl⁻, and SO₄²⁻ in the coastal zone, indicating coupled processes of saline water mixing and cation exchange. Statistical analysis identifies mineralization processes, carbonate weathering, redox conditions, and anthropogenic inputs as the main controlling factors. PHREEQC simulations demonstrate that groundwater in the alluvial zone evolves along the flow path through CO₂ degassing, dolomite precipitation, and sulfate mineral dissolution, whereas in the coastal zone, continuous dissolution of halite and gypsum leads to the formation of high-mineralized Na–Cl water. This study establishes a geochemical evolution framework from recharge to discharge zones in a typical alluvial–coastal transitional setting, providing theoretical guidance for salinization boundary identification and groundwater management. Full article

Background: Malnutrition is common in chronic hemodialysis (HD) patients and often remains underdiagnosed. While body composition, functional status, and anthropometric measures can support nutritional assessment, their associations with nutritional status are not fully established in this population. This study aimed to evaluate the diagnostic performance of various measures for assessing malnutrition in chronic HD patients, using the Subjective Global Assessment (SGA) as the reference standard. Methods: This cross-sectional study involved chronic HD patients, stratified by nutritional status using the SGA. Data collection consisted of clinical interviews, anthropometric and functional measurements, bioelectrical impedance analysis (BIA), and biochemical analyses. Statistical analysis included Spearman’s correlation, logistic regression, receiver operating characteristic (ROC) curve analysis with area under the curve (AUC) to assess predictive accuracy, standardized effect sizes to show the magnitude of differences, and kappa statistics to evaluate concordance between variables. Results: This study included 103 chronic HD patients. Malnutrition was diagnosed in 50.5% of patients based on the SGA. Phase angle (PA) was the strongest single predictor of malnutrition (AUC = 0.79; specificity 0.88; sensitivity 0.58). PA ≤ 5.1° was significantly associated with higher malnutrition risk (OR: 10.23; 95% CI: 3.93–30.61; p < 0.001). Handgrip strength (HGS) also demonstrated good diagnostic value (AUC = 0.71; specificity 0.84; sensitivity 0.59). A multivariable model incorporating eight parameters—gender, post-dialysis ECW/ICW ratio, post-dialysis lean and fat mass, serum albumin, normalized protein catabolic rate (nPCR), arm circumference (AC), and HGS—achieved an AUC of 0.88 (95% CI: 0.81–0.95) and pseudo-R² of 0.46, demonstrating improved predictive performance. Conclusions: An integrated panel of anthropometric, bioimpedance, functional, and biochemical markers provides superior diagnostic accuracy compared to individual predictors, supporting a holistic diagnostic approach in HD patients. Full article

Background and Objectives: This study aimed to evaluate and compare the early postoperative outcomes of patients who underwent pancreaticoduodenectomy (PD) with total mesopancreatic excision (TMpE) versus conventional pancreaticoduodenectomy (Co-PD) for pancreatic head ductal adenocarcinoma (PDAC). Materials and Methods: Patients who underwent PD for pancreatic head cancer between January 2021 and December 2024 in our clinic and had a pathological diagnosis of PDAC were included. Patients were stratified into two groups according to the surgical technique performed (TMpE-PD vs. Co-PD). Demographic characteristics and early postoperative clinicopathological data were compared between the groups. Results: A total of 41 patients were included: 17 (41.5%) underwent TMpE-PD and 24 (58.5%) underwent Co-PD. Demographic and clinicopathological parameters were comparable between the groups. Although not statistically significant, the TMpE-PD group demonstrated higher R0 resection rates (58.8% vs. 45.8%; p = 0.412) and greater lymph node yield (33.9 vs. 29.1; p = 0.757) compared to the Co-PD group. Overall postoperative complications were more frequent in the TMpE-PD group (82.4% vs. 63.4%; p = 0.034). A smaller pancreatic duct diameter was associated with an increased risk of postoperative complications in both groups, approaching statistical significance (p = 0.053). Multivariable logistic regression analysis revealed that the surgical technique was not an independent risk factor for postoperative complications (OR: 0.64; 95% CI: 0.14–2.83; p = 0.56). No direct correlation was found between resection margin status (R0 vs. R1) and the development of postoperative complications. Conclusions: TMpE demonstrated non-significant trends toward higher R0 resection rates and greater lymph node yield compared with conventional PD. These findings suggest possible oncological benefits without significantly increasing perioperative morbidity. Full article

Many studies have examined how species are shifting their ranges poleward in response to climate change, using statistical approaches such as graphical analyses, t-tests, correlation analyses, and circular data methods. However, these methods are often constrained by assumptions of linearity or reliance on a single explanatory variable, which limits their ecological applicability. This study introduces a new statistical methodology to evaluate the significance of poleward range expansion, aiming to overcome these limitations and improve the robustness of ecological inference. We developed four parameterized nonlinear models—simple, multivariable, fixed, and transformed—to characterize the relationship between latitude and species richness across 1253 islands. Model parameters were estimated using the Gauss–Newton algorithm, and residuals were calculated as the difference between observed and predicted values. To test for distributional shifts, likelihood ratio tests were applied to the residuals, with statistical significance assessed using chi-square statistics and p-values derived from the −2 log-likelihood ratio. Finally, an intuitive indicator based on the fitted models was introduced to evaluate the direction of range shifts, thereby providing a direct means of identifying northward expansion trends under climate change. Applying this framework revealed significant poleward shifts of warm temperate and subtropical species (χ² = 52.4–61.3; p < 0.001). Among the four models, the multivariable model incorporating island area provided the best fit (AIC, BIC), reflecting its ability to account for collinearity. Taken together, these results underscore the robustness and ecological relevance of the methodology, demonstrating its utility for detecting species-specific range shifts and comparing alternative models under climate change. Full article

Diabetes mellitus constitutes a significant global public health problem. It is a chronic disease characterized by persistent hyperglycemia, which is a consequence of inadequate insulin secretion, deficient insulin action, or a combination of both factors. A crucial component in the effective management of this pathology is therapeutic adherence, as it helps prevent complications, improve patient quality of life, reduce associated mortality, and decrease the need for hospitalization. In this context, it is crucial to implement a comprehensive care model that offers continuous support and a multidisciplinary approach. Primary care should be central, coordinating the entire care process. Understanding the clinical and social characteristics of people with diabetes is key to guiding more effective interventions. Objective: The objective of this study was to describe the sociodemographic and anthropometric characteristics, degree of metabolic control, and treatment adherence in patients with diabetes mellitus enrolled in primary care programs in Ceuta. Materials and Methods: This was a descriptive, observational, and cross-sectional study conducted during the second half of 2024. The study population included individuals enrolled in the primary care diabetes program in Ceuta. We analyzed sociodemographic variables with a self-administered questionnaire, the level of therapeutic adherence using the MMAS-8 scale, and glycemic control through glycosylated hemoglobin (HbA1c) values. Results: The sample consisted of 370 individuals, with 50.3% being men. The average age was 62.82 years (SD = 13.46). A significant portion of participants, 61.07%, had no formal education or had only received primary education. Additionally, 84.9% of the participants had at least one other associated chronic pathology. Regarding adherence, 36.8% of the patients showed a high level, and for all patients, the mean HbA1c value was 7.5% (SD = 1.55). Furthermore, our analysis revealed statistically significant associations between cultural background and both therapeutic adherence (weak positive correlation: r = 0.213, p ≤ 0.001; multivariate significance: sig: <0.001; Exp(B) = 2.448) and glycemic control (multivariate significance: sig: <0.001; Exp(B) = 2.686). Conclusions: We observed high treatment adherence in the study population, with HbA1c values within the limits recommended by the World Health Organization for older adults. Furthermore, a relationship between cultural background and both treatment adherence and glycemic control was identified. This suggests a need for further research into these and other social determinants, like study level or monthly income, in future studies. Full article

Background/Objectives: The differential diagnosis between primary polycythemia vera (PV) and secondary polycythemia (SP) presents significant clinical challenges owing to substantial phenotypic overlap. This investigation utilized untargeted metabolomic approaches to elucidate disease-specific metabolic perturbations and evaluate the metabolic consequences of cytoreductive therapeutic interventions. Methods: Plasma specimens obtained from PV patients (n = 40) and SP patients (n = 25) underwent comprehensive metabolomic profiling utilizing liquid chromatography–mass spectrometry (LC-MS) platforms. Multivariate statistical analyses, including principal component analysis (PCA), were employed in conjunction with pathway enrichment analyses to characterize disease-associated metabolic dysregulation. Additionally, receiving treatment (tPV) (n = 25) and not receiving treatment (ntPV) (n = 15) PV patients were compared to assess therapeutic metabolic effects. Results: Comprehensive metabolomic analysis identified 67 significantly altered metabolites between PV and SP patients, with 36 upregulated and 31 downregulated in PV. Key upregulated metabolites in PV included thyrotropin-releasing hormone, 3-sulfinoalanine, nicotinic acid adenine dinucleotide, and protoporphyrin IX, while 4-hydroxyretinoic acid and deoxyuridine were notably downregulated. Pathway enrichment analysis revealed disruptions in taurine, glutamate, nicotinate, and cysteine metabolism in PV. ntPV patients exhibited higher glucose and octanoyl-CoA levels compared to treated patients, indicating the normalization of glucose and fatty acid metabolism with cytoreductive therapy. ntPV was also associated with altered B-vitamin metabolism, including decreased nicotinic acid adenine dinucleotide and increased nicotinamide ribotide levels. Cross-comparison analysis revealed overlapping pathway enrichment in glutamate metabolism, nicotinate and nicotinamide metabolism, and cysteine metabolism between both comparisons. Conclusions: This study demonstrates that PV and SP exhibit fundamentally distinct metabolic signatures, providing novel insights into disease pathogenesis and potential diagnostic biomarkers. The identification of oxidative stress signatures, disrupted energy metabolism, and altered B-vitamin cofactor pathways distinguishes PV from SP at the molecular level. Cytoreductive therapy significantly normalizes metabolic dysregulation, particularly glucose and nucleotide metabolism, validating current therapeutic approaches while revealing broader systemic treatment effects. The metabolic signatures identified, particularly the combination of deoxyuridine, thyrotropin-releasing hormone, and oxidative stress metabolites, may serve as complementary diagnostic tools to traditional morphological and molecular approaches. These findings advance our understanding of myeloproliferative neoplasm pathophysiology and provide a foundation for developing metabolically targeted therapeutic strategies and precision medicine approaches in PV management. Full article

The effects of climate change, such as melting ice and permafrost in northern and Arctic regions, raise serious concerns about the risk of accidents at oil production, transportation, and storage facilities. This risk is compounded by the lack of comprehensive statistical data on accidental spills, which complicates the development of effective preventive measures. This study introduces an innovative approach to systematizing and analyzing official data on accidental oil and petroleum product spills in the Russian Arctic and Far North. Using association analysis and multivariate methods, the research explores relationships between the causes and objects of accidents. The findings indicate no distinct patterns in the distribution of spill incidents across the Russian Arctic and Far North compared to other regions. However, a notable correlation between the causes of spills and their locations was identified, which may inform the development of targeted preventive measures. Full article

Background: Glioblastoma (GBM) patients exhibit a median overall survival of 12–18 months post-diagnosis, with disease recurrence typically emerging within 6–9 months. Due to the absence of standardized therapeutic protocols at recurrence, management is highly individualized. This study comprehensively evaluates overall survival (OS) time to subsequent progression, and clinical status evolution following diverse interventions for first GBM recurrence. Methods: Data from 350 patients were retrospectively analyzed. The entire cohort was divided into the following four groups: (A) patients with no further therapy at recurrence, (B) combined re-radiation and chemotherapy with temozolomide with or without lomustine or other individual medication, (C) surgery without re-adjuvant treatment, and (D) surgery and at least one cycle of chemotherapy or re-radiation or a combination. Statistical analyses were performed using non-parametric tests. Additionally, various regression analyses were performed. Results: Patients receiving invasive therapeutic regimens with or without adjuvant re-therapy (groups C and D) demonstrated significantly prolonged OS (p < 0.001) alongside superior Karnofsky performance status (KPS) at both 3-month (p = 0.016) and 6-month (p < 0.001) intervals post-intervention. Multivariate analysis confirmed surgical resection, temozolomide (TMZ) chemotherapy, and radiotherapy as independent positive predictors of OS (respective p-values: <0.001, <0.001, and 0.048). Notably, surgical resection significantly improved clinical status (p < 0.001), whereas radiotherapy had a significant negative effect on clinical status (p = 0.016). Conclusions: Contrary to the prevailing hypothesis that survival extension through extensive therapy at recurrence necessitates compromised clinical status, our findings demonstrate that contemporary recurrence therapies—particularly multimodal approaches—simultaneously enhance both OS and functional outcomes in GBM patients. This paradigm challenges conventional expectations of therapeutic trade-offs at disease recurrence. Full article

Traditional methods of analyzing biofouling in emitters fail to capture the complexity and heterogeneity of their components. Therefore, the objective of this work was to develop and validate an innovative approach that integrates fractal metrics and scanning electron microscopy (SEM) to accurately characterize, quantify, and diagnose biofouling in drippers used with brackish water. For this purpose, tests were conducted on benches that applied brackish water and fresh water through drippers with a flow exponent (x) of 0.46 (NJ), 0.45 (SL), and 0.48 (ST) over 160 h. Biofouling was mapped using advanced diagnostics using SEM and factual metrics, and the results were analyzed using multivariate statistics. The results obtained present important findings for the study, detection, mapping, and proposal of mitigation measures for biofouling in drippers, presenting factual metrics that may be new indicators of clogging. Biofouling is a phenomenon resulting from the interaction between the spatial evolution of the obstructing material, emitter geometry, and irrigation water quality. The combination of SEM and fractal metrics has proven to be an advanced and innovative diagnostic tool for detecting the presence and distribution of biofouling, enabling clogging monitoring and creating more realistic scenarios in hydrodynamic studies to improve or develop emitter designs. Full article

Background: Cerebral small vessel disease (cSVD) is a major contributor to intracerebral hemorrhage (ICH). Its presence carries significant implications for stroke prevention, acute management, post-stroke recovery, and socioeconomic burden. Despite its clinical significance, the impact of cSVD on functional outcomes after ICH, particularly concerning aging, remains uncertain. Objective: This study evaluated how cSVD influences post-ICH functional recovery, using age stratification (<65 and ≥65 years) and a multidomain functional assessment approach. Methods: We retrospectively analyzed data from 356 patients with primary spontaneous ICH. Functional status was evaluated at baseline and at three months post-ICH across multiple domains, including global disability, activities of daily living, gait, upper-extremity function, and swallowing ability, using validated assessment tools. Patients were categorized based on age and the presence or absence of cSVD. Results: Patients without cSVD consistently exhibited better functional status than those with cSVD at both baseline and three-month evaluations across age groups. Although all groups showed statistically significant functional improvement over time, the degree of improvement was significantly lower in patients with cSVD, particularly among those aged 65 years or older. Multivariable logistic regression analysis confirmed that cSVD was a strong and independent predictor of poor functional outcomes at three months after ICH. Conclusions: Our findings emphasize that cSVD is not merely a passive comorbidity but an active and independent determinant of poor prognosis and limited recovery following ICH. The clinical importance of early detection of cSVD supports the need for more intensive, individualized rehabilitation strategies in ICH survivors. Full article

Objectives: Post-acute venous thromboembolism (VTE) is a well-recognized complication of COVID-19, driven by persistent endothelial dysfunction and thromboinflammation. Identifying simple clinical predictors of VTE may optimize therapy and limit adverse outcomes. We propose a pragmatic risk-stratification approach, based on clinical and echocardiographic parameters. Methods: We conducted a retrospective cohort study in a Romanian tertiary hospital (March 2020–April 2022) in 54 adults with laboratory-confirmed COVID-19 and imaging-confirmed VTE. Demographics, comorbidities, laboratory markers, and echocardiographic variables—particularly tricuspid annular plane systolic excursion (TAPSE), peripheral oxygen saturation (SpO₂), and left-ventricular end-diastolic diameter (LVEDD)—were collected. The primary outcome was the percentage of patients receiving systemic thrombolysis. Statistical analyses included Mann–Whitney U tests, chi-square, Spearman correlations, and multivariable logistic regression. Results: The mean age was 61.2 ± 14.7 years, and 63% were men. Eleven patients (20.4%) underwent thrombolysis. Compared with conservatively managed patients, those receiving thrombolysis had lower TAPSE (13.0 vs. 20.8 mm), lower SpO₂ (90.1 vs. 97.0%), and smaller LVEDD (24.4 vs. 46.1 mm); all differences were statistically significant. Each 1 mm decrease in TAPSE and 1% decrease in SpO₂ increased the likelihood of thrombolysis (adjusted odds ratios 1.58 and 1.34, respectively). Inflammatory markers and right-ventricular diameter were not associated with treatment. Conclusions: Reduced TAPSE, lower SpO₂, and decreased LVEDD identify post-COVID VTE patients at elevated risk of hemodynamic compromise requiring thrombolysis. A point-of-care assessment incorporating these variables may improve early risk stratification and guide therapeutic decisions. Full article

Long-term multivariate time series forecasting is crucial for real-world applications, including energy consumption, traffic flow, healthcare, and finance. Usually, some statistical approaches are used for predicting future observations based on historical temporal data. Recently, transformer-based models with patch mechanisms have demonstrated significant potential in enhancing computational efficiency. However, their inability to fully capture intra-patch temporal dependencies often limits the accuracy of predictions. To address this issue, we propose the Frequency Compensation Patch-wise transFormer (FCP-Former), which integrates a frequency compensation layer into the patching mechanism. This layer extracts frequency-domain features via Fast Fourier Transform (FFT) and incorporates them into patched data, thereby enriching patch representations and mitigating intra-patch information loss. To verify the feasibility of this model, FCP-Former was conducted on eight benchmark datasets via PyTorch 2.4.0 and trained on an NVIDIA RTX 4090 GPU (Santa Clara, CA, USA). Results demonstrate that FCP-Former 48 optimal experiment results and 17 suboptimal experiment results across all datasets. Especially on the ETTh1 dataset, it achieves an average MSE of 0.437 and an average MAE of 0.430, while on the Electricity dataset, it achieves an average MSE of 0.186 and an average MAE of 0.277. This demonstrates that FCP-Former has better forecasting accuracy and a superior ability to capture periodic and trend patterns. Full article