Journal of Medical Biochemistry最新文献

Background: Glucose-6-phosphate dehydrogenase (G6PD) deficiency is an X-linked recessive Mendelian genetic disorder characterized by neonatal jaundice and hemolytic anemia, affecting more than 400 million people worldwide. The purpose of this research was to investigate prevalence rates of G6PD deficiency and to evaluate and establish specific cut-off values in early prediction of G6PD deficiency by regions (HeFei, FuYang, AnQing) on different seasons, as well as to investigate the frequencies of G6PD gene mutations among three regions mentioned above.

Methods: A total of 31,482 neonates (21,402, 7680, and 2340 for HeFei, FuYang, and AnQing cities, respectively) were recruited. Positive subjects were recalled to attend genetic tests for diagnosis. G6PD activity on the Genetic screening processor (GSP analyzer, 2021-0010) was measured following the manufactureržs protocol. The cut-off value was first set to 35 U/dL. The receiver operating characteristics (ROC) curve was employed to assess and compare the efficiency in predicting G6PD deficiency among HeFei, FuYang, and AnQing cities in different seasons.

Background: Establishing reference intervals (RIs) in clinical laboratories is essential, as these can vary due to inter-individual variability as well as the analytical methods used. The purpose of this study was to determine RIs for markers and ratios biochemical in apparently healthy Chilean adults.

Methods: A sample of 1,143 data was selected from the Universidad Católica de Temuco, Clinical Laboratory database, La Araucanía Region, Chile, which were analysed by sex. The Tukey's Fences was used to detect outliers and the RIs were established using the non-parametric method.

The use of artificial intelligence (AI) has become widespread in many areas of science and medicine, including laboratory medicine. Although it seems obvious that the analytical and post-analytical phases could be the most important fields of application in laboratory medicine, a kaleidoscope of new opportunities has emerged to extend the benefits of AI to many manual labor-intensive activities belonging to the pre-analytical phase, which are inherently characterized by enhanced vulnerability and higher risk of errors. These potential applications involve increasing the appropriateness of test prescription (with computerized physician order entry or demand management tools), improved specimen collection (using active patient recognition, automated specimen labeling, vein recognition and blood collection assistance, along with automated blood drawing), more efficient sample transportation (facilitated by the use of pneumatic transport systems or drones, and monitored with smart blood tubes or data loggers), systematic evaluation of sample quality (by measuring serum indices, fill volume or for detecting sample clotting), as well as error detection and analysis. Therefore, this opinion paper aims to discuss the state-of-the-art and some future possibilities of AI in the preanalytical phase.

Background: This study aims to investigate the correlation between serum levels of interleukin-2 (IL-2) and interferong (IFN-g) and the clinical prognosis of patients with nasopharyngeal carcinoma (NPC). Additionally, the study aims to analyse the risk factors associated with this correlation.

Methods: The clinical data of 195 NPC patients admitted to our hospital from October 2020 to October 2022 were selected for a retrospective study. Based on the Glasgow score, patients were divided into two groups: the good prognosis group (group g), consisting of patients who scored 0 points, and the poor prognosis group (group p), consisting of patients who scored 1-2 points. The levels of serum IL-2 and IFN-g were compared between the two groups, and the clinical values of serum IL-2 and IFN-g in the prognosis of patients were analysed. The clinical parameters of the patients were collected, and the risk factors affecting the prognosis of NPC were analysed by univariate and multivariate logistic regression.

Background: Iron deficiency anemia (IDA) and b-thalassemia minor (BTM) are the two most common causes of microcytic anemia, and although these conditions do not share many symptoms, differential diagnosis by blood tests is a time-consuming and expensive process. CBC can be used to diagnose anemia, but without advanced techniques, it cannot differentiate between iron deficiency anemia and BTM. This makes the differential diagnosis of IDA and BTM costly, as it requires advanced techniques to differentiate between the two conditions. This study aims to develop a model to differentiate IDA from BTM using an automated machine-learning method using only CBC data.

Methods: This retrospective study included 396 individuals, consisting of 216 IDAs and 180 BTMs. The work was divided into three parts. The first section focused on the individual effects of hematological parameters on the differentiation of IDA and BTM. The second part discusses traditional methods and discriminant indices used in diagnosis. In the third section, models developed using artificial neural networks (ANN) and decision trees are analysed and compared with the methods used in the first two sections.

Background: Cerebral haemorrhage is a critical condition that often requires surgical treatment, and postoperative intracranial infection can significantly impact patient outcomes. The aim of the study was to examine the relationship between the levels of lactic acid and glucose in cerebrospinal fluid (CSF) of patients with cerebral haemorrhage and their postoperative intracranial infection and clinical prognosis.

Methods: The study selected the clinical data of 324 patients with cerebral haemorrhage who underwent surgical treatment in our hospital from March 2020 to March 2022 for retrospective analysis and divided these patients into the intracranial infection group (Group A, n=22, leukocyte values in CSF>5×106/L) and the non-intracranial infection group (Group B, n=302, leukocyte values in CSF 5×106/L) according to the occurrence of postoperative intracranial infection in patients to detect the levels of lactic acid and glucose in CSF at different times in the two groups. Pearson method was adopted to analyze the correlation of the levels of lactic acid and glucose in CSF of patients with intracranial infection, and the Glasgow Outcome Scale (GOS) was used to assess the clinical prognosis of patients. According to their scores, these patients were divided into the good prognosis group (GPG, scores of 4-5 points, n=178) and the poor prognosis group (PPG, scores of 1-3 points, n=146). The levels of lactic acid and glucose in the CSF of patients in the two groups were measured, and the Pearson method was adopted to analyze the relationship between these levels and clinical prognosis.

Background: Psoriasis is an autoinflammatory disease that affects not only skin but multiple organs thus being associated with many comorbidities. Oxidative stress and inflammation play the major role in the pathogenesis of this disease. Studies that examined by-products of oxidative stress in psoriasis show discrepant results. Hence, we aimed to examine the oxidative stress, inflammation and metabolic markers and to explore their potential relationship with disease severity in patients with psoriasis.

Methods: This case-control study comprised of 35 patients with psoriasis and 35 age, sex and body mass index-matched healthy controls. Metabolic and oxidative stress biomarkers [i.e., malondialdehyde (MDA), advanced oxidation protein products (AOPP), and catalase (CAT)] were measured. The principal component analysis (PCA) was employed to reduce the number of measured variables into smaller number of factors. PCA factors were subsequently used in logistic regression analysis for severe psoriasis prediction.