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MANF overexpression ameliorates oxidative stress-induced apoptosis of human nucleus pulposus cells by facilitating mitophagy through promoting MFN2 expression
Effectiveness of tacrolimus therapy in refractory ulcerative colitis compared to infliximab with propensity score matching
AbstractThere is insufficient evidence comparing the outcomes of tacrolimus-based remission induction therapy with infliximab in refractory ulcerative colitis (UC) and evidence regarding optimal strategies after tacrolimus-based remission induction therapy. We conducted a multi-institutional retrospective study of patients with UC treated with tacrolimus or infliximab between January 2010 and March 2019. The proportion of clinical remission at week 8 and cumulative colectomy-free rate were examined using propensity score matching analysis. The predictors for colectomy after tacrolimus induction were also investigated. Ninety patients in the tacrolimus group and 151 in the infliximab group were enrolled. The proportion of patients in clinical remission at week 8 was 65.2% in the matched tacrolimus group and 37.3% in the matched infliximab group (P = 0.0016), and the long-term colectomy-free rate was lower in the matched tacrolimus group than in the matched infliximab group (P = 0.0003). After clinical remission with tacrolimus, a serum albumin level of ≤ 3.5 g/dL at week 8 was extracted as a factor predicting colectomy (area under the curve: 0.94). Tacrolimus showed a higher remission induction effect for UC compared to infliximab. However, a high rate of colectomy after transition to maintenance treatment was found to be a concern for tacrolimus therapy.
Patterns and predictors of mortality in the first 24 hours of admission among children aged 1–59 months admitted at a Regional Referral Hospital in South Western Uganda
Most deaths among children under 5 years occur within the first 24 hours of hospital admission from preventable causes such as diarrhea, pneumonia, malaria, and HIV/AIDS. The predictors of these deaths are not yet well documented in our setting. This study aimed to describe the patterns and predictors of these mortalities among children aged 1–59 months at a regional hospital in South Western Uganda. We conducted a prospective cohort study among 208 children aged 1–59 months admitted to Mbarara Regional Referral Hospital. The mortality rate within the first 24 hours was 7.7% (95% CI 4–12) and the median time to death was 7.3(2.62–8.75) hours. Most deaths occurred in infants, with severe pneumonia, severe acute malnutrition, and malaria as leading causes. Factors predicting mortality included admission during the night (AHR: 3.7, 95% CI 1.02–13.53, p-value 0.047) and abnormal neutrophil count(AHR: 3.5, 95% CI 1.10–11.31, p-value 0.034). The study highlights the importance of timely interventions, particularly for infants, and suggests extra monitoring for those admitted at night or with abnormal neutrophil counts.
Comparison of clinical outcomes between double arterial cannulation and single arterial cannulation in type A aortic dissection
Assessing habitat selection parameters of Arabica coffee using BWM and BCM methods based on GIS
Why the gaze behavior of expert physicians and novice medical students differ during a simulated medical interview: A mixed methods study
Human cognition is reflected in gaze behavior, which involves eye movements to fixate or shift focus between areas. In natural interactions, gaze behavior serves two functions: signal transmission and information gathering. While expert gaze as a tool for gathering information has been studied, its underlying cognitive processes remain insufficiently explored. This study investigated differences in gaze behavior and cognition between expert physicians and novice medical students during a simulated medical interview with a simulated patient, drawing implications for medical education. This study employed an exploratory sequential mixed methods design. During the simulated medical interview, participants’ gaze behavior was measured across five areas: the patient’s eyes, face, body trunk, medical chart, and medical questionnaire. A hierarchical Bayesian model analyzed differences in gaze behavior between expert physicians and novice medical students. Then, a semi-structured interview was conducted with participants to discern their perceptions during their gaze behavior; their recorded gaze behavior was presented to them, and analyzed using a qualitative descriptive approach. Model analyses indicated that experts looked at the simulated patient’s eyes less frequently compared to novices during the simulated medical interview. Expert physicians stated that because of the potential for discomfort, looking at the patient’s eyes was less frequent, despite its importance for obtaining diagnostic findings. Conversely, novice medical students did not provide narratives for obtaining such findings, but increased the number of times they did so to improve patient satisfaction. This association between different perceptions of gaze behavior may lead to new approaches in medical education. This study highlights the importance of understanding gaze behavior in the context of medical education and suggests that different motivations underlie the gaze behavior of expert physicians and novice medical students. Incorporating training in effective gaze behavior may improve the quality of patient care and medical students’ learning outcomes.
Creation of a stable vector vortex beam with dual fractional orbital angular momentum
A case-crossover study of air pollution exposure during pregnancy and the risk of stillbirth in Tehran, Iran
Correlation analysis between micro and macro indicators of high modulus modified asphalt for asphalt pavement
The relationship between the micro technical indexes and the macro road performance of high modulus asphalt (HMA) is helpful for understanding its mechanism and performance, and promoting its application. To explore the relationship, two kinds of high modulus asphalt (HMA), LLDPE/SBS composite modified asphalt and rubber/PPA composite modified asphalt were prepared according to the HMA requirements. Secondly, Molecular models of two kinds of HMA were established through molecular dynamics (MD) simulations, and the high temperature parameters of LLDPE/SBS composite modified asphalt were obtained with the two methods, namely the micro molecular dynamics simulation and high temperature rheological test, respectively. Then, through correlation analysis and regression calculation, the estimation formula was established between the results of molecular dynamics simulation and high temperature rheological test. Finally, in order to evaluate and verify the rationality of the estimation formula, the two methods were carried out on the other HMA (rubber/PPA composite modified asphalt). The results show that the shear modulus obtained by molecular dynamics simulation has a good correlation with the high temperature rheological properties. The estimation formula based on molecular dynamics simulation can be used to estimate the high temperature shear modulus of high modulus asphalt, and the relative error is less than 7%, which means that the formula can be used to effectively predict the high temperature performance of high modulus asphalt.
Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimization
Risk factors for photic phenomena in two different multifocal diffractive intraocular lenses
AbstractPhotic phenomena are more pronounced in presbyopia-corrected than in monofocal intraocular lens (IOL), causing dissatisfaction after cataract surgery. Photic Phenomena Test (PPT) quantifies photic phenomena in eyes with two types of presbyopia-corrected IOL. We examined the relationship between preoperative eye shape and pupil diameter. We included patients with PanOptix IOLs (PanOptix group, n = 38; 65.7 ± 9.2 years old) and Synergy IOLs (Synergy group, n = 39; 61.9 ± 9.6 years old), who underwent the PPT between 1 and 3 months after cataract surgery, from January 2021 to April 2023. The relationships between age, sex, pupil diameter, and higher-order corneal aberrations were examined and mean values for PPT measurements were compared between the groups. There was no difference in glare between the two groups. The halo was larger and thicker, and the starburst was larger and stronger in the Synergy group (P < 0.01). Postoperative halo brightness was positively correlated with the corneal coma aberration in the PanOptix group (P < 0.05). The Synergy group showed a positive correlation between the size and brightness of the postoperative halo and starburst and pupil diameter (P < 0.01). PPT, thus, revealed risk factors in eyes with two types of presbyopia-corrected IOL, which can be examined before cataract surgery to provide critical information for IOL selection.
Birth prevalence and determinants of neural tube defects among newborns in Ethiopia: A systematic review and meta-analysis
Background Neural tube defects (NTDs) are complex multifactorial disorders in the neurulation of the brain and spinal cord that develop in humans between 21 and 28 days of conception. Neonates with NTDs may experience morbidity and mortality, with severe social and economic consequences. Therefore, the aim of this systematic review and meta-analysis is to assess the pooled prevalence and determinants for neural tube defects among newborns in Ethiopia. Methods The protocol of this study was registered in the International Prospective Register of Systematic Reviews (PROSPERO Number: CRD42023407095). We systematically searched the databases PubMed, Science Direct, Cochrane Library, Google Scholar and Research Gate. Grey literature was searched on Google. Heterogeneity among studies was assessed using the I2 test statistic and the Cochran Q test statistic. A random effects model was used to estimate the birth prevalence of neural tube defects. Result Twenty-five articles were included in the meta-analysis to estimate the prevalence and determinants of neural tube defects in Ethiopia. A total of 611,354 newborns were included in the analysis. The pooled birth prevalence of neural tube defects was 83.40 (95% CI: 60.78, 106.02) per 10,000 births. The highest and lowest prevalence rates were 130.9 (95% CI: 113.52, 148.29) in Tigray and 28.60 (95% CI: 18.70, 38.50) per 10,000 births in Amhara regional states. Women’s intake of folic acid supplements and planned pregnancy were identified as protective factors for NTDs, while stillbirth history, use of any drugs during pregnancy, exposure to radiation, and pesticides during pregnancy were risk factors for neural tube defects. Conclusion The pooled birth prevalence of neural tube defects in Ethiopia was found to be high. Effective prevention interventions, especially focusing on periconceptional folic acid supplementation as well as folate fortification, should be prioritized alongside nutrition education, maternal health care, and environmental safety measures.
Long non-coding RNA OSTM1-AS1 promotes renal cell carcinoma progression by sponging miR-491-5p and upregulating MMP-9
Constructing individualized follow-up strategies for locally advanced esophageal squamous cell carcinoma patients based on dynamic recurrence risk changes
Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms
Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability. In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor (XGBoost), K-neighbors Regressor (KNN), LGBM Regressor (LightGBM), and CatBoost Regressor (CatBoost). Leveraging a dataset of 21045 samples, factors like Humidity, Ambient temperature, Wind speed, Visibility, Cloud ceiling and Pressure serve as inputs for constructing these machine learning models in forecasting solar energy. Model accuracy is meticulously assessed and juxtaposed using metrics such as coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the CatBoost model emerges as the frontrunner in predicting solar energy, with training values of R2 value of 0.608, RMSE of 4.478 W and MAE of 3.367 W and the testing value is R2 of 0.46, RMSE of 4.748 W and MAE of 3.583 W. SHAP analysis reveal that ambient temperature and humidity have the greatest influences on the value solar energy generated from photovoltaic panel.
Chemical compatibility at the interface of garnet-type Ga-LLZO solid electrolyte and high-energy Li-rich layered oxide cathode for all-solid-state batteries
Zinc oxide nanoparticles foliar use and arbuscular mycorrhiza inoculation retrieved salinity tolerance in Dracocephalum moldavica L. by modulating growth responses and essential oil constituents
Artificial intelligence in dentistry: Assessing the informational quality of YouTube videos
Background and purpose The most widely used social media platform for video content is YouTubeTM. The present study evaluated the quality of information on YouTubeTM on artificial intelligence (AI) in dentistry. Methods This cross-sectional study used YouTubeTM (https://www.youtube.com) for searching videos. The terms used for the search were "artificial intelligence in dentistry," "machine learning in dental care," and "deep learning in dentistry." The accuracy and reliability of the information source were assessed using the DISCERN score. The quality of the videos was evaluated using the modified Global Quality Score (mGQS) and the Journal of the American Medical Association (JAMA) score. Results The analysis of 91 YouTube™ videos on AI in dentistry revealed insights into video characteristics, content, and quality. On average, videos were 22.45 minutes and received 1715.58 views and 23.79 likes. The topics were mainly centered on general dentistry (66%), with radiology (18%), orthodontics (9%), prosthodontics (4%), and implants (3%). DISCERN and mGQS scores were higher for videos uploaded by healthcare professionals and educational content videos(P<0.05). DISCERN exhibited a strong correlation (0.75) with the video source and with JAMA (0.77). The correlation of the video’s content and mGQS, was 0.66 indicated moderate correlation. Conclusion YouTube™ has informative and moderately reliable videos on AI in dentistry. Dental students, dentists and patients can use these videos to learn and educate about artificial intelligence in dentistry. Professionals should upload more videos to enhance the reliability of the content.