Tag: nevin manimala

Exp Brain Res. 2024 Jun 19. doi: 10.1007/s00221-024-06873-0. Online ahead of print.

ABSTRACT

Musculoskeletal trauma often leads to lasting psychological impacts stemming from concerns of future injuries. Often referred to as kinesiophobia or re-injury anxiety, such concerns have been shown to hinder return to physical activity and are believed to increase the risk for secondary injuries. Screening for re-injury anxiety is currently restricted to subjective questionnaires, which are prone to self-report bias. We introduce a novel approach to objectively identify electrocortical activity associated with the threat of destabilising perturbations. We aimed to explore its feasibility among non-injured persons, with potential future implementation for screening of re-injury anxiety. Twenty-three participants stood blindfolded on a translational balance perturbation platform. Consecutive auditory stimuli were provided as low (neutral stimulus [CS^–]) or high (conditioned stimulus [CS⁺]) tones. For the main experimental protocol (Protocol I), half of the high tones were followed by a perturbation in one of eight unpredictable directions. A separate validation protocol (Protocol II) requiring voluntary squatting without perturbations was performed with 12 participants. Event-related potentials (ERP) were computed from electroencephalography recordings and significant time-domain components were detected using an interval-wise testing procedure. High-amplitude early contingent negative variation (CNV) waves were significantly greater for CS⁺ compared with CS^– trials in all channels for Protocol I (> 521-800ms), most prominently over frontal and central midline locations (P ≤ 0.001). For Protocol II, shorter frontal ERP components were observed (541-609ms). Our test paradigm revealed electrocortical activation possibly associated with movement-related fear. Exploring the discriminative validity of the paradigm among individuals with and without self-reported re-injury anxiety is warranted.

PMID:38896295 | DOI:10.1007/s00221-024-06873-0

J Mater Sci Mater Med. 2024 Jun 19;35(1):31. doi: 10.1007/s10856-024-06794-y.

ABSTRACT

Orthopedic and dental implant failure continues to be a significant concern due to localized bacterial infections. Previous studies have attempted to improve implant surfaces by modifying their texture and roughness or coating them with antibiotics to enhance antibacterial properties for implant longevity. However, these approaches have demonstrated limited effectiveness. In this study, we attempted to engineer the titanium (Ti) alloy surface biomimetically at the nanometer scale, inspired by the cicada wing nanostructure using alkaline hydrothermal treatment (AHT) to simultaneously confer antibacterial properties and support the adhesion and proliferation of mammalian cells. The two modified Ti surfaces were developed using a 4 h and 8 h AHT process in 1 N NaOH at 230 °C, followed by a 2-hour post-calcination at 600 °C. We found that the control plates showed a relatively smooth surface, while the treatment groups (4 h & 8 h AHT) displayed nanoflower structures containing randomly distributed nano-spikes. The results demonstrated a statistically significant decrease in the contact angle of the treatment groups, which increased wettability characteristics. The 8 h AHT group exhibited the highest wettability and significant increase in roughness 0.72 ± 0.08 µm (P < 0.05), leading to more osteoblast cell attachment, reduced cytotoxicity effects, and enhanced relative survivability. The alkaline phosphatase activity measured in all different groups indicated that the 8 h AHT group exhibited the highest activity, suggesting that the surface roughness and wettability of the treatment groups may have facilitated cell adhesion and attachment and subsequently increased secretion of extracellular matrix. Overall, the findings indicate that biomimetic nanotextured surfaces created by the AHT process have the potential to be translated as implant coatings to enhance bone regeneration and implant integration.

PMID:38896291 | DOI:10.1007/s10856-024-06794-y

Environ Monit Assess. 2024 Jun 19;196(7):632. doi: 10.1007/s10661-024-12795-9.

ABSTRACT

In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM_2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM_2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM_2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM_2.5 concentration within Shanxi Province, with PM_2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM_2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m³ overall. The Hurst exponent indicated a projected decrease in PM_2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM_2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM_2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM_2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM_2.5 and provided a reference for similar studies.

PMID:38896290 | DOI:10.1007/s10661-024-12795-9

Inflamm Res. 2024 Jun 19. doi: 10.1007/s00011-024-01905-5. Online ahead of print.

ABSTRACT

BACKGROUND: Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized.

METHODS: We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the “Scissor” algorithm and the “BayesPrism” algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database.

RESULTS: We identified seven cell types. In the Scissor⁺ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor^– subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups.

CONCLUSION: Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

PMID:38896289 | DOI:10.1007/s00011-024-01905-5

Eur J Pediatr. 2024 Jun 19. doi: 10.1007/s00431-024-05596-2. Online ahead of print.

ABSTRACT

The assessment of body fat of children in primary care requires consideration of the dynamic changes in height, weight, lean mass, and fat mass during childhood growth. To achieve this, we aim to develop a predictive equation based on anthropometric values, with optimal diagnostic utility. This is a cross-sectional observational study, involving schoolgoers aged 11-17 years in the Vigo metropolitan area. Out of 10,747 individuals, 577 were randomly recruited.

VARIABLES: age, sex, ethnicity/country of origin, weight, height, 8 skinfolds, 3 diameters, 7 perimeters, and 85% percentile of body fat mass as the gold standard. Generalized additive regression was selected by cross-validation and compared using receiver operating characteristic curves (ROC curves). Sensitivity, specificity, positive and negative predictive values, true positive and true negative values, false positive and false negative values, accuracy, and positive and negative likelihood ratios were calculated. Two models were identified. The optimal model includes sex, weight, height, leg perimeter, and arm perimeter, with sensitivity of 0.93 (0.83-1.00), specificity of 0.91 (0.83-0.96), accuracy of 0.91 (0.84-0.96), and area under the curve (AUC) of 0.957 (0.928-0.986). The second model includes sex, age, and body mass index, with sensitivity of 0.93 (0.81-1.00), specificity of 0.90 (0.80-0.97), accuracy of 0.90 (0.82-0.96), and an AUC of 0.944 (0.903-0.984).

CONCLUSION: Two predictive models, with the 85th percentile of fat mass as the gold standard, built with basic anthropometric measures, show very high diagnostic utility parameters. Their calculation is facilitated by a complementary online calculator.

WHAT IS KNOWN: • In routine clinical practice, mainly in primary care, BMI is used to determine overweight and obesity. This index has its weaknesses in the assessment of children.

WHAT IS NEW: • We provide a calculator whose validated algorithm, through the determination of fat mass by impedanciometry, makes it possible to determine the risk of overweight and obesity in the community setting, through anthropometric measurements, providing a new practical, accessible and reliable model that improves the classification of overweight and obesity in children with respect to that obtained by determining BMI.

PMID:38896274 | DOI:10.1007/s00431-024-05596-2

J Neurol. 2024 Jun 19. doi: 10.1007/s00415-024-12511-0. Online ahead of print.

ABSTRACT

BACKGROUND: Myotonic dystrophy is a multisystem disorder characterized by widespread organic involvement including central nervous system symptoms. Although myotonic dystrophy disease types 1 (DM1) and 2 (DM2) cover a similar spectrum of symptoms, more pronounced clinical and brain alterations have been described in DM1. Here, we investigated brain volumetric and white matter alterations in both disease types and compared to healthy controls (HC).

METHODS: MRI scans were obtained from 29 DM1, 27 DM2, and 56 HC. We assessed macro- and microstructural brain changes by surface-based analysis of cortical thickness of anatomical images and tract-based spatial statistics of fractional anisotropy (FA) obtained by diffusion-weighted imaging, respectively. Global MRI measures were related to clinical and neuropsychological scores to evaluate their clinical relevance.

RESULTS: Cortical thickness was reduced in both patient groups compared to HC, showing similar patterns of regional distribution in DM1 and DM2 (occipital, temporal, frontal) but more pronounced cortical thinning for DM1. Similarly, FA values showed a widespread decrease in DM1 and DM2 compared to HC. Interestingly, FA was significantly lower in DM1 compared to DM2 within most parts of the brain.

CONCLUSION: Comparisons between DM1 and DM2 indicate a more pronounced cortical thinning of grey matter and a widespread reduction in microstructural integrity of white matter in DM1. Future studies are required to unravel the underlying and separating mechanisms for the disease courses of the two types and their neuropsychological symptoms.

PMID:38896263 | DOI:10.1007/s00415-024-12511-0

Urolithiasis. 2024 Jun 19;52(1):94. doi: 10.1007/s00240-024-01577-0.

ABSTRACT

Approximately 80% of kidney stone diseases contain calcium. Inherited genetic factors are among the variables that influence the development of calcium-containing kidney stone diseases (CKSD). Previous genome-wide association studies (GWAS) on stone diseases have been reported worldwide; however, these are not focused on calcium-containing stones. We conducted a GWAS to identify germline genetic polymorphisms associated with CKSD in a Medical Center in Taiwan; hence, this study was based primarily on a hospital-based database. CKSD was diagnosed using the chart records. Patients infected with urea-splitting-microorganisms and those with at least two urinary pH value below 5.5 were excluded. None of the patients had cystic stones based on stone analysis. Those over 40 years of age with no history of CKSD and no microscopic hematuria on urinalysis were considered as controls. The DNA isolated from the blood of 14,934 patients (63.7% male and 36.3% female) with CKSD and 29,868 controls (10,830 men and 19,038 women) at a medical center was genotyped for approximately 714,457 single nucleotide polymorphisms (SNPs) with minor allele frequency of ≥ 0.05. We used PLINK 1.9 to calculate the polygenic risk score (PRS) to investigate the association between CKSD and controls. The accuracy of the PRS was verified by dividing it into the training and testing groups. The statistical analyses were calculated with the area under the curve (AUC) using IBM SPSS version 22. We identified 432 susceptibility loci that reached a genome-wide threshold of P < 1.0 × 10^{– 5}. A total of 132 SNPs reached a threshold of P < 5 × 10^{– 8} using a stricter definition of significance on chromosomes 4, 13, 16, 17, and 18. At the top locus of our study, SNPs in DGKH, PDILT, BCAS3, and ABCG2 have been previously reported. RN7SKP27, HDAC4, PCDH15, AP003068.2, and NFATC1 were novel findings in this study. PRS was adjusted for sex and age, resulting in an AUC of 0.65. The number of patients in the top quartile of PRS was 1.39 folds in the risk of CKSD than patients in the bottom quartile. Our data identified the significance of GWAS for patients with CKSD in a hospital-based study. The PRS also had a high AUC for discriminating patients with CKSD from controls. A total of 132 SNP loci of SNPs significantly associated with the development of CKSD. This first survey, which focused on patients with CKSD, will provide novel insights specific to CKSD and its potential clinical biomarkers.

PMID:38896256 | DOI:10.1007/s00240-024-01577-0

Abdom Radiol (NY). 2024 Jun 19. doi: 10.1007/s00261-024-04419-0. Online ahead of print.

ABSTRACT

PURPOSE: To develop and validate a nomogram model that combines radiomics features, clinical factors, and coagulation function indexes (CFI) to predict intraoperative blood loss (IBL) during cesarean sections, and to explore its application in optimizing perioperative management and reducing maternal morbidity.

METHODS: In this retrospective consecutive series study, a total of 346 patients who underwent magnetic resonance imaging (156 for training and 68 for internal test, center 1; 122 for external test, center 2) were included. IBL+ was defined as more than 1000 mL estimated blood loss during cesarean sections. The prediction models of IBL were developed based on machine-learning algorithms using CFI, radiomics features, and clinical factors. ROC analysis was performed to evaluate the performance for IBL diagnosis.

RESULTS: The support vector machine model incorporating all three modalities achieved an AUC of 0.873 (95% CI 0.769-0.941) and a sensitivity of 1.000 (95% CI 0.846-1.000) in the internal test set, with an AUC of 0.806 (95% CI 0.725-0.872) and a sensitivity of 0.873 (95% CI 0.799-0.922) in the external test set. It was also scored significantly higher than the CFI model (P = 0.035) on the internal test set, and both the CFI (P = 0.002) and radiomics-CFI models (P = 0.007) on the external test set. Additionally, the nomogram constructed based on three modalities achieved an internal testing set AUC of 0.960 (95% CI 0.806-0.999) and an external testing set AUC of 0.869 (95% CI 0.684-0.967) in the pregnant population without a pernicious placenta previa. It is noteworthy that the AUC of the proposed model did not show a statistically significant improvement compared to the Clinical-CFI model in both internal (P = 0.115) and external test sets (P = 0.533).

CONCLUSION: The proposed model demonstrated good performance in predicting intraoperative blood loss (IBL), exhibiting high sensitivity and robust generalizability, with potential applicability to other surgeries such as vaginal delivery and postpartum hysterectomy. However, the performance of the proposed model was not statistically significantly better than that of the Clinical-CFI model.

PMID:38896245 | DOI:10.1007/s00261-024-04419-0

Anal Bioanal Chem. 2024 Jun 19. doi: 10.1007/s00216-024-05380-z. Online ahead of print.

ABSTRACT

The measurement uncertainty is a crucial quantitative parameter for assessing the reliability of the result. The study aimed to propose a new budget for uncertainty evaluation of a reference measurement procedure for the determination of total testosterone in human serum. The adaptive Monte Carlo method (aMCM) was used for the propagation of probability distributions assigned to various input quantities to determine the uncertainty of the testosterone concentration. The basic principles of the propagation and the statistical analysis were described based on the experimental results of the quality control serum sample. The analysis of the number of Monte Carlo trials was discussed. The procedure of validation of the GUM uncertainty framework using the aMCM was also provided. The number of Monte Carlo trials was 2.974 × 10⁶ when the results had stabilized. The total testosterone concentration was 16.02 nmol/L, and the standard uncertainty was 0.30 nmol/L. The coverage interval at coverage probability of 95% was 15.45 to 16.62 nmol/L, while the probability distribution for testosterone concentration was approximately described by a Gaussian distribution. The validation of results was not passed as the expanded uncertainty result obtained by the aMCM was slightly lower, about 7%, than that by the GUM uncertainty framework with consistent results of the concentration.

PMID:38896240 | DOI:10.1007/s00216-024-05380-z

Health Sci Rep. 2024 Jun 17;7(6):e2161. doi: 10.1002/hsr2.2161. eCollection 2024 Jun.

ABSTRACT

BACKGROUND AND AIM: Test-sets are standardized assessments used to evaluate reader performance in breast screening. Understanding how test-set results affect real-world performance can help refine their use as a quality improvement tool. The aim of this study is to explore if mammographic test-set results could identify breast-screening readers who improved their cancer detection in association with test-set training.

METHODS: Test-set results of 41 participants were linked to their annual cancer detection rate change in two periods oriented around their first test-set participation year. Correlation tests and a multiple linear regression model investigated the relationship between each metric in the test-set results and the change in detection rates. Additionally, participants were divided based on their improvement status between the two periods, and Mann-Whitney U test was used to determine if the subgroups differed in their test-set metrics.

RESULTS: Test-set records indicated multiple significant correlations with the change in breast cancer detection rate: a moderate positive correlation with sensitivity (0.688, p < 0.001), a moderate negative correlation with specificity (-0.528, p < 0.001), and a low to moderate positive correlation with lesion sensitivity (0.469, p = 0.002), and the number of years screen-reading mammograms (0.365, p = 0.02). In addition, the overall regression was statistically significant (F (2,38) = 18.456 p < 0.001), with an R² of 0.493 (adjusted R² = 0.466) based on sensitivity (F = 27.132, p < 0.001) and specificity (F = 9.78, p = 0.003). Subgrouping the cohort based on the change in cancer detection indicated that the improved group is significantly higher in sensitivity (p < 0.001) and lesion sensitivity (p = 0.02) but lower in specificity (p = 0.003).

CONCLUSION: Sensitivity and specificity are the strongest test-set performance measures to predict the change in breast cancer detection in real-world breast screening settings following test-set participation.

PMID:38895553 | PMC:PMC11183186 | DOI:10.1002/hsr2.2161