Fractal dimension of the brain in neurodegenerative disease and dementia: A systematic review

Boundary Complexity of (Sub-) Cortical Areas Predict Deep Brain Stimulation Outcomes in Parkinson's Disease

While deep brain stimulation (DBS) remains an effective therapy for Parkinson's disease (PD), sources of variance in patient outcomes are still not fully understood, underscoring a need for better prognostic criteria. Here we leveraged routinely collected T1-weighted (T1-w) magnetic resonance imaging (MRI) data to derive patient-specific measures of brain structure and evaluate their usefulness in predicting changes in PD medications in response to DBS. Preoperative T1-w MRI data from 231 patients with PD were used to extract regional measures of fractal dimension (FD), sensitive to the structural complexities of cortical and subcortical areas. FD was validated as a biomarker of Parkinson's disease (PD) progression through comparison of patients with PD and healthy controls (HCs). This analysis revealed significant group differences in FD across nine brain regions which supports its utility as a marker of PD. We evaluated the impact of adding imaging features (FD) to a clinical model that included demographics and clinical parameters-age, sex, total number and location of DBS electrodes, and preoperative motor response to levodopa. This model aimed to explain variance and predict changes in medication following DBS. Regression analysis revealed that inclusion of the FD of distributed brain areas correlated with post-DBS reductions in medication burden, explaining an additional 13.6% of outcome variance (R 2 =0.388) compared to clinical features alone (R 2 =0.252). Hypergraph-based classification learning tasks achieved an area under the receiver operating characteristic curve of 0.64 when predicting with clinical features alone, versus 0.76 when combining clinical and imaging features. These findings demonstrate that PD effects on brain morphology linked to disease progression influence DBS outcomes. The work also highlights FD as a potentially useful imaging biomarker to enhance DBS candidate selection criteria for optimized treatment planning.

Corneal Confocal Microscopy Identifies and Differentiates Patients With Multiple Sclerosis and Epilepsy

Purpose

To assess whether corneal nerve analysis can identify and differentiate patients with multiple sclerosis (MS) from those with epilepsy.

Methods

Participants with MS (n = 83), participants with epilepsy (n = 50), and healthy controls (HCs) (n = 20) underwent corneal confocal microscopy (CCM) and quantification of automated corneal nerve fiber length (ACNFL), automated corneal nerve fractal dimension (ACNFrD), and ACNFrD/ACNFL ratio of the subbasal nerve plexus.

Results

ACNFL (MS: P < 0.0001; epilepsy: P = 0.002) and ACNFrD (MS: P < 0.0001; epilepsy: P = 0.025) were significantly lower and the ACNFrD/ACNFL ratio (MS: P < 0.0001; epilepsy: P = 0.018) was significantly higher compared to HCs. ACNFL (P = 0.001), ACNFrD (P = 0.0003), and ACNFrD/ACNFL ratio (P = 0.006) were significantly lower in patients with MS compared to those with epilepsy. ACNFL had the highest diagnostic utility for identifying patients with MS (sensitivity/specificity 0.86/0.85, area under the curve [AUC] 0.90, P < 0.0001), and ACNFrD had the highest diagnostic utility for identifying patients with epilepsy (sensitivity/specificity 0.78/0.75, AUC 0.76, P = 0.0008). ACNFrD had the highest diagnostic utility for differentiating patients with MS from epilepsy (sensitivity/specificity 0.66/0.65, AUC 0.70, <0.0001).

Conclusions

Corneal neurodegeneration occurs in and is characterized by a distinct pattern that differentiates patients with MS and epilepsy.

Translational Relevance

CCM identifies and differentiates patients with MS and epilepsy, albeit with moderate performance. Further validation, with a larger sample size, is needed.

Detection of Parkinson's disease using nocturnal breathing signals based on multifractal detrended fluctuation analysis

Parkinson's disease (PD) is a highly prevalent neurodegenerative disorder that poses a significant challenge in terms of accurate and cost-effective diagnosis. This study focuses on the use of fractal features derived from nocturnal breathing signals to diagnose PD. Our study includes 49 individuals with Parkinson's disease (PD group), 49 relatively healthy individuals without PD (HC group), 49 individuals without PD but with other diseases (NoPD group), as well as 12 additional PD patients and 200 healthy individuals for testing. Using multifractal detrended fluctuation analysis, we extracted fractal features from nocturnal breathing signals, with logistic regression models applied to diagnose PD, as demonstrated in receiver operating characteristic curves. Eight fractal features show significant diagnostic potential for PD, including generalized Hurst exponents for the Airflow, Thorax, and Abdomen signals and the multifractal spectrum width of the SaO2 signal. Finally, the area under the receiver operating characteristic curve (AUC) of the training data set of the PD and HC groups for all four signals is 0.911, and the AUC of the testing data set is 0.929. These results demonstrate the potential of this work to enhance the accuracy of PD diagnosis in clinical settings.

Cortical morphological alterations in cognitively normal Parkinson's disease with severe hyposmia

Olfactory dysfunction is a common non-motor symptom of Parkinson's disease(PD) and may hold valuable insights into the disease's underlying pathophysiology. This study aimed to investigate cortical morphometry alterations in PD patients with severe hyposmia(PD-SH) and mild hyposmia(PD-MH) using surface-based morphometry(SBM) methods. Participants included 36 PD-SH patients, 38 PD-MH patients, and 40 healthy controls(HCs). SBM analysis revealed distinct patterns of cortical alterations in PD-SH and PD-MH patients. PD-MH patients exhibited reduced cortical thickness in the right supramarginal gyrus, while PD-SH patients showed widespread cortical thinning in regions including the bilateral pericalcarine cortex, bilateral lingual gyrus, left inferior parietal cortex, left lateral occipital cortex, right pars triangularis, right cuneus, and right superior parietal cortex. Moreover, PD-SH patients displayed reduced cortical thickness in the right precuneus compared to PD-MH patients. Fractal dimension analysis indicated increased cortical complexity in PD-MH patients' right superior temporal cortex and right supramarginal gyrus, as well as decreased complexity in the bilateral postcentral cortex, left superior parietal cortex, and right precentral cortex. Similarly, cortical gyrification index and cortical sulcal depth exhibited heterogeneous patterns of changes in PD-SH and PD-MH patients compared to HCs. These findings underscore the multifaceted nature of olfactory impairment in PD, with distinct patterns of cortical morphometry alterations associated with different degrees of hyposmia. The observed discrepancies in brain regions showing alterations reflect the complexity of PD's pathophysiology. These insights contribute to a deeper understanding of olfactory dysfunction in PD and provide potential avenues for early diagnosis and targeted interventions.

A viscoelastic-stochastic model of cell adhesion considering matrix morphology and medium viscoelasticity

Quantitative investigation of the adhesive behavior between cells and the extracellular matrix (ECM) through molecular bonds is essential for cell culture and bio-medical engineering in vitro. Cell adhesion is a complex multi-scale behavior that includes temporal and spatial scales. However, the influence of the cell and matrix creep effect and the complex spatial morphology characteristics of the matrix on the cell adhesion mechanism is unclear. In the present study, an idealized theoretical model has been considered, where the adhesion of cells and the matrix is simplified into a planar strain problem of homogeneous viscoelastic half-spaces. Furthermore, a new viscoelastic-stochastic model that considers the morphological characteristics of the matrix, the viscoelasticity of the cell and the viscoelasticity of the substrate was developed under the action of a constant external force. The model characterizes the matrix topographical features by fractal dimension (FD), interprets the effects of FD and medium viscoelasticity on the molecular bond force and the receptor-ligand bond re-association rate and reveals a new mechanism for the stable adhesion of molecular bond clusters by Monte Carlo simulation. Based on this model, it was identified that the temporal and spatial distribution of molecular bond force was affected by the matrix FD and the lifetime and stability of the molecular bond cluster could be significantly improved by tuning the FD. At the same time, the viscoelastic creep effect of the cell and matrix increased the re-association rate of open bonds and could expand the window of stable adhesion more flexibly.

Texture-based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population

Brain atrophy and cortical thinning are typically observed in people with Alzheimer's disease (AD) and, to a lesser extent, in those with mild cognitive impairment. In asymptomatic middle-aged apolipoprotein ε4 (ΑPOE4) carriers, who are at higher risk of future AD, study reports are discordant with limited evidence of brain structural differences between carriers and non-carriers of the ε4 allele. Alternative imaging markers with higher sensitivity at the presymptomatic stage, ideally quantified using typically acquired structural MRI scans, would thus be of great benefit for the detection of early disease, disease monitoring and subject stratification. In the present cross-sectional study, we investigated textural properties of T1-weighted 3T MRI scans in relation to APOE4 genotype, age and sex. We pooled together data from the PREVENT-Dementia and ALFA studies focused on midlife healthy populations with dementia risk factors (analysable cohort: 1585 participants; mean age 56.2 ± 7.4 years). Voxel-based and texture (examined features: contrast, entropy, energy, homogeneity) based morphometry was used to identify areas of volumetric and textural differences between APOE4 carriers and non-carriers. Textural maps were generated and were subsequently harmonised using voxel-wise COMBAT. For all analyses, APOE4, sex, age and years of education were used as model predictors. Interactions between APOE4 and age were further examined. There were no group differences in regional brain volume or texture based on APOE4 carriership or when age × APOE4 interactions were examined. Older people tended to have a less homogeneous textural profile in grey and white matter and a more homogeneous profile in the ventricles. A more heterogeneous textural profile was observed for females in areas such as the ventricles, frontal and parietal lobes and for males in the brainstem, cerebellum, precuneus and cingulate. Overall, we have shown the absence of volumetric and textural differences between APOE4 carriers and non-carriers at midlife and have established associations of textural features with ageing and sex.

Node-layer duality in networked systems

Real-world networks typically exhibit several aspects, or layers, of interactions among their nodes. By permuting the role of the nodes and the layers, we establish a new criterion to construct the dual of a network. This approach allows to examine connectivity from either a node-centric or layer-centric viewpoint. Through rigorous analytical methods and extensive simulations, we demonstrate that nodewise and layerwise connectivity measure different but related aspects of the same system. Leveraging node-layer duality provides complementary insights, enabling a deeper comprehension of diverse networks across social science, technology and biology. Taken together, these findings reveal previously unappreciated features of complex systems and provide a fresh tool for delving into their structure and dynamics.

Prediction Model for Cognitive Impairment among Disabled Older Adults: A Development and Validation Study

Disabled older adults exhibited a higher risk for cognitive impairment. Early identification is crucial in alleviating the disease burden. This study aims to develop and validate a prediction model for identifying cognitive impairment among disabled older adults. A total of 2138, 501, and 746 participants were included in the development set and two external validation sets. Logistic regression, support vector machine, random forest, and XGBoost were introduced to develop the prediction model. A nomogram was further established to demonstrate the prediction model directly and vividly. Logistic regression exhibited better predictive performance on the test set with an area under the curve of 0.875. It maintained a high level of precision (0.808), specification (0.788), sensitivity (0.770), and F1-score (0.788) compared with the machine learning models. We further simplified and established a nomogram based on the logistic regression, comprising five variables: age, daily living activities, instrumental activity of daily living, hearing impairment, and visual impairment. The areas under the curve of the nomogram were 0.871, 0.825, and 0.863 in the internal and two external validation sets, respectively. This nomogram effectively identifies the risk of cognitive impairment in disabled older adults.

Randomized controlled trials of non-pharmacological interventions for healthy seniors: Effects on cognitive decline, brain plasticity and activities of daily living-A 23-year scoping review

Little is known about the simultaneous effects of non-pharmacological interventions (NPI) on healthy older adults' behavior and brain plasticity, as measured by psychometric instruments and magnetic resonance imaging (MRI). The purpose of this scoping review was to compile an extensive list of randomized controlled trials published from January 1, 2000, to August 31, 2023, of NPI for mitigating and countervailing age-related physical and cognitive decline and associated cerebral degeneration in healthy elderly populations with a mean age of 55 and over. After inventorying the NPI that met our criteria, we divided them into six classes: single-domain cognitive, multi-domain cognitive, physical aerobic, physical non-aerobic, combined cognitive and physical aerobic, and combined cognitive and physical non-aerobic. The ultimate purpose of these NPI was to enhance individual autonomy and well-being by bolstering functional capacity that might transfer to activities of daily living. The insights from this study can be a starting point for new research and inform social, public health, and economic policies. The PRISMA extension for scoping reviews (PRISMA-ScR) checklist served as the framework for this scoping review, which includes 70 studies. Results indicate that medium- and long-term interventions combining non-aerobic physical exercise and multi-domain cognitive interventions best stimulate neuroplasticity and protect against age-related decline and that outcomes may transfer to activities of daily living.

Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter

Objective

Voxel-based morphometry (VBM), surface-based morphometry (SBM), and radiomics are widely used in the field of neuroimage analysis, while it is still unclear that the performance comparison between traditional morphometry and emerging radiomics methods in diagnosing brain aging. In this study, we aimed to develop a VBM-SBM model and a radiomics model for brain aging based on cognitively normal (CN) individuals and compare their performance to explore both methods' strengths, weaknesses, and relationships.

Methods

967 CN participants were included in this study. Subjects were classified into the middle-aged group (n = 302) and the old-aged group (n = 665) according to the age of 66. The data of 360 subjects from the Alzheimer's Disease Neuroimaging Initiative were used for training and internal test of the VBM-SBM and radiomics models, and the data of 607 subjects from the Australian Imaging, Biomarker and Lifestyle, the National Alzheimer's Coordinating Center, and the Parkinson's Progression Markers Initiative databases were used for the external tests. Logistics regression participated in the construction of both models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the two model performances. The DeLong test was used to compare the differences in AUCs between models. The Spearman correlation analysis was used to observe the correlations between age, VBM-SBM parameters, and radiomics features.

Results

The AUCs of the VBM-SBM model and radiomics model were 0.697 and 0.778 in the training set (p = 0.018), 0.640 and 0.789 in the internal test set (p = 0.007), 0.736 and 0.737 in the AIBL test set (p = 0.972), 0.746 and 0.838 in the NACC test set (p < 0.001), and 0.701 and 0.830 in the PPMI test set (p = 0.036). Weak correlations were observed between VBM-SBM parameters and radiomics features (p < 0.05).

Conclusion

The radiomics model achieved better performance than the VBM-SBM model. Radiomics provides a good option for researchers who prioritize performance and generalization, whereas VBM-SBM is more suitable for those who emphasize interpretability and clinical practice.

Differing patterns of cortical grey matter pathology identified by multifractal analysis in UMN-predominant ALS patients with and without corticospinal tract hyperintensity

The pathological hallmarks of amyotrophic lateral sclerosis (ALS) are degeneration of the primary motor cortex grey matter (GM) and corticospinal tract (CST) resulting in upper motor neuron (UMN) dysfunction. Conventional brain magnetic resonance imaging (MRI) shows abnormal CST hyperintensity in some UMN-predominant ALS patients (ALS-CST+) but not in others (ALS-CST-). In addition to the CST differences, we aimed to determine whether GM degeneration differs between ALS-CST+ and ALS-CST- patients by cortical thickness (CT), voxel-based morphometry (VBM) and fractal dimension analyses. We hypothesized that MRI multifractal (MF) measures could differentiate between neurologic controls (n = 14) and UMN-predominant ALS patients as well as between patient subgroups (ALS-CST+, n = 21 vs ALS-CST-, n = 27). No significant differences were observed in CT or GM VBM in any brain regions between patients and controls or between ALS subgroups. MF analyses were performed separately on GM of the whole brain, of frontal, parietal, occipital, and temporal lobes as well as of cerebellum. Estimating MF measures D (Q = 0), D (Q = 1), D (Q = 2), Δf, Δα of frontal lobe GM classified neurologic controls, ALS-CST+ and ALS-CST- groups with 98% accuracy and > 95% in F1, recall, precision and specificity scores. Classification accuracy was only 74% when using whole brain MF measures and < 70% for other brain lobes. We demonstrate that MF analysis can distinguish UMN-predominant ALS subgroups based on GM changes, which the more commonly used quantitative approaches of CT and VBM cannot.

Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease

BACKGROUND

Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline.

OBJECTIVES

The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features.

METHODS

Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models.

RESULTS

FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity.

CONCLUSIONS

Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Lower fractional dimension in Alzheimer's disease correlates with reduced locus coeruleus signal intensity

This study aimed to determine the pattern of fractional dimension (FD) in Alzheimer's disease (AD) patients, and investigate the relationship between FD and the locus coeruleus (LC) signal intensity.A total of 27 patients with AD and 25 healthy controls (HC) were collected to estimate the pattern of fractional dimension (FD) and cortical thickness (CT) using the Computational Anatomy Toolbox (CAT12), and statistically analyze between groups on a vertex level using statistical parametric mapping 12. In addition, they were examined by neuromelanin sensitive MRI(NM-MRI) technique to calculate the locus coeruleus signal contrast ratios (LC-CRs). Additionally, correlations between the pattern of FD and LC-CRs were further examined.Compared to HC, AD patients showed widespread lower CT and FD Furthermore, significant positive correlation was found between local fractional dimension (LFD) of the left rostral middle frontal cortex and LC-CRs. Results suggest lower cortical LFD is associated with LCCRs that may reflect a reduction due to broader neurodegenerative processes. This finding may highlight the potential utility for advanced measures of cortical complexity in assessing brain health and early identification of neurodegenerative processes.

Alzheimer's disease cortical morphological phenotypes are associated with TOMM40'523-APOE haplotypes

Both the APOE ε4 and TOMM40 rs10524523 ("523") genes have been associated with risk for Alzheimer's disease (AD) and neuroimaging biomarkers of AD. No studies have investigated the relationship of TOMM40'523-APOE ε4 on the structural complexity of the brain in AD individuals. We quantified brain morphology and multiple cortical attributes in individuals with mild cognitive impairment (MCI) and AD, then tested whether APOE ε4 or TOMM40 poly-T genotypes were related to AD morphological biomarkers in cognitively unimpaired (CU) and MCI/AD individuals. We identified several AD-specific phenotypes in brain morphology and found that TOMM40 poly-T short alleles are associated with early, AD-specific brain morphological differences in healthy aging. We observed decreased cortical thickness, sulcal depth, and fractal dimension in CU individuals with the poly-T short alleles. Moreover, in MCI/AD participants, the APOE ε4 (TOMM40 L) individuals had a higher rate of gene-related morphological markers indicative of AD. Our data suggest that TOMM40'523 is associated with early brain structure variations in the precuneus, temporal, and limbic cortices.

Fractal dimension analysis of resting state functional networks in schizophrenia from EEG signals

Fractal dimension (FD) has been revealed as a very useful tool in analyzing the changes in brain dynamics present in many neurological disorders. The fractal dimension index (FDI) is a measure of the spatiotemporal complexity of brain activations extracted from EEG signals induced by transcranial magnetic stimulation. In this study, we assess whether the FDI methodology can be also useful for analyzing resting state EEG signals, by characterizing the brain dynamic changes in different functional networks affected by schizophrenia, a mental disorder associated with dysfunction in the information flow dynamics in the spontaneous brain networks. We analyzed 31 resting-state EEG records of 150 s belonging to 20 healthy subjects (HC group) and 11 schizophrenia patients (SCZ group). Brain activations at each time sample were established by a thresholding process applied on the 15,002 sources modeled from the EEG signal. FDI was then computed individually in each resting-state functional network, averaging all the FDI values obtained using a sliding window of 1 s in the epoch. Compared to the HC group, significant lower values of FDI were obtained in the SCZ group for the auditory network (p < 0.05), the dorsal attention network (p < 0.05), and the salience network (p < 0.05). We found strong negative correlations (p < 0.01) between psychopathological scores and FDI in all resting-state networks analyzed, except the visual network. A receiver operating characteristic curve analysis also revealed that the FDI of the salience network performed very well as a potential feature for classifiers of schizophrenia, obtaining an area under curve value of 0.83. These results suggest that FDI is a promising method for assessing the complexity of the brain dynamics in different regions of interest, and from long resting-state EEG signals. Regarding the specific changes associated with schizophrenia in the dynamics of the spontaneous brain networks, FDI distinguished between patients and healthy subjects, and correlated to clinical variables.

Vessels characteristics in familial exudative vitreoretinopathy and retinopathy of prematurity based on deep convolutional neural networks

Purpose

The purpose of this study was to investigate the quantitative retinal vascular morphological characteristics of Retinopathy of Prematurity (ROP) and Familial Exudative Vitreoretinopathy (FEVR) in the newborn by the application of a deep learning network with artificial intelligence.

Methods

Standard 130-degree fundus photographs centered on the optic disc were taken in the newborns. The deep learning network provided segmentation of the retinal vessels and the optic disc (OD). Based on the vessel segmentation, the vascular morphological characteristics, including avascular area, vessel angle, vessel density, fractal dimension (FD), and tortuosity, were automatically evaluated.

Results

201 eyes of FEVR, 289 eyes of ROP, and 195 eyes of healthy individuals were included in this study. The deep learning system of blood vessel segmentation had a sensitivity of 72% and a specificity of 99%. The vessel angle in the FEVR group was significantly smaller than that in the normal group and ROP group (37.43 ± 5.43 vs. 39.40 ± 5.61, 39.50 ± 5.58, P = 0.001, < 0.001 respectively). The normal group had the lowest vessel density, the ROP group was in between, and the FEVR group had the highest (2.64 ± 0.85, 2.97 ± 0.92, 3.37 ± 0.88 respectively). The FD was smaller in controls than in the FEVR and ROP groups (0.984 ± 0.039, 1.018 ± 0.039 and 1.016 ± 0.044 respectively, P < 0.001). The ROP group had the most tortuous vessels, while the FEVR group had the stiffest vessels, the controls were in the middle (11.61 ± 3.17, 8.37 ± 2.33 and 7.72 ± 1.57 respectively, P < 0.001).

Conclusions

The deep learning technology used in this study has good performance in the quantitative analysis of vascular morphological characteristics in fundus photography. Vascular morphology was different in the newborns of FEVR and ROP compared to healthy individuals, which showed great clinical value for the differential diagnosis of ROP and FEVR.

Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson's Disease

Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson's disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very little is known on the fractal characteristics of EEG in PD measured by FD. In this study we performed a spatio-temporal analysis of EEG in PD using FD in four dimensions (4DFD). We analyzed 42 resting-state EEG recordings comprising two groups: 27 PD patients without dementia and 15 healthy control subjects (HC). From the original resting-state EEG we derived the cortical activations defined by a source reconstruction at each time sample, generating point clouds in three dimensions. Then, a sliding window of one second (the fourth dimension) was used to compute the value of 4DFD by means of the box-counting algorithm. Our results showed a significantly higher value of 4DFD in the PD group (p < 0.001). Moreover, as a diagnostic classifier of PD, 4DFD obtained an area under curve value of 0.97 for a receiver operating characteristic curve analysis. These results suggest that 4DFD could be a promising method for characterizing the specific changes in the brain dynamics associated with PD.

Fractal Analysis of Fuel Nozzle Surface Morphology Based on the 3D-Sandbox Method

The dual oil circuit centrifugal fuel nozzle is made of martensitic stainless steel, which has complex morphological characteristics. The surface roughness characteristics of the fuel nozzle directly affect the degree of fuel atomization and the spray cone angle. The surface characterization of the fuel nozzle is investigated by the fractal analysis method. A sequence of images of an unheated treatment fuel nozzle and a heated treatment fuel nozzle are captured by the super-depth digital camera. The 3-D point cloud of the fuel nozzle is acquired by the shape from focus technique, and its three-dimensional (3-D) fractal dimensions are calculated and analyzed by the 3-D sandbox counting method. The proposed method can characterize the surface morphology well, including the standard metal processing surface and the fuel nozzle surface, and the experiments show that the 3-D surface fractal dimension is positively correlated with the surface roughness parameter. The 3-D surface fractal dimensions of the unheated treatment fuel nozzle were 2.6281, 2.8697, and 2.7620, compared with the heated treatment fuel nozzles dimensions of 2.3021, 2.5322, and 2.3327. Thus, the 3-D surface fractal dimension value of the unheated treatment is larger than that of the heated treatment and is sensitive to surface defects. This study indicates that the 3-D sandbox counting fractal dimension method is an effective method to evaluate the fuel nozzle surface and other metal processing surfaces.

Non-coding RNAs as key players in the neurodegenerative diseases: Multi-platform strategies and approaches for exploring the Genome's dark matter

A growing amount of evidence in the last few years has begun to unravel that non-coding RNAs have a myriad of functions in gene regulation. Intensive investigation on non-coding RNAs (ncRNAs) has led to exploring their broad role in neurodegenerative diseases (NDs) owing to their regulatory role in gene expression. RNA sequencing technologies and transcriptome analysis has unveiled significant dysregulation of ncRNAs attributed to their biogenesis, upregulation, downregulation, aberrant epigenetic regulation, and abnormal transcription. Despite these advances, the understanding of their potential as therapeutic targets and biomarkers underpinning detailed mechanisms is still unknown. Advancements in bioinformatics and molecular technologies have improved our knowledge of the dark matter of the genome in terms of recognition and functional validation. This review aims to shed light on ncRNAs biogenesis, function, and potential role in NDs. Further deepening of their role is provided through a focus on the most recent platforms, experimental approaches, and computational analysis to investigate ncRNAs. Furthermore, this review summarizes and evaluates well-studied miRNAs, lncRNAs and circRNAs concerning their potential role in pathogenesis and use as biomarkers in NDs. Finally, a perspective on the main challenges and novel methods for the future and broad therapeutic use of ncRNAs is offered.

Integrated Pangenome Analysis and Pharmacophore Modeling Revealed Potential Novel Inhibitors against Enterobacter xiangfangensis

Enterobacter xiangfangensis is a novel, multidrug-resistant pathogen belonging to the Enterobacter genus and has the ability to acquire resistance to multiple antibiotic classes. However, there is currently no registered E. xiangfangensis drug on the market that has been shown to be effective. Hence, there is an urgent need to identify novel therapeutic targets and effective treatments for E. xiangfangensis. In the current study, a bacterial pan genome analysis and subtractive proteomics approach was employed to the core proteomes of six strains of E. xiangfangensis using several bioinformatic tools, software, and servers. However, 2611 nonredundant proteins were predicted from the 21,720 core proteins of core proteome. Out of 2611 nonredundant proteins, 372 were obtained from Geptop2.0 as essential proteins. After the subtractive proteomics and subcellular localization analysis, only 133 proteins were found in cytoplasm. All cytoplasmic proteins were examined using BLASTp against the virulence factor database, which classifies 20 therapeutic targets as virulent. Out of these 20, 3 cytoplasmic proteins: ferric iron uptake transcriptional regulator (FUR), UDP-2,3diacylglucosamine diphosphatase (UDP), and lipid-A-disaccharide synthase (lpxB) were chosen as potential drug targets. These drug targets are important for bacterial survival, virulence, and growth and could be used as therapeutic targets. More than 2500 plant chemicals were used to molecularly dock these proteins. Furthermore, the lowest-binding energetic docked compounds were found. The top five hit compounds, Adenine, Mollugin, Xanthohumol C, Sakuranetin, and Toosendanin demonstrated optimum binding against all three target proteins. Furthermore, molecular dynamics simulations and MM/GBSA analyses validated the stability of ligand-protein complexes and revealed that these compounds could serve as potential E. xiangfangensis replication inhibitors. Consequently, this study marks a significant step forward in the creation of new and powerful drugs against E. xiangfangensis. Future studies should validate these targets experimentally to prove their function in E. xiangfangensis survival and virulence.

Reduced cortical complexity in patients with end-stage kidney disease prior to dialysis initiation

End-stage kidney disease (ESKD) is associated with cognitive impairment (CI) and affects different aspects of cortical morphometry, but where these changes converge remains unclear. Fractal dimension (FD) is used to represent cortical complexity (CC), which describes the structural complexity of the cerebral cortex by integrating different cortical morphological measures. This study aimed to investigate changes in CC in patients with ESKD prior to initiation of dialysis and to evaluate the relationship between changes in CC, cognitive performance, and uremic toxins. Forty-nine patients with ESKD naive to dialysis and 31 healthy controls (HCs) were assessed using structural magnetic resonance imaging (MRI) and cognitive tests, including evaluations of global cognitive function, memory, and executive function. Clinical laboratory blood tests were performed on all patients with ESKD, including measurement of nine uremic toxin-related indices. CC was measured using MRI data to determine regional FD values. We estimated the association between cognitive performance, uremic toxin levels, and CC changes. Compared to HCs, patients with ESKD showed significantly lower CC in the left precuneus (p = 0.006), left middle temporal cortex (p = 0.010), and left isthmus cingulate cortex (p = 0.018). Furthermore, lower CC in the left precuneus was associated with impaired long-term delayed memory (Pearson r = 0.394, p = 0.042) in patients with ESKD. Our study suggests that regional decreases in CC are an additional characteristic of patients with ESKD naive to dialysis, related to impaired long-term memory performance. These findings may help further understand the underlying neurobiological mechanisms between brain structural changes and CI in patients with ESKD.