Category: Nevin Manimala

Virol J. 2025 Apr 18;22(1):104. doi: 10.1186/s12985-025-02706-w.

ABSTRACT

Listeriosis, caused by Listeria monocytogenes (Lm), is a severe foodborne illness with a high fatality rate. Listeria phages specifically target and lyse Lm, offer a promising alternative for biocontrol and phage therapy. However, most existing studies focus on the lytic characteristics of Listeria phages using limited sample sizes. In this study, a large number of Listeria phages were isolated from diverse sources, and their lytic profiles and morphology were characterized. A total of 317 Listeria phages were isolated from 90 food-related environmental samples and 196 natural environmental samples collected across seven provinces. The phages were tested for lytic activity against 35 Lm strains representing nine serotypes, and their morphology was characterized using transmission electron microscopy (TEM). Statistical analysis was conducted to evaluate the lytic patterns of phages. The phages were classified into three groups based on their total lysis ratios. Broad Host Range Phages (BHRP) were primarily members of the Myoviridae-like phages and demonstrated the ability to lyse a vast majority of nine serotype host strains. Medium Host Range Phages (MHRP) comprised both Siphoviridae-like and Myoviridae-like phages, and demonstrated lysis of 6-9 serotype strains. Narrow Host Range Phages (NHRP) belonged to the Siphoviridae-like phages and exhibited effective lysis of serotype 4 strains. Furthermore, phages isolated from food-related environmental sources demonstrated greater lytic activity against Listeria serotypes 1/2b, 4a, and 4c compared to those derived from natural environmental sources. The study first isolated a multitude of Listeria phages, elucidated their lytic patterns and ecological distribution, and provided a valuable resource for future research.

PMID:40251606 | DOI:10.1186/s12985-025-02706-w

BMC Med Imaging. 2025 Apr 18;25(1):127. doi: 10.1186/s12880-025-01668-3.

ABSTRACT

BACKGROUND: To use of statistical methods to assess the diagnostic value of arterial spin labeling (ASL) imaging for follow-up of treated arteriovenous malformations.

METHODS: We screened references from four databases, namely, the Cochrane Library, PubMed, Web of Science and Embase, that met the requirements. The methodology quality of the included studies was evaluated using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool. Data pertaining to diagnostic performance were extracted, and the pooled sensitivity and specificity were calculated using a bivariate mixed-effects model.

RESULTS: We included six studies with a total of 132 patients with arteriovenous malformation (AVM). The merged sensitivity and specificity of ASL for the diagnosis of brain AVMs with incomplete occlusion after treatment were 0.94[0.86-0.98] and 0.99 [0.59-1.00], respectively. According to the SROC curve summary, the AUC was found to be 0.98 [0.96-0.99]. No significant publication bias was observed.

CONCLUSION: While ASL does not currently match the diagnostic precision of DSA, it is instrumental in post-treatment surveillance of AVM patients. With the development of ASL technology in the future, this technique holds promise as a minimally invasive diagnostic strategy for AVMs with fewer side effects.

REGISTRATION NUMBER OF PROSPERO: CRD42023422087.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40251605 | DOI:10.1186/s12880-025-01668-3

BMC Psychol. 2025 Apr 18;13(1):407. doi: 10.1186/s40359-025-02715-y.

ABSTRACT

BACKGROUND: This study aimed to explore the link between cognitive impairment and levels of asymmetric dimethylarginine (ADMA).

METHODS: The study included 172 patients from the Department of Geriatrics and Neurology at the Second Affiliated Hospital of Harbin Medical University. The enrollment period spanned from October 2013 to July 2014. To assess their cognitive function, we used the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Additionally, automatic biochemical analyzers were employed to measure various biochemical blood indexes, while enzyme-linked immunosorbent assay was used to determine the serum ADMA concentrations.

RESULTS: The participants were categorized into four groups based on the MMSE scale, which reflects cognition (higher scores indicating better cognitive function), and five groups based on the MoCA scale, which also measures cognition (higher scores indicating better cognitive function). Various factors were analyzed for their statistical significance in relation to different cognitive impairment groups determined by each scale. Regarding the MoCA scale, the following factors were found to be statistically significant: Age (P = 0.0001), systolic blood pressure (P = 0.0261), ALT (P = 0.0104), AST (P = 0.0106), endogenous creatinine clearance (P = 0.0006), and serum ADMA concentration (P = 0.0383). For the MMSE scale, the following factors showed statistical significance: Age (P = 0.0008), ALT (P = 0.0002), AST (P = 0.0088), CRP (P = 0.0407), and endogenous creatinine clearance (P = 0.0027). Interestingly, as the scores on the MoCA scale decreased, the serum ADMA concentration increased (P=0.0383), but this trend was not observed in the groups classified based on the MMSE scale (P > 0.05).

CONCLUSION: The level of sensitivity measured by the MoCA scale indicated the presence of initial cognitive dysfunction. The extent of cognitive impairment showed a direct correlation with ADMA levels, indirectly implying a connection between impaired endothelial function and cognitive dysfunction.

PMID:40251603 | DOI:10.1186/s40359-025-02715-y

BMC Womens Health. 2025 Apr 19;25(1):193. doi: 10.1186/s12905-025-03714-2.

ABSTRACT

BACKGROUND: Post sexual-based violence (SBV) services are crucial for mitigating SBV-induced consequences. However, these services are reportedly rare and often underutilized, particularly by young women in Sub-Saharan Africa. This study aimed to explore the barriers and facilitators to accessing post-SBV services among young women (18-24 years) attending higher education institutions in Nigeria.

METHODS: An online survey, using a piloted questionnaire, was administered to a purposive sample of 114 participants recruited from social media platforms between the 8th and 22nd March 2022. Descriptive statistics were used to summarize the study findings.

RESULTS: The majority (71.1%) of the participants were between the ages of 21 and 24 years. Of the 37 participants who indicated they have had their first sexual intercourse, a quarter (9, 24.3%) indicated it was non-consensual. Also, 1 in 5 respondents did not identify SBV/abuse as abnormal. Half of the participants (50.9%) strongly agreed that a post-SBV health service should be the first place to seek care following an incident of rape, however, over half (53.2%) reported a lack of awareness of existing post-SBV health services as a key barrier affecting access. Less than half of the participants strongly agreed that healthcare workers could provide the post-SBV services highlighted in the study, including emergency contraceptives to prevent pregnancy (42.9%) and post-exposure prophylaxis (PEP) to prevent human immunodeficiency virus (HIV) (39.6%), highlighting awareness gaps. Other significant barriers included stigma, shame, and a lack of support systems. Key facilitators included assurance of confidentiality and access to free post-SBV health services.

CONCLUSION: Significant barriers and facilitators affect access to post-SBV health services in Nigeria, particularly among young women. Multilevel efforts by families, civil society organizations, communities, and governments are essential to address these barriers and improve access to post-SBV health services.

PMID:40251599 | DOI:10.1186/s12905-025-03714-2

Virol J. 2025 Apr 18;22(1):105. doi: 10.1186/s12985-025-02726-6.

ABSTRACT

BACKGROUND: There have been no previous studies on male HPV infection in the Huizhou region. This research aims to investigate the HPV infection rate and genotype distribution among male patients in this area, offering valuable insights for developing targeted preventive strategies against HPV infection in male population.

METHODS: This study included 1009 male patients from Huizhou Central People’s Hospital who underwent HPV genotype testing between 2014 and 2023. We analyzed the distribution of HPV genotypes by year, age group, and diagnosis. Additionally, clinical data from 308 HPV-positive patients were retrospectively collected, and differences in high-risk vs. low-risk types, single vs. multiple infections, and genotype correlations were analyzed.

RESULTS: The overall HPV positivity rate was 30.53%, with the positive rate(40.56%) in the 2014-2019 group being significantly higher than that in 2021 (25.56%) and 2022 (24.29%)(p<0.05). The most common genotypes were HPV6, HPV52, HPV11, and HPV16. HPV infection was most prevalent in the 41-50 age group, while males aged ≤ 30 were predominantly infected with low-risk types (41.73%). The 31-40 age group had a higher prevalence of high-risk types (52.07%), with males under 50 primarily infected with low-risk HPV6, while those aged 51 and above mostly had high-risk HPV52 infections. The highest HPV positivity rate was found in the viral wart group (79.01%). Single infections were more common (64.29%), with co-infection of HPV6 and HPV16 being the most prevalent type.

CONCLUSION: The overall HPV infection rate was relatively high among outpatient male patients in Huizhou, with single infections being predominant. Additionally, HPV infection rates exhibited significant differences across various years, age groups, and diagnostic types, suggesting that these factors should be considered when formulating HPV prevention and control strategies.

PMID:40251597 | DOI:10.1186/s12985-025-02726-6

Neural Netw. 2025 Apr 12;188:107432. doi: 10.1016/j.neunet.2025.107432. Online ahead of print.

ABSTRACT

While end-to-end Text-to-Speech (TTS) methods with limited target speaker corpus can generate high-quality speech, they often require a non-target speaker corpus (auxiliary corpus) which contains a substantial amount of <text, speech> pairs to train the model, significantly increasing training costs. In this work, we propose a fast and high-quality speech synthesis approach, requiring few target speaker recordings. Based on statistics, we analyzed the role of phonemes, function words, and utterance target domains in the corpus and proposed a Statistical-based Compression Auxiliary Corpus algorithm (SCAC). It significantly improves model training speed without a noticeable decrease in speech naturalness. Next, we use the compressed corpus to train the proposed non-autoregressive model CMDF-TTS, which uses a multi-level prosody modeling module to obtain more information and Denoising Diffusion Probabilistic Models (DDPMs) to generate mel-spectrograms. Besides, we fine-tune the model using the target speaker corpus to embed the speaker’s characteristics into the model and Conditional Variational Auto-Encoder Generative Adversarial Networks(CVAE-GAN) to enhance further the synthesized speech’s quality. Experimental results on multiple Mandarin and English corpus demonstrate that the CMDF-TTS model, enhanced by the SCAC algorithm, effectively balances training speed and synthesized speech quality. Overall, its performance surpasses that of state-of-the-art models.

PMID:40249999 | DOI:10.1016/j.neunet.2025.107432

Comput Biol Med. 2025 Apr 17;191:110166. doi: 10.1016/j.compbiomed.2025.110166. Online ahead of print.

ABSTRACT

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets and do not combine different feature extraction techniques for better classification. To address these challenges, we propose a robust and explainable stacking ensemble model for multiclass brain tumor classification. To address these challenges, we propose a stacking ensemble model that combines EfficientNetB0, MobileNetV2, GoogleNet, and Multi-level CapsuleNet, using CatBoost as the meta-learner for improved feature aggregation and classification accuracy. This ensemble approach captures complex tumor characteristics while enhancing robustness and interpretability. The proposed model integrates EfficientNetB0, MobileNetV2, GoogleNet, and a Multi-level CapsuleNet within a stacking framework, utilizing CatBoost as the meta-learner to improve feature aggregation and classification accuracy. We created two large MRI datasets by merging data from four sources: BraTS, Msoud, Br35H, and SARTAJ. To tackle class imbalance, we applied Borderline-SMOTE and data augmentation. We also utilized feature extraction methods, along with PCA and Gray Wolf Optimization (GWO). Our model was validated through confidence interval analysis and statistical tests, demonstrating superior performance. Error analysis revealed misclassification trends, and we assessed computational efficiency regarding inference speed and resource usage. The proposed ensemble achieved 97.81% F1 score and 98.75% PR AUC on M1, and 98.32% F1 score with 99.34% PR AUC on M2. Moreover, the model consistently surpassed state-of-the-art CNNs, Vision Transformers, and other ensemble methods in classifying brain tumors across individual four datasets. Finally, we developed a web-based diagnostic tool that enables clinicians to interact with the proposed model and visualize decision-critical regions in MRI scans using Explainable Artificial Intelligence (XAI). This study connects high-performing AI models with real clinical applications, providing a reliable, scalable, and efficient diagnostic solution for brain tumor classification.

PMID:40249992 | DOI:10.1016/j.compbiomed.2025.110166

Comput Biol Med. 2025 Apr 17;191:110137. doi: 10.1016/j.compbiomed.2025.110137. Online ahead of print.

ABSTRACT

In this work, we developed deep neural networks for the fast and comprehensive estimation of the most salient features of aortic blood flow. These features include velocity magnitude and direction, 3D pressure, and wall shear stress. Starting from 40 subject-specific aortic geometries obtained from 4D Flow MRI, we applied statistical shape modeling to generate 1,000 synthetic aorta geometries. Complete computational fluid dynamics (CFD) simulations of these geometries were performed to obtain ground-truth values. We then trained deep neural networks for each characteristic flow feature using 900 randomly selected aorta geometries. Testing on remaining 100 geometries resulted in average errors of 3.11% for velocity and 4.48% for pressure. For wall shear stress predictions, we applied two approaches: (i) directly derived from the neural network-predicted velocity, and, (ii) predicted from a separate neural network. Both approaches yielded similar accuracy, with average error of 4.8 and 4.7% compared to complete 3D CFD results, respectively. We recommend the second approach for potential clinical use due to its significantly simplified workflow. In conclusion, this proof-of-concept analysis demonstrates the numerical robustness, rapid calculation speed (less than seconds), and good accuracy of the CFD-based machine learning approach in predicting velocity, pressure, and wall shear stress distributions in subject-specific aortic flows.

PMID:40249990 | DOI:10.1016/j.compbiomed.2025.110137

J Aging Health. 2025 Apr 18:8982643251336801. doi: 10.1177/08982643251336801. Online ahead of print.

ABSTRACT

ObjectivesThis study aims to examine the relationship between social connectedness and health-related quality of life (HRQoL) among older adults, focusing on the impact of technology accessibility and geographic context (urban vs. rural).MethodsData from the 2021 National Health and Aging Trends Study (NHATS) with 2303 participants aged 65 and older were used. Confirmatory factor analysis validated measures of social connectedness and HRQoL, followed by regression analysis to explore their relationship, including the moderating roles of technology accessibility and geographic context.ResultsThe findings indicate a significant positive relationship between social connectedness and HRQoL. However, technology accessibility moderates this relationship only in rural areas, where lower technology access enhances the positive effects of social connectedness on HRQoL.DiscussionThe results suggest that interventions to improve HRQoL among older adults should consider different geographical locations. Notably, promoting in-person interactions is crucial for enhancing the HRQoL of rural older adults.

PMID:40249964 | DOI:10.1177/08982643251336801

Phlebology. 2025 Apr 18:2683555251336447. doi: 10.1177/02683555251336447. Online ahead of print.

ABSTRACT

BackgroundPrevious research has indicated a correlation between chronic venous insufficiency (CVI) and cardiovascular disease. However, whether CVI is an independent risk factor for coronary artery disease (CAD) remains underexplored. This study aimed to investigate this relationship by utilizing coronary artery calcium score (CACS) assessment during CVI screening and comparing it with CACS in patients undergoing cardiac ablation treatment.Methodsand Subjects: A retrospective cohort study was conducted, approved by the ethical committee (IRB 2024012). From February to July 2023 Simultaneous non-contrast lower limb vein CT and CACS measurements were performed on CVI patients aged 50 and above and less than 90, excluding cases with history of heart failure, post-thrombotic syndrome, percutaneous coronary intervention (PCI), cardiac ablation, cardiac surgery, peripheral arterial disease, and renal failure. Parameters included coronary risk factors and CACS. Control group was composed of sex- and age-matched patients receiving cardiac ablation treatment from April 2020 to December 2023. A comparison between the two groups was made, and univariate and multivariate analyses were conducted. Statistical significance was set at p < .05.ResultsComparison between CVI group (n = 234) and cardiac ablation group (n = 234) were as follows: mean age (71 ± 9:71 ± 9, not significant (NS)), females (154:145, NS), body mass index (BMI) (23.6 ± 3.9:22.6 ± 3.5, p = .004), hypertension (103:121, NS), dyslipidemia (100:66, p = .001), diabetes (30:24, NS), Creatinine (0.76 ± 0.25:0.87 ± 0.64, p = .02), respectively. The total CACS was 214 ± 578 in the CVI group and 64.8 ± 233 in the cardiac ablation group (p < .001). The median CACS values were 14.8 (IQR: 0-178) and 0 (IQR: 0-16), respectively. CVI group included 35% with CACS >100 and the cardiac ablation group did 12%, respectively (p < .001). Univariate analysis identified age [beta 9.6 (95% CI 5.2 to 13.9), p < .001], hypertension [beta 142.4 (95% CI 62.5 to 222), diabetes [beta 179.1 (95% CI 53.5 to 305), p = .005], dyslipidemia [beta 164.5 (95% CI 81.2 to 248), p < .001],creatinine [beta 85.4 (95% CI 2.6 to 168), p = .04], and CVI [beta 149 (95% CI 69.4 to 229), p < .001], p = .001] as risk factors. Multivariate analysis revealed age [beta 7.1 (95% CI 2.7 to 11.5), p = .002], hypertension [beta 86.5 (95% CI 1.7 to 171), p = .046], dyslipidemia [beta87.5 (95% CI 1.4 to 174), p = .047], and CVI [beta 143.6 (95% CI 63.7 to 223), p < .001] as strong correlates of CACS.ConclusionsMultivariate analysis indicated that CVI is an independent risk factor for coronary artery disease, even after adjusting for age, hypertension, and dyslipidemia as confounding factors.

PMID:40249957 | DOI:10.1177/02683555251336447