Macrophages in atherosclerotic lesions exhibit a spectrum of behaviours or phenotypes. The phenotypic distribution of monocyte-derived macrophages (MDMs), its correlation with MDM lipid content, and relation to blood lipoprotein densities are not well understood. Of particular interest is the balance between low density lipoproteins (LDL) and high density lipoproteins (HDL), which carry bad and good cholesterol respectively. To address these issues, we have developed a mathematical model for early atherosclerosis in which the MDM population is structured by phenotype and lipid content. The model admits a simpler, closed subsystem whose analysis shows how lesion composition becomes more pathological as the blood density of LDL increases relative to the HDL capacity. We use asymptotic analysis to derive a power-law relationship between MDM phenotype and lipid content at steady-state. This relationship enables us to understand why, for example, lipid-laden MDMs have a more inflammatory phenotype than lipid-poor MDMs when blood LDL lipid density greatly exceeds HDL capacity. We show further that the MDM phenotype distribution always attains a local maximum, while the lipid content distribution may be unimodal, adopt a quasi-uniform profile or decrease monotonically. Pathological lesions exhibit a local maximum in both the phenotype and lipid content MDM distributions, with the maximum at an inflammatory phenotype and near the lipid content capacity respectively. These results illustrate how macrophage heterogeneity arises in early atherosclerosis and provide a framework for future model validation through comparison with single-cell RNA sequencing data.

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Paranoia is a common delusion type found in clinical and non-clinical populations. A hierarchical, dimensional model of paranoia in the general population has been proposed, with four categories representing increasing levels of paranoia: interpersonal sensitivity (IP), mistrust (M), ideas of reference (IR), persecutory ideas (PI). What is currently lacking and could provide insights into etiology is a comprehensive clinical characterization of the lower end of the paranoia spectrum, psychological domains that are associated with symptom severity, and how paranoia and its structure fluctuate over time. This study conducted both cross-sectional and longitudinal surveys with 802 participants from the German population assessing paranoia and general psychopathology. Data was collected through Ecological Momentary Assessment (EMA). Several statistical approaches were used including confirmatory factor analysis (CFA), latent class analysis (LCA) and mixed modelling analyses (ME). Paranoid experiences appear to be a common phenomenon that occur in people with and without mental illness. Subjects clustered into four paranoia severity subgroups (IP, M, IR, PI) and showed significant associations in various psychological domains like increased psychiatric symptoms and maladaptive coping. Paranoia fluctuates over time in all four severity subgroups, but the hierarchical subgrouping was stable. Both persecutory ideations and interpersonal sensitivity were significant predictors of paranoia. Findings provide important insights into the architecture of paranoia in the German population by characterizing their hierarchical, dimensional, and dynamic structure and its link to psychopathology.

BACKGROUND: Exposure to traumatic events is a frequent source of distress, provoking isolated symptoms such as distressing memories (DM) to full-blown post-traumatic stress disorder (PTSD). We aimed to assess the continuum theory using DM as an isolated symptom, and to examine trauma consequences in a exposed to traumatic events.
METHODS: Using data from the National Epidemiologic Study of Alcohol and Related Conditions III, we assessed the prevalence of DM in a trauma exposed sample, and examined their sociodemographic and lifetime psychiatric correlates, comparing three groups: (i) controls (no DM, no PTSD); (ii) participants with isolated DM without PTSD; (iii) participants with PTSD. We estimated the sensitivity and specificity of DM for PTSD diagnosis.
RESULTS: In our sample of 17,505 participants exposed to trauma, 13 % had PTSD and 42 % had DM without PTSD. The sensitivity of DM for the diagnosis of PTSD was 95.14 %, specificity was 51.91 %. Participants with DM and those with PTSD shared the same socio-demographic correlates. Participants with DM reported more lifetime psychiatric disorders (mood disorders - mainly depressive disorders and bipolar type 1 disorder; anxiety disorders - mainly social anxiety disorder, substance use disorders - mainly opioid use disorder and cannabis disorder; eating disorders - mainly binge eating disorder; personality disorders - mainly borderline personality disorder- and suicidality) than controls, but less than participants with PTSD.
CONCLUSIONS: DM represent an intermediate state between well-being and post-traumatic stress disorder; DM is also associated with other psychiatric disorders. It should be considered as a transdiagnostic psychiatric symptom useful for clinicians in identifying psychiatric vulnerability.

Brain injuries resulting from mechanical trauma represent an ongoing global public health issue. Several in vitro and in vivo models for traumatic brain injury (TBI) continue to be developed for delineating the various complex pathophysiological processes involved in its onset and progression. Developing an in vitro TBI model that is based on cortical spheroids is especially of great interest currently because they can replicate key aspects of in vivo brain tissue, including its electrophysiology, physicochemical microenvironment, and extracellular matrix composition. Being able to mechanically deform the spheroids are a key requirement in any effective in vitro TBI model. The spheroids\' shape and size, however, make mechanically loading them, especially in a high-throughput, sterile, and reproducible manner, quite challenging. To address this challenge, we present an idea for a spheroid-based, in vitro TBI model in which the spheroids are mechanically loaded by being spun by a centrifuge. (An experimental demonstration of this new idea will be published shortly elsewhere.) An issue that can limit its utility and scope is that imaging techniques used in 2D and 3D in vitro TBI models cannot be readily applied in it to determine spheroid strains. In order to address this issue, we developed a continuum mechanics-based theory to estimate the spheroids\' strains when they are being spun at a constant angular velocity. The mechanics theory, while applicable here to a special case of the centrifuge-based TBI model, is also of general value since it can help with the further exploration and development of TBI models.

The soil-plant-animal continuum represents an evolving realm in biological research that\'s why this study was undertaken in the middle Gangetic plain region of India. Trace and ultra-trace elements were analyzed in 100 soil samples, 147 feed and fodder samples, as well as 69 blood and 127 hair samples with the help of inductively coupled plasma optical emission spectroscopy (ICP-OES). The levels of trace and ultra-trace elements in the soil were significantly higher than those in the feed, and similarly, the concentrations in the feed were notably higher than those in the blood of dairy cattle. Blood and hair samples from the cattle showed deficiencies in copper (Cu) and manganese (Mn), with reaching approximately 20% and 50%, respectively. Correlation analysis indicated significant (P < 0.05) associations between the trace and ultra-trace elements in plants and the corresponding elements found in cattle\'s hair, specifically for iron (Fe) and molybdenum (Mo). Conversely, a significant (P < 0.05) negative correlation was observed between soil composition and cattle\'s blood, while a positive correlation was evident only in the case of silver content between plant and cattle\'s hair. Regression analyses revealed positive linear relationships between minerals in soils and plants, as well as between plants and cattle. However, the correlation coefficients were statistically insignificant. The regression equations established to predict mineral concentrations in cattle based on soil and plant mineral contents indicated a positive relationship for both trace and ultra-trace elements, suggesting the potential to measure the mineral status in dairy cattle through this approach.

The composition and continuous succession of natural microbial communities during grape growth play important roles in grape health and flavor quality as well as in characterizing the regional wine terroir. This study explored the diversity and dynamics of fruit epidermal microbes at each growth and developmental stage of Ecolly grapes under an extremely simplified eco-cultivation model, analyzed microbial interactions and associations of weather parameters to specific communities, and emphasized metabolic functional characteristics of microecology. The results indicated that the natural microbial community changed significantly during the grape growth phase. The dominant fungal genera mainly included Gibberella, Alternaria, Filobasidium, Naganishia, Ascochyta, Apiotrichum, Comoclathris, and Aureobasidium, and the dominant bacterial genera mainly contained Sediminibacterium, Ralstonia, Pantoea, Bradyrhizobium, Brevundimonas, Mesorhizobium, Planococcus, and Planomicrobium. In summary, filamentous fungi gradually shifted to basidiomycetous yeasts along with fruit ripening, with a decline in the number of Gram-negative bacteria and a relative increase in Gram-positive bacteria. The community assembly process reflects the fact that microbial ecology may be influenced by a variety of factors, but the fungal community was more stable, and the bacterial community fluctuated more from year to year, which may reflect their response to weather conditions over the years. Overall, our study helps to comprehensively profile the ecological characteristics of the grape microbial system, highlights the natural ecological viticulture concept, and promotes the sustainable development of the grape and wine industry.

The highly heterogeneous nature of alcohol use and problems has presented significant challenges to those attempting to understand, treat or prevent what is commonly termed alcohol use disorder (AUD). However, any attempts to capture this complex phenomenon, including the various current criterion of AUD, come with a number of limitations. One particular limitation has been how alcohol problems are represented or understood in ways which do not capture the broad spectrum of alcohol use and harms and the many potential routes to prevention, treatment, and recovery. One possible response to this has been proposed as more explicitly framing or conceptualizing a continuum model of alcohol use and harms. In this commentary, we attempt to identify the key implications of a continuum model for policy and practice, examining the historical and current context of alcohol problem classifications and models. We argue a continuum model of alcohol use and problems holds a number of advantages for advancing public health goals, but also some potential limitations, both of which require further examination.

Alzheimer\'s Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease\'s progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granu la ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer\'s Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.

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