Jorjani Biomedicine Journal

Single-cell analysis: Illuminating the path to early diagnosis of systemic lupus erythematosus

Saeed Mohammadi

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease characterized by heterogeneous clinical manifestations and the production of autoantibodies, making early diagnosis challenging. Traditional diagnostic methods lack sensitivity and specificity, leading to delayed intervention and irreversible organ damage. Single-cell technologies offer a novel opportunity to investigate the cellular landscape of SLE at the level of individual cells. By profiling the gene expression, protein expression, and functional states of thousands of individual cells simultaneously, these technologies can reveal critical findings such as the expansion of type I interferon-producing pDCs and dysregulated T/B cell subsets involved in SLE pathogenesis. This editorial highlights the transformative potential of single-cell analysis in identifying disease-relevant cell populations and their functional states, ultimately paving the way for earlier diagnosis, personalized treatment, and improved outcomes for patients with SLE.

Enhanced missing value imputation and gaussian mixture model-based semi-supervised learning for predicting type 2 diabetes

Fatemeh Bagheri

Background: Diabetes is a prevalent condition with no definitive cure, often referred to as a” silent killer.” Diabetes is primarily categorized into three types: Type I, Type II, and gestational diabetes. In Type I diabetes, the body's immune system attacks and damages the insulin-producing cells. Conversely, Type II diabetes, which is more common than Type I, occurs when the body does not respond adequately to the insulin being produced, resulting in elevated blood sugar levels. Effectively treating pre-diabetes can prevent its progression to full-blown diabetes.
Methods: In the present research, a semi-supervised approach is proposed to predict diabetes. Improved missing value imputation (MVI) is achieved by utilizing Gaussian mixture model (GMM) clustering. The proposed classifier integrates GMM with a machine learning algorithm, specifically random forest (RF), thereby inducing a more robust predictive model via the fusion of clustering and classification techniques.
Results: The proposed method achieves an accuracy of 84%, a precision of 82.03%, a recall of 69.75%, and an F1-score of 75.12% base on experiments conducted on the PIMA Indian population.
Conclusion: Employing GMM to fill in missing values provides the advantage of replacing invalid data with the most similar records, thereby enhancing the quality of the dataset. The proposed classifier also exhibits strong predictive capabilities in identifying diabetes. By integrating this combined approach, this study offers an effective method for predicting diabetes, making a significant contribution to healthcare analytics as a whole.

Antibiotic resistance and blaTEM β-lactamase gene carriage in Pseudomonas aeruginosa isolates from burn patients: A multicenter study in Tabriz, 2024

Mehrdad Pashazadeh

Background: A major contributor to hospital-acquired infections, particularly in burn units, is Pseudomonas aeruginosa (P. aeruginosa). Because this bacterium produces extended-spectrum β-lactamases (ESBLs), antibiotic resistance is a significant treatment concern. In this work, P. aeruginosa isolates from burn victims in Tabriz were examined for antibiotic resistance patterns and the presence of the bla_TEM gene.
Methods: In this descriptive-cross-sectional study, 100 clinical isolates of P. aeruginosa were collected from patients hospitalized in the burn wards of Tabriz hospitals over a six-month period. Standard biochemical methods were used to identify microorganisms. Antibiotic resistance patterns were assessed by the disk diffusion technique according to clinical and laboratory standards institute protocols. Additionally, the presence of the bla_TEMgene was investigated by polymerase chain reaction, and ESBL production was confirmed by the combined disk test.
Results: The highest resistance rates were observed for levofloxacin (97%) and meropenem (92%), while the lowest was for ceftazidime (69%). Furthermore, 58% (58/100) of the isolates were ESBL-positive, half of which (50%, 29/58) carried the bla_TEM gene.
Conclusion: The results of this study indicated that P. aeruginosa strains in burn units of Tabriz hospitals exhibited high antibiotic resistance. Half of ESBL-positive isolates carried the bla_TEM gene, highlighting the need for continuous monitoring of antibiotic resistance patterns and prudent use of antibiotics.

The relationship between parental authority and the mental health of nursing and midwifery students at Golestan University of Medical Sciences in 2022

Elham Adib Moghadam

Background: Mental health is significantly influenced by parenting styles, and undesirable parental authority styles can have detrimental effects on children’s mental health. Addressing mental health and its contributing factors is essential for improving individuals' personal and social lives. On the other hand, research findings on the relationship between parental authority styles and mental health have yielded conflicting results. Therefore, the present study was conducted to determine the relationship between parental authority styles and the mental health of nursing and midwifery students.
Methods: This cross-sectional, descriptive-analytical study was conducted on 435 nursing and midwifery students using a census method. The research instruments included a demographic information form, Buri’s Parental Authority Questionnaire (PAQ), and Goldberg and Hillier’s General Health Questionnaire (GHQ). Questionnaires were completed virtually via Google Docs. Data were analyzed using descriptive statistics and Pearson's correlation coefficient with SPSS software, and a significance level of P < 0.05 was considered.
Results: The mean age of the participating students was 24.71 ± 5.90 years. Most of the students surveyed were female (75.9%) and undergraduate (75.9%). The mean scores for the permissive, authoritarian, and authoritative parenting styles, and for general health, were 21.80 ± 4.64, 21.33 ± 5.64, 24.93 ± 4.92, and 19.22 ± 7.07, respectively. There was a significant and direct correlation between overall public health and the permissive parenting style (r = 0.14, p = 0.003). The correlation between the authoritative parenting style and students' mental health was inverse; however, this finding was not statistically significant (r = −0.05, p = 0.26).
Conclusion: The research results underscore the influence of parental authority styles on students' mental health. It is suggested that university counselors who provide counseling services to young adults consider the influence of parental authority styles on the diagnosis, etiology, and treatment of their mental and behavioral disorders, and take measures to assess the type of parental authority style.

The synergistic effects of glycyrrhizin and swimming exercise on CXCL12/CXCR4 gene

Farah Nameni

Background: This study aimed to investigate the modulating effects of two interventions, glycyrrhizin and swimming exercise, on the expression of CXCR4 and CXCL12 genes in the blood and heart tissue of diabetic rat models.
Methods: A total of 55 male Wistar rats were purchased, of which 44 were subcutaneously injected with streptozotocin between the ears. Four days post-injection, blood glucose levels were assessed. Rats exhibiting levels above 250 mg/dL were classified as diabetic and subsequently randomized into four groups of 11 animals each (diabetic control, diabetic + glycyrrhizin, diabetic + swimming training, and diabetic + swimming training + glycyrrhizin). The healthy control group consisted of 11 rats. Two groups of diabetic rats were treated with glycyrrhizin. The core experimental protocol involved swimming training, which was implemented in two of the experimental groups. Glycyrrhizin was dissolved in 0.9% saline with sterile distilled water and administered by gavage at a dose of 120 mg/kg on average, transferred to the stomach of the animals every night. The main experimental protocol consisted of eight weeks of swimming training, which was performed in two experimental groups. The swimming training time started from 25 minutes with 7 liters of water in the adaptation period and increased to 60 minutes of activity in 17 liters of water in the eighth week. After eight weeks, the rats were anesthetized with ketamine and xylazine before blood collection, and then blood samples were taken from their hearts. Data analysis was performed using two-way analysis of variance and Tukey's test.
Results: A marked reduction in the expression levels of CXCR4 and CXCL12 was observed in the groups subjected to exercise and glycyrrhizin supplementation, indicating the potential of these interventions in modulating inflammatory signaling pathways (p<0.05). The greatest reduction in CXCR4 (49.77%) and CXCL12 (68.19%) was observed in the combined exercise + glycyrrhizin group, indicating a stronger effect of the combined therapy than the single treatments.
Conclusion: Swimming training combined with glycyrrhizin supplementation significantly downregulated the expression of CXCR4 and CXCL12 genes and demonstrated potent antioxidant and anti-inflammatory effects, contributing effectively to the modulation and prevention of diabetes.

Interactive effects of endurance training combined with saffron and crocin on the hippocampal oxidant-antioxidant system in trimethyltin-induced Alzheimer’s rats

Fatemeh Akbari

Background: Alzheimer’s disease (AD) disrupts hippocampal oxidant-antioxidant balance. Although endurance training, saffron, and crocin have shown antioxidant benefits individually, their comparative effects in Alzheimer’s models are not well established. The present study investigated changes in hippocampal oxidant-antioxidant markers in trimethyltin (TMT)-induced Alzheimer’s rats following eight weeks of endurance training (ET), with or without saffron (S) and crocin (Cr) supplementation.
Methods: The current study was performed on 49 male Sprague-Dawley rats (age: 8 weeks old, weight: ~220 g). Forty-two rats received TMT (8 mg/kg, [intraperitoneal] IP) and were randomly assigned to six groups: (1) AD, (2) ET, (3) S, (4) Cr, (5) ET+S, and (6) ET+Cr. Seven healthy rats served as controls (HC). Training groups ran on a treadmill for eight weeks (5 sessions/week, 15-30 min/session, 15-20 m/min). S and Cr groups received daily IP injections (25 mg/kg). Forty-eight hours following the intervention, hippocampal tissue was collected to measure superoxide dismutase (SOD), malondialdehyde (MDA), and total antioxidant capacity (TAC). The data were analyzed using one-way analysis of variance (ANOVA) and Tukey’s post hoc test (SPSS software [version 22], P ≤ 0.05).
Results: Compared to the AD group, MDA levels decreased by 35-56%, and SOD levels increased by 193-257% in all the intervention groups. Moreover, TAC levels showed marked improvement, rising by 185%, 220%, 253%, 309%, and 335% in Cr, S, ET, ET+Cr, and ET+S groups, respectively (P < 0.05). The aforementioned findings highlight the superior antioxidant response in the combined intervention groups, compared to the response in single treatments (P < 0.05).
Conclusion: Endurance training, saffron, and crocin improved hippocampal antioxidant status in Alzheimer’s rats, with combined interventions yielding superior effects.

The effect of six weeks HIIT exercise on the gene expression of some inflammatory and anti-inflammatory markers of hippocampal tissue in aged male Wistar rats

Mohammad Rami

Background: Research has shown that high-intensity interval training (HIIT) is a convenient and time-efficient approach that promotes oxidative capacity. Therefore, this study aimed to investigate the effect of HIIT on the gene expression of some inflammatory and anti-inflammatory markers of hippocampal tissue in aged male Wistar rats.
Methods: In this experimental study, 21 male Wistar rats were divided into three equal groups: 1- control (C), 2- elderly control (EC), and 3- elderly training (ET). After two weeks of adaptation to the laboratory environment, six weeks of HIIT were performed for the training group. Forty-eight hours after the last training session, hippocampal tissue was isolated to examine the mean levels of interleukin-10 (IL-10), IL-6, and IL-1β gene expression. One-way analysis of variance and Tukey's post-hoc test with a significance level of 0.05 were used to analyze the data.
Results: There was a significant increase in the expression of IL-6 and IL-1β, while a significant decrease in IL-10 expression (p=0.001) in the C group compared to the EC. After six weeks of HIIT, expression of IL-6 (p=0.001) and IL-1β (p=0.001) significantly decreased, whereas IL-10 expression significantly increased (p=0.001) in the ET group compared to the EC group.
Conclusion: It seems that HIIT activity has beneficial effects on preventing the process of neural degeneration. Moreover, it is promising as a non-pharmacological strategy for controlling complications resulting from the aging process due to its anti-inflammatory properties.

Presenting a neural network-based framework for drug-target interaction prediction

Mahdi Yaghoubi

Background: Identifying drug-target interactions (DTIs) is a central focus in pharmaceutical research, as accurately recognizing these interactions can play a crucial role in developing modern and targeted therapies. In recent years, numerous deep learning-based models have been introduced to predict these interactions. However, several challenges remain. Existing methods often fail to incorporate the three-dimensional structures of drugs and proteins alongside their SMILES and FASTA sequences, resulting in lower prediction accuracy. Furthermore, many approaches utilize only partial sequence data, thereby overlooking critical information. This lack of spatial and comprehensive sequence awareness ultimately limits the accurate modeling of molecular interactions and binding mechanisms.
Methods: In this study, we introduced TGATS2S-v1 and TGATS2S-v2, two novel deep learning frameworks designed to address the critical challenge of Drug-Target Interaction (DTI) prediction by integrating 3D structural information of both drugs and target proteins alongside their canonical sequence representations (SMILES and FASTA). The proposed methods leveraged three-dimensional structural information to enhance DTI prediction and were tested on the Davis dataset.
Results: The results of the proposed methods were thoroughly analyzed. By integrating 3D structural data, the predictive power of the models improved significantly. Evaluations showed that these models consistently outperformed advanced baseline models, delivering higher accuracy and robustness in all cases. The proposed model achieves state-of-the-art performance, improving PR-AUC by over 20%.
Conclusion: These findings indicate that incorporating 3D structural information plays a pivotal role in overcoming the limitations of previous models and paves the way for the discovery of more effective drugs and therapeutic advancements.