Diagnosis and Management Approaches for Cerebellar Hydatid Cysts: A Systematic Review of Cases.

Cerebellar hydatid cysts are uncommon lesions, with limited cases reported in the literature. This systematic review aimed to summarize current diagnostic and management approaches, given the low suspicion index of hydatid cysts in the cerebellum. The review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD42023437853. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) reporting guidelines. Two independent researchers searched PubMed, Scopus, and Google Scholar databases on June 27, 2023. We included 15 studies published between 1965 and 2022, comprising 12 case reports and three case series. A pooled analysis of reported cases (nine females and seven males) with cerebellar hydatid cysts revealed a mean age of 24 ± 20 years. Most of the cases were reported in Turkish hospitals (n = 8). The prominent signs and symptoms observed were headaches (10, 62.5%), ataxic gait (9, 56.25%), and visual disturbances (9, 56.25%). The time from symptom onset to hospital visit varied, with most patients seeking medical attention within the first three months. The left cerebellar hemisphere was the most common location of the cysts (6, 37.5%), and compression of the fourth ventricle was frequently observed. Computed tomography (CT) and magnetic resonance imaging (MRI) were the primary diagnostic tools used in three-fourths of cases, and surgical intervention was the primary treatment approach. Albendazole and praziquantel were commonly prescribed postoperatively, and two patients underwent preoperative needle decompression. This systematic review contributes to a better understanding of cerebellar hydatid cysts and guides future research and clinical management of this entity.

Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification.

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, such as convolutional neural networks (CNNs), have emerged as transformative technologies with vast potential in healthcare. Body iron load is usually assessed using slightly invasive blood tests (serum ferritin, serum iron, and serum transferrin). Serum ferritin is widely used to assess body iron and drive medical management; however, it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients. Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron. The ML and DL algorithms can be used to enhance the detection of minor changes. However, a lack of open-access datasets may delay the advancement of medical research in this field. In this letter, we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging. Despite the current limitations, embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.

Medical research production in native languages: A descriptive analysis of PubMed database.

INTRODUCTION: Language barriers in medicine can hinder effective communication, comprehension, and patient care. While English has emerged as the dominant language in global medicine, the importance of native languages should not be overlooked. This article aims to examine the extent of publishing in native languages by analyzing the PubMed database literature to gain further insights into the usage of native languages in medicine and medical research. METHODS: In December 2023, a comprehensive examination of the PubMed literature was conducted for each of the 55 registered languages. We searched for records published in each language (e.g., German[lang]) by applying language filters. Ethnologue provided data on the number of worldwide native speakers for each language, facilitating a comparative analysis. RESULTS: By December 2023, PubMed contained over 36 million publications, with 86.5% of them published in English. German, French, and Russian came after English, with over 700 thousand publications each. Among the languages analyzed, fourteen had fewer than 50 publications, nineteen had fewer than 100, twenty-two had fewer than 500, and twenty-five had fewer than one thousand publications. European languages were well-represented with thousands of publications each, while widely spoken languages such as Hindi and Arabic had limited representation. CONCLUSION: The production of medical research in native languages reflects the attention given to native languages in medicine and medical education within each country. It is crucial to provide due attention to these language-related issues and explore strategies for including native languages in medicine to bridge the gaps in language and medicine.

Intra-biliary hydatid cyst rupture: A rare case report with superinfection.

Key Clinical Message: Hydatid cysts, primarily found in the liver (70%), are caused by parasitic infections and can lead to severe complications such as cyst rupture. This case report describes a unique instance of a hydatid liver cyst occupying the right lobe with a communicating part with the biliary tree that ruptured showing a concurrent superinfection. Abstract: Hydatid cysts are a clinical pathology resulting from parasitic infections. They may occur in different organs of the body. However, these are mostly found in the liver (70%). This can cause significant complications including cyst rupture. Several case reports have described various hydatid cyst ruptures; however, only a few have reported an intra-biliary hydatid cyst rupture. A 24-year-old male patient presented with right upper quadrant pain, jaundice, dark urine, and pale stool. Imaging studies, including Magnetic resonance cholangiopancreatography (MRCP) and computed tomography (CT), revealed a beavertail liver, cystobiliary communication and intrahepatic biliary tree-ruptured hydatid cysts. The cyst was in the right liver lobe, which is the most common site for hydatid cysts. Surgical intervention involving laparoscopic de-roofing and cyst removal resulted in a smooth recovery without complications. Several case reports have described various hydatid cyst ruptures; however, only a few have reported originally placed intra-biliary hydatid cyst ruptures. This case report describes a unique instance of a hydatid liver cyst occupying the right lobe with a communicating part with the biliary tree that ruptured showing a concurrent superinfection.

Prune Belly Syndrome in a Female Newborn following In Vitro Fertilization-Induced Pregnancy: A Case Report and Literature Review.

Prune belly syndrome (PBS) is a rare congenital anomaly characterized by a triad of abdominal flaccidity, varying degrees of urinary system involvement, and cryptorchidism. The exact cause of PBS is unknown. Clinical symptoms can range from stillbirth to significant renal and respiratory abnormalities to almost normal children. Treatment typically involves surgical repair of the abdominal wall defect and urinary tract abnormalities, early orchiopexy, and supportive management of related problems. We report the first case of a female newborn with PBS following in vitro fertilization-induced pregnancy with a comprehensive systematic review of all relevant cases.

Streamlining Systematic Reviews: Harnessing Large Language Models for Quality Assessment and Risk-of-Bias Evaluation.

This editorial explores the innovative application of large language Models (LLMs) in conducting systematic reviews, specifically focusing on quality assessment and risk-of-bias evaluation. As integral components of systematic reviews, these tasks traditionally require extensive human effort, subjectivity, and time. Integrating LLMs can revolutionize this process, providing an objective, consistent, and rapid methodology for quality assessment and risk-of-bias evaluation. With their ability to comprehend context, predict semantic relationships, and extract relevant information, LLMs can effectively appraise study quality and risk of bias. However, careful consideration must be given to potential risks and limitations associated with over-reliance on machine learning models and inherent biases in training data. An optimal balance and combination between human expertise and automated LLM evaluation might offer the most effective approach to advance and streamline the field of evidence synthesis.