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Effects of Ramadan fasting on fetal health: A systematic review

Ong AKW, Yee AL, Fong AJH, Arasoo VJT, Ramadas A

Aust N Z J Obstet Gynaecol, 2023 Oct;63(5):625-637.
PMID: 37283305 DOI: 10.1111/ajo.13706

BACKGROUND: Ramadan fasting (RF) is mandatory for all healthy Muslims in the ninth month of the Islamic calendar. Although pregnant women are exempt from fasting, many make the decision to practise it. Concerns that fasting during pregnancy harms the fetus remain, as there are no firm recommendations regarding its safety.
OBJECTIVES: To provide a systematic review on the effects of RF on fetal health.
SEARCH STRATEGY: We conducted a literature search for peer-reviewed articles through Ovid MEDLINE, PubMed, SCOPUS, EMBASE and Cochrane Central Register of Controlled Trials (CENTRAL) until 31 December 2021.
SELECTION CRITERIA: All case-control and observational cohort studies that reported on fetal outcomes of pregnant women who underwent RF for at least one day during pregnancy are included.
DATA COLLECTION AND ANALYSIS: Two researchers independently reviewed the eligibility of all studies. A third researcher resolved any conflict between researchers. Findings are extracted from eligible papers and presented as narratives.
MAIN RESULTS: Fourteen articles are included based on eligibility criteria, with a total sample size of 2889. Studies demonstrate negative associations between RF and neonatal weight, amniotic fluid index, preterm birth and growth parameters mainly during the second and third trimesters. However, the evidence is not strongly supported.
CONCLUSION: There is limited data to elucidate the relationship between RF and fetal health, hence the need for more studies to provide a better understanding.
Fulltext Biportal endoscopic surgery for lumbar spine herniated discs: a narrative review of its clinical application and outcomes

Awuah WA, Adebusoye FT, Alshareefy Y, Cheng Ng J, Tomas Ferreira AL, Abdus Salam AL, et al.

Ann Med Surg (Lond), 2023 Aug;85(8):3965-3973.
PMID: 37554866 DOI: 10.1097/MS9.0000000000001053

Lumbar disk herniation (LDH) is a common condition affecting millions worldwide. The management of LDH has evolved over the years, with the development of newer surgical techniques that aim to provide better outcomes with minimal invasiveness. One promising emerging technique is biportal endoscopic spinal surgery (BESS), which utilizes specialized endoscopic equipment to treat LDH through two small incisions. This review aims to assess the effectiveness of BESS as a management option for LDH by analyzing the available literature on surgical outcomes and potential complications associated with the technique. Our review shows that BESS is associated with favorable postoperative results as judged by clinical scoring systems, such as visual analog scale, Oswestry disability index, and MacNab criteria. BESS has several advantages over traditional open surgery, including minimized blood loss, a shorter duration of hospitalization, and an expedited healing process. However, the technique has limitations, such as a steep learning curve and practical challenges for surgeons. Our review offers recommendations for the optimal use of BESS in clinical practice, and provides a foundation for future research and development in this field, aiming to improve patient outcomes and quality of life.
Fulltext Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Awuah WA, Adebusoye FT, Wellington J, David L, Salam A, Weng Yee AL, et al.

World Neurosurg X, 2024 Jul;23:100301.
PMID: 38577317 DOI: 10.1016/j.wnsx.2024.100301

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

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