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A new nanocomposite for multiwalled carbon nanotubes with zinc oxide and silver oxide was prepared by utilizing hydrothermal method with methanol as solvent. Zinc oxide (ZnO) and silver oxide (Ag2O) were synthesized using co-precipitation method under basic medium. They were identified by several techniques such as UV-Vis, X-ray powder diffraction (XRD), scanning electron microscopy (SEM) and EDX analysis. The crystal size of the prepared nano compounds was revealed 17.6, 26.8, 20.3 and 21.5nm for MWCNT (after their functionalized by utilizing acids mixture from sulfuric and nitric acid by ratio 3:1(v/v)), ZnO, Ag2O and MWCNTs/ZnO & Ag2O nanocomposite respectively. The batch adsorption was used for removing two different class as cationic and anionic dyes from its aqueous solutions under various conditions such as pH level, temperature, contact time and agitation speed, the data exhibited a high value to remove dyes methyl green (MG) and erythrosin B (EB) onto the surface nanocomposite were 96.4 and 99.71% respectively. Adsorption equilibrium isotherm appeared the Langmuir model is more fitted than Freundlich to remove erythrosin B dyes with adsorption capacity 184.9 mg/g, while the adsorbed of methyl green dye more fitted with Freundlich isotherm and adsorption capacity is 836.9 mg/g. Thermodynamic parameters (∆G°, ∆H° and ∆S°) have been computed and revealed the negative values for the free energy.

The main aim of this paper is to propose Cubic Spline-Quantum Neural Network (CS-QNN) model for analysis and classification of Electroencephalogram (EEG) signals. Experimental data used here were taken from seven different electrodes. The work has been done in three stages, normalization of the signals, extracting the features by Cubic Spline Technique (CST) and classification using Quantum Neural Network (QNN). The simulation results showed that five types of EEG signals were classified with an average accuracy for seven electrodes that is 94.3% when training 70% of the features while with an average accuracy of 92.84% when training 50% of the features.

Purpose. This case series demonstrate diagnostic features, treatment options, and challenges for Brittle Cornea Syndrome. Observations. Three cases presented with bluish sclera and extremely thin cornea. Genetic workup was performed and confirmed the diagnosis of Brittle Cornea Syndrome, a rare autosomal recessive disorder characterized by corneal thinning and blue sclera. Case 1 was a 4-year-old boy who developed cataract and glaucoma after undergoing right tectonic penetrating keratoplasty (PK) secondary to a spontaneous corneal rupture. Glaucoma was controlled medically. Later, the kid underwent right transcorneal lensectomy and vitrectomy with synechiolysis. After 6 weeks, he sustained graft dehiscence that was repaired using onlay patch graft. Case 2 was a 7-year-old boy who underwent PK in the right eye, then a pericardial patch graft in the left eye following spontaneous corneal rupture. Glaucoma in both eyes was controlled medically. Case 3 was the 2-year-old sister of the 2nd case. She had a pachymetry of 238 μm OD and 254 μm OS. In the 3 cases, parents were instructed to take protective measures for both eyes and to continue with follow-up visits. Also, they were instructed to have regular screenings for late-onset hearing loss, dental abnormalities, and bone deformities. Conclusions. Long-term follow-up of children diagnosed with Brittle Cornea Syndrome is paramount to minimize the morbidity of corneal rupture and late-onset extraocular conditions.

Widespread acceptance of COVID-19 vaccines is crucial for achieving sufficient immunization coverage to end the global pandemic, yet few studies have investigated COVID-19 vaccination attitudes in lower-income countries, where large-scale vaccination is just beginning. We analyze COVID-19 vaccine acceptance across 15 survey samples covering 10 low- and middle-income countries (LMICs) in Asia, Africa and South America, Russia (an upper-middle-income country) and the United States, including a total of 44,260 individuals. We find considerably higher willingness to take a COVID-19 vaccine in our LMIC samples (mean 80.3%; median 78%; range 30.1 percentage points) compared with the United States (mean 64.6%) and Russia (mean 30.4%). Vaccine acceptance in LMICs is primarily explained by an interest in personal protection against COVID-19, while concern about side effects is the most common reason for hesitancy. Health workers are the most trusted sources of guidance about COVID-19 vaccines. Evidence from this sample of LMICs suggests that prioritizing vaccine distribution to the Global South should yield high returns in advancing global immunization coverage. Vaccination campaigns should focus on translating the high levels of stated acceptance into actual uptake. Messages highlighting vaccine efficacy and safety, delivered by healthcare workers, could be effective for addressing any remaining hesitancy in the analyzed LMICs.

Emerging research in empirical economics posits a question on the relation between underlying risk preferences and reflective cognitive ability. In an experimental setting, a preliminary sample of 260 participants undergo a series of incentivized choice experiments to elicit risk preferences and a Cognitive Reflection Test (CRT) to obtain estimates of their reflective ability. We sidestep potential biases by using a Fechner error specification along with a contextualized version of the utility function. Individuals who are more likely to avoid risky outcomes have significantly lower scores on the CRT. The analysis validates a prominent relationship spanning the economics and psychology literature and suggests a potential direction of causal inference for future research.

An Electrocardiogram (ECG) is a test that is done with the objective of monitoring the heartâs rhythm and electrical activity. It is conducted by attaching a specific type of sensor to the subjectâs skin to detect the signals generated by the heartbeats. These signals can reveal significant information about the wellness of the subjectsâ heart state, and cardiologists use them to detect abnormalities. Due to the prevalence of heart diseases amongst individuals around the globe, there is an urgent need to design computer-aided approaches to automatically analyze ECG signals. Recently, computer vision-based techniques have demonstrated remarkable performance in medical image analysis in a variety of applications and use cases. This paper proposes an approach based on Convolutional Autoencoders (CAEs) and Transfer Learning (TL). Our approach is an ensemble way of learning, the most useful features from both the signal itself, which is the input of the CAE, and the spectrogram version of the same signal, which is fed to a convolutional feature extractor named MobileNetV1. Based on the experiments conducted on a dataset collected from 3 well-known hospitals in Baghdad, Iraq, the proposed method claims good performance in classifying four types of problems in the ECG signals. Achieving an accuracy of 97.3% proves that our approach can be remarkably fruitful in situations where access to expert human resources is scarce.

Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disease of the central nervous system that injures the myelin sheath, provoking progressive axonal degeneration and functional impairments. No efficient therapy is available at present to combat such insults, and hence, novel safe and effective alternatives for MS therapy are extremely required. Rutin (RUT) is a flavonoid that exhibits antioxidant, anti-inflammatory, and neuroprotective effects in several brain injuries. The present study evaluated the potential beneficial effects of two doses of RUT in a model of pattern-III lesion of MS, in comparison to the conventional standard drug; dimethyl fumarate (DMF). Demyelination was induced in in male adult C57BL/6 mice by dietary 0.2% (w/w) cuprizone (CPZ) feeding for 6 consecutive weeks. Treated groups received either oral RUT (50 or 100 mg/kg) or DMF (15 mg/kg), along with CPZ feeding, for 6 consecutive weeks. Mice were then tested for behavioral changes, followed by biochemical analyses and histological examinations of the corpus callosum (CC). Results revealed that CPZ caused motor dysfunction, demyelination, and glial activation in demyelinated lesions, as well as significant oxidative stress, and proinflammatory cytokine elevation. Six weeks of RUT treatment significantly improved locomotor activity and motor coordination. Moreover, RUT considerably improved remyelination in the CC of CPZ + RUT-treated mice, as revealed by luxol fast blue staining and transmission electron microscopy. Rutin also significantly attenuated CPZ-induced oxidative stress and inflammation in the CC of tested animals. The effect of RUT100 was obviously more marked than either that of DMF, regarding most of the tested parameters, or even its smaller tested dose. In silico docking revealed that RUT binds tightly within NF-κB at the binding site of the protein-DNA complex, with a good negative score of −6.79 kcal/mol. Also, RUT-Kelch-like ECH-associated protein 1 (Keap1) model clarifies the possible inhibition of Keap1–Nrf2 protein–protein interaction. Findings of the current study provide evidence for the protective effect of RUT in CPZ-induced demyelination and behavioral dysfunction in mice, possibly by modulating NF-κB and Nrf2 signaling pathways. The present study may be one of the first to indicate a pro-remyelinating effect for RUT, which might represent a potential additive benefit in treating MS. Graphical Abstract

Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions. Bill & Melinda Gates Foundation.

Alopecia areata (AA) is a non-scarring tissue-specific autoimmune disorder. Many therapeutic modalities are available for the treatment of AA, but none has yet proven to be uniformly effective. Fractional carbon dioxide (FRCO 2 ) laser has been introduced as a treatment modality for AA. The objective is to evaluate and compare the efficacy and safety of FRCO 2 laser in treatment of AA alone or in combination with betamethasone valerate cream. 30 patients were assigned to one of the following groups, Group A FRCO 2 , Group B FRCO 2 plus betamethasone valerate cream or Group C (betamethasone valerate cream). Patients received eight laser sessions 2 weeks apart, treatment period was 4 months. A statistically significant decrease in SALT score, dystrophic hair and a statistically significant increase in terminal hair was observed in all groups. Patient satisfaction level and reduction in SALT score were significantly higher among FRCO 2 and FRCO 2 plus betamethasone valerate group. However, no statistical significant difference was found between FRCO 2 group and FRCO 2 combined with betamethasone valerate cream group. FRCO 2 laser is a safe and effective treatment modality for AA when used alone or in combination with betamethasone valerate cream. However, it was found superior to betamethasone valerate cream monotherapy.