Shanghai's Breastfeeding Habits and Related Factors: A Cross-Sectional Research

Sulu Wang^*
*Correspondence: Sulu Wang, Department of Pediatrics, Laval University, Québec, Canada, Email:

Keywords

Gut microbiome • Hormonal • Intermittent fasting

Introduction

The healthcare system, the job market, and the economy. According to the results of a multivariate study, mothers who exclusively breastfed had the following characteristics: knowledge of breastfeeding, intention to exclusively breastfeed throughout pregnancy, and higher levels of satisfaction with postpartum care. With an EBF incidence of over 40% in Shanghai, supporting breastfeeding necessitates action on a number of fronts, including individual traits, workplace conditions for women, breastfeeding knowledge, and health services.

Literature Review

Breastfeeding is essential for a child's survival, sustenance, and physical and mental development. Children may gain from higher IQ scores and maybe lower long-term risks of obesity and diabetes in addition to being shielded from respiratory infections and diarrhoea in the short term. Nursing women are protected from diseases including non-alcoholic fatty liver disease, postpartum depression, breast cancer, ovarian cancer, and even diabetes when they breastfeed. The World Health Organization recommends beginning breastfeeding as soon as possible after birth, having new-borns nurse exclusively for the first six months of life before introducing complementary meals, and continuing to breastfeed until the child is two years old or older. Yet, marketing of milk-based formula for intense breast milk replacement rose by 41% and by 72% globally. 41% globally and by 72% in high- and middleincome nations like Brazil, China, and Turkey. This is true even though only 2 in 5 infants under the age of 6 months are exclusively breastfed worldwide. China has much lower rates of exclusive breastfeeding than the global average [1,2].

Human health may be examined using high-resolution retinal imaging. Finding novel treatments and bringing them to market are the fundamental objectives of pharmaceutical research and development, which is a timeconsuming and expensive process. The entire procedure, from target selection through drug clinical trials, might potentially be eased by AI. Identification of the biological elements that interfere with the disease is the first stage in the development of a new medicine. Throughout the medication development process, thousands of synthetic compounds are produced in an effort to attach to a target and alter that target's behaviour in order to cure a specific illness. Computer-aided drug design and quantitative structure are used in this approach to determine the physicochemical and pharmacokinetic features. Relationships between quantitative structure and attribute or activity deep neural network [3].

Discussion

Deep learning and neural networks built on the predictor and software are used to forecast an object's lipophilicity and solubility. Chem mapper and the similarity ensemble approach are two examples of AI systems that predict drug-target interactions. How advanced are the toxicity testing techniques used to predict the toxicity of a tiny chemical? By removing potentially dangerous substances from preclinical or clinical trials, such forecasts can help the industry save time and money. Clinical trials for new compounds consume the bulk of the time and resources utilised in the drug development process. AI has been used to raise the bar for trial design, patient selection, dose selection, patient adherence, trial monitoring, and endpoint analysis. Although are helpful tools for patients, BNMs can be used for identification and characterization, as well as for dose planning in clinical trials [4-6].

Conclusion

Over the world, there are regularly many pandemic and epidemic outbreaks that are extremely painful and deadly. The COVID-19 pandemic, which has claimed almost six million lives, is presently raging throughout the world. There have been horrific epidemics of the Spanish and other diseases in the past. AI is a helpful tool for detecting, preventing, responding to, and recovering from pandemics or epidemics. It has been demonstrated that deep learning is more effective in tracking pandemics and epidemics. Some helpful AI technologies for epidemic or pandemic forecasting include neural networks, , and the machine learning algorithm. AI offers a number of uses and advantages over traditional methods, but it also has a number of important limitations or challenges. The system's requirement for massive amounts of data, as well as logistical challenges during development, includes.

Acknowledgement

None.

Conflict of Interest

There are no conflicts of interest by author.

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