IntroductionTurning is a complex locomotor task that can accentuate gait impairments and precipitate freezing of gait (FOG) in Parkinson’s disease (PD). However, gait-derived spatiotemporal parameters during inter-episodic turning remain incompletely characterized. We compared turning-related gait patterns among healthy older adults, patients with PD without FOG, and patients with PD and a history of FOG.MethodsTen healthy older controls, 10 patients with PD without FOG (PD-P), and 10 patients with PD and FOG (FOG-P) performed 90°, 180°, and 360° turns while undergoing optical motion capture. Spatiotemporal gait parameters were analyzed using Bayesian generalized linear models. Trials containing observable FOG episodes were excluded; consequently, the analyses represent inter-episodic turning rather than freezing episodes themselves.ResultsCompared with controls, PD-P participants demonstrated reduced stride width of the internal leg and increased stride width of the external leg, whereas FOG-P participants demonstrated shorter step and stride lengths. Both PD groups predominantly used step-out or mixed turning strategies and required more time and steps to complete larger-amplitude turns.DiscussionGait-derived spatial parameters during turning differed between the PD groups. PD-P participants showed more pronounced mediolateral foot-placement differences, whereas FOG-P participants showed more pronounced reductions in anteroposterior step and stride length. Because the groups differed in overall motor severity and the analyses were intentionally restricted to inter-episodic turning, these findings should be interpreted as descriptive turning gait patterns in patients with a history of FOG rather than as evidence of FOG-specific mechanisms.

Precise control of keratinocyte proliferation and differentiation is critical for oral epithelial regeneration, yet the mechanobiological cues guiding these processes remain incompletely defined. Here, we systematically evaluated how electrospun polycaprolactone (PCL) scaffolds with defined fiber orientations (aligned vs. random) and diameters (600–800 nm, 1.2–1.7 µm, 2.0–2.5 µm) direct gingival keratinocyte fate. Using immortalized human gingival keratinocytes, we assessed cell and nuclear morphology, proliferation dynamics, differentiation marker expression, and the effects of basal keratin (KRT5/KRT14) knockdown. Quantitative morphological analysis revealed scaffold-dependent changes in cell shape: aligned medium-diameter fibers (with fiber diameters of 1.2–1.7 µm) induced pronounced cell and nuclear elongation, whereas random fibers (600–800 nm) promoted larger, more rounded cell and nuclear shapes. Time-resolved EdU assays indicated that aligned scaffolds supported sustained proliferation, whereas random scaffolds elicited a transient proliferative burst followed by a decline. Gene expression analysis (ddPCR) demonstrated that random scaffolds (especially 600–800 nm fibers) upregulated basal keratins (KRT5, KRT14) and early differentiation markers (KRT1, KRT10, KRT4, KRT13) relative to aligned scaffolds. At the protein level, differentiation markers involucrin (IVL) and filaggrin (FLG) were likewise elevated on random scaffolds, corroborating the mRNA findings. Functional KRT5/KRT14 knockdown experiments revealed scaffold-specific dependencies: cells on random scaffolds required these keratins for viability, whereas aligned cultures remained viable upon KRT5/14 loss. Furthermore, KRT5/14 depletion differentially altered downstream differentiation markers (IVL, KRT1) and mechanotransduction markers (LMNB1, YAP1) in a scaffold-dependent manner. Collectively, these findings establish fiber orientation and diameter as key design parameters for controlling keratinocyte fate. As a translational concept, layered scaffolds combining aligned and random fibers may enable spatially controlled proliferation and differentiation in engineered oral epithelia.

Background:The GPT-4 is a large language model (LLM) trained and fine-tuned on an extensive dataset. After the public release of its predecessor in November 2022, the use of LLMs has seen a significant spike in interest, and a multitude of potential use cases have been proposed. In parallel, however, important limitations have been outlined. Particularly, current LLMs encounter limitations, especially in symbolic representation and accessing contemporary data. The recent version of GPT-4, alongside newly released plugin features, has been introduced to mitigate some of these limitations. Objective:Before this background, this work aims to investigate the performance of GPT-3.5, GPT-4, GPT-4 with plugins, and GPT-4 with plugins using pretranslated English text on the German medical board examination. Recognizing the critical importance of quantifying uncertainty for LLM applications in medicine, we furthermore assess this ability and develop a new metric termed "confidence accuracy" to evaluate it. Methods:We used GPT-3.5, GPT-4, GPT-4 with plugins, and GPT-4 with plugins and translation to answer questions from the German medical board examination. Additionally, we conducted an analysis to assess how the models justify their answers, the accuracy of their responses, and the error structure of their answers. Bootstrapping and CIs were used to evaluate the statistical significance of our findings. Results:This study demonstrated that available GPT models, as LLM examples, exceeded the minimum competency threshold established by the German medical board for medical students to obtain board certification to practice medicine. Moreover, the models could assess the uncertainty in their responses, albeit exhibiting overconfidence. Additionally, this work unraveled certain justification and reasoning structures that emerge when GPT generates answers. Conclusions:The high performance of GPTs in answering medical questions positions it well for applications in academia and, potentially, clinical practice. Its capability to quantify uncertainty in answers suggests it could be a valuable artificial intelligence agent within the clinical decision-making loop. Nevertheless, significant challenges must be addressed before artificial intelligence agents can be robustly and safely implemented in the medical domain.

Cell micro-patterning controls cell fate and function and has potential for generating therapeutically usable mesenchymal stromal cell (MSC) populations with precise functions. However, to date, the micro-patterning of human cells in a translational context has been impossible because only ruminant media supplements, e.g. fetal bovine serum (FBS), are established for use with micro-patterns (MPs). Thus, there are currently no good manufacturing practice (GMP)-compliant media available for MPs. This study tested a xenogenic-free human plasma and platelet lysate (hP + PL) medium supplement to determine its compatibility with MPs. Unfiltered hP + PL medium resulted in significant protein deposition, creating a 'carpet-like' layer that rendered MPs ineffective. Filtration (3x/5x) eliminated this effect. Importantly, quantitative comparison using droplet digital PCR revealed that human MSCs in all media types exhibited similar profiles with strong myogenic Calponin 1/Transgelin 2 (TAGLN2) and weaker osteogenic alkaline phosphatase/Runt-related transcription factor 2 marker expression, and much weaker adipogenic (lipoprotein lipase/peroxisome proliferator-activated receptor gamma) and chondrogenic (collagen type II/aggrecan) expression, with profiles being dominated by myogenic markers. Within these similar profiles, an even stronger induction of the myogenic marker TAGLN2 by all hP + PL- compared to FBS-containing media. Overall, this suggested that FBS can be replaced with hP + PL without altering differentiation profiles. However, assessing individual MSC responses to various MP types with defined categories revealed that unfiltered hP + PL medium was unusable. Importantly, FBS- and 3x filtered hP + PL media were comparable in each differentiation category. Summarized, this study recommends 3x filtered hP + PL as a xenogenic-free and potentially GMP-compliant alternative to FBS as a culture medium supplement for micro-patterning cell populations in both basic and translational research that will ensure consistent and reliable MSC micro-patterning for therapeutic use.

Previous studies hint at possible differences in osteogenic, osteoimmunologic, and angiogenetic potential among primary human osteoblasts (HOBs) from different origins (iliac and alveolar bone) within the same patient. In this study, HOBs from the jaw and the fibula were investigated for the first time to gain further knowledge about the similarities and differences on the cellular morphological level.Patient-paired HOB cultures from the jaw and fibula of 14 patients (60.3 ± 11.1 years; male: 9; female: 5) were isolated and further processed. Cells were stained with Calcein and Hoechst 33342, and single-cell morphometric shape analysis was performed. For each osteoblast, the shape descriptors area, length, width, aspect ratio, circularity, roundness, and solidity were determined. A site-specific and a gender-specific comparison were conducted.None of the shape descriptors showed any significant differences between HOBs derived from the jaw and the fibula. The same applied to the gender-specific comparison between osteoblasts from female and male patients. Significant correlations between shape descriptors were found.HOBs from both bones possess a comparable cell shape, which might positively influence the ossification between the recipient and the donor bone. Since cell morphology often reflects cell function, both bones might exhibit comparable osteoblast behavior, adding to the favorable outcomes observed with free fibula flaps in reconstructive surgery.

The Generative Pre-trained Transformer (GPT-4) is a large language model (LLM) trained and fine-tuned on an extensive dataset. After the public release of its predecessor in November 2022, the use of LLMs has seen a significant spike in interest, and a multitude of potential use cases have been proposed. In parallel, however, important limitations have been outlined. Particularly, current LLM encounters limitations, especially in symbolic representation and accessing contemporary data. The recent version of GPT-4, alongside newly released plugin features, has been introduced to mitigate some of these limitations. Before this background, this work aims to investigate the performance of GPT-3.5, GPT-4, GPT-4 with plugins, and GPT-4 with plugins using pre-translated English text on the German medical board examination. Recognizing the critical importance of quantifying uncertainty for LLM applications in medicine, we furthermore assess this ability and develop a new metric termed 'confidence accuracy' to evaluate it. We employed GPT-3.5, GPT-4, GPT-4 with plugins, and GPT-4 with plugins and translation to answer questions from the German medical board examination. Additionally, we conducted a thorough analysis to assess how the models justify their answers, the accuracy of their responses, and the error structure of their answers. Bootstrapping and confidence intervals were utilized to evaluate the statistical significance of our findings. This study demonstrated that available GPT models, as LLM examples, exceeded the minimum competency threshold established by the German medical board for medical students to obtain board certification to practice medicine. Moreover, the models could assess the uncertainty in their responses, albeit exhibiting overconfidence. Additionally, this work unraveled certain justification and reasoning structures that emerge when GPT generates answers. The high performance of GPTs in answering medical questions positions it well for applications in academia and, potentially, clinical practice. Its capability to quantify uncertainty in answers suggests it could be a valuable AI agent within the clinical decision-making loop. Nevertheless, significant challenges must be addressed before AI agents can be robustly and safely implemented in the medical domain.

Micropatterns (MPs) are widely used as a powerful tool to control cell morphology and phenotype. However, methods for determining the effectiveness of how well cells are controlled by the shape of MPs have been inconsistently used and studies rarely report on this topic, indicating lack of standardization. We introduce an evaluation score that quantitatively assesses the MP fabrication quality and effectiveness, which can be broadly used in conjunction with all currently available MP design types. This score uses four simple and quick steps: (i) scoring MP and (ii) background fabrication quality, (iii) defining the type(s) of MP of interest, and (iv) assigning so-called efficiency descriptors describing cell behavior. These steps are based on visual inspection and quick categorization of various aspects of MP fabrication quality and cell behavior, presented in illustrations and microscopy image examples intended to serve as a reference "atlas." To illustrate the advantage of using this score, we determined differences in cell morphology and F-actin intensity between scored versus nonscored cells. These measurements, which could be different in other studies, were chosen because both are understood as markers of cell phenotype and function. We combined intensity-calibrated immunofluorescence microscopy and image-based single cell protein analysis. Most important, significant differences in cell morphology and cytoskeletal protein content between scored versus nonscored cells were noted: the unconditional inclusion of all experimental read-outs (i.e., all MP data regardless of MP quality and effectiveness) into the final results significantly misjudged the experimental readouts versus only including experimental read-outs of quality-controlled and effective MPs, identified by scoring. Specifically, nonscoring underestimated the F-actin intensity per cell and quantitative cellular morphometric descriptors circularity and solidity and overestimated aspect ratio. Scoring improved the precision of cellular readouts, advocating the use of a MP quality and efficiency score as a quantitative decision-supporting tool in deciding whether or not particular MPs should be used for experiments, saving time and money. This simple scoring methodology can be used for improving MP fabrication, comparing results across studies, benefiting basic science studies and potential future clinical use of MPs by introducing standardization.

COVID-19 vaccination has been shown to prevent and reduce the severity of COVID-19 disease. The aim of this study was to explore the cardioprotective effect of COVID-19 vaccination in hospitalized COVID-19 patients. In this retrospective, single-center cohort study, we included hospitalized COVID-19 patients with confirmed vaccination status from July 2021 to February 2022. We assessed outcomes such as acute cardiac events and cardiac biomarker levels through clinical and laboratory data. Our analysis covered 167 patients (69

This study aimed to analyze the effect of COVID-19 vaccination on the occurrence of ARDS in hospitalized COVID-19 patients. The study population of this retrospective, single-center cohort study consisted of hospitalized COVID-19 patients with known vaccination status and chest computed tomography imaging between July 2021 and February 2022. The impact of vaccination on ARDS in COVID-19 patients was assessed through logistic regression adjusting for demographic differences and confounding factors with statistical differences determined using confidence intervals and effect sizes. A total of 167 patients (69% male, average age 58 years, 95% CI [55; 60], 42% fully vaccinated) were included in the data analysis. Vaccinated COVID-19 patients had a reduced relative risk (RR) of developing ARDS (RR: 0.40, 95% CI [0.21; 0.62]). Consequently, non-vaccinated hospitalized patients had a 2.5-fold higher probability of developing ARDS. This risk reduction persisted after adjusting for several confounding variables (RR: 0.64, 95% CI [0.29; 0.94]) in multivariate analysis. The protective effect of COVID-19 vaccination increased with ARDS severity (RR: 0.61, 95% CI [0.37; 0.92]). Particularly, patients under 60 years old were at risk for ARDS onset and seemed to benefit from COVID-19 vaccination (RR: 0.51, 95% CI [0.20; 0.90]). COVID-19 vaccination showed to reduce the risk of ARDS occurrence in hospitalized COVID-19 patients, with a particularly strong effect in patients under 60 years old and those with more severe ARDS.

IntroductionThe last decade has led to rapid developments and increased usage of computational tools at the single-cell level. However, our knowledge remains limited in how extracellular cues alter quantitative macrophage morphology and how such morphological changes can be used to predict macrophage phenotype as well as cytokine content at the single-cell level.MethodsUsing an artificial intelligence (AI) based approach, this study determined whether (i) accurate macrophage classification and (ii) prediction of intracellular IL-10 at the single-cell level was possible, using only morphological features as predictors for AI. Using a quantitative panel of shape descriptors, our study assessed image-based original and synthetic single-cell data in two different datasets in which CD14+ monocyte-derived macrophages generated from human peripheral blood monocytes were initially primed with GM-CSF or M-CSF followed by polarization with specific stimuli in the presence/absence of continuous GM-CSF or M-CSF. Specifically, M0, M1 (GM-CSF-M1, TNFα/IFNγ-M1, GM-CSF/TNFα/IFNγ-M1) and M2 (M-CSF-M2, IL-4-M2a, M-CSF/IL-4-M2a, IL-10-M2c, M-CSF/IL-10-M2c) macrophages were examined.ResultsPhenotypes were confirmed by ELISA and immunostaining of CD markers. Variations of polarization techniques significantly changed multiple macrophage morphological features, demonstrating that macrophage morphology is a highly sensitive, dynamic marker of phenotype. Using original and synthetic single-cell data, cell morphology alone yielded an accuracy of 93% for the classification of 6 different human macrophage phenotypes (with continuous GM-CSF or M-CSF). A similarly high phenotype classification accuracy of 95% was reached with data generated with different stimuli (discontinuous GM-CSF or M-CSF) and measured at a different time point. These comparably high accuracies clearly validated the here chosen AI-based approach. Quantitative morphology also allowed prediction of intracellular IL-10 with 95% accuracy using only original data.DiscussionThus, image-based machine learning using morphology-based features not only (i) classified M0, M1 and M2 macrophages but also (ii) classified M2a and M2c subtypes and (iii) predicted intracellular IL-10 at the single-cell level among six phenotypes. This simple approach can be used as a general strategy not only for macrophage phenotyping but also for prediction of IL-10 content of any IL-10 producing cell, which can help improve our understanding of cytokine biology at the single-cell level.

BackgroundTurning during walking and volitionally modulating walking speed introduces complexity to gait and has been minimally explored.Research questionHow do the spatiotemporal parameters vary between young adults walking at a normal speed and a slower speed while making 90°, 180°, and 360° turns?MethodsIn a laboratory setting, the spatiotemporal parameters of 10 young adults were documented as they made turns at 90°, 180°, and 360°. A generalized linear model was utilized to determine the effect of both walking speed and turning amplitude.ResultsYoung adults volitionally reducing their walking speed while turning at different turning amplitudes significantly decreased their cadence and spatial parameters while increasing their temporal parameters. In conditions of slower movement, the variability of certain spatial parameters decreased, while the variability of some temporal parameters increased.SignificanceThis research broadens the understanding of turning biomechanics in relation to volitionally reducing walking speed. Cadence might be a pace gait constant synchronizing the rhythmic integration of several inputs to coordinate an ordered gait pattern output. Volition might up-regulate or down-regulate this pace gait constant (i.e., cadence) which creates the feeling of modulating walking speed.

While cell morphology has been recognized as a fundamental regulator of cell behavior, few studies have measured the complex cell morphological changes of chondrocytes using quantitative cell morphometry descriptors in relation to inflammation and phenotypic outcome. Acute vs. persistent exposure to IL-1β and how IL-1β modulated dynamic changes in cell morphology in relation to the phenotype, donor and OA grade in healthy and osteoarthritis (OA) chondrocytes was investigated. A panel of quantitative cell morphometry descriptors was measured using an automated high-throughput method. Absolute quantification of gene expression was measured by ddPCR followed by correlation analyses. In OA chondrocytes, chronic IL-1β significantly decreased COL2A1, SOX9, and ACAN, increased IL-6 and IL-8 levels and caused chondrocytes to become less wide, smaller, longer, slimmer, less round and more circular, consistent with a de-differentiated phenotype. In healthy chondrocytes, 3 days after acute (72 h) IL-1β exposure, COL1A2 and IL-6 significantly increased but had minor effects on cell morphology. However, in healthy chondrocytes, persistent IL-1β led to more profound effects in all cell morphology descriptors and chondrocytes expressed significantly less COL2A1 and more IL-6 and IL-8 vs. controls and acutely-stimulated chondrocytes. In both OA and healthy chronically-stimulated chondrocytes, area, width and circularity were sensitive to the persistent presence of the IL-1β cytokine. Moreover, there were many significant and strong correlations among the measured parameters, with several indications of an IL-1β-mediated mechanism. Cell morphology combined with gene expression analysis could guide researchers interested in understanding inflammatory effects in the complex domain of cartilage/chondrocyte biology. Use of quantitative cell morphometry could complement classical approaches by providing numerical data on a large number of cells, thereby providing a biological fingerprint for describing chondrocyte phenotype, which could help to understand how changes in cell morphology lead to disease progression.

IntroductionLittle is known how inflammatory processes quantitatively affect chondrocyte morphology and how single cell morphometric data could be used as a biological fingerprint of phenotype.MethodsWe investigated whether trainable high-throughput quantitative single cell morphology profiling combined with population-based gene expression analysis can be used to identify biological fingerprints that are discriminatory of control vs. inflammatory phenotypes. The shape of a large number of chondrocytes isolated from bovine healthy and human osteoarthritic (OA) cartilages was quantified under control and inflammatory (IL-1β) conditions using a trainable image analysis technique measuring a panel of cell shape descriptors (area, length, width, circularity, aspect ratio, roundness, solidity). The expression profiles of phenotypically relevant markers were quantified by ddPCR. Statistical analysis, multivariate data exploration, and projection-based modelling were used for identifying specific morphological fingerprints indicative of phenotype.ResultsCell morphology was sensitive to both cell density and IL-1β. In both cell types, all shape descriptors correlated with expression of extracellular matrix (ECM)- and inflammatory-regulating genes. A hierarchical clustered image map revealed that individual samples sometimes responded differently in control or IL-1β conditions than the overall population. Despite these variances, discriminative projection-based modeling revealed distinct morphological fingerprints that discriminated between control and inflammatory chondrocyte phenotypes: the most essential morphological characteristics attributable to non-treated control cells was a higher cell aspect ratio in healthy bovine chondrocytes and roundness in OA human chondrocytes. In contrast, a higher circularity and width in healthy bovine chondrocytes and length and area in OA human chondrocytes indicated an inflammatory (IL-1β) phenotype. When comparing the two species/health conditions, bovine healthy and human OA chondrocytes exhibited comparable IL-1β-induced morphologies in roundness, a widely recognized marker of chondrocyte phenotype, and aspect ratio.DiscussionOverall, cell morphology can be used as a biological fingerprint for describing chondrocyte phenotype. Quantitative single cell morphometry in conjunction with advanced methods for multivariate data analysis allows identifying morphological fingerprints that can discriminate between control and inflammatory chondrocyte phenotypes. This approach could be used to assess how culture conditions, inflammatory mediators, and therapeutic modulators regulate cell phenotype and function.

Despite lignin's global abundance and its use in biomedical studies, our understanding of how lignin regulates disease through modulation of cell morphology and associated phenotype of human cells is unknown. We combined an automated high-throughput image cell segmentation technique for quantitatively measuring a panel of cell shape descriptors, droplet digital Polymerase Chain Reaction for absolute quantification of gene expression and multivariate data analyses to determine whether lignin could therapeutically modulate the cell morphology and phenotype of inflamed, degenerating diseased human cells (osteoarthritic (OA) chondrocytes) towards a healthier cell morphology and phenotype. Lignin dose-dependently modified all aspects of cell morphology and ameliorated the diseased shape of OA chondrocytes by inducing a less fibroblastic healthier cell shape, which correlated with the downregulation of collagen 1A2 (COL1A2, a major fibrosis-inducing gene), upregulation of collagen 2A1 (COL2A1, a healthy extracellular matrix-inducing gene) and downregulation of interleukin-6 (IL-6, a chronic inflammatory cytokine). This is the first study to show that lignin can therapeutically target cell morphology and change a diseased cells' function towards a healthier cell shape and phenotype. This opens up novel opportunities for exploiting lignin in modulation of disease, tissue degeneration, fibrosis, inflammation and regenerative medical implants for therapeutically targeting cell function and outcome.