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Nucleophilic addition of bulk chemicals with imines using N-functionalized hydroxylamine reagents as precursors
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
Self-assembled hole-selective contact for efficient Sn-Pb perovskite solar cells and all-perovskite tandems
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
C–C bond coupling with sp3 C–H bond via active intermediates from CO2 hydrogenation
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.
Nomophobia among nursing students: prevalence and associated factors
Effectiveness of movement representation techniques in non-specific shoulder pain: a systematic review and meta-analysis
TOPS-speed complex-valued convolutional accelerator for feature extraction and inference
AbstractComplex-valued neural networks process both amplitude and phase information, in contrast to conventional artificial neural networks, achieving additive capabilities in recognizing phase-sensitive data inherent in wave-related phenomena. The ever-increasing data capacity and network scale place substantial demands on underlying computing hardware. In parallel with the successes and extensive efforts made in electronics, optical neuromorphic hardware is promising to achieve ultra-high computing performances due to its inherent analog architecture and wide bandwidth. Here, we report a complex-valued optical convolution accelerator operating at over 2 Tera operations per second (TOPS). With appropriately designed phasors we demonstrate its performance in the recognition of synthetic aperture radar (SAR) images captured by the Sentinel-1 satellite, which are inherently complex-valued and more intricate than what optical neural networks have previously processed. Experimental tests with 500 images yield an 83.8% accuracy, close to in-silico results. This approach facilitates feature extraction of phase-sensitive information, and represents a pivotal advance in artificial intelligence towards real-time, high-dimensional data analysis of complex and dynamic environments.
A GPU-accelerated fuzzy method for real-time CT volume filtering
During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy images results in a higher radiation dose being administered to the patient. Filtering techniques can be utilized to reduce radiation without losing diagnosis capabilities. The objective in this work is to obtain an implementation of a filter capable of processing medical images in real-time. To achieve this we have developed several filter methods based on fuzzy logic, and their GPU implementations, to reduce mixed Gaussian-impulsive noise. These filters have been developed to work in attenuation coefficients so as to not lose any information from the CT scans. The testing volumes come from the Mayo clinic database and consist of CT volumes at full and at simulated low dose. The GPU parallelizations reach speedups of over 2700 and take less than 0.1 seconds to filter more than 300 slices. In terms of quality the filter is competitive with other state of the art algorithmic and AI filters. The proposed method obtains good performance in terms of quality and the parallelization results in real-time filtering.
A single vector system for tunable and homogeneous dual gene expression in Escherichia coli
Role of sclerostin in mastocytosis bone disease
AbstractMastocytosis is a heterogeneous group of disorders, characterized by accumulation of clonal mast cells which can infiltrate several organs, most often spine (70%). The pathogenesis of mastocytosis bone disease is poorly understood. The main aim of the study was to investigate whether neoplastic mast cells may be the source of sclerostin and whether there is an association between sclerostin and selected bone remodeling markers with mastocytosis related bone disease. We assessed sclerostin, bioactive sclerostin, and SOST gene expression in HMC-1.2 human mast cell culture supernatants and plasma of SM patients (n = 39). We showed that human mast cells can secrete sclerostin, and after their stimulation with IL-6, there is a significant increase in SOST gene expression. We observed significantly higher levels of sclerostin in patients diagnosed with more advanced disease. We observed a statistically significant correlation between concentations of sclerostin and its bioactive form and the concentration of alkaline phosphatase (ALP), and between sclerostin and interleukin-6 (IL-6). We observed that significantly higher sclerostin concentrations are present in patients with increased sclerosis of the spongy bone. Sclerostin may serve as a marker of more advanced disease and bone disease in mastocytosis. Further studies are justified to evaluate its role in mastocytosis.
In vivo spontaneous Ca2+ activity in the pre-hearing mammalian cochlea
AbstractThe refinement of neural circuits towards mature function is driven during development by patterned spontaneous calcium-dependent electrical activity. In the auditory system, this sensory-independent activity arises in the pre-hearing cochlea and regulates the survival and refinement of the auditory pathway. However, the origin and interplay of calcium signals during cochlear development is unknown in vivo. Here we show how calcium dynamics in the cochlear neuroepithelium of live pre-hearing mice shape the activity of the inner hair cells (IHCs) and their afferent synapses. Both IHCs and supporting cells (SCs) generate spontaneous calcium-dependent activity. Calcium waves from SCs synchronise the activity of nearby IHCs, which then spreads longitudinally recruiting several additional IHCs via a calcium wave-independent mechanism. This synchronised IHC activity in vivo increases the probability of afferent terminal recruitment. Moreover, the modiolar-to-pillar segregation in sound sensitivity of mature auditory nerve fibres appears to be primed at pre-hearing ages.