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Abstract Background Artificial intelligence (AI) chatbots have demonstrated proficiency in structured knowledge assessments; however, there is limited research on their performance in scenarios involving diagnostic uncertainty, which requires careful interpretation and complex decision-making. This study aims to evaluate the efficacy of AI chatbots, GPT-4o and Claude-3, in addressing medical scenarios characterized by diagnostic uncertainty relative to Family Medicine residents. Methods Questions with diagnostic uncertainty were extracted from the Progress Tests administered by the Department of Family and Community Medicine at the University of Toronto between 2022 and 2023. Diagnostic uncertainty questions were defined as those presenting clinical scenarios where symptoms, clinical findings, and patient histories do not converge on a definitive diagnosis, necessitating nuanced diagnostic reasoning and differential diagnosis. These questions were administered to a cohort of 320 Family Medicine residents in their first (PGY-1) and second (PGY-2) postgraduate years and inputted into GPT-4o and Claude-3. Errors were categorized into statistical, information, and logical errors. Statistical analyses were conducted using a binomial generalized estimating equation model, paired t-tests, and chi-squared tests. Results Compared to the residents, both chatbots scored lower on diagnostic uncertainty questions (p

Abstract The simulation of chemical reactions and mechanical properties including failure from atoms to the micrometer scale remains a longstanding challenge in chemistry and materials science. Bottlenecks include computational feasibility, reliability, and cost. We introduce a method for reactive molecular dynamics simulations using a clean replacement of non-reactive classical harmonic bond potentials with reactive, energy-conserving Morse potentials, called the Reactive INTERFACE Force Field (IFF-R). IFF-R is compatible with force fields for organic and inorganic compounds such as IFF, CHARMM, PCFF, OPLS-AA, and AMBER. Bond dissociation is enabled by three interpretable Morse parameters per bond type and zero energy upon disconnect. Use cases for bond breaking in molecules, failure of polymers, carbon nanostructures, proteins, composite materials, and metals are shown. The simulation of bond forming reactions is included via template-based methods. IFF-R maintains the accuracy of the corresponding non-reactive force fields and is about 30 times faster than prior reactive simulation methods.

Background: Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis. Methods: Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm2 (2.56 mm2 of epithelium and 24.87 mm2 of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138− cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (n = 73), women with PCOS (n = 91), and RIF patients (n = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model. Results: The AI algorithm consistently and reliably distinguished CD138− and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36–0.93, p = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, p

Background: The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency. Methods: We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition. Results: Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists’ assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women’s samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses. Conclusion: The AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.

The poor cost-effectiveness of mechanized planting (MECP) is the main reason for the low mechanization rate of planting. In this study, we investigated the productivity of the mechanized excavator-based planting of Norway spruce (Picea abies [L.] H. Karst.) seedlings based on data collected by the Risutec Asta documentation system. We also compared the costs of a MECP chain with two different manual planting (MAP) chains, where mounding was carried out by a crawler excavator (EXC) or a continuously advancing mounder (CONT). The MECP of seedlings was carried out using an EXC equipped with a Risutec PM-160 planting device. Generally, the nine study sites in western Finland contained few surface obstacles (e.g., the logging residues had mainly been harvested), which made the conditions very suitable for MECP. The average production time taken by the MECP was 9 h ha-1. The operating hour (G15-h) productivity averaged 215 seedlings G15-h-1 , with the mean planting time being 13.8 s seedling-1. Loading 160 seedlings into the seedling cassette took approximately 10 min (3.8 s seedling-1). Overall, the cost of the MECP was about 5% lower than for the EXC + MAP. However, when productivity was set at

Abstract Background Though next-generation sequencing (NGS) tests like exome sequencing (ES), genome sequencing (GS), and panels derived from exome and genome data (EGBP) are effective for rare diseases, the ideal diagnostic approach is debated. Limited research has explored reanalyzing raw ES and GS data post-negative EGBP results for diagnostics. Results: We analyzed complete ES/GS raw sequencing data from Mayo Clinic's Program for Rare and Undiagnosed Diseases (PRaUD) patients to assess whether supplementary findings could augment diagnostic yield. ES data from 80 patients (59 adults) and GS data from 20 patients (10 adults), averaging 43 years in age, were analyzed. Most patients had renal (n=44) and auto-inflammatory (n=29) phenotypes. Ninety-six cases had negative findings and in four cases additional genetic variants were found, including a variant related to a recently described disease (RRAGD-related hypomagnesemia), a variant missed due to discordant inheritance pattern (COL4A3), a variant with high allelic frequency (NPHS2) in the general population, and a variant associated with an initially untargeted phenotype (HNF1A). Conclusion: ES and GS show diagnostic yields comparable to EGBP for single-system diseases. However, EGBP's limitations in detecting new disease-associated genes underscore the necessity for periodic updates.

Abstract Purpose In a learning health system (LHS), data gathered from clinical practice informs care and scientific investigation. To demonstrate how a novel data and analytics platform can enable an LHS at a regional cancer center by characterizing the care provided to breast cancer patients. Methods Socioeconomic information, tumor characteristics, treatments and outcomes were extracted from the platform and combined to characterize the patient population and their clinical course. Oncologists were asked to identify examples where clinical practice guidelines (CPGs) or policy changes had varying impacts on practice. These constructs were evaluated by extracting the corresponding data. Results Breast cancer patients (5768) seen at the Juravinski Cancer Centre between January 2014 and June 2022 were included. The average age was 62.5 years. The commonest histology was invasive ductal carcinoma (74.6%); 77% were estrogen receptor‐positive and 15.5% were HER2 Neu positive. Breast‐conserving surgery (BCS) occurred in 56%. For the 4294 patients who received systemic therapy, the initial indications were adjuvant (3096), neoadjuvant (828) and palliative (370). Metastases occurred in 531 patients and 495 patients died. Lowest‐income patients had a higher mortality rate. For the adoption of CPGs, the uptake for adjuvant bisphosphonate was very low, 8% as predicted, compared to 64% for pertuzumab, a HER2 targeted agent and 40.2% for CD4/6 inhibitors in metastases. During COVID‐19, the provincial cancer agency issued a policy to shorten the duration of radiation after BCS. There was a significant reduction in the average number of fractions to the breast by five fractions. Conclusion Our platform characterized care and the clinical course of breast cancer patients. Practice changes in response to regulatory developments and policy changes were measured. Establishing a data platform is important for an LHS. The next step is for the data to feedback and change practice, that is, close the loop.

Abstract Background The compounded effect of a migratory background and ageing increases the risk of unequal medical treatment opportunities. The aim of this article is to investigate the social determinants of barriers to health services. Methods The study uses population-based survey data of Russian-speaking migrants (50 + years) residing in Finland (n = 1082, 57% of men, mean age 63 years). Multiple correspondence analysis was performed as a dimension reduction procedure on six barriers to health services. Multiple ordinary least-squares linear regression was used for the predicted score of the barriers as an outcome variable. Results Most of the sociodemographic characteristics were not associated with barriers to health services, except gender, as women tended to face more disadvantages. Migration-related factors, such as the need for interpreters for health services and experienced discrimination, were associated with an increased likelihood of reporting barriers to health services. Using the internet as a primary source of health information was associated with more access barriers to health services. Conclusions Migrants 50 years of age or older face multiple barriers to health services. Given that the healthcare needs increase with age, addressing this issue becomes crucial, necessitating improved access to health services for older migrants.

We are addressing a parabolic equation with fractional derivatives in time and space that governs the scaling limit of continuous-time random walks with anomalous diffusion. For these equations, the fundamental solution represents the probability density of finding a particle released at the origin at time 0 at a given position and time. Using some estimates of the asymptotic behaviour of the fundamental solution, we evaluate the probability of the process returning infinite times to the origin in a heuristic way. Our calculations suggest that the process is always recurrent.

Abstract Background In the United States, rare disease (RD) is defined as a condition that affects fewer than 200,000 individuals. Collectively, RD affects an estimated 30 million Americans. A significant portion of RD has an underlying genetic cause; however, this may go undiagnosed. To better serve these patients, the Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD) was created under the auspices of the Center for Individualized Medicine (CIM) aiming to integrate genomics into subspecialty practice including targeted genetic testing, research, and education. Methods Patients were identified by subspecialty healthcare providers from 11 clinical divisions/departments. Targeted multi-gene panels or custom exome/genome-based panels were utilized. To support the goals of PRaUD, a new clinical service model, the Genetic Testing and Counseling (GTAC) unit, was established to improve access and increase efficiency for genetic test facilitation. The GTAC unit includes genetic counselors, genetic counseling assistants, genetic nurses, and a medical geneticist. Patients receive abbreviated point-of-care genetic counseling and testing through a partnership with subspecialty providers. Results Implementation of PRaUD began in 2018 and GTAC unit launched in 2020 to support program expansion. Currently, 29 RD clinical indications are included in 11 specialty divisions/departments with over 142 referring providers. To date, 1152 patients have been evaluated with an overall solved or likely solved rate of 17.5% and as high as 66.7% depending on the phenotype. Noteworthy, 42.7% of the solved or likely solved patients underwent changes in medical management and outcome based on genetic test results. Conclusion Implementation of PRaUD and GTAC have enabled subspecialty practices advance expertise in RD where genetic counselors have not historically been embedded in practice. Democratizing access to genetic testing and counseling can broaden the reach of patients with RD and increase the diagnostic yield of such indications leading to better medical management as well as expanding research opportunities.

Abstract The aim of this study was to develop a convolutional neural network (CNN) for classifying positron emission tomography (PET) images of patients with and without head and neck squamous cell carcinoma (HNSCC) and other types of head and neck cancer. A PET/magnetic resonance imaging scan with 18F-fluorodeoxyglucose (18F-FDG) was performed for 200 head and neck cancer patients, 182 of which were diagnosed with HNSCC, and the location of cancer tumors was marked to the images with a binary mask by a medical doctor. The models were trained and tested with five-fold cross-validation with the primary data set of 1990 2D images obtained by dividing the original 3D images of 178 HNSCC patients into transaxial slices and with an additional test set with 238 images from the patients with head and neck cancer other than HNSCC. A shallow and a deep CNN were built by using the U-Net architecture for classifying the data into two groups based on whether an image contains cancer or not. The impact of data augmentation on the performance of the two CNNs was also considered. According to our results, the best model for this task in terms of area under receiver operator characteristic curve (AUC) is a deep augmented model with a median AUC of 85.1%. The four models had highest sensitivity for HNSCC tumors on the root of the tongue (median sensitivities of 83.3–97.7%), in fossa piriformis (80.2–93.3%), and in the oral cavity (70.4–81.7%). Despite the fact that the models were trained with only HNSCC data, they had also very good sensitivity for detecting follicular and papillary carcinoma of thyroid gland and mucoepidermoid carcinoma of the parotid gland (91.7–100%).

Personal data has become an important resource in today’s market and in platform business models. In fact, the essence of many platform business models is to create value for an individual by offering a free service and to capture value by collecting and selling collected data. In this study, we analyse the drivers for platform business model innovation in the context of personal data used in the healthcare sector. Platform business models are shaped and designed with respect to the internal and external drivers in the market. However, few studies have increased our understanding of the drivers for platform business model innovation. For that, we conducted an exploratory study for five personal data platform providers that enable individuals to control the use of their personal data in digital services by interviewing the personal data platform providers and using company presentation material. The findings show that by adopting a human-centred approach to personal data, giving control over data to the individual, personal data platform providers change how value is created and captured. This research has managerial implications and contributes to the business model innovation literature by extending the current knowledge about drivers for personal data-based platform business model innovation in the healthcare sector.

Abstract Dermatitis herpetiformis (DH) is an inflammatory skin disorder often considered as an extra intestinal manifestation of celiac disease (CeD). Hallmarks of CeD and DH are auto‐antibodies to transglutaminase 2 (TG2) and transglutaminase 3 (TG3), respectively. DH patients have auto‐antibodies reactive with both transglutaminase enzymes. Here it is reported that in DH both gut plasma cells and serum auto‐antibodies are specific for either TG2 or TG3 with no TG2–TG3 cross reactivity. By generating monoclonal antibodies from TG3‐specific duodenal plasma cells of DH patients, three conformational epitope groups are defined. Both TG2‐specific and TG3‐specific gut plasma cells have few immunoglobulin (Ig) mutations, and the two transglutaminase‐reactive populations show distinct selection of certain heavy and light chain V‐genes. Mass spectrometry analysis of TG3‐specific serum IgA corroborates preferential usage of IGHV2‐5 in combination with IGKV4‐1. Collectively, these results demonstrate parallel induction of anti‐TG2 and anti‐TG3 auto‐antibody responses involving separate B‐cell populations in DH patients.

The complex structural and chemical changes that occur during polymerization and pyrolysis critically affect material properties but are difficult to characterize in situ. This work presents a novel, experimentally validated methodology for modeling the complete polymerization and pyrolysis processes for phenolic resin using reactive molecular dynamics. The polymerization simulations produced polymerized structures with mass densities of 1.24 ± 0.01 g/cm3 and Young's moduli of 3.50 ± 0.64 GPa, which are in good agreement with experimental values. The structural properties of the subsequently pyrolyzed structures were also found to be in good agreement with experimental X-ray data for the phenolic-derived carbon matrices, with interplanar spacings of 3.81 ± 0.06 Å and crystallite heights of 10.94 ± 0.37 Å. The mass densities of the pyrolyzed models, 2.01 ± 0.03 g/cm3, correspond to skeletal density values, where the volume of pores is excluded in density calculations for the phenolic resin-based pyrolyzed samples. Young's moduli are underpredicted at 122.36 ± 16.48 GPa relative to experimental values of 146 – 256 GPa for nanoscale amorphous carbon samples.

Land-use change is a perennial driver of biodiversity decline. However, climate change may pose novel pressures on species populations, adding to the mixture of anthropogenic threats. Determining the main causes for species decline is important for effective allocation of conservation efforts and for understanding the linkages between biodiversity decline and climate change. Here, we study the impacts of forest management, urbanization, and changes in spring climatic conditions on the presence of a threatened plant species, Pulsatilla patens. In addition, we examine if topographic heterogeneity has supported the persistence of species populations under climatic change starting from 1961. We modelled the effect of land-use and climate change both separately and jointly, finding that both drivers have individual and potentially direct impacts on the persistence of P. patens. This is in agreement with the historic standpoint of land-use change being the main threat for the species, as well as recent findings of the potentially harmful effects of altered climatic conditions in spring, e.g., through late frost damage following earlier emergence of flowers. However, we found no indication that topographic heterogeneity would offer a buffering effect, suggesting that changes in spring climatic conditions form a tangible threat to this species that is not alleviated in the current landscape. As climate change continues, it is likely that the number and intensity of stressors increases for a variety of species, which warrants further attention to the role of climate change and appropriate conservation action.

Abstract Background In older adults, including those with a migrant background, ill health is associated with less internet use. However, it is not known what are the specific self-perceived barriers to internet use among older migrants with different health conditions. The aim of this study was to investigate the associations between different health conditions and self-perceived barriers to internet use among older migrants. Methods We used the Care, Health and Ageing of Russian-speaking Minority in Finland (CHARM) study, which is a nationally representative survey of community-dwelling Russian-speaking adults aged ≥50 years living in Finland (N=1082, 57% men, mean age 63.2 years, standard deviation 8.4 years, response rate 36%). Postal survey data were collected in 2019. Health indicators were self-rated health (SRH), depressive symptoms, cognitive functioning, and doctor-diagnosed conditions. Linear regression analyses were used to investigate the associations between health indicators and a summary scale consisting of the following barriers of internet use: (1) internet use is too complicated and hard to learn; (2) having concerns about safety issues; (3) internet use is too expensive; (4) physical limitations hinder the internet use; (5) memory problems hinder the internet use. In addition, the two most commonly reported barriers (the first two) were examined separately using logistic regression analyses. The analyses were adjusted for age, sex, education, marital status, local language proficiency, and income support, and the health conditions, and were performed with weights accounting for the survey design and non-response. Results After adjustments, spine/back problems (b=0.13; p=0.049), depressive symptoms (b=0.40; p=0.007), and problems in learning new things (b=0.60; p

BackgroundManaging HIV treatment is a complex multi-dimensional task because of a combination of factors such as stigma and discrimination of some populations who frequently get infected with HIV. In addition, patient-provider encounters have become increasingly multicultural, making effective communication and provision of ethically sound care a challenge.PurposeThis article explores ethical issues that health service providers in the United States and Botswana encountered in their interaction with patients in HIV care.Research designA descriptive qualitative design was used to collect data from health service providers and patients using focused group discussions. This article is based on responses from health service providers only.Participants and contextThis article is based on 11 focused group discussions with a total sample of 71 service providers in seven US sites and one Botswana site.Ethical considerationsEthical review boards at all the study sites reviewed the study protocol and approved it. Ethical review boards of the study's coordinating centers, Rutgers University and the University of California at San Francisco, also approved it. The study participants provided a written informed consent to participate.FindingsHIV service providers encountered ethical challenges in all the four Beauchamp and Childress' biomedical ethics of respect for patients' autonomy, beneficence, justice, and nonmaleficence.DiscussionThe finding that HIV service providers encounter ethical challenges in their interaction with patients is supported by prior studies. The ethical challenges are particularly prominent in multicultural care and resource-constrained care environments.ConclusionProvision of HIV care is fraught with ethical challenges that tend to pose different issues depending on a given care environment. It is important that strong partnerships are developed among key stakeholders in HIV care. In addition, health service providers need to be provided with resources so they can provide quality and ethically sound care.

Glucocorticoids have short- and long-term effects on adrenal gland function and development. RNA sequencing (RNA-seq) was performed to identify early transcriptomic responses to the synthetic glucocorticoid, dexamethasone (Dex), in vitro and in vivo. In total, 1711 genes were differentially expressed in the adrenal glands of the 1-h Dex-treated mice. Among them, only 113 were also considered differentially expressed genes (DEGs) in murine adrenocortical Y-1 cells treated with Dex for 1 h. Gene ontology analysis showed that the upregulated DEGs in the adrenal gland of the 1-h Dex-treated mice were highly associated with the development of neuronal cells, suggesting the adrenal medulla had a rapid response to Dex. Interestingly, only 4.3% of Dex-responsive genes in the Y-1 cell line under Dex treatment for 1 h were differentially expressed under Dex treatment for 24 h. The heatmaps revealed that most early responsive DEGs in Y-1 cells during 1 h of treatment exhibited a transient response. The expression of these genes under treatment for 24 h returned to basal levels similar to that during control treatment. In summary, this research compared the rapid transcriptomic effects of Dex stimulation in vivo and in vitro. Notably, adrenocortical Y-1 cells had a transient early response to Dex treatment. Furthermore, the DEGs had a minimal overlap in the 1-h Dex-treated group in vivo and in vitro.

The rare A673T variant was the first variant found within the amyloid precursor protein (APP) gene conferring protection against Alzheimer's disease (AD). Thereafter, different studies have discovered that the carriers of the APP A673T variant show reduced levels of amyloid beta (Aβ) in the plasma and better cognitive performance at high age. Here, we analyzed cerebrospinal fluid (CSF) and plasma of APP A673T carriers and control individuals using a mass spectrometry-based proteomics approach to identify differentially regulated targets in an unbiased manner. Furthermore, the APP A673T variant was introduced into 2D and 3D neuronal cell culture models together with the pathogenic APP Swedish and London mutations. Consequently, we now report for the first time the protective effects of the APP A673T variant against AD-related alterations in the CSF, plasma, and brain biopsy samples from the frontal cortex. The CSF levels of soluble APPβ (sAPPβ) and Aβ42 were significantly decreased on average 9–26% among three APP A673T carriers as compared to three well-matched controls not carrying the protective variant. Consistent with these CSF findings, immunohistochemical assessment of cortical biopsy samples from the same APP A673T carriers did not reveal Aβ, phospho-tau, or p62 pathologies. We identified differentially regulated targets involved in protein phosphorylation, inflammation, and mitochondrial function in the CSF and plasma samples of APP A673T carriers. Some of the identified targets showed inverse levels in AD brain tissue with respect to increased AD-associated neurofibrillary pathology. In 2D and 3D neuronal cell culture models expressing APP with the Swedish and London mutations, the introduction of the APP A673T variant resulted in lower sAPPβ levels. Concomitantly, the levels of sAPPα were increased, while decreased levels of CTFβ and Aβ42 were detected in some of these models. Our findings emphasize the important role of APP-derived peptides in the pathogenesis of AD and demonstrate the effectiveness of the protective APP A673T variant to shift APP processing towards the non-amyloidogenic pathway in vitro even in the presence of two pathogenic mutations.

Current urban green space planning processes are typically guided by greenspace-to-residence proximity indicators. Alone, these indicators are unable to assess, and respond, to the growing socio-spatial polarisation of environmental risks, impacts, and benefits among distinct societal groups within a city. In this research, we present a municipal green space planning tool for measuring the spatial distribution of urban green space diversity within a city, developed using a novel spatial application of the Shannon diversity index to urban green spaces. We illustrate the use of this planning tool in the case of Vantaa, a city in Southern Finland, and examine the association of this diversity indicator with three spatially polarising socio-demographic factors in the region. Contrary to the dominant environmental distributive justice narrative, and stark trends towards socio-spatial polarisation observed in many cities internationally, our findings reveal that urban green space diversity does not currently correlate with income, age structure, or share of population with a foreign background in Vantaa. However, given the changing social dynamics present in our case city, ongoing monitoring and evaluation of who benefits and how from ecosystem service benefits of a diverse urban green space network is necessary. This research offers a practical tool for urban green space planners in European cities facing similar periods of significant differential spatial expansion and changing social dynamics.

IntroductionLipoprotein(a) (Lp(a)) is an LDL-like particle with an additional apolipoprotein (apo)(a) covalently attached. Elevated levels of circulating Lp(a) are a risk factor for atherosclerosis. A proinflammatory role for Lp(a) has been proposed, but its molecular details are incompletely defined.Methods and resultsTo explore the effect of Lp(a) on human macrophages we performed RNA sequencing on THP-1 macrophages treated with Lp(a) or recombinant apo(a), which showed that especially Lp(a) induces potent inflammatory responses. Thus, we stimulated THP-1 macrophages with serum containing various Lp(a) levels to investigate their correlations with cytokines highlighted by the RNAseq, showing significant correlations with caspase-1 activity and secretion of IL-1β and IL-18. We further isolated both Lp(a) and LDL particles from three donors and then compared their atheroinflammatory potentials together with recombinant apo(a) in primary and THP-1 derived macrophages. Compared with LDL, Lp(a) induced a robust and dose-dependent caspase-1 activation and release of IL-1β and IL-18 in both macrophage types. Recombinant apo(a) strongly induced caspase-1 activation and IL-1β release in THP-1 macrophages but yielded weak responses in primary macrophages. Structural analysis of these particles revealed that the Lp(a) proteome was enriched in proteins associated with complement activation and coagulation, and its lipidome was relatively deficient in polyunsaturated fatty acids and had a high n-6/n-3 ratio promoting inflammation.DiscussionOur data show that Lp(a) particles induce the expression of inflammatory genes, and Lp(a) and to a lesser extent apo(a) induce caspase-1 activation and IL-1 signaling. Major differences in the molecular profiles between Lp(a) and LDL contribute to Lp(a) being more atheroinflammatory.

Abstract Mutations in the COL13A1 gene result in congenital myasthenic syndrome type 19 (CMS19), a disease of neuromuscular synapses and including various skeletal manifestations, particularly facial dysmorphisms. The phenotypic consequences in Col13a1 null mice (Col13a1 −/− ) recapitulate the muscle findings of the CMS19 patients. Collagen XIII (ColXIII) is exists as two forms, a transmembrane protein and a soluble molecule. While the Col13a1 −/− mice have poorly formed neuromuscular junctions, the prevention of shedding of the ColXIII ectodomain in the Col13a1 tm/tm mice results in acetylcholine receptor clusters of increased size and complexity. In view of the bone abnormalities in CMS19, we here studied the tubular and calvarial bone morphology of the Col13a1 −/− mice. We discovered several craniofacial malformations, albeit less pronounced ones than in the human disease, and a reduction of cortical bone mass in aged mice. In the Col13a1 tm/tm mice, where ColXIII is synthesized but the ectodomain shedding is prevented due to a mutation in a protease recognition sequence, the cortical bone mass decreased as well with age and the cephalometric analyses revealed significant craniofacial abnormalities but no clear phenotypical pattern. To conclude, our data indicates an intrinsic role for ColXIII, particularly the soluble form, in the upkeep of bone with aging and suggests the possibility of previously undiscovered bone pathologies in patients with CMS19.