Miracle-dz/UniFAR-FTUs huggingface dataset:
Methacrylate Polymers With “Flipped External” Ester Groups: A Review

Current resin composites have favorable handling and upon polymerization initial physical properties that allow for efficient material replacement of removed carious tooth structure. Dental resin composites have long term durability limitations due to the hydrolysis of ester bonds within the methacrylate based polymer matrix. This article outlines the importance of ester bonds positioned internal to the carbon-carbon double bond in current methacrylate monomers. Water and promiscuous salivary/bacterial esterase activity can initiate ester bond hydrolysis that can sever the polymer backbone throughout the material. Recent studies have custom synthesized, with the latest advances in modern organic chemical synthesis, a novel molecule named ethylene glycol bis (ethyl methacrylate) (EGEMA). EGEMA was designed to retain the reactive acrylate units. Upon intermolecular polymerization of vinyl groups, EGEMA ester groups are positioned outside the backbone of the polymer chain. This review highlights investigation into the degradation resistance of EGEMA using buffer, esterase, and microbial storage assays. Material samples of EGEMA had superior final physical and mechanical properties than traditional ethylene glycol dimethacrylate (EGDMA) in all degradation assays. Integrating bioinformatics-based biodegradation predictions to the experimental results of storage media analyzed by LC/GC-MS revealed that hydrolysis of EGEMA generated small amounts of ethanol while preserving the strength bearing polymer backbone. Prior studies support investigation into additional custom synthesized methacrylate polymers with “flipped external” ester groups. The long term goal is to improve clinical durability compared to current methacrylates while retaining inherent advantages of acrylic based chemistry, which may ease implementation of these novel methacrylates into clinical practice.
Alteration of Visuospatial System as an Early Marker of Cognitive Decline: A Double-Center Neuroimaging Study

Amnestic-type mild cognitive impairment (a-MCI) represents the prodromal phase of Alzheimer's disease associated with a high conversion rate to dementia and serves as a potential golden period for interventions. In our study, we analyzed the role of visuospatial (VS) functions and networks in the recognition of a-MCI. We examined 78 participants (32 patients and 46 controls) in a double-center arrangement using neuropsychology, structural, and resting-state functional MRI. We found that imaging of the lateral temporal areas showed strong discriminating power since in patients only the temporal pole (F = 5.26, p = 0.034) and superior temporal gyrus (F = 8.04, p < 0.001) showed reduced cortical thickness. We demonstrated significant differences between controls and patients in various neuropsychological results; however, analysis of cognitive subdomains revealed that the largest difference was presented in VS skills (F = 8.32, p < 0.001). Functional connectivity analysis of VS network showed that patients had weaker connectivity between the left and right frontotemporal areas, while stronger local connectivity was presented between the left frontotemporal structures (FWE corrected p < 0.05). Our results highlight the remarkable potential of examining the VS system in the early detection of cognitive decline. Since resting-state setting of functional MRI simplifies the possible automatization of data analysis, detection of VS system alterations might provide a non-invasive biomarker of a-MCI.
mTORC1 Inhibition Protects Human Regulatory T Cells From Granzyme-B-Induced Apoptosis

Regulatory T cells (Tregs) have shown great promise as a means of cellular therapy in a multitude of allo- and auto-immune diseases—due in part to their immunosuppressive potency. Nevertheless, the clinical efficacy of human Tregs in patients has been limited by their poor in vivo homeostasis. To avert apoptosis, Tregs require stable antigenic (CD3ζ/T-cell-receptor-mediated), co-stimulatory (CD28-driven), and cytokine (IL-2-dependent) signaling. Notably, this sequence of signals supports an activated Treg phenotype that includes a high expression of granzymes, particularly granzyme B (GrB). Previously, we have shown that aside from the functional effects of GrB in lysing target cells to modulate allo-immunity, GrB can leak out of the intracellular lysosomal granules of host Tregs, initiating pro-apoptotic pathways. Here, we assessed the role of inhibiting mechanistic target of rapamycin complex 1 (mTORC1), a recently favored drug target in the transplant field, in regulating human Treg apoptosis via GrB. Using ex vivo models of human Treg culture and a humanized mouse model of human skin allotransplantation, we found that by inhibiting mTORC1 using rapamycin, intracytoplasmic expression and functionality of GrB diminished in host Tregs; lowering human Treg apoptosis by in part decreasing the phosphorylation of S6K and c-Jun. These findings support the already clinically validated effects of mTORC1 inhibition in patients, most notably their stabilization of Treg bioactivity and in vivo homeostasis.
Differential Biodistribution of Adenoviral-Vectored Vaccine Following Intranasal and Endotracheal Deliveries Leads to Different Immune Outcomes

Infectious diseases of the respiratory tract are one of the top causes of global morbidity and mortality with lower respiratory tract infections being the fourth leading cause of death. The respiratory mucosal (RM) route of vaccine delivery represents a promising strategy against respiratory infections. Although both intranasal and inhaled aerosol methods have been established for human application, there is a considerable knowledge gap in the relationship of vaccine biodistribution to immune efficacy in the lung. Here, by using a murine model and an adenovirus-vectored model vaccine, we have compared the intranasal and endotracheal delivery methods in their biodistribution, immunogenicity and protective efficacy. We find that compared to intranasal delivery, the deepened and widened biodistribution in the lung following endotracheal delivery is associated with much improved vaccine-mediated immunogenicity and protection against the target pathogen. Our findings thus support further development of inhaled aerosol delivery of vaccines over intranasal delivery for human application.
Advanced Cross-Correlation Function Application to Identify Arterial Baroreflex Sensitivity Variations From Healthy to Diabetes Mellitus

Diabetes mellitus (DM) is a chronic disease characterized by elevated blood glucose levels, which leads over time to serious damage to the heart, blood vessels, eyes, kidneys, and nerves. DM is of two types–types 1 or 2. In type 1, there is a problem with insulin secretion, and in type 2–insulin resistance. About 463 million people worldwide have diabetes, and 80% of the majority live in low- and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each year. Autonomic neuropathy (AN) is one of the common diabetic complications, leading to failure in blood pressure (BP) control and causing cardiovascular disease. Therefore, early detection of AN becomes crucial to optimize treatment. We propose an advanced cross-correlation function (ACCF) between BP and heart rate with suitable threshold parameters to analyze and detect early changes in baroreflex sensitivity (BRS) in DM with AN (DM+). We studied heart rate (HR) and systolic BP responses during tilt in 16 patients with diabetes mellitus only (DM−), 19 diabetes mellitus with autonomic dysfunction (DM+), and 10 healthy subjects. The ACCF analysis revealed that the healthy and DM groups had different filtered percentages of significant maximum cross-correlation function (CCF) value (p < 0.05), and the maximum CCF value after thresholds was significantly reduced during tilt in the DM+ group (p < 0.05). The maximum CCF index, a parameter for the phase between HR and BP, separated the healthy group from the DM groups (p < 0.05). Due to the maximum CCF index in DM groups being located in the positive range and significantly different from healthy ones, it could be speculated that BRS dysfunction in DM and AN could cause a phase change from lead to lag. ACCF could detect and separate DM+ from DM groups. This fact could represent an advantage of the ACCF algorithm. A common cross-correlation analysis was not easy to distinguish between DM− and DM+. This pilot study demonstrates that ACCF analysis with suitable threshold parameters could explore hidden changes in baroreflex control in DM+ and DM−. Furthermore, the superiority of this ACCF algorithm is useful in distinguishing whether AN is present or not in DM.
Association Between Prognostic Nutritional Index and Prognosis in Patients With Heart Failure: A Meta-Analysis

Background The prognostic nutritional index (PNI) has been proposed as a marker of malnutrition and associated with the prognosis of cardiovascular disease. However, whether PNI can serve as a potential biomarker for the prognosis of heart failure (HF) upon those established risk factors were still controversial. This meta-analysis aimed to generate comprehensive evidence on the prognostic value of PNI in patients with HF. Methods Multiple databases (PubMed, Embase, the Cochrane Library, and Google Scholar) were searched for related studies up to January 31, 2022. Observational studies accessed associations between PNI levels and the prognosis in patients with HF were included for meta-analysis. The hazard ratios (HRs) and 95% confidence intervals (CI) were calculated. Results Fourteen studies, comprising 19,605 patients with HF were included for meta-analysis. The median follow-up duration was 18.5 months. Compared with those with higher PNI (normal nutritional status), patients with HF with lower PNI (malnourished) were associated with a higher risk of all-cause mortality (HR 1.53, 95% CI 1.27–1.85) and composite major adverse cardiac outcomes (MACEs; HR 2.26, 95% CI 1.54–3.31) in the multivariable-adjusted model. Furthermore, when PNI was defined as per 1 increment as a continuous metric, higher PNI was associated with a decrease in all-cause mortality (per 1 increment of PNI: HR 0.94, 95% CI 0.88–0.96) and MACEs (per 1 increment of PNI: HR 0.97, 95% CI 0.95–0.98). Conclusions The PNI can serve as an easily calculated bedside “malnutrition-inflammation” biomarker in HF. Lower PNI was associated with a worse prognosis in patients with HF.
Lipid Profile in Patients With Primary Ovarian Insufficiency: A Systematic Review and Meta-Analysis

Backgrounds A large number of studies have investigated the effect of early menopause on cardiovascular disease (CVD) outcomes and the relationship between the levels of lipid profile and primary ovarian insufficiency (POI). However, the results are inconsistent. The aim of this meta-analysis was to assess whether the levels of total cholesterol (TC), triglyceride (TG), high density lipoprotein (HDL) and low density lipoprotein (LDL) changed in women with POI relative to healthy controls. Methods To identify eligible studies, references published prior to December 2021 were searched in the PubMed, Embase, Cochrane Library and Web of Science databases. DerSimonian-Laird random-effects model was used to estimate the overall standard mean difference (SMD) between POI and healthy control subjects. Subgroup analysis and sensitivity analysis were preformed, and publication bias was assessed. Results A total of 12 studies featuring 846 women with primary ovarian insufficiency and 959 healthy women were selected for analysis. The meta-analysis showed that the levels of TC (SMD: 0.60; 95% CI: 0.32 to 0.89; P<0.0001), TG (SMD: 0.36; 95% CI: 0.12 to 0.60; P=0.003), LDL (SMD: 0.46; 95% CI: 0.16 to 0.76; P=0.003) were significantly increased in women with POI. There was no significant change in the level of HDL (SMD: 0.25; 95% CI: -0.12 to 0.61; P=0.19). Subgroup analysis showed that the heterogeneity in this meta-analysis of the correlation between lipid profile and POI might come from by region, sample size, number of cases, mean body mass index (BMI) value of cases and mean age of cases. Conclusions Scientific evidence suggests that the lipid profile levels were altered in patients with primary ovarian insufficiency compared to healthy controls. Therefore, we recommend that early medical intervention (e.g., hormone replacement therapy) to minimize the risk of CVD morbidity and mortality associated with dyslipidemia in patients with POI. Systematic Review Registration PROSPERO, identifier CRD42021297088
Relationship Between Entrepreneurship Education Curriculum and Agricultural Students’ Satisfaction in China

Developing agriculture is an important way to get rid of poverty and boost economic development. Entrepreneurship, especially entrepreneurship education, is considered to be an important contributor to the realization of the above objectives. Entrepreneurship education has received more and more attention. Improving the entrepreneurial willingness and skills of agricultural students is of great significance to the economic and social development of developing countries. In order to explore the relationship between entrepreneurship education curricula and satisfaction, especially the agricultural students, we conducted a questionnaire survey in 35 universities across the country between 2018 and 2019 to evaluate the entrepreneurship education of agricultural students in Chinese universities. And 1223 valid questionnaires with 7 interviews were obtained. Furthermore, we used the structural equation model to empirically analyze the questionnaire data and found that entrepreneurship practice plays a part in the mediating effect between entrepreneurship curriculum and satisfaction with entrepreneurship education. This paper expands literature on entrepreneurship education and has certain reference significance for training a large number of agricultural entrepreneurial talents the practice of entrepreneurship education in other developing countries.
Idiopathic Hypersomnia—A Dynamic Simulation Model

Aims of the study: Commonly used approach to illness assessment focuses on the patient's actual state supplemented by binary records of past events and conditions. This research project was designed to explain subjective experience in idiopathic hypersomnia (IH) patients influenced by their clinical symptoms and comorbidities. Material and Methods Forty-three IH patients of both sexes (female 60.5%, male 39.5%) were assessed using a detailed structured examination. The interview covered neurologic, psychiatric, and internal medicine anamnesis, medication past and current, substance abuse, work impairment, detailed sleep-related data, specific sleep medication, and a full-length set of questionnaires including depression, quality of life, sleepiness, anxiety, fatigue, insomnia, and sleep inertia. The data were digitized and imported into statistical software (SPSS by IBM), and dynamic simulation software (Vensim by Ventana Systems Inc.) was used to build a causal loop diagram and stocks and flows diagram as a simulation structure. Results The overall raw data and simulation-based patterns fit at 76.1%. The simulation results also identified the parameters that contribute the most to patients' subjective experience. These included sleep inertia, the refreshing potential of naps, the quality of nocturnal sleep, and the social aspects of the patient's life. Psychiatric disorders influence the overall pattern at a surprisingly low level. The influence of medication has been studied in detail. Although its contribution to the dynamics looks marginal at first sight, it significantly influences the contribution of other variables to the overall patient experience of the disease. Conclusion Even the simplified dynamic structure designed by the research team reflects the real-life events in patients with IH at the acceptable level of 76.1% and suggests that a similar structure plays an important role in the course of the disease. Therapeutic focus on the parameters identified by the model should enhance the patients' subjective experience throughout illness duration and might even turn the progress from negative into positive. Further research is needed to understand the dynamics of idiopathic hypersomnia in greater detail to better understand the causes and design therapeutic approaches to improve patients' quality of life.
Efficient and Reliable Geocoding of German Twitter Data to Enable Spatial Data Linkage to Official Statistics and Other Data Sources

More and more, social scientists are using (big) digital behavioral data for their research. In this context, the social network and microblogging platform Twitter is one of the most widely used data sources. In particular, geospatial analyses of Twitter data are proving to be fruitful for examining regional differences in user behavior and attitudes. However, ready-to-use spatial information in the form of GPS coordinates is only available for a tiny fraction of Twitter data, limiting research potential and making it difficult to link with data from other sources (e.g., official statistics and survey data) for regional analyses. We address this problem by using the free text locations provided by Twitter users in their profiles to determine the corresponding real-world locations. Since users can enter any text as a profile location, automated identification of geographic locations based on this information is highly complicated. With our method, we are able to assign over a quarter of the more than 866 million German tweets collected to real locations in Germany. This represents a vast improvement over the 0.18% of tweets in our corpus to which Twitter assigns geographic coordinates. Based on the geocoding results, we are not only able to determine a corresponding place for users with valid profile locations, but also the administrative level to which the place belongs. Enriching Twitter data with this information ensures that they can be directly linked to external data sources at different levels of aggregation. We show possible use cases for the fine-grained spatial data generated by our method and how it can be used to answer previously inaccessible research questions in the social sciences. We also provide a companion R package, nutscoder, to facilitate reuse of the geocoding method in this paper.
Effectivity of ILF Neurofeedback on Autism Spectrum Disorder—A Case Study

Autism spectrum disorder (ASD) is a neural and mental developmental disorder that impacts brain connectivity and information processing. Although application of the infra-low frequency (ILF) neurofeedback procedure has been shown to lead to significant changes in functional connectivity in multiple areas and neuronal networks of the brain, rather limited data are available in the literature for the efficacy of this technique in a therapeutic context to treat ASD. Here we present the case study of a 5-year-old boy with ASD, who received a treatment of 26 sessions of ILF neurofeedback over a 6-month period. A systematic and quantitative tracking of core ASD symptoms in several categories was used to document behavioral changes over time. The ILF neurofeedback intervention decreased the average symptom severity of every category to a remarkable degree, with the strongest effect (80 and 77% mean severity reduction) for physical and sleep symptoms and the lowest influence on behavioral symptoms (15% mean severity reduction). This case study is representative of clinical experience, and thus shows that ILF neurofeedback is a practical and effective therapeutic instrument to treat ASD in children.
Characteristics of Pyroptosis-Related Subtypes and Novel Scoring Tool for the Prognosis and Chemotherapy Response in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is usually associated with poor prognosis and low complete remission (CR) rate due to individual biological heterogeneity. Pyroptosis is a special form of inflammatory programmed cell death related to the progression, treatment response, and prognosis of multiple tumors. However, the potential connection of pyroptosis-related genes (PRGs) and AML still remains unclear. We described the genetic and transcriptional alterations of PRGs in 151 AML samples and presented a consensus clustering of these patients into two subtypes with distinct immunological and prognostic characteristics. Cluster A, associated with better prognosis, was characterized by relatively lower PRG expression, activated immune cells, higher immune scores in the tumor microenvironment (TME), and downregulation of immunotherapy checkpoints. Subsequently, a PRG score was constructed to predict overall survival (OS) of AML patients by using univariate and multivariate Cox regression analysis, and its immunological characteristics and predictive capability were further validated by 1,054 AML samples in external datasets. Besides an immune-activated status, low-PRG score cohorts exhibited higher chemotherapeutic drug sensitivity and significant positive correlation with the cancer stem cell (CSC) index. Combined with age, clinical French-American-British (FAB) subtypes, and PRG score, we successfully constructed a nomogram to effectively predict the 1-/3-/5-year survival rate of AML patients, and the predictive capability was further validated in multiple external datasets with a high area under the curve (AUC) value. The various transcriptomic analysis helps us screen significant pyroptosis-related signatures of AML and provide a new clinical application of PRG scores in predicting the prognosis and benefits of treatment for AML patients.
Context-Aware Human Behaviour Forecasting in Dyadic Interactions

Non-verbal behaviours play an indispensable role in social interaction. People tend to use a wide range of non-verbal channels, including eye gaze, body, and facial gestures, to communicate their intentions and emotions to their interacting partners. Such social signals encourage verbal messages of the communicator can be transmitted to other interlocutors in a facile and transparent manner. On the other hand, an essential aspect of communication behaviours is the dynamic exchange of non-verbal signals among interlocutors for adapting current social norms and building a common ground. This factor suggests that data observed from the interacting partners should be considered when modeling the target individual’s behaviours. Our paper introduces a generative framework with context awareness that captures the influence of the interacting partner’s non-verbal signals on the target individual. The model consists of three components, namely, Context Encoder , Generator , and Discriminator . Context Encoder is employed to extract social signals observed from the interacting partner while Generator and Discriminator are utilized to generate and optimize the target person’s gestures. We verify the efficiency of the framework on two different dyadic interaction datasets. The experimental results demonstrate that compared to baselines, our solution can produce human-like gestures better supporting interaction contexts. Undoubtedly, in dyadic interaction, the influence of the interacting partner’s social signals on the target individual is observable, and the proposed approach can efficiently capture those effects. The source code of our framework can be found at https://github.com/sairlab/Context-Aware-Human-Behavior-Forecasting .
Cell-type classification from microscope imaging

—This paper reviews the use of Machine Learning tools applied to microscope images to investigate the interactions between tumor cells and their micro-environments which is a step towards personalized therapy for cancer. We compared traditional ML methods, both supervised and unsupervised, with deep ML. On labeled images, our models performed satisfyingly, but all generalized poorly to the mixed cell types images suggesting a lack of domain transferability.