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Effectiveness of Piper longum Extract as Natural Irrigant on Antimicrobial Properties and Smear Layer Removal: A Scanning Electron Microscope Study
Glutathionylated DNA adducts accumulate in mitochondrial DNA and are regulated by AP endonuclease 1 and tyrosyl-DNA phosphodiesterase 1
Mitochondrial DNA (mtDNA) is crucial for cellular energy production, metabolism, and signaling. Its dysfunction is implicated in various diseases, including mitochondrial disorders, neurodegeneration, and diabetes. mtDNA is susceptible to damage by endogenous and environmental factors; however, unlike nuclear DNA (nDNA), mtDNA lesions do not necessarily lead to an increased mutation load in mtDNA. Instead, mtDNA lesions have been implicated in innate immunity and inflammation. Here, we report a type of mtDNA damage: glutathionylated DNA (GSH-DNA) adducts. These adducts are formed from abasic (AP) sites, key intermediates in base excision repair, or from alkylation DNA damage. Using mass spectrometry, we quantified the GSH-DNA lesion in both nDNA and mtDNA and found its significant accumulation in mtDNA of two different human cell lines, with levels one or two orders of magnitude higher than in nDNA. The formation of GSH-DNA adducts is influenced by TFAM and polyamines, and their levels are regulated by repair enzymes AP endonuclease 1 (APE1) and tyrosyl-DNA phosphodiesterase 1 (TDP1). The accumulation of GSH-DNA adducts is associated with the downregulation of several ribosomal and complex I subunit proteins and the upregulation of proteins related to redox balance and mitochondrial dynamics. Molecular dynamics (MD) simulations revealed that the GSH-DNA lesion stabilizes the TFAM-DNA binding, suggesting shielding effects from mtDNA transactions. Collectively, this study provides critical insights into the formation, regulation, and biological effects of GSH-DNA adducts in mtDNA. Our findings underscore the importance of understanding how these lesions may contribute to innate immunity and inflammation.
Light bulb-inspired high-temperature catalytic depolymerization of polyolefin plastic with high monomer selectivity
Robust trajectory maneuvering of VTOL aircraft using higher-order sliding mode control (HOSMC)
Perceptions and attitudes of healthcare workers towards the use of digital facial recognition application in a health setting in Uganda: An exploratory pilot study
Background Unique patient identification is often challenging in healthcare systems, especially in low- and middle-income countries. Digital facial recognition is a promising alternative to traditional identification methods. This pilot study explores the perceptions and attitudes of healthcare workers towards using facial recognition technology in a healthcare setting in Uganda. Methods We conducted an explorative qualitative study using key informant interviews with healthcare workers in Kampala, Uganda, to assess perceptions and attitudes towards digital facial recognition. We interviewed a total of 10 healthcare workers, including five doctors and five nurses, aged 20–39 years, with at least one year of professional experience. A trained interviewer provided a brief overview and demonstration of the facial recognition application and then used an open-ended interview guide to elicit responses about perceptions and attitudes. The interviews were audio recorded and transcribed verbatim. Data obtained from Key Informant Interviews were manually analyzed using thematic content analysis. Results Overall, the healthcare workers perceived digital facial recognition as a more effective and acceptable way to identify patients who receive service at outpatient clinics. Four themes emerged, including: i) Challenges affecting current patient identification standards, ii) Healthcare workers’ views on facial recognition, iii) Perceived digital facial recognition implementation challenges, and iv) Solutions to challenges of digital facial recognition. The healthcare workers recommended ensuring the protection patients’ images privacy, providing adequate technological infrastructure in clinics, and securing stable internet access for the successful implementation of digital facial recognition. Conclusion Our exploratory study indicates that overall, healthcare workers have a positive perception of the digital facial recognition application. However, it is crucial to acknowledge and address concerns regarding confidentiality and privacy to pave the way for the future implementation of the system.
Assessment of the Sealing Ability and Penetration Depth of Different Endodontic Sealers: A Scanning Electron Microscope Study
MAPK signaling and angiopoietin-2 contribute to endothelial permeability in capillary malformations
Nonsyndromic capillary malformations (CM) are seen predominantly in skin. In Sturge–Weber Syndrome (SWS), CMs occur in the skin, leptomeninges of the brain, and choroid of the eye. >90% of CM are caused by a somatic mutation— GNAQ p.R183Q, the gene encoding the G-protein subunit Gα q . Longitudinal MRI of the brain in one SWS patient suggests developing vascular permeability. We modeled this in a transendothelial electrical resistance assay and found endothelial cells with GNAQ p.R183Q (EC-R183Q) exhibited increased permeability compared to EC wild-type. Increased vascular permeability was confirmed in a Gnaq p.R183Q mouse model. Knockdown of elevated angiopoietin-2 (ANGPT2) in EC-R183Q partially restored the EC barrier, as did a MEK1,2 inhibitor, implicating MAPK/ERK signaling. The combination of ANGPT2 knockdown and trametinib further restored the EC barrier in an additive manner. indicating the two operate in separate pathways. In summary, we found that EC-R183Q exhibits increased permeability, reflecting the compromised endothelial barrier in CMs.
Evidence for divergent cortical organisation in Parkinson’s disease and Lewy Body Dementia
Abstract Dementia is a defining feature of Lewy body disease: its timing and onset distinguish different clinical diagnoses, and its effect on quality of life is profound. However, it remains unclear whether processes leading to cognitive and motor symptoms in Lewy body disease differ. To clarify this, we use in-vivo neuroimaging to assess spatial gradients of inter-regional differences in structural and functional connectivity in 108 people across the Lewy body disease spectrum (46 Parkinson’s with normal cognition (PD-NC), 62 Lewy body dementia (LBD)) and 23 controls. We show divergent structural gradient differences with cognitive impairment: PD-NC show increased inter-regional differentiation, whilst LBD show overall gradient distribution similar to controls despite widespread organisational differences at the regional level. We then assess cellular and molecular underpinnings of these organisational changes. We reveal similarities and also important differences in the drivers of cortical organisation between LBD and PD-NC, particularly in layer 4 excitatory neurons.
Exploring practitioners’ perceptions of health behavior changes associated with psychedelic experiences
Abstract In addition to evidence supporting the use of psychedelics in treating alcohol and tobacco misuse, emerging research suggests that psychedelic experiences may also facilitate beneficial changes in other health behaviors, such as diet, exercise, and meditation. This retrospective, survey-based study explored perceived health behavior changes following psychedelic experiences, from the perspective of 96 psychedelic practitioners, working in both legal and underground contexts. Practitioners reported on perceived changes in their clients’ or patients’ behaviors, including physical activity, diet and nutrition, contemplative practices, and time spent in nature, as well as on their own behavior changes, and potential mechanisms underlying these changes. Behaviors with the highest proportion of clients perceived as having changed in a positive direction included contemplative practices (61.4%), time spent in nature (60.1%), eating patterns (56.9%), and diet and nutrition (56%). These findings suggest that practitioners often perceive psychedelic experiences as associated with positive health behavior change. Future research is needed to validate these findings, explore the extent and mechanisms of potential changes, and assess whether such effects are sustained over the long-term. Understanding these effects may help inform future behavior change interventions and public health policy.
Analysis of arrhythmia and its risk factors in patients with COVID-19
Background To investigate the incidence of arrhythmia in patients with coronavirus disease 2019 (COVID-19) and analyze its risk factors. Methods This was a retrospective cross-sectional study that surveyed 324 COVID-19 patients admitted to the Second Hospital of Shanxi Medical University from 2020 to 2022. General data, vital signs, myocardial enzyme and imaging data of the patients were collected. The characteristics of COVID-19 patients with arrhythmia were analysed, and multivariate logistic regression was used to analyze the risk factors for arrhythmia in patients. The receiver operating characteristic (ROC) curve was plotted to evaluate the efficacy of the regression equation in predicting arrhythmia in COVID-19 patients. Results Compared with COVID-19 patients without arrhythmia, those with arrhythmia were found to have significant differences in heart rate, prothrombin time (PT), activated partial thromboplastin time (APTT), blood glucose, uric acid, serum potassium, serum total cholesterol, high-sensitivity troponin I (hs-TnI), N-terminal pro B-type natriuretic peptide (NT-proBNP), aortic root diameter, left atrial diameter (LAD), left ventricular end-diastolic diameter (LVEDD), left ventricular ejection fraction (LVEF), fractional shortening (FS), end-systolic volume (ESV), and diabetes (all P < 0.05). Multivariate logistic regression analysis revealed that heart rate, PT, hs-TnI, ESV, serum potassium, blood glucose and diabetes were risk factors for arrhythmia in COVID-19 patients (all P < 0.05). ROC curve analysis showed that the area under the curve (AUC) was 0.773 (95%CI: 0.711-0.834, P < 0.001). Conclusion Heart rate, PT, hs-TnI, ESV, serum potassium, blood glucose and diabetes are risk factors for arrhythmia in COVID-19 patients.
Berberine Induces Ferroptosis and Impairs Migration via Glutathione Peroxidase4/Six-transmembrane Epithelial Antigen of Prostate3 Signaling Pathway in Oral Cancer Cells
Ultra high-Q tunable microring resonators enabled by slow light
Classifying schizophrenia subtypes via resting-state EEG complexity networks
The impact of negative emotions on adolescents’ nonsuicidal self-injury thoughts: an integrated application of machine learning and multilevel logistic models
Non-Suicidal Self-Injury (NSSI) is a prevalent and complex behavior among adolescents, often linked to negative emotions such as loneliness, anxiety, and emptiness. Traditional self-report and experimental methods rely on autobiographical recall and are therefore vulnerable to bias and low ecological validity. Accordingly, approaches that repeatedly sample NSSI-related feelings and contexts in daily life such as Ecological Momentary Assessment (EMA) are needed. This study aimed to identify emotional predictors of NSSI thoughts among adolescents using machine learning and multilevel logistic regression. The study included 42 adolescents (aged 12–15 years) who had engaged in NSSI in the past year. Participants reported their mood and NSSI behaviors three times daily over a 14-day EMA period via a smartphone application. Predictor variables included depression, anxiety, loneliness, self-anger, anger towards others, shame, and emptiness. A random forest model identified loneliness (feature importance: 0.40), anxiety (0.18), and emptiness (0.14) as the most significant predictors of NSSI thoughts. Multilevel logistic regression confirmed these findings, showing that each one-unit increase in anxiety, loneliness, and emptiness corresponded to a 24%, 19%, and 24% increase in the odds of experiencing NSSI thoughts, respectively. The ICC value of 0.26 indicated substantial between-individual variance, justifying multilevel modeling. However, random effects analysis revealed no significant individual differences, suggesting uniform effects across participants. These findings highlight loneliness as the most influential predictor, emphasizing the need to address social connections in interventions. Combining machine learning with traditional statistical methods enhanced interpretability, providing practical insights for developing tailored, emotion-focused interventions for adolescents engaging in NSSI.
Effect of Fluoridated and Non-fluoridated Mouthwash on Tooth Movement and Surface Topography Changes of Arch Wires: An In Vivo Study
QnAs with Graham F. Hatfull
Multilayer relaxor ferroelectric polymer stacks as data transmitter for real-time and programmable infrared information encryption
Abstract Infrared information encryption is an emerging technique that leverages infrared (IR) radiation for secure data transmission. However, current IR encryption strategies fail to achieve real-time and precise data transmission, increasing the risk of information leakage. Here, utilizing a bistable adhesion polymer as a transfer medium, a multilayer relaxor ferroelectric polymer (RFP) stack is fabricated without additional thermal loads. An 8-layer RFP stack generates a rectangular temperature wave upon the application of an electric field. Its temperature rapidly increases from 22.1 °C to 26.3 °C and remains above 26 °C for over 8 s under an applied electric field of 80 MV/m at 0.01 Hz. Due to the instantaneous electrocaloric effects (temperature change rate up to10 -8 s/K) upon voltage application, the stack enables a real-time and programmable IR information encryption and decryption strategy. This approach highlights the potential of IR-based communication for secure data transmission, with applications in confidential messaging and optical encryption systems.
Brain benefits of deep learning-based noise management in experienced hearing aid users using functional near infrared spectroscopy
Abstract There is growing interest in using neuroimaging to understanding listening effort in individuals with hearing loss, with a particular focus on how innovative hearing aid features impact listening effort. This study used functional near infrared spectroscopy (fNIRS) to investigate the neural basis of listening effort in experienced hearing aid users. This study evaluated the impact of a new deep learning-based noise management hearing aid feature on cerebral blood oxygenation, with the expectation that it would be associated with less oxygenation in the left prefrontal cortex compared to a standard quiet-listening hearing aid program. Twenty-six experienced hearing aid users repeated sentence-final words from sentences presented in noise while wearing individually prescribed hearing aids in two conditions: a standard-listening program with an omnidirectional microphone setting and a DNN-listening program combining directional microphones with a deep-neural-network-based noise management algorithm. Listening accuracy, subjective listening effort ratings, and prefrontal oxygenation via fNIRS were measured throughout testing. As expected, the DNN-listening program was associated with higher listening accuracy, lower subjective listening effort ratings, and lower fNIRS-measured oxygenation in the left prefrontal cortex relative to the standard-listening program. The utility of fNIRS for hearing aid research and the interaction of listening effort and other cognitive processes are discussed further.
Harnessing agro-waste for the high-efficiency removal of methylene blue using ball-milled magnetic Fe3O4@pistachio shell composites: From waste to resource
The development of a cost-effective and ecologically beneficial method for removing toxic dye molecules from wastewater is urgently needed for environmental and health reasons. Here, four Fe 3 O 4 /PS nanocomposites with different Fe 3 O 4 percentages were fabricated through the mechanochemical technique. This study provided a new monolithic adsorbent from abundant materials via a facile synthetic procedure, which can greatly reduce the problems of the tedious separation of adsorbents from treated waste. The adsorbent is composed of pistachio shell/Fe 3 O 4 composite, which has been used as a highly effective and sustainable adsorbent to eliminate methylene blue (MB) dye under ambient conditions. Diverse characterization analyses, including SEM, EDX, and FTIR techniques, were utilized to investigate the morphology and structure of the synthesized composite. The formed Fe 3 O 4 particles have an average diameter of 274 nm and an average area of 0.11 μm 2 . The batch adsorption methodology was employed, wherein several parameters, such as adsorbent dose, pH, initial dye concentration, adsorption temperature, and contact time, were adjusted to examine their effectiveness and adsorption capacity in removing the MB dye. Furthermore, the adsorption behavior was evaluated by studying the linear and non-linear adsorption isotherms. The maximum MB removal efficiency of 95% was achieved with the optimized parameters of MB dye concentration (2.5 mg/L), adsorbent dose (15 mg), contact time (15 min), and adsorption temperature (25 °C). The selectivity of the developed adsorbent was examined towards 25 mg/L of MB cationic dye and anionic dye (methylene orange), which demonstrated higher removal efficiency for the cationic dye (46.93%) compared to the anionic dye (12.46%). Moreover, the pistachio shell/Fe 3 O 4 adsorbent demonstrated excellent capability to remove MB dye from industrial wastewater samples. Overall, this approach presents a new, sustainable, and effective strategy for mitigating the harmful effects of MB, with potential applications in treated industrial wastewater samples.