Category: Nevin Manimala

Sci Rep. 2025 Sep 26;15(1):33202. doi: 10.1038/s41598-025-17413-3.

ABSTRACT

This study aimed to investigate the biological properties of probiotic and synbiotic Lactobacillus gasseri SM 05 (L. gasseri) and Lactobacillus casei subsp. casei (L. casei) in the black raspberry (BR) matrix. A distinctive aspect of this research was its assessment of both fermented and non-fermented raspberry juices as active components in chitosan-based edible coatings, supplemented by coconut sap at concentrations of 5% and 10%, along with the application of high hydrostatic pressure (HHP) processing. These interventions were evaluated for their synergistic potential in enhancing the antioxidant capacity of coatings applied to Lates calcarifer (barramundi) fillets. What distinguishes this study from prior works is the integration of cutting-edge HHP technology, the deliberate exclusion of essential oils and synthetic preservatives, and the novel incorporation of coconut sap across coating formulations. Experimental outcomes revealed that synbiotic formulations-specifically L. gasseri combined with Oligofructose-Enriched Inulin (LG-OEI) and L. casei with OEI (LC-OEI)-exerted the most pronounced bioactivity. The key findings underscore the efficacy of Oligofructose-Enriched Inulin in promoting lactic acid bacterial metabolism and metabolite production within fruit juice substrates. Additionally, the deployment of 10% coconut sap in conjunction with HHP treatment-rather than conventional VAT pasteurization-yielded superior coating performance, effectively preserving the quality of fish samples over a 15-day period. In conclusion, the LG-OEI formulation, when used in chitosan coatings augmented with 10% coconut sap and processed via HHP, demonstrated the highest preservation efficacy.

PMID:41006467 | DOI:10.1038/s41598-025-17413-3

Sci Rep. 2025 Sep 26;15(1):33102. doi: 10.1038/s41598-025-17008-y.

ABSTRACT

This study presents a forensic diagnostic framework aimed at enhancing the early detection, fault classification and remaining useful life (RUL) prediction of rolling bearing failures. The proposed network integrates a novel three-stage machine learning formulation – (1) identification of health state using voting ensemble, (2) prognostic analysis via a hybrid convolutional neural network and gated recurrent unit (CNN-GRU), and (3) fault type identification through the segment anything model (SAM) based on time-frequency representations. The ensemble and CNN-GRU models are trained on both time- and frequency-domain features from vibration signals, while SAM leverages this data in visual sense through iterative masking for zero-shot spatial-temporal fault segmentation. Pre-processing techniques, including piecewise aggregate approximation and singular spectrum analysis, are used to denoise and compress the vibration response without impacting key statistical traits. The proposed e-CNN-GRU-SAM network demonstrates better accuracy in diagnosing fault types, predicting RUL and identifying root causes under different operational conditions. This is established using diverse operating benchmark datasets that simulate induced and real-world degradation scenarios for generalization. Thus, the proposed framework offers a comprehensive forensic analysis toolkit for diagnosis and prognosis of bearings.

PMID:41006465 | DOI:10.1038/s41598-025-17008-y

Sci Rep. 2025 Sep 26;15(1):32952. doi: 10.1038/s41598-025-09011-0.

ABSTRACT

This cross-sectional observational study involved 1052 non-pregnant women aged 18-65 who had undergone TCC and sought care at a tertiary-level gynecology clinic. We documented participants’ medical histories, Pap smear results, and TCC histories. Pap smear results were analyzed using the 2014 Bethesda classification, and obstetric outcomes, including pregnancies, deliveries, miscarriages, and preterm births (PTD), were evaluated. Data analysis was performed using SPSS 22.0, with statistical significance set at p < 0.05. This cross-sectional observational study involved 1052 non-pregnant women aged 18-65 who had undergone TCC and sought care at a tertiary-level gynecology clinic. We documented participants’ medical histories, Pap smear results, and TCC histories. Pap smear results were analyzed using the 2014 Bethesda classification, and obstetric outcomes, including pregnancies, deliveries, miscarriages, and preterm births (PTD), were evaluated. Data analysis was performed using SPSS 22.0, with statistical significance set at p < 0.05. The mean age of participants was 34.4 ± 7.4 years. Abnormal Pap smear results were observed in 11.5% of patients, with a higher prevalence (29.9%) among those who underwent TCC within the last year. The frequency of TCC was inversely related to the rate of abnormal smear results (p < 0.005). However, frequent TCC procedures were associated with significantly increased rates of abortion (18.9%) and PTD (10.3%) (both p < 0.005). While the time elapsed since the last TCC procedure influenced abortion and PTD rates, both were statistically significant (p = 0.016 and p = 0.029, respectively). TCC is associated with a higher incidence of cervical abnormalities and adverse pregnancy outcomes, particularly when performed more frequently. These findings suggest a potential link between TCC and disruptions in cervical health, which may increase the risk of pregnancy complications, underscoring the need for cautious use and further research. Educating communities about the potential risks and advocating for safer medical practices are crucial steps towards improving gynecological and obstetric care in regions where TCC is still prevalent.

PMID:41006449 | DOI:10.1038/s41598-025-09011-0

Sci Rep. 2025 Sep 26;15(1):33015. doi: 10.1038/s41598-025-18500-1.

ABSTRACT

In this study, the biochemical and cytogenetic toxicity induced by the antibiotic active ingredient ornidazole in the non-target organism Allium cepa L. was investigated. In the toxicity assessment, the level of malondialdehyde (MDA), a biochemical marker of lipid peroxidation; genotoxicity indicators such as micronucleus (MN) frequency and mitotic index (MI); the incidence of chromosomal abnormalities (CAs); activities of antioxidant defense enzymes catalase (CAT) and superoxide dismutase (SOD); and the levels of chlorophyll a and b pigments reflecting photosynthetic capacity were analyzed. Additionally, DNA damage was assessed using the Comet test method, and the interaction of ornidazole with macromolecules-particularly DNA-was examined using the molecular docking approach. Four groups of A. cepa bulbs-one control and three treatments-were created. Three distinct dosages (0.0179, 0.0357 and 0.0714 mg/L) of ornidazole were used to germinate the bulbs in the treatment group, while tap water was used to germinate the bulbs in the control group. Following germination, samples from the roots and leaves were gathered and ready for examination. As a result, there was no cytogenetic damage or biochemical alteration that was statistically significant (p > 0.05) in the control group (Group I). MI value, DNA, and chlorophyll levels significantly (p < 0.05) decreased with ornidazole treatment, while MN frequency, CAs, MDA levels, SOD, and CAT activities significantly (p < 0.05) increased. At the ornidazole dosage of 0.0714 mg/L, these rises and declines were shown to be more noticeable. Ornidazole promoted several CAs in root meristem cells, the most common being the sticky chromosome. DNA damage was highlighted by the comet assay results, which indicated a drop in head DNA and an increase in tail DNA. In the control group, the Tail DNA was 1.00 ± 1.05 (%), whereas in the group treated with 0.0714 mg/L ornidazole, it increased to 72.0 ± 1.63 (5), indicating high DNA fragmentation. Molecular docking results showed ornidazole-DNA, ornidazole-tubulin, ornidazole-topoisomerase, ornidazole-glutamate-1-semialdehyde aminotransferase and ornidazole-protochlorophyllide reductase interaction supporting the biochemical and cytogenetic toxicity results. In conclusion, ornidazole exposure induced significant toxic effects in the non-target organism Allium cepa. The study further validated the efficacy of the Allium test as a reliable bioassay for detecting such toxicity. These findings underscore the urgent need for implementing appropriate environmental management strategies to mitigate pharmaceutical contamination and protect non-target organisms from drug-induced toxicity.

PMID:41006444 | DOI:10.1038/s41598-025-18500-1

Sci Rep. 2025 Sep 26;15(1):33276. doi: 10.1038/s41598-025-18097-5.

ABSTRACT

Minimizing ionizing radiation is crucial in paediatric imaging due to children’s increased radiation sensitivity, especially at younger ages. To evaluate a CT attenuation-based Auto Prescription protocol for paediatric head imaging, testing whether it provides image quality and radiation dose comparable to age-based protocols. Auto Prescription was implemented on a 256-slice scanner for axial volumetric head CT, adjusting kV and mAs based on attenuation data from scout images. Radiation dose parameters (CTDIvol, SSDE, ED, DLP) and image quality metrics (SNR, CNR, subjective Likert scale from 1-unacceptable to 4-higher than needed) were assessed in 79 consecutive studies using Auto Prescription protocols. These were compared to 68 studies obtained with age-based protocols using non-parametric tests. A total of 147 patients (60 females, mean age 6.7 ± 5.1 years) were included. The auto prescription group included 29 patients aged 0-5, 20 aged 5-10, 25 aged 10-15, and 5 over 15 years; the age-based group included 36, 18, 9, and 5 patients respectively in the same age groups. The Auto Prescription protocol achieved a more balanced radiation dose distribution across age and water-equivalent diameter. The greatest dose reduction was observed in the 0-1 year (48.2%, p < 0.001) and 10-15 year (40.4%, p < 0.001) age groups. While diagnostic image quality was adequate in both settings, it was lower with the auto prescription protocols (mean image quality 3.0 ± 0.2 versus 2.8 ± 0.2; SNR 7.2 ± 1.5 vs. 5.1 ± 1.1; CNR 0.9 ± 0.5 vs. 0.7 ± 0.2; all p < 0.001). All subjective image quality parameters were statistically non-inferior to the age- based protocol (p < 0.05). Attenuation-based Auto Prescription resulted in a more homogeneous and head density adapted radiation dose across paediatric patients, with non-inferior image quality. Dose reduction was a secondary benefit of individualized scan settings based on patient attenuation rather than age alone.

PMID:41006436 | DOI:10.1038/s41598-025-18097-5

Sci Rep. 2025 Sep 26;15(1):32954. doi: 10.1038/s41598-025-16149-4.

ABSTRACT

Cyberbullying (CB) has emerged as a growing concern among adolescents, with nearly 10% of European children affected monthly and almost half experiencing it at least once. Unlike traditional bullying, CB thrives in digital environments where anonymity and impunity are prevalent. Despite its increasing prevalence, understanding the causal mechanisms behind CB remains challenging due to the limitations of conventional statistical methods, which often rely on correlations and are prone to spurious associations. In this paper, we introduce a novel human-machine consensus framework for causal discovery, aimed at supporting social scientists in unraveling the complex dynamics of CB. We leverage recent advances in data-driven causal inference, particularly the use of Directed Acyclic Graphs (DAGs), to identify and interpret causal relationships from observational data. Our approach integrates automatic causal discovery algorithms with expert knowledge, addressing the limitations of both purely algorithmic and purely expert-driven methods, and allows for the creation of a model ensemble estimation of the causal effects. To enhance interpretability and usability, we advocate for the use of Probabilistic Graphical Causal Models (PGCMs), or Bayesian Networks, which combine probabilistic reasoning with graphical representation. This hybrid methodology not only mitigates cognitive biases and inconsistencies in expert input but also fosters transparency and critical reflection in model construction. Cyberbullying serves as a compelling case study where ethical constraints preclude experimental designs, highlighting the value of interpretable, expert-informed causal models for guiding policy and intervention strategies.

PMID:41006423 | DOI:10.1038/s41598-025-16149-4

Sci Rep. 2025 Sep 26;15(1):33098. doi: 10.1038/s41598-025-17736-1.

ABSTRACT

Artificial neuro-cognitive models can simulate human brain intelligence to enable accurate decision-making during complex agricultural operations in situ. To investigate this, twelve machine learning algorithms were employed to sequentially train 72 neurocomputing architectures using Deep Neural Network (DNN) and Artificial Neural Network (ANN) models for the neurocognitive prediction of tractive force (F_Tr). Fourteen soil-machine input variables were used, and the hyperparameters of the neuro-cognitive models were optimized through metaheuristic algorithms, targeting 50,000 neuro-perceptron epochs to minimize convergence error. The performance of the neurocomputing models was evaluated using standard accuracy metrics, including coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and prediction accuracy (PA). The DNN Levenberg-Marquardt (trainlm) model with a 14-7-5-1 architecture demonstrated superior predictive performance (RMSE = 1.03e-5, R² = 1.0, MAE = 8.0e-6, and PA = 99.994), closely followed by the ANN Bayesian Regularization (trainbr) model with a 14-72-1 architecture (RMSE = 4.0e-4, R² = 0.9999, MAE = 0.0002, and PA = 99.935). Although the DNN trainlm model required slightly more epochs to reach optimal performance (55 vs. 51), it achieved faster computation (2s vs. 29s) than the ANN trainbr model. The reliability indices, i.e., a20-index (a20), scatter index (IOS), and agreement index (IOA), revealed that the DNN trainlm (14-7-5-1) and ANN trainbr (14-72-1) models are highly reliable. Notably, the ANN trainbr model attained the highest [Formula: see text] (= 240), while the DNN trainlm model was the most reliable under its configuration ([Formula: see text]= 196). Taylor’s analysis revealed no statistically significant deviation between the experimental and predicted F_Tr values for both models. Furthermore, all prediction instances (100%) for both trainbr (14-72-1) and trainlm (14-7-5-1) fell within the 95% prediction uncertainty range (PPU_95%), with a near-zero neurocognitive uncertainty index ([Formula: see text] = 0.00;0.03) and minimal logical deviation factors (d_factor=0.03;1.58), confirming Monte Carlo consistency between predicted and observed F_Tr values. The Anderson-Darling test confirmed the normality of the predictions ([Formula: see text] = 0.001), with both models satisfying the condition (P_model< P_ideal,0.05)​. Finally, the ANN model under trainbr also achieved excellent performance. The DNN trainlm (14-7-5-1) model employed a log-sig-tan-sig-purelin activation sequence, whereas the ANN trainbr (14-72-1) model used a tan-sig and purelin configuration, both suitable for in-situ prediction of F_Tr. In addition, it was observed that the Spider Wasp Optimization (SWO) algorithm enhanced the performance of the conventional DNN (trainlm) and ANN (trainbr) models in predicting agricultural traction.

PMID:41006420 | DOI:10.1038/s41598-025-17736-1

Horm Behav. 2025 Sep 25;176:105821. doi: 10.1016/j.yhbeh.2025.105821. Online ahead of print.

ABSTRACT

The care that a mother rat provides is essential for the ability of her pups to survive and thrive. Maternal care naturally varies between litters, including among animals with close genetic relatedness. There are also significant differences in behavior even among offspring reared together. Our lab and others have documented stable, naturally occurring individual differences in maternal care received by individual pups within the litter that persist throughout at least the first ten days of postnatal life. In this study, we hypothesized that within-litter variation in maternal care received constitutes a significant source of variation in offspring behavior and neurochemistry in Long-Evans rats. We analyzed measures related to maternal care behavior, offspring anxiety-like and social behaviors, and neurotransmitter levels in specific brain regions after the offspring became mothers themselves. For statistical modeling, we used the coefficient of variation (CV) to standardize and directly compare between- and within-litter variation across a range of behavioral and neurophysiological outcomes. Several variables analyzed showed greater within-litter CVs than between-litter CVs, especially for offspring behavior and levels of the monoamines dopamine, serotonin, and their primary metabolites DOPAC (3,4-dihydroxyphenylacetic acid) and 5-HIAA (5-hydroxyindoleacetic acid) in the nucleus accumbens, ventral tegmental area, medial preoptic area, hippocampus, and prefrontal cortex. Our findings suggest that within-litter variation in maternal care plays a prominent role in behavioral and physiological outcomes. This study provides a methodological advance by demonstrating that within-litter variability often exceeds between-litter variability across maternal, behavioral, and neurochemical domains, challenging a key assumption in experimental designs using littermate controls.

PMID:41004891 | DOI:10.1016/j.yhbeh.2025.105821

Appl Radiat Isot. 2025 Sep 23;226:112204. doi: 10.1016/j.apradiso.2025.112204. Online ahead of print.

ABSTRACT

There are very few radioactive environmental reference materials (RM) traceable to the International System of Units. Existing radioactive RMs for environmental samples that can be measured by mass spectrometry are even more limited and their characterisation does not always include relevant parameters such as isotopic ratios. This paper focuses on the development of two environmentally relevant candidate RMs, one liquid and one solid, which could be used for routine quality control measurements. The liquid RM was prepared by spiking seawater sampled from the North Sea, and therefore the matrix is representative of a real environmental sample, while the solid RM was prepared using a synthetic approach by spiking a mixture of silica precursors before a sol-gel reaction. The homogeneity, between-bottles and within-bottles, of both RMs was assessed using gamma-ray spectrometry and mass spectrometry. For the liquid RM, the variation among sub-samples was due mainly to the within-bottle variance, and was lower than 1 %, for all the radionuclides tested. For the solid RM, the ²⁴¹Am content measured with gamma-ray spectrometry revealed a statistically significant variation between-bottles, but was lower than 1 %. The ²³⁸U and ²³⁹Pu contents, measured by mass spectrometry, showed higher measurement variability (∼5 %), with the main contribution coming from within the bottles.

PMID:41004888 | DOI:10.1016/j.apradiso.2025.112204

Curr Res Transl Med. 2025 Sep 19;73(4):103544. doi: 10.1016/j.retram.2025.103544. Online ahead of print.

ABSTRACT

BACKGROUND: Ensuring accurate statistical reporting is critical in oncology research, where data-driven conclusions impact clinical decision-making. Despite standardized guidelines such as the Statistical Analyses and Methods in the Published Literature (SAMPL), adherence remains inconsistent. This study evaluates the performance of Gemini Advanced 2.0 Flash, an AI model, in assessing compliance with SAMPL guidelines in oncology research articles.

METHODS: A total of 100 original research articles published in four peer-reviewed oncology journals (October 2024-February 2025) were analyzed. Gemini Advanced 2.0 Flash assessed adherence to ten key SAMPL guidelines, categorizing each as “not met,” “partially met,” or “fully met.” AI evaluations were compared with independent assessments by a statistical editor, with agreement quantified using Cohen’s Kappa coefficient.

RESULTS: The overall weighted Kappa coefficient was 0.77 (95 % CI: 0.6-0.94), indicating substantial agreement between AI and manual assessment. Full agreement (Kappa = 1) was found for four guidelines, including naming statistical packages and reporting confidence intervals. High agreement was observed for specifying statistical methods (Kappa = 0.85) and confirming test assumptions (Kappa = 0.75). Moderate agreement was noted for summarizing non-normally distributed data (Kappa = 0.42) and specifying test directionality (Kappa = 0.43). The lowest agreement (Kappa = 0.37) was observed in multiple comparison adjustments due to missing justifications for post hoc tests.

CONCLUSION: AI-assisted evaluation showed substantial agreement with expert assessment, demonstrating its potential in statistical review. However, discrepancies in specific guidelines suggest human oversight remains essential for ensuring statistical rigor in oncology research. Further refinement of AI models may enhance their reliability in scientific publishing.

PMID:41004884 | DOI:10.1016/j.retram.2025.103544