Nevin Manimala

Biomater Sci. 2025 May 12. doi: 10.1039/d5bm00033e. Online ahead of print.

ABSTRACT

Inflammation plays a key role in cartilage damage that occurs in osteoarthritis (OA). However, in vitro assessments of tissue-engineered constructs for cartilage regeneration generally do not consider their performance in the presence of inflammation. In this work, the chondrogenic differentiation potential of mesenchymal stromal cells (MSCs) was evaluated in the presence of both chondrogenic factors and inflammatory cytokines, and cartilage formation, degradative response, and inflammatory response were characterized. The addition of cytokines reduced cartilage production, increased cell proliferation, and resulted in an increase in inflammatory markers. Incorporation of hyaluronic acid (HA) had little impact on both collagen fibril microstructure and mechanical properties, two gel properties known to affect cell response, and thus allows the work to probe the biological impact of HA without the confounding effect of these gel properties. Regardless of in vitro environment, HA did not change cartilage production. The inflammatory response was similar with or without HA in terms of IL-6 and IL-10 secretion whereas IL-8 production exhibited some correlation with HA concentration as observed via a linear regression model. Additionally, in the presence of cytokines, inclusion of HA statistically decreased the gene- and protein-level expression of matrix metalloproteinase-13 (MMP-13). Thus, when exposed to both chondrogenic growth factors and inflammatory cytokines within a chondrogenic-promoting collagen I/II blended hydrogel, chondrogenic differentiation of MSCs was limited by the inflammatory environment. These findings emphasize the importance of understanding how biomaterials affect cell responses within disease-relevant inflammatory environments.

PMID:40354044 | DOI:10.1039/d5bm00033e

Eur Arch Paediatr Dent. 2025 May 12. doi: 10.1007/s40368-025-01046-1. Online ahead of print.

ABSTRACT

PURPOSE: An effective Deep learning (DL) based Early Childhood Caries (ECC) prediction model is crucial for early detection of ECC. This study aims to develop and evaluate a deep learning (DL) based hybrid statistical model for ECC prediction.

METHODS: The study employed a computational cross-sectional design, conducted over a three-year period from March 2021 to March 2024. Data analysis was carried out using a hybrid statistical approach that integrated bootstrap methods, Logistic Regression Modelling (LRM), and Multilayer Feed-Forward Neural Networks (MLFFNN). The sample comprised 157 parent-child pairs, providing a robust dataset for examining the research questions.

RESULTS: In the current study, the predictors named, “mother’s education” (β₁: 0.423; p < 0.25), “parent’s knowledge of bottle-feeding habit during sleep can cause tooth decay” (β₂: -1.264; p < 0.25), “attitude towards the importance of oral health as general health” (β₄: -1.052; p < 0.25) and “parent’s self-reported oral pain among their children” (β₅: -2.107; p < 0.25) showed significant association with ECC. For this model, the Mean Absolute Deviation (MAD) was 0.02211, Predictive Mean Squared Error (PMSE) was 0.07909, and the accuracy level was 99.98%. No significant difference was observed from the t-test between the actual values and the predicted values of the model (p > 0.05).

CONCLUSION: It has been shown that this unique deep learning-based ECC prediction model appears an effective tool with high accuracy and interpretability for ECC prediction. After implementing the oral health intervention program, focusing on the potential predictors of ECC obtained from this innovative model, policymakers could be able to evaluate their prediction models comparing their results with the findings of the current study. This comparison will guide them in understanding, designing, and implementing a more effective intervention program for ECC prevention.

PMID:40354021 | DOI:10.1007/s40368-025-01046-1

Cogn Process. 2025 May 12. doi: 10.1007/s10339-025-01275-8. Online ahead of print.

ABSTRACT

The human brain operates as a complex network, and understanding its functional connectivity is a core challenge in neuroscience. Functional near-infrared spectroscopy (fNIRS) offers a non-invasive, portable method for studying brain activity and connectivity, providing valuable insights into the brain’s network dynamics. In this study, we used fNIRS to examine the functional connectivity of the human brain during the Dual n-back task, a cognitive challenge that varies in memory load (0-back, 1-back, and 2-back). Data were collected from 24 channels in the frontal cortex and pre-processed with discrete wavelet transform. Functional connectivity matrices for each task level were calculated using correlation analysis, and graph theory metrics such as clustering coefficient and local and global efficiency were assessed. Statistical comparisons (t-tests and ANOVA) revealed significant differences in these metrics across memory load levels, with higher memory loads leading to altered brain connectivity patterns (p < 0.05 for clustering coefficient and local efficiency, p < 0.04 for global efficiency). These findings suggest that as cognitive demand increases, the functional connectivity of the brain’s frontal network changes, reflecting the dynamic nature of brain activity during complex tasks. This research highlights the potential of fNIRS for exploring brain network functions and has broader implications for understanding cognitive processes and developing neurocognitive diagnostics and interventions.

PMID:40354005 | DOI:10.1007/s10339-025-01275-8

Discov Oncol. 2025 May 12;16(1):735. doi: 10.1007/s12672-025-02523-1.

ABSTRACT

The growing burden of cancer and recent surge in healthcare data availability call for new ways of analysing this multifactorial disease and improving patient outcomes. The aim of this study is to develop and evaluate prognostic cancer survival models across ten common cancer types based on a large patient sample. We compare the performance of different machine learning algorithms and assess the added value of genetic information in cancer prognosis. We also provide ways to improve model explainabilty which is critical for model adoption in clinical practice. This study included data from 9977 patients with bladder, breast, colorectal, endometrial, glioma, leukaemia, lung, ovarian, prostate, and renal cancers. Genetic data collected through the 100,000 Genomes Project was linked with clinical and demographic data provided by the National Cancer Registration and Analysis Service, Hospital Episode Statistics and Office for National Statistics. More than 500 prognostic features were assessed and four machine learning algorithms including Elastic Net Cox proportional hazards regression, random survival forest, gradient boosting survival and DeepSurv neural network were developed in this study. Most models achieved good performance varying from 60% in bladder cancer to 80% in glioma with the average C-index of 72% across all cancer types. Different machine learning methods achieved similar performance with DeepSurv model slightly underperforming compared to other methods. Addition of genetic data improved performance in endometrial, glioma, ovarian and prostate cancers, showing its potential importance for cancer prognosis. Patient’s age, stage, grade, referral route, waiting times, pre-existing conditions, previous hospital utilisation, tumour mutational burden and mutations in gene TP53 were among the most important features in cancer survival modelling. By offering a comprehensive set of predictive models for cancer survival, this study fills a critical gap in our understanding of cancer prognosis and provides new tools for informing cancer treatment and consequently improving patient outcomes.

PMID:40353995 | DOI:10.1007/s12672-025-02523-1

Biol Futur. 2025 May 12. doi: 10.1007/s42977-025-00260-8. Online ahead of print.

ABSTRACT

Monitoring the root system plays an important role in understanding plant physiological processes; however, its assessment using non-destructive methods remains challenging. Here, we evaluate the utility of root capacitance (C_R) as a practical indicator of root function and its relationship to plant growth parameters in Capsicum annuum L. To improve the accuracy of root function assessment, we applied artificial neural networks (ANN) as a novel data evaluation approach, comparing its predictive performance against multiple linear regression (MLR). Across two soil types (sandy and sandy loam), we applied multiple treatments ranging from microbial inoculants to wool pellet and inorganic nitrogen sources primarily to test whether C_R could detect differences in root activity and biomass production under different conditions. We measured root dry biomass, shoot dry biomass, and leaf N content, treating these variables as independent predictors in a statistical framework. Multiple linear regression (MLR) initially showed strong relationship between C_R and both root and shoot biomass in sandy soil, and between C_R and total plant N content in sandy loam. However, an ANN model consistently outperformed MLR in predicting C_R from plant physiological parameters, as evidenced by lower mean absolute error (MAE) in all treatments. These findings confirm that C_R correlates strongly with plant growth parameters and can reliably distinguish the effects of different soil amendments even those with markedly different nutrient-release profiles.

PMID:40353984 | DOI:10.1007/s42977-025-00260-8

Eur J Nutr. 2025 May 12;64(4):177. doi: 10.1007/s00394-025-03662-5.

ABSTRACT

OBJECTIVE: To investigate the complex relationship between oxidative balance score (OBS), hypertension (HTN) and arterial stiffness in children and adolescents utilizing data gathered from the National Health and Nutrition Examination Survey (NHANES).

STUDY DESIGN: Through utilizing NHANES data (2001-2018), OBS, comprising dietary and lifestyle components, was calculated and categorized into tertiles. The correlation between OBS and HTN was explored employing weighted multivariate logistic regression. Stratified analyses were further performed to evaluate the associations across different subgroups.

RESULTS: A total of 11,754 children and adolescents were ultimately enrolled in analyses. High OBS tertiles demonstrated a consistent negative association with HTN across models. Compared with the lowest OBS tertile, the risk of HTN in the highest OBS tertile was decreased by 37% (95% CI 0.44-0.90, p = 0.011). After dividing OBS into dietary OBS and lifestyle OBS, Lifestyle OBS exhibited a significant inverse association with HTN, while dietary OBS showed no significant correlation. Stratified analyses notably revealed the protective impacts of OBS on the risk of HTN in males. Restricted cubic spline analysis confirmed a nonlinear association between OBS and HTN. Moreover, the elevated OBS was significantly associated with decreased ePWV, indicating a potential link between arterial stiffness and OBS.

CONCLUSION: In summary, the risk of HTN was inversely correlated with high OBS. Adopting a wholesome lifestyle enriched with antioxidants to boost OBS may help shield children from HTN risk.

PMID:40353983 | DOI:10.1007/s00394-025-03662-5

Dokl Biochem Biophys. 2025 May 11. doi: 10.1134/S1607672925700164. Online ahead of print.

ABSTRACT

To study the repertoire of the T-cell receptor in chronically exposed persons in the long-term period. The study involved 48 people who were divided into two groups: a group of exposed persons (31 individuals with the mean accumulated dose to red bone marrow (RBM) of 981 ± 130 mGy) and a comparison group (17 individuals, the mean accumulated dose to RBM of 25.3 ± 5.91 mGy). The study groups did not differ significantly in age, gender, and ethnicity. The repertoire of Vβ-segments of the T-cell receptor of the peripheral blood T-lymphocytes of exposed persons was analyzed by flow cytometry method. 24 Vβ-segments of the T-cell receptor were studied. Statistical processing of the obtained data was carried out using the Wilcoxon signed-rank test, and a direct description of Vβ-segment repertoire of the T-cell receptor was performed using the Lorenz curve and the Gini-TCR index. The study revealed a statistically significant increase in the number of Vβ3 and Vβ5.2 T-cell receptor segments in exposed individuals relative to the comparison group (p = 0.03 and p = 0.003, respectively). It was also shown that the distribution of the Vβ-segments of the T-cell receptor was uneven in both study groups. However, there was no significant difference between the repertoires of the T-cell receptor of the studied groups by the Gini-TCR index (p = 0.14).

PMID:40353972 | DOI:10.1134/S1607672925700164

Dokl Biochem Biophys. 2025 May 11. doi: 10.1134/S1607672925700140. Online ahead of print.

ABSTRACT

The study aims to evaluate the clinical efficacy, safety, pharmacokinetics, pharmacodynamics and immunogenicity of seniprutug (BCD-180) in patients with active radiographic axial spondyloarthritis (r-axSpA, or ankylosing spondylitis).

MATERIALS AND METHODS: . Two hundred sixty patients with active r-axSpA and inadequate response to nonsteroidal anti-inflammatory drugs (NSAIDs) were randomized into three groups to receive either seniprutug (BCD-180) 5 or 7 mg/kg, or placebo. BCD-180 was administered in the respective group dose using a 0-12-36 week regimen. The placebo group patients were switched to BCD-180 5 mg/kg at Week 24, with therapy continued at Week 36. The primary endpoint was the proportion of patients achieving 40% improvement in the Assessment in Spondyloarthritis International Society (ASAS40) score at Week 24. The secondary endpoints included the proportion of patients achieving an ASAS20/40 response, improvement in 5 of 6 ASAS criteria (ASAS5/6), partial remission according to ASAS, ASDAS-CRP clinically important improvement in (Ankylosing Spondylitis Disease Activity Score with C-reactive protein level, ASDAS-CII) and ASDAS-CRP major improvement (ASDAS-MI). An analysis of changes over time in the disease activity status according to ASDAS-CRP, BASDAI (Bath Ankylosing Spondylitis Disease Activity Index) and BASFI (Bath Ankylosing Spondylitis Functional Index) scores, as well as changes over time in laboratory markers (CRP and erythrocyte sedimentation rate (ESR)) was also conducted. Safety was assessed based on the frequency and profile of adverse events (AE) and adverse reactions (AR).

RESULTS: : The proportion of patients who achieved an ASAS40 response at Week 24 on seniprutug (BCD-180) at doses of 7 and 5 mg/kg was 51.4 and 40.8%, respectively, compared with 24% in the Placebo group (p = 0.0012 and p = 0.0417, respectively). Analysis of secondary endpoints showed that the efficacy of BCD-180 at both study doses was statistically significantly superior to placebo in patients with r-axSpA at Week 24 in the following respects: reduction in the proportion of subjects with very high disease activity (ASDAS-CRP > 3.5), achieving ASDAS-CII, ASAS20, ASAS5/6 response. A statistically significant decrease in the ASDAS-CRP, BASDAI, BASFI score, as well as CRP and ESR levels was demonstrated. Tolerability of seniprutug therapy was assessed as acceptable. The most common AEs were infusion-related reactions, most of which were mild to moderate according to CTCAE 5.0 (Common Terminology Criteria for Adverse Events) and developed mainly during the first administration. The proportion of patients with detected binding antibodies was 5.1%. No neutralizing antibodies were detected.

CONCLUSIONS: . Seniprutug (BCD-180) as a therapy for r-axSpA has demonstrated superiority over placebo in the clinical efficacy, a good safety profile and low immunogenicity.

PMID:40353961 | DOI:10.1134/S1607672925700140

Dokl Biochem Biophys. 2025 May 11. doi: 10.1134/S1607672925700139. Online ahead of print.

ABSTRACT

The aim of the study was to determine the clinical and diagnostic value of IL-17A, IL-17F, and IL-23 in RA patients in the advanced stage of the disease. MATERIALS AND METHODS: . We examined 154 patients with a reliable diagnosis of RA according to ACR/EULAR criteria (2010), predominantly (73.4%) female, middle-aged (56.0 (50.0; 64.0) years), disease duration of 9.4 (3.0; 13.0) years, radiologic stages II (34.4%) and III (37.0%), and moderate to high activity (DAS28-ESR 5.40 (4.65; 6.00). Of these, 83.8% patients were seropositive for IgM rheumatoid factor (IgM RF), and 68.8% had antibodies to cyclic citrullinated peptide (ACCP). As many as 144 (93.5%) patients were taking DMARDs (methotrexate, leflunamide, and sulfasalazine), as well as nonsteroidal antiinflammatory drugs (NSAIDs) and glucocorticoids (GCs) up to 10 mg/day in terms of prednisolone. The serum levels of IL-17A, IL-17F, and IL-23 were investigated using multiplex xMAR technology. The upper limit of the norm (M+3σ) in 20 sera of healthy donors was 1.78 pg/mL for IL-17A, 9.5 pg/mL for IL-17F, and 91.55 pg/mL for IL-23. RESULTS. : IL-17A (1.16 (0.50; 2.39) pg/mL) and IL-17F (5.02 (1.00; 138.80) pg/mL) concentrations in RA patients were not significantly different from controls (0.78 (0.00; 1.65) pg/mL and 4.02 (1.46; 7.31) pg/mL, p > 0.05). In contrast, IL-23 levels were significantly higher in patients than in donors (21.36 (2.50; 4626.22) pg/mL and 14.63 (0.00; 91.55) pg/mL, p < 0.05). High values of IL-17F (71 patients, 46.1%) and IL-23 (66 patients, 42.9%) were significantly more frequently detected than IL-17A (46 patients, 29.9%: p = 0.003 and p = 0.02, respectively). Overproduction of IL-17A and IL-17F was simultaneously observed in 37 (24.0%) patients, and 32 (20.8%) patients had an increase in IL-17A, IL-17F, and IL-23. Correlations between IL-17A and IL-17F concentrations (r = 0.44, p < 0.05), IL-17A and IL-23 (r = 0.40, p < 0.05), IL-17F and IL-23 (r = 0.94, p < 0.05) were found. No statistically significant differences were found between the concentration of IL-17A, IL-17F, and IL-23 and the frequency of their elevation in RA patients positive or negative for IgM RF, as well as for ACCP. When IL-17A level was elevated, CDAI and SDAI indices and IgM RF concentration were significantly higher than in the comparison group (p < 0.05). In patients with IL-17F overproduction, predominance of ESR and CRP values was revealed in comparison with the normal values of this index (p < 0.05). At the same time, IL-17A concentration correlated with SDAI (r = 0.17, p < 0.05), IgM RF values (r = 0.19, p < 0.05), and ACCP (r = 0.19, p < 0.05). When IL-23 values were high, the HR was significantly lower (28, p < 0.05), and the groups did not differ in other indices of disease activity, IgM RF and ACCP. No differences in clinical and laboratory indices of RA activity were found between patients with simultaneous elevation of one, two, or three cytokines and groups of patients with their normal concentrations. In RA patients in the advanced stage of the disease, IL-17F overproduction prevails over the frequency of IL-17A elevation. The concentration of IL-23 in serum is significantly higher in patients with RA compared to the control group, and its high values are found in 42.7% of patients. The combined overproduction of IL-17A and IL-17F; IL-17A, IL-17F, and IL-23 does not increase the proinflammatory potential of each individual cytokine.

PMID:40353959 | DOI:10.1134/S1607672925700139

J Neurol Surg B Skull Base. 2024 May 11;86(3):332-341. doi: 10.1055/s-0044-1786738. eCollection 2025 Jun.

ABSTRACT

Background Natural language processing (NLP), a subset of artificial intelligence (AI), aims to decipher unstructured human language. This study showcases NLP’s application in surgical health care, focusing on vestibular schwannoma (VS). By employing an NLP platform, we identify prevalent text concepts in VS patients’ electronic health care records (EHRs), creating concept panels covering symptomatology, comorbidities, and management. Through a case study, we illustrate NLP’s potential in predicting postoperative cerebrospinal fluid (CSF) leaks. Methods An NLP model analyzed EHRs of surgically managed VS patients from 2008 to 2018 in a single center. The model underwent unsupervised (trained on one million documents from EHR) and supervised (300 documents annotated in duplicate) learning phases, extracting text concepts and generating concept panels related to symptoms, comorbidities, and management. Statistical analysis correlated concept occurrences with postoperative complications, notably CSF leaks. Results Analysis included 292 patients’ records, yielding 6,901 unique concepts and 360,929 occurrences. Concept panels highlighted key associations with postoperative CSF leaks, including “antibiotics,” “sepsis,” and “intensive care unit admission.” The NLP model demonstrated high accuracy (precision 0.92, recall 0.96, macro F1 0.93). Conclusion Our NLP model effectively extracted concepts from VS patients’ EHRs, facilitating personalized concept panels with diverse applications. NLP shows promise in surgical settings, aiding in early diagnosis, complication prediction, and patient care. Further validation of NLP’s predictive capabilities is warranted.

PMID:40351873 | PMC:PMC12064303 | DOI:10.1055/s-0044-1786738