Issue 2 (April)

In recent years, enhanced artificial intelligence algorithms and more access to training data have enabled artificial intelligence to augment or supplant certain functions of physicians. Nonetheless, the interest of diverse stakeholders in the application of artificial intelligence in medicine has not resulted in extensive acceptance. Numerous experts have indicated that a primary cause for the limited adoption is the lack of openness surrounding certain artificial intelligence algorithms, particularly black-box algorithms. Clinical medicine, particularly evidence-based practice, depends on transparency in decision-making. If there is no medically explicable artificial intelligence and the physician cannot adequately elucidate the decision-making process, the patient's trust in them will diminish. To resolve the transparency concern associated with specific artificial intelligence models, explainable artificial intelligence has arisen.

Background

Asthma is a common chronic illness in children, contributing significantly to healthcare costs and morbidity. Despite the use of systemic corticosteroids (SC) as the standard treatment for acute asthma, their delayed onset and incomplete efficacy in reducing hospitalization have led to interest in inhaled corticosteroids (ICS) as adjunctive therapy.

Methods

This meta-analysis aimed to evaluate the efficacy of combining budesonide (an ICS) with SC in children with acute asthma. A systematic search of databases identified 10 studies involving 1,588 children, of whom 794 received budesonide in addition to SC, while the remaining 794 received SC alone.

Results

The addition of budesonide was associated with a non-significant reduction in hospital admission rates (OR 0.48, 95% CI 0.25–0.91, p=0.02) and length of stay. Studies with higher doses of budesonide (≥2mg) showed a greater but still non-significant improvement in asthma severity scores.

Conclusions

While combining ICS and SC resulted in a potential cost-effective benefit, further trials with higher doses of budesonide are needed. Major limitations include the absence of studies involving MDI-based ICS delivery, limited data on lung function, and the lack of recent trials after 2020.

“Myasthenia gravis” has its origins in Greek. The terms denote muscle weakening (myasthenia) and heaviness (gravis). Myasthenia gravis (myasthenia gravis) is an autoimmune disorder marked by muscle weakness that exacerbates with activity, predominantly impacting the oculobulbar muscles and principally targeting postsynaptic nicotinic acetylcholine receptors. ¹ This condition, characterized by a significantly high mortality rate due to respiratory failure, holds unique significance among neuromuscular disorders and within the broader field of neurology, as patients can enjoy perfectly normal lives with appropriate treatment. The prevalence of the condition in women is bimodal, with the highest incidence occurring between the ages of 20-30 and above 50 years. In contrast to males above the age of 50. The disease's most notable characteristic is muscle weakness, which exacerbates with tiredness and is at least partially alleviated by rest. Patients indicate improvement in the morning, with symptoms exacerbating in the evening or when fatigued ² The manifestation of disease progresses with periods of remission and exacerbation. Remissions can last for durations ranging from a few days to several years.

Recurrent spontaneous abortion (RSA), a significant reproductive challenge, involves multiple pregnancy losses and impacts fertility. Although various factors contribute to RSA, immune system dysfunction is frequently implicated. Recent technological advancements in bioinformatics, particularly high-throughput sequencing and computational modeling, have enabled a more detailed examination of immune responses associated with RSA. This text explores how bioinformatics tools, including gene expression analysis, machine learning, and network analysis, facilitate the prediction of immune factors relevant to RSA. Additionally, it reviews current progress and potential future applications in personalized treatment strategies.

Background: Afghanistan is among the countries which have the highest maternal mortality in the world. The inadequate utilization of antenatal care (ANC) services increased the risk of maternal mortality in Afghanistan.

The goal of this study is to understand key associated factors with antenatal care and its impact on maternal mortality in Afghanistan. It also elaborates on the impacts and utilization of antenatal care services among women in Afghanistan.

Methods: For this research, data from the Afghanistan Demographic Health Survey 2015 has been used, the study participants were 18139 reproductive-aged women. The relevant variable was chosen, and data was analyzed for general descriptive, Chi-square, and logistic regression tests. Analysis was carried out using JAMOVI 2.2.5.

Results: The overall number of ANC visits among women was 10040 (55.4%) and 8099 women (44.6%) never used ANC visits. The utilization of one ANC visit was 11.2% and two ANC visits were most frequent among women with 17%. Women who undertook three or four and more than four were 11.6%, 7.1%, and 4.3% respectively.

In this study, we examined the socio-economic and demographic backgrounds of 18139 reproductive-aged women attending ANC clinics. The highest age group (30-39 years) represented 32.3% (OR=0.947, 95%CI: 0.885-1014), followed by the age group of over 40 which was 25.1% (OR=0.919, 95%CI: 0854-0.990) compared to age group under 29 among women. According to residency, urban areas represented 77.2% of the total (18139) women, and 22.8% were from rural areas (OR=1.097, 95%CI: 1.023-1.177) compared to urban areas. In terms of education, 86% of women had received no education, 6.5% received primary education (OR=1.159, 95%CI: 1.020-1.316), the number of secondary educations was 5.7% with (OR=0.981, 95%CI: 0.828-1.163) and higher education was 1.8%with (OR=0.917, 95%CI: 0.712-1.181) compared to not education women group in Afghanistan. ANC visits were used more frequently by Pashtuns, 38.8%, followed by Tajiks, 35.1%. (OR=0.192,95% CI:0.852-0.977), Uzbek was 10% (OR=1.224, 95% CI:0.869-1.068) followed by Hazara at 6.6% (OR=0.963, 95% CI:0.869-1.068) the Turkmen at 5.4% (OR=2.571, 95% CI:2.208-2.924), the percentage by Aimaq was 1.2% (OR=1.574, 95% CI:1.196-2.072) and for Baloch 0% (OR=1.917, 95% CI:0.320-11.4812.072) followed by others with 2.6% (OR=0.910, 95% CI:0.753-1.099) compared to Pashtun ethnic group. For media exposure the number of not listening to the radio per week was 58.1% and one time per week was 16% (OR=0.974, 95%CI: 0.897-1.058), for two times per week it was 25.8% (OR=0.854, 95%CI: 0.797-0.915) and more than three plus times per week was 0.1% (OR=0.970, 95%CI: 0.402-2.343) compared with the zero time of listening to radio per week. Also, for not watching TV was 56% and one time per week was 11.3% (OR=0.888, 95%CI: 0.807-0.977) also for two times per week it was 32.6% (OR=0.913, 95%CI: 0.856-0.974) and the number of three plus times of watching TV was 0.2% OR=1.188, 95%CI: 0.617-2.286) compared with zero time of watching the TV.

Conclusion: The most important key associated factors were residency, education level, ethnic groups, and media exposure. We suggest that the Afghanistan government and with international community improve their health policy, increasing accessibility of health care and equal distribution of health services in rural and urban areas in Afghanistan.