Because transseptal catheterization is felt to be contraindicated in patients with severe kyphoscoliosis, there have been no reports of percutaneous transvenous mitral commissurotomy performed in such patients. This report describes percutaneous transvenous mitral commissurotomy in three patients with severe thoracic kyphoscoliosis, with special emphasis on the transseptal puncture technique. Biplane right atrial angiography and the contrast septal flush method are very useful in landmark selection for a safe transseptal puncture.
Functional meat products to reduce appetite is a feasible approach to decreasing meat consumption, especially in the current global pandemic of overweight and sustainability issues in meat production. The meat itself is highly satiating because of its high protein content. Dietary fibre and plant protein, both naturally occurring macromolecular compounds with appetite-suppressant properties, work synergistically with the meat itself to create a feeling of fullness. Dietary fibre consumption induces satiety by improving structural complexity and chewing time. Increasing protein content improves the volume and texture of the product. The overall effect on appetite varies widely with ingredient and meat product types. By-products, mycoproteins, and insects have the potential to generate functional and sustainable meat products. The incorporation of functional ingredients improves the yield and textural complexity of meat products, albeit at the expense of sensory properties. The complex interactions among food structure, texture, oral processing, and satiety/palatability warrant additional study to inform the design of meat products that maximise the contribution to appetite control. This review aims to provide an overview of the types of ingredients used in the preparation of functional meat products and their effect on controlling appetite.
This study aims to estimate the global, regional, and national burden of depressive disorders in 204 countries and territories from 1990 to 2019. All data were obtained from the 2019 Global Burden of Disease (GBD) study. Age-period-cohort (APC) modeling was conducted to disentangle age, period, and birth cohort effects on depression incidence. We compared these estimates across regions classified based on their socio-demographic index (SDI). The Estimated Annual Percentage Change (EAPC) was calculated for each of the 204 countries and territories to identify the top five countries with increased depression incidence (Spain, Mexico, Malaysia, the United States of America, and Uruguay) and the top five countries with decreased depression incidence (Singapore, Estonia, Cuba, Maldives, and Sri Lanka). The results from APC analysis indicate that although depression incidence has decreased globally, the incidence rate in high SDI regions is still increasing, especially in the younger generations. Findings suggest that currently some populations are in need of receiving more psychological support (i.e., individuals born after 1950s in high SDI regions; males in middle SDI regions). Forthcoming studies could corroborate our findings using individual-level data which may guide future prevention and intervention of depression in high-risk populations or regions.
Hydroxyethylcellulose (HEC) is a non-ionic water-soluble polymer with poor mucoadhesive properties. The mucoadhesive properties of hydroxyethylcellulose can be improved by modifying it through conjugation with molecules containing maleimide groups. Maleimide groups interact with the thiol groups present in cysteine domains in the mucin via Michael addition reaction under physiological conditions to form a strong mucoadhesive bond. This will prolong the residence time of a dosage form containing this modified polymer and drug on mucosal surfaces. In this study HEC was modified by reaction with 4-bromophenyl maleimide in varying molar ratios and the successful synthesis was confirmed using 1H NMR and FTIR spectroscopies. The safety of the newly synthesised polymer derivatives was assessed with in vivo planaria assays and in vitro MTT assay utilising Caco-2 cell line. The synthesized maleimide-functionalised HEC solutions were sprayed onto blank tablets to develop a model dosage form. The physical properties and mucoadhesive behavior of these tablets were evaluated using a tensile test with sheep buccal mucosa. The maleimide-functionalised HEC exhibited superior mucoadhesive properties compared to unmodified HEC.
The development of a Digital Intelligence Quotient (DQ) scale for primary school students is the basis for research on the DQ of primary school students, which helps to scientifically diagnose the level and the current average DQ among Chinese primary school students. This study developed and validated a scale applicable to the assessment of DQ in Chinese primary school students where, the initial scale was first constructed; Then 1109 valid datasets were collected through purposive sampling and divided into Sample A and Sample B; Sample A was subjected to exploratory factor analysis and Sample B was tested by confirmatory factor analysis; The final validated scale consists of 22 items in 7 dimensions: digital identity, digital use, digital safety, digital security, digital emotional intelligence, digital literacy and digital rights. The scale has high reliability and validity and thus can be used as a reliable instrument for assessing DQ in Chinese primary school students.
The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.