Miracle-dz/UniFAR-FTUs huggingface dataset:
Dependence of diamond nanoparticle cytotoxicity on physicochemical parameters: comparative studies of glioblastoma, breast cancer, and hepatocellular carcinoma cell lines

Abstract Reports on the cytotoxicity of diamond nanoparticles (ND) are ambiguous and depend on the physicochemical properties of the material and the tested cell lines. Thus, the aim of this research was to evaluate the influence of thirteen types of diamond nanoparticles, differing in production method, size, and surface functional groups, on their cytotoxicity against four tumor cell lines (T98G, U-118 MG, MCF-7, and Hep G2) and one non-tumor cell line (HFF-1). In order to understand the dependence of diamond nanoparticles on physicochemical properties, the following parameters were analyzed: viability, cell membrane damage, morphology, and the level of intracellular general ROS and mitochondrial superoxide. The performed analyses revealed that all diamond nanoparticles showed no toxicity to MCF-7, Hep G2, and HFF-1 cells. In contrast, the same nanomaterials were moderately toxic for the glioblastoma T98G and U-118 MG cell lines. In general, the effect of the production method did not influence ND toxicity. Some changes in cell response after treatment with modified nanomaterials were observed, with the presence of carboxyl groups having a more detrimental effect than the presence of other functional groups. Although nanoparticles of different sizes caused similar toxicity, nanomaterials with bigger particles caused a more pronounced effect.
Safety and effectiveness of the short (0-1h) high sensitive troponin protocol in real-life practice

Abstract Aim Recent guidelines recommend the use of a short 0-1h high sensitive cardiac troponin (hs-cTn) algorithm in patients presenting with chest pain at the emergency department (ED). This retrospective observational study evaluates the safety and effectiveness of the new 0-1h hs-cTn I protocol in comparison with the standard 0-3h cTn I protocol for the diagnosis of acute myocardial infarction (AMI). Methods A total of two times 100 consecutive chest pain patients presenting at the ED in November/December 2018 (standard 0-3h cTn I group) and in November/December 2020 (short 0-1h hs-cTn I group) were enrolled. Decision making was based upon validated assay-specific cut-off values. Results The new 0-1h hs-cTn I protocol had a sensitivity of 100% (95% CI 83.2–100) and a negative predictive value of 100% to rule out AMI. The accuracy of rule-in was slightly lower with a specificity of 92.5% (95% CI 84.4–97.2). The overall protocol accuracy was 94% (95% CI 87.4–97.8) in the short 0-1h hs-cTn I group compared to 88% (95% CI 80.0–93.6) in the standard 0-3h cTn I group (p-value 0.14). The 0-1h hs-cTn I protocol was associated with a numerically higher rate of early hospital discharge compared to the conventional 0-3h cTn I protocol (47% versus 59%; p-value 0.09) and with a shorter median length of stay for those patients (mean 316 min versus 289 min; p-value 0.09). Conclusion The abbreviated protocol based on the 0-1h hs-cTn I assays is effective and safe for the exclusion of AMI at the ED.
Implications of left atrial volume index in patients with three-vessel coronary disease: A 6.6-year follow-up cohort study

Abstract Background: Risk assessment and treatment stratification for three-vessel coronary disease (TVD) remain challenging. This study aimed to investigate the prognostic value of left atrial volume index (LAVI) with the Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score II, and its association with the long-term prognosis after three strategies (percutaneous coronary intervention [PCI], coronary artery bypass grafting [CABG], and medical therapy [MT]) in patients with TVD. Methods: This study was a post hoc analysis of a large, prospective cohort of patients with TVD in China, that aimed to determine the long-term outcomes after PCI, CABG, or optimal MT alone. A total of 8943 patients with TVD were consecutively enrolled between 2004 and 2011 at Fuwai Hospital. A total of 7818 patients with available baseline LAVI data were included in the study. Baseline, procedural, and follow-up data were collected. The primary endpoint was major adverse cardiac and cerebrovascular events (MACCE), which was a composite of all-cause death, myocardial infarction (MI), and stroke. Secondary endpoints included all-cause death, cardiac death, MI, revascularization, and stroke. Long-term outcomes were evaluated among LAVI quartile groups. Results: During a median follow-up of 6.6 years, a higher LAVI was strongly associated with increased risk of MACCE (Q3: hazard ratio [HR] 1.20, 95% confidence interval [CI] 1.06–1.37, P = 0.005; Q4: HR 1.85, 95%CI 1.64–2.09, P <0.001), all-cause death (Q3: HR 1.41, 95% CI 1.17–1.69, P <0.001; Q4: HR 2.54, 95%CI 2.16–3.00, P <0.001), and cardiac death (Q3: HR 1.81, 95% CI 1.39–2.37, P <0.001; Q4: HR 3.47, 95%CI 2.71–4.43, P <0.001). Moreover, LAVI significantly improved discrimination and reclassification of the SYNTAX score II. Notably, there was a significant interaction between LAVI quartiles and treatment strategies for MACCE. CABG was associated with lower risk of MACCE than MT alone, regardless of LAVI quartiles. Among patients in the fourth quartile, PCI was associated with significantly increased risk of cardiac death compared with CABG (HR: 5.25, 95% CI: 1.97–14.03, P = 0.001). Conclusions: LAVI is a potential index for risk stratification and therapeutic decision-making in patients with three-vessel coronary disease. CABG is associated with improved long-term outcomes compared with MT alone, regardless of LAVI quartiles. When LAVI is severely elevated, PCI is associated with higher risk of cardiac death than CABG.
The default mode network and rumination in individuals at risk for depression

Abstract The default mode network (DMN) is a network of brain regions active during rest and self-referential thinking. Individuals with major depressive disorder (MDD) show increased or decreased DMN activity relative to controls. DMN activity has been linked to a tendency to ruminate in MDD. It is unclear if individuals who are at risk for, but who have no current or past history of depression, also show differential DMN activity associated with rumination. We investigated whether females with high levels of neuroticism with no current or lifetime mood or anxiety disorders (n = 25) show increased DMN activation, specifically when processing negative self-referential information, compared with females with average levels of neuroticism (n = 28). Participants heard criticism and praise during functional magnetic resonance imaging (MRI) scans in a 3T Siemens Prisma scanner. The at-risk group showed greater activation in two DMN regions, the medial prefrontal cortex and the inferior parietal lobule (IPL), after hearing criticism, but not praise (relative to females with average levels of neuroticism). Criticism-specific activation in the IPL was significantly correlated with rumination. Individuals at risk for depression may, therefore, have an underlying neurocognitive vulnerability to use a brain network typically involved in thinking about oneself to preferentially ruminate about negative, rather than positive, information.
An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning

Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in literature and has shown very good performance. However, this performance assumes that the training data is centralized, which is often not the case in real-life scenarios, where data resides in multiple places and only a small portion of it is labeled. Therefore, in this work, we propose an ECG classification system that focuses on preserving data privacy and enhancing overall system efficiency. We analyzed the complexity of previously proposed deep learning-based models and showed that the temporal convolutional network-based models (TCN) were the most efficient. Then, we built on the TCN models a modified split-learning (SL) system that achieves the same classification performance as the basic SL but reduces the communication overhead between the server and the client by 71.7% as well as reducing the computations at the client by 46.5% compared to the original SL system based on the TCN network. Finally, we implement semi-supervised learning in our system to enhance its classification performance by 9.1%–15.7%, when the training data consists only of 10% labeled data. We have tested our proposed system on a test IoT setup and it achieved satisfactory classification accuracy while being private and energy efficient for green-AI applications.
Social media’s influence on momentary emotion based on people’s initial mood: an experimental design

ABSTRACT Can you think of a meme that made you laugh or a political post that made you angry? These examples illustrate how social media use (SMU) impacts how people feel. Similarly, how people feel when they initiate SMU may impact the emotional effects of SMU. Someone feeling happy may feel more positively during SMU, whereas someone feeling sad may feel more negatively. Using an experimental design, we examined whether following SMU, those in a happy mood would experience increases in positive affect (PA) and those in a sad mood would experience increases in negative affect (NA). A large sample of college students (N = 703) were randomly assigned to a happy, sad, or neutral mood induction before SMU. PA and NA were assessed at baseline, post-mood induction, and after SMU. Contrary to hypotheses, after SMU, people in happy moods experienced decreases in PA, and those in sad moods experienced decreases in NA, reflecting SMU having a dampening effect on emotions. PA and NA were significantly lower after SMU compared to baseline and did not vary by condition. How young adults feel when they log onto SMU matters in understanding how SMU impacts PA and NA, but on average, emotional experiences are dampened.
Quantitative phosphoproteomics reveals novel roles of cAMP in plants

3′,5′‐cyclic adenosine monophosphate (cAMP) is finally recognized as an essential signaling molecule in plants where cAMP‐dependent processes include responses to hormones and environmental stimuli. To better understand the role of 3′,5′‐cAMP at the systems level, we have undertaken a phosphoproteomic analysis to elucidate the cAMP‐dependent response of tobacco BY‐2 cells. These cells overexpress a molecular “sponge” that buffers free intracellular cAMP level. The results show that, firstly, in vivo cAMP dampening profoundly affects the plant kinome and notably mitogen‐activated protein kinases, receptor‐like kinases, and calcium‐dependent protein kinases, thereby modulating the cellular responses at the systems level. Secondly, buffering cAMP levels also affects mRNA processing through the modulation of the phosphorylation status of several RNA‐binding proteins with roles in splicing, including many serine and arginine‐rich proteins. Thirdly, cAMP‐dependent phosphorylation targets appear to be conserved among plant species. Taken together, these findings are consistent with an ancient role of cAMP in mRNA processing and cellular programming and suggest that unperturbed cellular cAMP levels are essential for cellular homeostasis and signaling in plant cells.
Research on Source of Error and Analytical Processing of Atmospheric Electric Field Data

The accuracy and consistency of measuring data will directly affect preciseness of their application on the research on regional atmospheric electricity and the lightning warning.By using simulative calculation and comparing the measuring data of atmospheric electric field,this paper points out that the measurement error may be produced by the height of electric field mill and its ambient environment,and there is a certain positive linear correlation between the electric field at different height and that at surface.Because the charge sources generating atmospheric electric field under sunny and thunderstorm condition are different,the statistical and linear fitting methods are proposed to revise the measuring data,respectively.
Population-Based Pharmacodynamic Modeling of Omalizumab in Pediatric Patients with Moderate to Severe Persistent Inadequately Controlled Allergic Asthma

Omalizumab is the first approved anti-immunoglobulin E (IgE) agent for the treatment of moderate to severe persistent inadequately controlled allergic asthma in adults and adolescents (≥ 12 years old). In 2016, it was approved in pediatric patients (6–11 years old). The objective of this study was to quantitatively characterize the relationship between serum free IgE and pulmonary function (as measured by forced expiratory volume in 1 s [FEV1]) in pediatrics using a population-based pharmacodynamic model. Data collected during the steroid-stable period (first 24 weeks) of an omalizumab trial with pediatric asthma patients (Study IA05) were used to build the pediatric IgE–FEV1 model. The previously developed population IgE–FEV1 model in adults/adolescents was adapted to characterize the FEV1 and IgE relationship in pediatrics with different magnitude and onset of response. The pediatric IgE–FEV1 model adequately characterized the IgE–FEV1 relationship in pediatrics, particularly at the extremes of the observed body weights (i.e., ≤ 30 kg) and IgE values at screening (i.e., > 700 IU/mL). The estimated sigmoidal free IgE–FEV1 curves were similar in shape and maximum effect, but the estimated free IgE concentration leading to 50% maximum effect (IC50) in pediatric patients (39.4, 95% confidence interval [CI] 24.3–63.9 ng/mL) was higher than estimated in adults (19.8, 95% CI 15.1–24.5 ng/mL). The model further confirmed that the current omalizumab dosing rationale based on the mean target free IgE level of 25 ng/ml was appropriate. The pediatric model can be used to predict population FEV1 response for omalizumab when combined with an omalizumab pharmacokinetic–IgE model.
Role of the Gut Microbiota and Its Metabolites in Tumorigenesis or Development of Colorectal Cancer

Colorectal cancer (CRC) is the most common cancer of the digestive system with high mortality and morbidity rates. Gut microbiota is found in the intestines, especially the colorectum, and has structured crosstalk interactions with the host that affect several physiological processes. The gut microbiota include CRC‐promoting bacterial species, such as Fusobacterium nucleatum, Escherichia coli, and Bacteroides fragilis, and CRC‐protecting bacterial species, such as Clostridium butyricum, Streptococcus thermophilus, and Lacticaseibacillus paracasei, which along with other microorganisms, such as viruses and fungi, play critical roles in the development of CRC. Different bacterial features are identified in patients with early‐onset CRC, combined with different patterns between fecal and intratumoral microbiota. The gut microbiota may be beneficial in the diagnosis and treatment of CRC; some bacteria may serve as biomarkers while others as regulators of chemotherapy and immunotherapy. Furthermore, metabolites produced by the gut microbiota play essential roles in the crosstalk with CRC cells. Harmful metabolites include some primary bile acids and short‐chain fatty acids, whereas others, including ursodeoxycholic acid and butyrate, are beneficial and impede tumor development and progression. This review focuses on the gut microbiota and its metabolites, and their potential roles in the development, diagnosis, and treatment of CRC.
Anterior fontanel size in Korean nursery newborns and clinical implications of large anterior fontanel: A retrospective cohort, observational study

Anterior fontanel (AF) sizes in newborns are influenced by ethnicity, gender, genetics, nutrition, and other pathological conditions. However, AF sizes in recent Korean newborns have not been reported yet. Thus, this study aimed to understand variation in AF size and clinical implications of large AF sizes in newborns. This cohort study was conducted on nursery newborns born at a University Hospital between September 2019 and August 2020. AF size was measured at 24 to 48 hours after birth. Newborns with a large AF (> 3.6cm) were examined for other pathological reasons using radiological and laboratory tests. Demographic data were analyzed in relation to AF size. A total of 573 newborns were investigated. Their mean gestational age was 38.5 ± 1.2 weeks at birth and their mean birth weight was 3140 ± 450 g. Their mean AF size was 1.85 ± 0.83 cm (90 and 97-percentile of AF size were 2.96 and 3.65 cm, respectively). Among demographic factors, small for gestational age (SGA) weight, SGA head circumference, and multiple births were correlated with larger AF size. Among 18 infants with a large AF, 2 had intracranial abnormalities and 11 had vitamin D deficiency. This is the first recent study on AF sizes of Korean newborn infants. Their mean AF size was the smallest so far. For the largest AF size, a cutoff of 3.65 cm met the 97-percentile of the cohort. Both SGA weight and head circumference were risk factors for large AF size. For the largest AF size, intracranial lesions and vitamin D deficiency as well as SGA presented clinical implications.
Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training

As electroencephalography (EEG) is nonlinear and nonstationary in nature, an imperative challenge for brain-computer interfaces (BCIs) is to construct a robust classifier that can survive for a long time and monitor the brain state stably. To this end, this research aims to improve BCI performance by incorporation of electroencephalographic and cerebral hemodynamic patterns. A motor imagery (MI)-BCI based visual-haptic neurofeedback training (NFT) experiment was designed with sixteen participants. EEG and functional near infrared spectroscopy (fNIRS) signals were simultaneously recorded before and after this transient NFT. Cortical activation was significantly improved after repeated and continuous NFT through time-frequency and topological analysis. A classifier calibration strategy, weighted EEG-fNIRS patterns (WENP), was proposed, in which elementary classifiers were constructed by using both the EEG and fNIRS information and then integrated into a strong classifier with their independent accuracy-based weight assessment. The results revealed that the classifier constructed on integrating EEG and fNIRS patterns was significantly superior to that only with independent information (<inline-formula> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula>10% and <inline-formula> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula>18% improvement respectively), reaching <inline-formula> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula>89% in mean classification accuracy. The WENP is a classifier calibration strategy that can effectively improve the performance of the MI-BCI and could also be used to other BCI paradigms. These findings validate that our proposed methods are feasible and promising for optimizing conventional motor training methods and clinical rehabilitation.
Dysmenorrhea and psychological wellbeing among females with attention deficit hyperactivity disorder

Although rarely examined together, ADHD, emotional regulation (ER), and dysmenorrhea may be associated, which could create additive burdens on psychological well-being (PWB). Clinicians working with ADHD populations may need to take these challenges into consideration to maximize treatment outcomes. This study investigated the relationships among ADHD, dysmenorrhea, ER, and PWB within a sample of 266 adult females with a self-reported ADHD diagnosis. ADHD symptom severity was positively correlated with dysmenorrhea severity, but ER skills were not a significant moderator of this relationship. ADHD symptom severity was negatively correlated with PWB; however, this relationship was not moderated by dysmenorrhea severity nor ER ability. Overall, a positive association between ADHD symptom severity and dysmenorrhea severity was found in our sample. Further research is needed to understand the nature of this association, as well as factors that may contribute to PWB among individuals with these comorbid conditions.
The Dual Labor Market and Workers’ Health Inequality in South Korea

Findings in this study would help better understand the workers’ health status was significantly associated with company size as well as employment type. Strategies for reducing work-related injuries of workers at small-sized workplaces should be prioritized to alleviate inequality in workers’ health according to the size of the workplace. Objective We investigated how worker health inequality is potentially linked to the dual labor market in South Korea. Methods Using the data from the fifth Korean Working Conditions Survey, we analyzed the data from 21,088 paid workers. We used logistic regression analyses to assess whether employment type or company size had a significant effect on workers’ physical and mental health and work-related injuries. Results Workers’ health status was significantly associated with employment type and company size. Nonstandard workers had higher rates of musculoskeletal diseases or injuries, and their self-rated health and psychological well-being were lower than standard workers. Company size showed a significant correlation only for work-related injuries. Conclusions The structure of inequality in Korea’s labor market according to employment type and company size seems to be affecting the health and safety of workers.
On Testing Shock Induced Asymmetries in Time Series

This paper develops a Lagrange multiplier test statistic and its variants to test for the null hypothesis of no asymmetric effects of shocks on time series. In asymmetric time series models that allow for different responses to positive and negative past shocks the likelihood functions are, in general, non-differentiable. By making use of the theory of generalized functions Lagrange multiplier type tests and the resulting asymptotics are derived. The test statistics possess standard asymptotic limiting behavior under the null hypothesis. Monte Carlo experiments illustrate the accuracy of the asymptotic approximation and show that conventional model selection criteria can be used to estimate the required lag length. We provide an empirical application to the U.S. unemployment rate.
Therapeutic applications of contact lens-based drug delivery systems in ophthalmic diseases

Abstract Traditional ophthalmic drugs, such as eye drops, gels and ointments, are accompanied by many problems, including low bioavailability and potential drug side effects. Innovative ophthalmic drug delivery systems have been proposed to overcome the limitations associated with traditional formulations. Recently, contact lens-based drug delivery systems have gained popularity owing to their advantages of sustained drug delivery, prolonged drug retention, improved bioavailability, and few drug side effects. Various methods have been successfully applied to drug-loaded contact lenses and prolonged the drug release time, such as chemical crosslinking, material embedding, molecular imprinting, colloidal nanoparticles, vitamin E modification, drug polymer film/coating, ion ligand polymerization systems, and supercritical fluid technology. Contact lens-based drug delivery systems play an important role in the treatment of multifarious ophthalmic diseases. This review discusses the latest developments in drug-loaded contact lenses for the treatment of ophthalmic diseases, including preparation methods, application in ophthalmic diseases and future prospects.