Quantification Of Fibers Research Articles

Rapid biofabrication of cell-free, anisotropic collagen tissues using a novel horizontal shear flow technique

Ultrastructure and organisation of collagen fibres is essential to tissue function, due to the loadbearing properties of collagen. Current techniques used to create aligned collagen tissue equivalents use the contractile ability of cells to remodel and align collagen fibres or utilise highly specialised pieces of equipment. The aim of this study is to develop a novel and rapid method to produce acellular aligned collagen sheets by combining horizontal shear flow (HSFlow) and the established RAFT method to remove excess fluid from a hydrogel.Force applied to the gel during the HSFlow process was measured to allow replication of the method. Quantification of fibres and cellular alignment revealed a significant difference between HSFlow and control samples, where both cells and collagen fibres showed alignment in the direction of shear flow, compared to the randomly aligned RAFT controls. Mechanical properties were also measured and revealed that HSFlow does not appear to improve the strength of the constructs despite the improved alignment, therefore further optimisation is needed to strengthen the constructs.In conclusion, we developed a novel and rapid technique to generate flat sheets of aligned collagen without relying on the contractile ability of cells to rearrange collagen fibres. This rapid method has potential to be used in the fabrication of a scaffold to mimic anisotropic tissues for regenerative medicine.

Automated Fiber Quantification Analysis Identifies Tract-specific Microstructural Alterations in Brain in Intermittent exotropia

BackgroundGrowing evidence of neuroimaging has indicated brain microstructural abnormalities in comitant strabismus. Nonetheless, few studies have investigated neuropathological alterations in patients with intermittent exotropia (IXT). This study aimed at examining the characteristics of brain microstructure along major fiber tracts in IXT patients using an automated fiber quantification analysis. MethodsA total of 25 patients with IXT as well as 25 healthy participants matched for age and gender finished the diffusion tensor imaging scanning and the ophthalmic examination. Automated fiber quantification analysis of 20 major fiber tracts was carried out for IXT patients and healthy subjects, respectively. Diffusion metrics of 100 equidistant nodes resampled along each tract were measured for every subject and compared between two groups. Effect size analysis was performed to identify the most affected fiber tracts in IXT. ResultsWidely declined mean diffusivity was noted in IXT along major tracts containing bilateral thalamic radiations, bilateral corticospinal fasciculi, bilateral cingulum cingulate, left inferior fronto-occipital fasciculus, right arcuate fasciculus and superior longitudinal fasciculus. Local reduction in fractional anisotropy was observed in IXT along left cingulum hippocampus, right inferior longitudinal fasciculus and right uncinate fasciculus, in contrast to the regionally increased fractional anisotropy along bilateral thalamic radiation, left corticospinal tract and left arcuate fasciculus. Among the tracts with significantly changed diffusion metrics in IXT, right inferior longitudinal fasciculus was the most affected one in fractional anisotropy while left thalamic radiation was the most influenced one in mean diffusivity. ConclusionsAbnormalities in microstructural properties along visual-related fiber tracts are likely to contribute to difficulties in visual information processing in IXT patients, which could serve as the neural basis of underlying pathological mechanism of IXT.

Segmental abnormalities of white matter microstructure in multiple sclerosis and neuromyelitis optica spectrum disorder identified by automated fiber quantification

BackgroundMultiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) are different in pathogenesis, but both could lead to white matter (WM) microstructural damage. The aim of this study was to explore the differences in the patterns of WM fiber tract damage in relapsing-remitting MS (RRMS) and NMOSD by automated fiber quantification (AFQ). Materials and MethodsForty-one RRMS patients, 30 NMOSD patients and 30 healthy controls (HC) underwent MRI examination. AFQ was applied to identify and quantify 100 equally spaced nodes of specific WM fiber tracts for each participant. Measurements of fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD) and radial diffusivity (RD) for each segment of a specific fiber tract were compared between RRMS, NMOSD and HC. ResultsThe decrease in FA was found in 7 fiber tracts in entire tract comparison and 9 fiber tracts in pointwise comparison in RRMS patients. However, the FA in left thalamic radiation (TR) and right uncinate fasciculus showed significant differences between RRMS and HC only in the pointwise comparison, but not in the entire tract comparison. The MD, AD and RD of WM fiber tracts in RRMS patients were extensively increased both in the entire level and in the pointwise level. NMOSD patients showed significant FA decrease in left TR and callosum forceps minor (CF_minor), and significant RD increase in CF_minor in the pointwise level. In the pointwise comparison between RRMS and NMOSD, significant FA decrease was found in right inferior fronto-occipital fasciculus and bilateral inferior longitudinal fasciculus in RRMS patients, focal or widespread MD, AD and RD increase was found in multiple fiber tracts. ConclusionThe AFQ approach is a more sensitive way to reflect WM microstructural abnormalities, revealing extensive WM microstructural damage in RRMS and limited WM fiber tract damage in NMOSD.

Fiber Microstructure Quantile (FMQ) Regression: A Novel Statistical Approach for Analyzing White Matter Bundles from Periphery to Core.

The structural connections of the brain's white matter are critical for brain function. Diffusion MRI tractography enables the in-vivo reconstruction of white matter fiber bundles and the study of their relationship to covariates of interest, such as neurobehavioral or clinical factors. In this work, we introduce Fiber Microstructure Quantile (FMQ) Regression, a new statistical approach for studying the association between white matter fiber bundles and scalar factors (e.g., cognitive scores). Our approach analyzes tissue microstructure measures based on quantile-specific bundle regions . These regions are defined according to the quantiles of fractional anisotropy (FA) from the periphery to the core of a population fiber bundle, which pools all individuals' bundles. To investigate how fiber bundle tissue microstructure relates to covariates of interest, we employ the statistical technique of quantile regression. Unlike ordinary regression, which only models a conditional mean, quantile regression models the conditional quantiles of a response variable. This enables the proposed analysis, where a quantile regression is fitted for each quantile-specific bundle region. To demonstrate FMQ Regression, we perform an illustrative study in a large healthy young adult tractography dataset derived from the Human Connectome Project-Young Adult (HCP-YA), focusing on particular bundles expected to relate to particular aspects of cognition and motor function. Importantly, our analysis considers sex-specific effects in brain-behavior associations. In comparison with a traditional method, Automated Fiber Quantification (AFQ), which enables FA analysis in regions defined along the trajectory of a bundle, our results suggest that FMQ Regression is much more powerful for detecting brain-behavior associations. Importantly, FMQ Regression finds significant brain-behavior associations in multiple bundles, including findings unique to males or to females. In both males and females, language performance is significantly associated with FA in the left arcuate fasciculus, with stronger associations in the bundle's periphery. In males only, memory performance is significantly associated with FA in the left uncinate fasciculus, particularly in intermediate regions of the bundle. In females only, motor performance is significantly associated with FA in the left and right corticospinal tracts, with a slightly lower relationship at the bundle periphery and a slightly higher relationship toward the bundle core. No significant relationships are found between executive function and cingulum bundle FA. Our study demonstrates that FMQ Regression is a powerful statistical approach that can provide insight into associations from bundle periphery to bundle core. Our results also identify several brain-behavior relationships unique to males or to females, highlighting the importance of considering sex differences in future research.

Diagnosis of nascent fibrosis: Interest of marking the relaxed form of fibronectin fibres

Fibrosis is a pathological and irreversible state of excessively synthesized extracellular matrix fibres induced by abnormal tissue scarring. Effective diagnostic tools for early management of patients with fibrosis are missing. Here, I discuss the recent advances in the quantification of Fibronectin fibres and the interest of novel biologics for diagnosis.

White matter microstructure damage measured by automated fiber quantification correlates with pain symptoms in lung cancer patients.

To investigative the white matter (WM) alterations in lung cancer patients with cancer pain (CP+), and explore the correlations between damaged WM fiber tracts and clinical indicators. Twenty-six CP+, 26 lung cancer patients without CP (CP-), and 31 healthy controls (HC) were recruited. All participants underwent diffusion tensor imaging (DTI) and clinical assessments. Automated fiber quantification (AFQ) technique was performed to identify the 20 WM fiber bundles, and the fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were extracted. Intergroup comparisons of these diffusion metrics were conducted based on the entire fiber bundle level and 100 node levels along each tract. The associations between altered diffusion metrics and the numeric rating scale (NRS) scores, as well as the pain duration, were analyzed. At the entire level, the CP + group showed impaired WM structure in the right cingulum hippocampus (CH_R). At the pointwise level, the CP + group exhibited extensive nodal FA reduction or MD, RD, and AD elevation. In addition, the AD of the posterior portion of the right inferior longitudinal fasciculus (ILF_R, nodes 71-75) in the CP + group was positively correlated with the pain duration, and the FA of CH_R (nodes 22-38) was negatively correlated with NRS score. Extensive WM microstructural damage may be a pattern of brain abnormalities in lung cancer patients with CP, and in particular, specific nodal disruption along pain-related fiber tracts may be a sensitive imaging biomarker to characterize the severity and duration of CP.

Assessment of Optical and Scanning Electron Microscopies for the Identification and Quantification of Asbestos Fibers and Typical Asbestos Bodies in Human Colorectal Cancer Tissues

Asbestos research, identification, and quantification have been performed over the years, and the relationship between fiber inhalation and lung disease development is well defined. The same cannot be said for the gastroenteric system: the International Agency for Research on Cancer (IARC) believes that colorectal cancer (CRC) could be associated with asbestos exposure, but research has not demonstrated a casual nexus between exposure and CRC, despite highlighting an association tendency. The combination of scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) is the most applied technique in asbestos fiber identification in tissues and intestinal mucosa. In this study, SEM/EDS was applied to evaluate the presence of asbestos fibers and bodies (ABs) inside the tissue of eleven patients affected by CRC who had undergone environmental exposure due to living in an asbestos-polluted area where an Eternit plant had been active in the past. This technique was coupled with optical microscopy (OM) to verify whether the latter could be applied to evaluate the presence of these mineral phases, with the goal of understanding its suitability for identifying fibers and ABs in colon tissues. In addition to verifying the presence of fibers, this study allowed us to identify the deposition site of said fibers within the sample and possibly detect associated tissue reactions using OM, over a shorter time and at lower costs. Despite being a preliminary and descriptive work, the obtained results allowed us to propose a method involving first-sample OM observation to identify regulated (fibers with a length ≥ 5 μm, a thickness ≤ 3 μm, and a length/thickness ratio > 3) asbestos phases and ABs in the extra-respiratory system. In fact, OM and SEM/EDS provided similar information: no asbestiform morphology or ABs were found, but phyllosilicates and other inorganic materials were identified. This research needs to be continued using higher-resolution techniques to definitively rule out the presence of these fibers inside tissues whilst also increasing the number of patients involved.

When detection and quantification of mineral fibres in natural raw materials are at their limit – the case of a clay from the Gomsiqe–Puka mining area (Albania)

Abstract. In today's global market, the movement of raw materials and goods in the free global market can lead to unintended consequences. One significant concern is the potential presence of contaminants and carcinogens, particularly when products originate from regions with less strict regulations and enforcement. This issue is particularly pertinent in the natural raw materials utilized in the global building materials market, where contamination by asbestos minerals can occur. Therefore, the screening of natural raw materials for asbestos content is crucial to mitigate the risk of exposure to carcinogens for both workers and the general public. In this study, we examine a challenging case involving a smectite–kaolinite clay from Gomsiqe–Puka, Albania, possibly containing mineral fibres. Detection and quantification of asbestos in this material push the boundaries of current experimental methods. Using transmission electron microscopy (TEM), micro-Raman spectroscopy, and electron probe microanalysis (EPMA), we identified the presence of asbestos tremolite, along with a rare fibrous variety of diopside. EPMA data allowed the advancement of some speculations on the origin of the observed tremolites, showing that Al-rich tremolites are typical of oceanic settings and Al-poor tremolites are more similar to continental tremolites. We also investigated the impact of milling on the detection and quantification of mineral fibres, testing different milling times. This investigation is crucial as it can influence the classification of the raw material as asbestos-containing material or not. Our findings indicate that tremolite, cleavage fragments, and elongated particles break down into smaller World Health Organization (WHO) fibres with increasing milling times (1–5 min). However, prolonged milling (10 min) leads to overgrinding, resulting in a decrease in the number of counted WHO fibres with a length exceeding 5 µm.

Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging

Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally non-quantitative or are low throughput due to technical limitations of ‘splitting hairs’. We developed a deep learning-based computer vision approach for the high throughput quantification of individual hair fibers at a high resolution. Our innovative computer vision tool can distinguish and extract overlapping fibers for quantification of multivariate features including length, width, and color to generate single-hair phenomes (shPhenome) of diverse conditions across the lifespan of mice. Using our tool, we explored the effects of hormone signaling, genetic modifications, and aging on hair follicle output. Our analyses revealed hair phenotypes resultant of endocrinological, developmental, and aging-related alterations in the fur coats of mice. These results demonstrate the efficacy of our deep hair phenomics tool for characterizing factors that modulate the hair follicle and developing new diagnostic methods for detecting disease through the hair fiber. Finally, we have generated a searchable, interactive web tool for the exploration of our hair fiber data at skinregeneration.org.

Glymphatic function assessment with diffusion tensor imaging along the perivascular space in patients with major depressive disorder and its relation to cerebral white-matter alteration.

The link between glymphatic system function in the brain and alterations in white-matter microstructure among individuals with major depressive disorder (MDD) remains unclear. This study aimed to examine the assessment of glymphatic system function in patients with MDD using the diffusion tensor imaging along the perivascular space (DTI-ALPS) index and to evaluate its association with cerebral-white-matter abnormalities and neuropsychological scores. From February 2023 to November 2023, this cross-sectional study recruited 35 patients with MDD from the Psychosomatic Diseases Department of the First Affiliated Hospital of Dalian Medical University. In this time period, 23 healthy controls (HCs) were enlisted from the community and matched with the MDD cohort in terms of years of education, gender, and age. All participants underwent magnetic resonance imaging, depression, anxiety, and cognitive assessments. The tract-based spatial statistics (TBSS) analyzed DTI parameters and identified significant clusters. Automated fiber quantification (AFQ) was used to automatically identify fiber bundles with statistical differences. Mann-Whitney tests or two-sample t-tests were used for comparisons. Interobserver consistency of the DTI-ALPS measurements was evaluated using the interclass correlation coefficient (ICC). Partial correlation analyses and linear regression analyses were used to examine relationships. A comparison of the DTI-ALPS index was made between the two groups. Correlations among diffusion characteristics, neuropsychological scores, and the DTI-ALPS index were analyzed. Compared to HCs, patients with MDD exhibited a lower DTI-ALPS score (P=0.001). According to using linear regression analysis, the ALPS index was found to be an independent predictor of the Hamilton Depression Rating Scale [B=-25.32; P=0.001; 95% confidence interval (CI): -40.35 to -11.55], Hamilton Anxiety Rating Scale (B=-33.48; P=0.003; 95% CI: -55.38 to -11.24), and Montreal Cognitive Assessment total score (B=8.59; P=0.008; 95% CI: 2.38 to 14.79). According to the TBSS analysis, there were clusters of increased axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) in patients with MDD as compared to HCs (all P values <0.05). A lower DTI-ALPS score was correlated with higher AD (r=-0.592; P<0.001), MD (cluster 1: r=-0.567, P=0.001; cluster 2: r=-0.581, P<0.001), and RD (r=-0.491; P=0.004) values. AFQ analysis identified the significantly different diffusion indicators in the left cingulum bundle (CB_L), left inferior longitudinal fasciculus (ILF_L), and left uncinate fasciculus (UF_L) between the two groups (all false discovery rate P values <0.05). DTI-ALPS score was negatively correlated with the AD value of CB_L (r=-0.304; P=0.024), ILF_L (r=-0.35; P=0.008), and UF_L (r=-0.354; P=0.008) in AFQ tract-level analysis. In point-wise analysis, the MD value of CB_L at nodes 33 to 36 was negatively correlated with DTI-ALPS score (r ranging from -0.504 to -0.535; P<0.01). Our results indicated a decrease in DTI-ALPS index score in patients with MDD. DTI-ALPS score was associated with depression, anxiety, declined cognitive ability, and white-matter microstructural abnormalities and may thus be a promising biomarker for the partial evaluation of glymphatic system function in patients with MDD.

Structural Development of Speech Networks in Young Children at Risk for Speech Disorder.

Characterizing the structural development of the neural speech network in early childhood is important for understanding speech acquisition. To investigate speech in the developing brain, 94 children aged 4-7-years-old at risk for early speech disorder were scanned using diffusion weighted imaging (DWI) magnetic resonance imaging (MRI). Additionally, each child completed the Syllable Repetition Task (SRT), a validated measure of phoneme articulation. The DWI data were modeled using multi-compartment restriction spectrum imaging (RSI) to measure restricted and hindered diffusion properties in both grey and white matter. Consequently, we analyzed the diffusion data using both whole brain analysis, and automated fiber quantification (AFQ) analysis to establish tract profiles for each of six fiber pathways thought to be important for supporting speech development. In the whole brain analysis, we found that SRT performance was associated with restricted diffusion in bilateral inferior frontal gyrus ( pars opercularis ), right pre-supplementary/ supplementary motor area (pre-SMA/SMA), and bilateral cerebellar grey matter ( p < .005). Age moderated these associations in left pars opercularis and frontal aslant tract (FAT). However, in both cases only the cerebellar findings survived a cluster correction. We also found associations between SRT performance and restricted diffusion in cortical association fiber pathways, especially left FAT, and in the cerebellar peduncles. Analyses using automatic fiber quantification (AFQ) highlighted differences in high and low performing children along specific tract profiles, most notably in left but not right FAT. These findings suggest that individual differences in speech performance are reflected in structural gray and white matter differences as measured by restricted and hindered diffusion metrics, and offer important insights into developing brain networks supporting speech in very young children.

Automated immunohistochemistry of intra-epidermal nerve fibres in skin biopsies: A proof-of-concept study.

To develop a standardised, automated protocol for detecting protein gene product 9.5 (PGP9.5) positive intra-epidermal nerve fibres (IENFs) in skin biopsies, transitioning from the established manual technique to an automated platform. This automated method, although currently intended for research applications, may improve the accessibility of this diagnostic test for small fibre neuropathy in clinical settings. Skin biopsies (n = 274) from 100 participants (fibromyalgia syndrome n = 62; idiopathic small fibre neuropathy: n = 16; healthy volunteers: n = 22) were processed using an automated immunohistochemistry platform. IENF quantification was performed by blinded examiners, with reliability assessed via a two-way mixed-effects model to evaluate inter- and intra-observer variability. The automated staining system reproduced intra-epidermal nerve fibre density (IENFD) counts consistent with free-floating sections (mean ± standard deviation: free-floating: 5.6 ± 3.4 fibres/mm; automated: 5.9 ± 3.2 fibres/mm). A median difference of 0.3 with a lower bound 95% Confidence Interval (CI) at -0.00005 established non-inferiority against a margin of -0.4 (p = .08). Specifically, the inter-class correlation coefficient (class denotes consistency in measured observations) was 99% (95% CI: 0.9-1), indicating excellent agreement between free-floating and automated methods. The inter- and intra-class coefficient between examiners were both 99% (95% CI: 0.9-0.1) for IENFD, demonstrating high reliability using sections stained using the automated method. Automated immunohistochemistry provides high-throughput reliable and reproducible intra-epidermal nerve fibre quantification. This method, although currently proof-of-concept, for research use only, may be more widely deployed in histopathology laboratories to increase the adoption of IENFD assessment for the diagnosis of peripheral neuropathies.

Precise quantification of skeletal muscle fibers reveals the physiological basis for growth rate discrepancies in broilers1

Skeletal muscle is composed of multinucleated muscle fibers, which play a crucial role in determining the quality of meat products in livestock. Quantifying the total number of muscle fibers (TNM) is essential for understanding muscle composition, yet remains challenging in poultry, particularly due to the size of the livestock that complicates the preparation of tissue sections for analysis and renders the counting process laborious. Our previous study developed an automatic muscle fiber quantification tool powered by deep learning, named MyoV, which has addressed this bottleneck. This study aimed to employ the tool for the accurate quantification of the TNM in the pectoral muscles of slow-growing (SL), medium-growing (ML), and fast-growing (FL) broilers. Results showed that FL exhibited higher growth performance compared to ML and SL from embryonic to rearing stages. Processing of whole slide images of pectoral muscle revealed significantly higher TNM in FL and ML than in SL (P < 0.01). The TNM of FL, ML and SL were 693,568.00 ± 54,169.80, 652,122.00 ± 65,822.60 and 539,778.57±40,722.94 at 7 days of age (D7), respectively. And the TNM at D35 were 663,014.93±58,801.11, 645,784.76±80,204.34 and 507,280.29±98,092.16 of FL, ML and SL. Differences in cross-sectional area (CSA) of muscle fibers among the three groups were consistent with TNM results. Correlation analysis showed a correlation coefficient of 0.73-0.89 between body weight (BW) and TNM and a correlation coefficient of 0.78-0.87 between BW and CSA. These findings directly indicate that the number of muscle fibers in broilers is an important foundation for their rapid growth and development. This study precisely quantifies the muscle fiber number of important skeletal muscle in poultry for the first time, providing the direct evidence for the physiological basis of rapid development in broilers and offering important data support for further in-depth researches on muscle fiber development.

The mediating effect of the amygdala-frontal circuit on the association between depressive symptoms and cognitive function in Alzheimer’s disease

Depressive symptoms occur commonly in Alzheimer’s disease (AD). Although abnormalities in the amygdala-frontal circuit have been linked to emotional dysregulation and cognitive impairment, the neurological basis underlying these associations in AD patients with depressive symptoms (ADD) is unclear. We aimed to investigate the relationship between the amygdala-frontal circuit and depressive symptoms and cognitive function in ADD. We recruited 60 ADD, 60 AD patients without depressive symptoms (ADND), and 60 healthy controls (HC). Functional connectivity (FC) maps of the bilateral amygdala were compared. Fractional anisotropy (FA) of the amygdala-frontal circuit connected by the uncinate fasciculus (UF) was calculated using automated fiber quantification (AFQ). In addition, mediation analysis was performed to explore the effects of the amygdala-frontal circuit on the relationship between depressive symptoms and cognitive function. We found decreased bilateral amygdala FC with the inferior frontal gyrus (IFG) in the ADD group compared to the ADND and HC groups. Moreover, FA in the left frontal UF (nodes 64–97) was significantly lower in the ADD group than ADND group. Notably, amygdala-based FC with IFG and the left frontal UF FA mediated the relationship between depressive symptoms and cognitive function in ADD, with mediating effects ranging between 15 and 18%. Our study is the first to demonstrate the mediating effect of functional and microstructural abnormalities in the amygdala-frontal circuit in ADD. The findings suggest that the amygdala-frontal circuit may underlie emotional dysregulation in ADD, providing potential targets for treatment strategies.

Altered white matter connectivity of ventral language networks in autism spectrum disorder: An automated fiber quantification analysis with multi-site datasets

Comprehension and pragmatic deficits are prevalent in autism spectrum disorder (ASD) and are potentially linked to altered connectivity in the ventral language networks. However, previous magnetic resonance imaging studies have not sufficiently explored the microstructural abnormalities in the ventral fiber tracts underlying comprehension dysfunction in ASD. Additionally, the precise locations of white matter (WM) changes in the long tracts of patients with ASD remain poorly understood. In the current study, we applied the automated fiber-tract quantification (AFQ) method to investigate the fine-grained WM properties of the ventral language pathway and their relationships with comprehension and symptom manifestation in ASD. The analysis included diffusion/T1 weighted imaging data of 83 individuals with ASD and 83 age-matched typically developing (TD) controls. Case-control comparisons were performed on the diffusion metrics of the ventral tracts at both the global and point-wise levels. We also explored correlations between diffusion metrics, comprehension performance, and ASD traits, and conducted subgroup analyses based on age range to examine developmental moderating effects. Individuals with ASD exhibited remarkable hypoconnectivity in the ventral tracts, particularly in the temporal portions of the left inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). These WM abnormalities were associated with poor comprehension and more severe ASD symptoms. Furthermore, WM alterations in the ventral tract and their correlation with comprehension dysfunction were more prominent in younger children with ASD than in adolescents. These findings indicate that WM disruptions in the temporal portions of the left ILF/IFOF are most notable in ASD, potentially constituting the core neurological underpinnings of comprehension and communication deficits in autism. Moreover, impaired WM connectivity and comprehension ability in patients with ASD appear to improve with age.

Liver fibrosis analysis using digital pathology.

Digital pathology has enabled the noninvasive quantification of pathological parameters. In addition, the combination of digital pathology and artificial intelligence has enabled the analysis of a vast amount of information, leading to the sharing of much information and the elimination of knowledge gaps. Fibrosis, which reflects chronic inflammation, is the most important pathological parameter in chronic liver diseases, such as viral hepatitis and metabolic dysfunction-associated steatotic liver disease. It has been reported that the quantitative evaluation of various fibrotic parameters by digital pathology can predict the prognosis of liver disease and hepatocarcinogenesis. Liver fibrosis evaluation methods include 1 fiber quantification, 2 elastin and collagen quantification, 3s harmonic generation/two photon excitation fluorescence (SHG/TPE) microscopy, and 4 Fibronest™.. In this review, we provide an overview of role of digital pathology on the evaluation of fibrosis in liver disease and the characteristics of recent methods to assess liver fibrosis.

Microscopic fractional anisotropy outperforms multiple sclerosis lesion assessment and clinical outcome associations over standard fractional anisotropy tensor.

We aimed to compare the ability of diffusion tensor imaging and multi-compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy-six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid-attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate-severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R-squared value of .57, significantly outperforming the R-squared value of .24 for FA (p-value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS-related impairments.

Cross-sectional and longitudinal evaluation of white matter microstructure damage and cognitive correlations by automated fibre quantification in relapsing-remitting multiple sclerosis patients.

The purpose of this study was to characterize whole-brain white matter (WM) fibre tracts by automated fibre quantification (AFQ), capture subtle changes cross-sectionally and longitudinally in relapsing-remitting multiple sclerosis (RRMS) patients and explore correlations between these changes and cognitive performance A total of 114 RRMS patients and 71 healthy controls (HCs) were enrolled and follow-up investigations were conducted on 46 RRMS patients. Fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD), and radial diffusivity (RD) at each node along the 20 WM fibre tracts identified by AFQ were investigated cross-sectionally and longitudinally in entire and pointwise manners. Partial correlation analyses were performed between the abnormal metrics and cognitive performance. At baseline, compared with HCs, patients with RRMS showed a widespread decrease in FA and increases in MD, AD, and RD among tracts. In the pointwise comparisons, more detailed abnormalities were localized to specific positions. At follow-up, although there was no significant difference in the entire WM fibre tract, there was a reduction in FA in the posterior portion of the right superior longitudinal fasciculus (R_SLF) and elevations in MD and AD in the anterior and posterior portions of the right arcuate fasciculus (R_AF) in the pointwise analysis. Furthermore, the altered metrics were widely correlated with cognitive performance in RRMS patients. RRMS patients exhibited widespread WM microstructure alterations at baseline and alterations in certain regions at follow-up, and the altered metrics were widely correlated with cognitive performance in RRMS patients, which will enhance our understanding of WM microstructure damage and its cognitive correlation in RRMS patients.

Lensless shadow microscopy-based shortcut analysis strategy for fast quantification of microplastic fibers released to water

Fast quantification is the primary challenge in monitoring microplastic fiber (MPF) pollution in water. The process of quantifying the number of MPFs in water typically involves filtration, imaging on a filter membrane, and manual counting. However, this routine workflow has limitations in terms of speed and accuracy. Here, we present an alternative analysis strategy based on our high-resolution lensless shadow microscope (LSM) for rapid imaging of MPFs on a chip and modified deep learning algorithms for automatic counting. Our LSM system was equipped with wide field-of-view submicron-pixel imaging sensors (>1 cm2; ∼500 nm/pixel) and could simultaneously capture the projection image of >3-μm microplastic spheres within 90 s. The algorithms enabled accurate classification and detection of the number and length of >10-μm linear and branched MPFs derived from melamine cleaning sponges in each image (∼0.4 gigapixels) within 60 s. Importantly, neither MPF morphology (dispersed or aggregated) nor environmental matrix had a notable impact on the automatic recognition of the MPFs by the algorithms. This new strategy had a detection limit of 10 particles/mL and significantly reduced the time of MPF imaging and counting from several hours with membrane-based methods to just a few minutes per sample. The strategy could be employed to monitor water pollution caused by microplastics if an efficient sample separation and a comprehensive sample image database were available.

Association between segmental alterations of white matter bundles and cognitive performance in first-episode, treatment-naïve young adults with major depressive disorder

BackgroundCumulative evidence has consistently shown that white matter (WM) disruption is associated with cognitive decline in geriatric depression. However, limited research has been conducted on the correlation between these lesions and cognitive performance in untreated young adults with major depressive disorder (MDD), particularly with the specific segmental alterations of the fibers. MethodDiffusion tensor images were performed on 60 first-episode, treatment-naïve young adult patients with MDD and 54 matched healthy controls (HCs). Automated fiber quantification was applied to calculate the tract profiles of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) to evaluate the WM microstructural organization. Correlation analysis was performed to find the associations between the diffusion properties and cognitive performance. ResultsCompared with HCs, patients with MDD exhibited predominantly different diffusion properties in bilateral uncinate fasciculus (UF), corticospinal tracts (CSTs), left superior longitudinal fasciculus and anterior thalamic radiation. The FA of the temporal cortex portion of right UF was positively correlated with working memory. The MD of the temporal component of left UF was negatively correlated with working memory and positively correlated with symptom severity. Additionally, a positive correlation between the MD of left CST and the psychomotor speed, negative correlation between the MD of left CST and the executive functions and complex attentional processes were observed. ConclusionsOur study validated the alterations in spatial localization of WM microstructure and its correlations with cognitive performance in first-episode, treatment-naïve young adults with MDD. This study added to the knowledge of the neuropathological basis of MDD.