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.

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.

“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.

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.

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.

Small cell lung cancer exhibits rapid proliferation and significant metastatic potential. There are significant epidemiologic and biological associations with tobacco carcinogens. While most small cell lung cancers display neuroendocrine characteristics, a significant subset of tumors does not possess these features. Genomic profiling of small cell lung cancer demonstrates genetic instability, nearly universal inactivation of the tumor suppressor genes TP53 and RB1, and a significant mutation burden. Due to early metastasis, a limited number of patients are suitable for curative lung resection, necessitating adjuvant platinum-etoposide chemotherapy for these individuals. Consequently, most patients are presently receiving chemoradiation, with or without the addition of immunotherapy. For patients with disease localized to the chest, standard treatment comprises thoracic radiotherapy alongside concurrent platinum-etoposide chemotherapy. Patients diagnosed with metastatic disease receive treatment that combines platinum-etoposide chemotherapy with immunotherapy utilizing an anti-programmed death-ligand 1 monoclonal antibody. Small cell lung cancer initially exhibits a strong response to platinum-based chemotherapy; however, these responses are temporary due to the emergence of drug resistance. Recent years have seen an accelerated accumulation of biological insights into the disease, resulting in a redefinition of the classification scheme for small cell lung cancer. The emerging understanding of molecular subtypes in small cell lung cancer may delineate distinct therapeutic vulnerabilities. The integration of these recent findings with existing knowledge of small cell lung cancer biology and clinical management could result in significant improvements in patient care for this condition. The authors provide an overview of multimodal clinical approaches in small cell lung cancer, emphasizing recent advancements in research that may expedite clinical development.

Autism spectrum disorder encompasses a range of neurobehavioral and neurodevelopmental conditions marked by deficits in social interaction and communication, as well as restricted and repetitive behaviors or interests, alongside atypical sensory processing. Environmental, immunological, genetic, and epigenetic factors contribute to the pathophysiology of autism, triggering neuroanatomical and neurochemical changes early in central nervous system development. Numerous neurochemical pathways contribute to the etiology of autism spectrum disorder; however, the interactions among these intricate networks and their role in the emergence of core autism symptoms remain poorly understood. Additional research on neurochemical changes in autism is essential to elucidate the early neurodevelopmental differences that contribute to the significant heterogeneity of autism spectrum disorder, thereby informing new strategies for treatment and prevention.

The use of beta-blockers (bisoprolol) and ACE inhibitors (perindopril) is pathogenetically and clinically justified in patients with stable coronary artery disease (CAD) and concomitant arterial hypertension (AH) . The study showed that prescribing a fixed combination of previously used drugs (perindopril, bisoprolol) for 4 weeks in patients with clinical symptoms or documented CAD can effectively reduce heart rate (HR) and blood pressure (BP) (heart rate levels ≤ 70 bpm were reached in 84.9%, blood pressure ≤ 140/90 mm Hg - 86.9% of patients), reduces the number of angina attacks from 4.4 to 2.6 per week and the need for nitroglycerin - from 4.8 to 2.7 tablets per week, and improve adherence to therapy (66.5% of patients). 

Joint trauma constitutes a substantial proportion of emergency room visits and patients requiring urgent care, imposing considerable financial burdens on society. Diagnostic imaging is essential in the evaluation and treatment of trauma victims. Diagnostic imaging constitutes a complex, multifaceted system, with numerous elements of its workflow susceptible to inefficiencies or human error. Recent advancements in artificial intelligence and machine learning have the potential to transform our medical care delivery systems. This review will offer a comprehensive analysis of the present status of artificial intelligence and machine learning applications in various facets of trauma imaging and propose a vision for how these applications could be utilized to improve diagnostic imaging systems and optimize patient outcomes.

Background

Eye is one of the primary sensory organs, responsible to provide the necessary visual information to the brain. Any abnormality in eye cause mild to severe vision issues and hence appropriate detection and treatment is preferred. Clinical level examination of the eye is commonly performed using image examination procedures and this work considered the Optical Coherence Tomography (OCT) for the study. In the present study, the proposed system aims to develop a tool to classify the OCT-images into normal and abnormal class using the traditional deep-learning (DL) and machine-learning (ML) features.

Methods

This tool consists the following stages; OCT-image collection and resizing based on the chosen DL scheme, image features extraction using DL and ML methods, DL feature reduction using 50% dropout and serially fusing the deep- and machine-features to generate hybrid-features, and binary classification using 5-fold cross-validation.

Results

The developed tool’s merit is verified using; (i) deep-features, (ii) machine-features, and (iii) hybrid-features. The merit of the developed scheme is verified using different binary classifiers and the overall quality metric is considered to verify the tool’s performance.

Conclusions

The study confirms that the hybrid-features based detection provides 97.6% accuracy, when Random Forest classifier is employed, which verifies the tool’s merit on the chosen OCT database. In the future, this tool can be considered to detect the common AEAs, like AMD and the DME.