Breastfeeding Practices and Associated Factors in Shanghai: A Cross-Sectional Study

Lulu Wang^*
*Correspondence: Lulu Wang, Department of Pediatrics, Laval University, Québec, QC G1V 0A6, Canada, Email:

Keywords

Gut microbiome • Hormonal • Intermittent fasting

Introduction

Economy, employment and the workplace, and the healthcare system. Following a multivariate analysis, it was concluded that moms who had a higher percentage of exclusive breastfeeding had the following traits: awareness of breastfeeding, intention to breastfeed throughout pregnancy, and greater satisfaction with postpartum care. Shanghai has an EBF rate of over 40%, and promoting breastfeeding calls for actions at several levels, including personal characteristics, women's job and working situations, breastfeeding expertise, and health services.

Literature Review

Child survival, nourishment, and the physical and mental growth all depend on breastfeeding. In addition to being protected against respiratory infections and diarrhoea in the near term, children may also benefit from better IQ levels and maybe decreased risks of obesity and diabetes in the long run Breastfeeding shields nursing mothers from illnesses such non-alcoholic fatty liver disease, postpartum depression, breast cancer, ovarian cancer, and even diabetes . The World Health Organization advises starting breastfeeding as soon as possible after delivery and that infants should nurse exclusively for the first six months of their lives before introducing supplementary meals and continuing to breastfeed until they are 2 years old or older However, intensive breast milk replacement marketing milk-based formula increased internationally by 41% and by 72% in upper/middle-income countries like Brazil, China, and Turkey .This is despite the fact that only 2 in 5 infants under 6 months of age are exclusively breastfed globally. Even less exclusive breastfeeding occurs in China than the average percentage worldwide.

High-resolution retinal imaging allows for the examination of human health. The main goal of pharmaceutical research and development, which is a timeconsuming and expensive process, is to identify new drugs and bring them to market. The process, from target identification through medication clinical trials, has the potential to be made easier by AI. The first step in developing a new drug is to pinpoint the biological components that interfere with the condition. Thousands of synthetic compounds are created throughout the drug development process so they may attach to a target and change that target's activity to treat a certain ailment. The physicochemical and pharmacokinetic characteristics are ascertained in this procedure using computer-aided drug design and quantitative structure-activity relationships or quantitative structureproperty relationships neural network deep learning.

Discussion

The prediction of an lipophilicity and solubility uses deep learning and neural networks based on the ADMET predictor and ALGOPS software. The AI tools that foretell drug-target interactions include chem mapper and similarity ensemble technique. Deep are the technologies used in toxicity testing to forecast the toxicity of a small chemical? Such predictions can aid industry in saving time and money during preclinical or clinical investigations by excluding potentially harmful compounds. The majority of the time and money spent on the drug development process is spent on clinical trials for novel molecules, and AI has been used to enhance the standard of trial design, patient selection, dosage selection, patient adherence, trial monitoring, and endpoint analysis. While OCR and NLP offer a useful tool for patient identification and characterisation, BNMs have potential use in clinical trial design and dosage selection [1-5].

Conclusion

Numerous pandemic and epidemic outbreaks that cause a great deal of pain and death are frequently seen across the world. With approximately six million fatalities, the COVID-19 epidemic is currently sweeping the globe. The Spanish Flu, AIDS, and other terrible outbreaks have all occurred in the past. AI is a useful tool for pandemic or epidemic detection, prevention, reaction, and recovery. Deep learning has been proven to be more successful in tracking epidemics and pandemics. For forecasting an epidemic or pandemic, other useful AI technologies include neural networks, AMM, and the ML algorithm SVR. In addition to its uses and benefits above conventional approaches, AI has significant drawbacks or difficulties. The system's need for enormous volumes of data, as well as implementation's logistical hurdles, are the main challenges.

Acknowledgement

None.

Conflict of Interest

There are no conflicts of interest by author.

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