Novelty in Biomedicine

Background: The presence of Escherichia coli among uropathogens is increasing significantly worldwide. It accounts for a considerable amount of morbidity and high medical costs and also can lead to mortality. The current research aims to investigate E. coli antimicrobial susceptibility patterns and the molecular causes of E. coli resistance trends and virulence factors among phylogenetic groups of Uropathogenic Escherichia coli (UPEC) in Urinary tract infection (UTI) patients in a hospital in Tehran, Iran. The antimicrobial susceptibility of urinary E. coli isolates.

Materials and Methods: The antimicrobial susceptibility of urinary E. coli isolates was tested using the Kirby-Bauer agar disc diffusion method. In addition, resistance and virulence genes were monitored by polymerase chain reaction (PCR), and the clonal relation of isolates was studied by pulsed-field gel electrophoresis (PFGE).

Results: Studied isolates showed the highest susceptibility rates to MEM (95.7%), followed by TZP (90%). In contrast, resistance rates were found for AMP (100%), SXT (74%), and CIP (51.5%). ESBL-producing isolates were positive for blaTEM, blaCTX-M, and blaSHV by PCR, respectively. According to the adhesion gene analyses, fimH (85.8%) was the most prevalent among E. coli isolates, followed by aer (49.7%), hlyA (46.1%), and pap (38.9%). A total of 57 PFGE patterns and three clusters (A–C) were identified by the PFGE method. (cluster A: Non-ESBL & Sensitive to all Antibiotics use; Cluster B: The most common cluster in terms of TEM, CTX-M, or both; Cluster C: containing CTX-M gene and resistant to ceftriaxone and ciprofloxacin).

Conclusion: Our data showed ESBL rates were high in UTI E. coli isolated in the studied hospital. The UPEC isolates exhibited a high resistance rate to first- and second-generation cephalosporins and fluoroquinolone, which could result in serious public health risks. The relationship between virulence factors and resistance genes is complex and needs more studies specific to each area.

Background: Pituitary macroadenoma is a global health concern. Advanced imaging techniques, such as multiparametric magnetic resonance imaging (MRI), have emerged as valuable tools for comprehensively evaluating macroadenomas. This study aimed to evaluate the association between multiparametric MRI findings, pathological features, and type of secretion in patients with pituitary macroadenoma.

Materials and Methods: This cross-sectional study evaluated the association between multiparametric MRI findings and pathological features in patients with pituitary macroadenoma. Patients with pituitary macroadenoma referred to Loghman Hakim Hospital (Tehran-Iran) in 2023 were assessed. Preoperative MRI, including T1W, T2W, DWI, ADC, and CE-MRI, were evaluated for signal intensity, maximum diameter, and tumoral extension. The association between the results of MR imaging and the pathologic findings of the resected macroadenoma was assessed.

Results: Forty-five patients were assessed. The mean age was 48.22 ± 14.58 years, and 55.6% of the patients were male. Most patients (48.9%) had isointense lesion in T1W, and most lesions (57.8%) had heterogeneous signals in T2W. Apparent diffusion coefficient levels (ADC) had no diagnostic value for predicting pathologic subtypes. The most invasion was to the infrasellar among macro adenomas (P-value: 0.037). Among patients with gonadotroph pathology results, invasion to the third ventricle with heterogeneous signal on T2 was more common (P-values<0.05). The mean tumor volume is higher in the Prolactin (PRL) secreting type compared to other categories (P-value: 0.002).

Conclusion: Multiparametric MRI helps predict the pathological diagnosis of pituitary macroadenoma.

Background: Cardiovascular disorders are the most common cause of death in the world. It seems that doctors are usually at high cardiovascular risk due to high stress and anxiety, high work pressure, and an inactive lifestyle. Therefore, this study aimed to evaluate the residents' atherosclerotic cardiovascular disease (ASCVD) risk score.

Materials and Methods: This cross-sectional study was conducted on Imam Hosein Hospital's residents to evaluate their ASCVD risk score in 2022. Participants filled out a general checklist to obtain baseline features, and a blood sample was taken to measure biochemical factors. The 10-year cardiovascular risk was estimated by the Framingham Risk Score (FRS), and depression severity was determined using the Patient Health Questionnaire-9.

Results: In this study, 150 medical residents were evaluated whose mean age was 30.94 ± 3.26, and 50% were male (n=75). Obesity, smoking, and alcohol were more common in male compared to female residents (P<0.05). Also, laboratory abnormalities were more common in men (P<0.05). Additionally, it was observed that the overall mean score FRS was 1.44±1.31%, while this figure in men (2.23 ± 1.44%) was higher than that of women (0.65±0.35%). In terms of mental health, the prevalence of major depression in women (37.33%) was higher than in men (21.33%) (P = 0.178).

Conclusion: Medical residents are at low risk of ASCVD, but risk factors such as obesity, alcohol consumption, smoking, and depression are remarkably high in them.

Background: Tuberculosis (TB) is a major public health issue, especially among Human immunodeficiency virus (HIV)-infected individuals, where early detection of latent tuberculosis infection (LTBI) is crucial. Interferon-gamma release assays (IGRAs), like quantiferon-TB gold "in tube" (QFT-IT) and QFT-Plus, are more accurate alternatives to the tuberculin skin test (TST). This study compares the diagnostic performance of QFT-IT and QFT-Plus for detecting LTBI in HIV patients in Iran.

Materials and Methods: A cross-sectional study was conducted at Masih Daneshvari Hospital, Iran’s national tuberculosis center, between 2020 and 2023. HIV-infected individuals were tested using both QFT-IT and QFT-Plus assays. Agreement between the two tests was evaluated using the kappa coefficient, and McNemar's test was used to assess discrepancies.

Results: Of the 100 HIV-infected patients, 93% demonstrated agreement between the two tests. However, 7% of participants showed discrepancies, with six patients testing negative on QFT-Plus but positive on QFT-IT. The kappa coefficient for agreement was 0.92, indicating high concordance between the two assays. McNemar's test revealed no significant difference in diagnostic performance.

Conclusion: Both QFT-IT and QFT-Plus exhibited strong agreement in detecting LTBI in HIV-infected Iranian patients, supporting their use as reliable diagnostic tools for LTBI screening in this population. Further studies are recommended to assess their utility in other settings and patient populations.

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with various cardiovascular manifestations. Anti-Ro antibodies are commonly found in SLE patients and may contribute to cardiac abnormalities, including electrocardiographic (ECG) changes. This study assessed the association between Anti-Ro antibodies and ECG abnormalities in SLE patients.

Materials and Methods: In this cross-sectional study, 85 SLE patients, all positive for Anti-Ro antibodies, were enrolled from a university hospital. Demographic data, clinical features, and 12-lead ECG results were collected. ECG abnormalities were evaluated, including sinus tachycardia, premature ventricular contractions, QRS fragmentation, ST changes, and QT interval prolongation. Data analysis was performed using SPSS version 26, with appropriate statistical tests to compare variables.

Results: Among the 85 patients, 6 (7.06%) exhibited sinus tachycardia, 1 (1.18%) had premature ventricular contractions, and 12.94% showed QRS fragmentation. ST elevation was observed in a minority of patients (30.59%), while 88.24% had normal ST depression results. QTc was prolonged in 11.76% of patients. No significant associations were found between Anti-Ro antibodies and specific ECG abnormalities.

Conclusion: This study did not find a significant correlation between Anti-Ro antibodies and ECG abnormalities in SLE patients. However, further research with a larger sample size and more in-depth analysis is needed to clarify Anti-Ro antibodies' role in SLE's cardiovascular manifestations.

Background: Research on antifungal agents for infectious diseases caused by multidrug-resistant microorganisms, particularly in immunocompromised patients, has continued. Mucormycosis, candidiasis, aspergillosis, and cryptococcosis are mortal and need more attention due to their spreading potential. Natural products with long-time usage as preservatives, food additives, and medicine in traditional and local resources have been investigated for too many purposes. Some medicinal herbs show antimicrobial properties. This study focused on the antifungal properties of Sugar beet (Beta vulgaris subsp. vulgaris), Yarrow (Achillea wilhelmsii K. Koch), Cinnamon (Cinnamomum zeylanicum), and Den or Don (Oliveria decumbens Vent.) against Candida albicans, Cryptococcus neoformans, Mucor circinelloides, and Aspergillus flavus.

Materials and Methods: The minimum inhibitory concentration (MIC) of hydroethanolic crude extract of Sugar beet leaves (Beta vulgaris subsp. vulgaris), aerial parts of Yarrow (Achillea wilhelmsii K. Koch), Cinnamon barks (Cinnamomum zeylanicum), and Den or Don aerial parts (Oliveria decumbens Vent.) against clinical and standard isolates of Candida albicans, Cryptococcus neoformans, Mucor circinelloides, and Aspergillus flavus, determined according to CLSI M60 and CLSI-M38-A2 for yeasts and filamentous strains, respectively in comparison to Fluconazole and Amphotericin B as positive controls.

Results: Sugar beet extract revealed the best minimum inhibitory concentration (MIC) values against clinical/standard Candida albicans (1 µg/ml and 0.5 µg/ml), respectively, but was insufficient against other species. Other herbal extracts showed a higher MIC range compared to controls.

Conclusion: These findings suggest more research in in-vivo studies, finding safe herbal products with antifungal properties.

Background: Hydroxychloroquine, a commonly prescribed treatment for systemic lupus erythematosus (SLE), has been associated with potential cardiac complications, including QTc prolongation. QTc prolongation increases the risk of arrhythmias and sudden cardiac death. This study aimed to evaluate electrocardiographic (ECG) abnormalities in Iranian SLE patients receiving hydroxychloroquine treatment.

Materials and Methods: A cross-sectional study was conducted at the rheumatology clinic of Imam Hossein Hospital in Tehran, Iran. The study included patients diagnosed with systemic lupus erythematosus (SLE) receiving hydroxychloroquine treatment. Demographic and clinical data were collected through patient interviews and medical records. All participants underwent a 12-lead electrocardiogram (ECG), and the QT interval was corrected using the Bazett formula. QTc prolongation was defined as QTc≥450 ms. Additionally, fragmented QRS complexes, premature ventricular contractions (PVCs), and other ECG abnormalities were recorded.

Results: A total of 81 SLE patients on hydroxychloroquine were analyzed. The mean age was 48.5 years, and the average QTc interval was 426.52 ms (SD: 28.82 ms). QTc prolongation was observed in 16.05% of cases. Fragmented QRS complexes were found in the inferior, and V1-V3 leads in several patients, while no right or left bundle branch blocks (RBBB or LBBB) were detected. Three patients presented with PVCs, and one case showed a Brugada pattern.

Conclusion: QTc prolongation and fragmented QRS complexes were identified in many SLE patients treated with hydroxychloroquine. Regular ECG monitoring may be necessary for early detection of potential cardiac risks in this population.

Background: Diabetes and hypertension are two common diseases all over the world. There are several treatment options for the management of these diseases. Some studies have reported that drugs used to manage diabetes and hypertension may have an impact on psychiatric disorders such as depression and dementia. In this article, we aimed to review the results of recent studies about the impact of anti-hypertensive and antidiabetic drugs on psychiatric disorders.

Materials and Methods: We reviewed studies with keywords of “diabetes”, OR “diabetic”, OR “anti-diabetes”, OR “anti-diabetes”, OR “antidiabetic”, AND “drug”, OR “hypertension”, OR “hypertensive”, OR “anti-hypertension”, OR “anti-hypertensive”, OR “anti-hypertensive”, AND “psychology”, OR “psychological” in PUBMED, ELSEVIER, and CENTRAL databases from 2015 to 2025.

Results: The results of the studies were contradictory. There was no consensus idea about antidiabetic drugs, but it seems that sodium-glucose cotransporter two inhibitors can reduce the risk of depression and dementia. The results of studies about anti-hypertensive drugs had more similarities, and some anti-hypertensive drugs, like angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, may reduce the risk of psychiatric disorders.

Conclusion: There is a strong need for evaluation of the effects of anti-hypertensive and antidiabetic drugs on psychiatric disorders because the results of current studies are discrepant.

A key issue raised by increased artificial intelligence (AI) applications in healthcare is whether robots will replace physicians or work alongside them. As technologies incorporating machine learning (ML), natural language processing (NLP), and deep learning continue to evolve rapidly, artificial intelligence (AI) is increasingly improving its skill sets in disease diagnosis, image interpretation, and therapeutic guidance^1-3. Does this imply that physicians are becoming obsolete?

Artificial intelligence has proved to be noteworthy. In radiology, dermatology, and ophthalmology, algorithms can now detect problems with a similar level of accuracy, or even better, than that of professionals^4-6. Google LYNA has aided the detection of breast cancer metastases and helped pathologists focus their attention on areas of highest risk, where it is most crucial⁷. In medical imaging, deep learning algorithms, especially those trained with genetic algorithms, have demonstrated immense promise to improve the accuracy of pneumonia and COVID-19 diagnosis from chest X-rays. Such breakthroughs demonstrate the pragmatic utility of AI to ease respiratory disease detection and improve healthcare response to pandemics⁸. In the same vein, Transfer learning with DenseNet121 and CheXNeXt has demonstrated comparable accuracy to that of humans in classifying many chest ailments, highlighting the potential of AI to be a reliable clinical tool in multi-disease diagnosis based on X-rays⁹. In ophthalmology, convolutional and deep learning have made it possible to quickly and non-invasively interpret the retina. This has made possible the detection and measurement of pathological changes in macular and retinal pathologies. AI systems, now FDA-approved to screen diabetic retinopathy and promising to enable large-scale screening programs, disease monitoring, and customized treatment planning, prove the ophthalmologist's role in the current care of the eyes to be essential¹⁰. In dermatology, AI systems, and deep learning algorithms in particular, have reached levels of comparable accuracy to that of dermatologists in the detection of skin malignancies and inflammatory conditions, highlighting their potential as useful clinical decision-support tools¹¹. Additionally, IBM Watson systems have started to help oncologists by suggesting treatment plans customized to each patient¹². Such remarkable achievements raise concerns in physicians' minds over their professional fate.

However, while AI excels at recognizing patterns and data handling, it fundamentally misses an essential ingredient: human touch. The physician-patient bond is built on communication, empathy, and trust, and includes knowledge of the patient’s condition, values, and life situation—dimensions that no program can truly grasp^{13, 14}. Clinical judgments usually involve managing vague indications, incomplete patient histories, and complex psychosocial dynamics. Such dilemmas require physicians to resort to moral reasoning, draw on clinical experience, and take ethical and contextual considerations into play—dimensions beyond artificial intelligence, thus underscoring the critical role of physician judgment^15,16.

AI can alleviate time spent by physicians doing non-clinically related work by reducing the burden of workload, improving diagnostics, and enabling evidence-based practice, enabling physicians to concentrate more on patient-centered practice¹. For instance, AI-based tools can help with real-time transcription, clinical documentation, and disease progression prediction². These tools alleviate mental fatigue and help clinicians work more efficiently and effectively. Therefore, we should welcome AI not as a rival but as a mighty companion^{17, 18}. This enhances rather than diminishes the role of the physician. The best contenders to take the next leap in patient care will be physicians with dual expertise in both technology and medicine^{19, 20}.

In order to make advancements, education in medicine should include AI literacy and equip the next physicians with the faculty to use new technologies judiciously. Physicians need to know all about AI and critically evaluate its output to provide safe and ethical care²¹. The aim is to enable physicians to function efficiently with machines rather than to make robots physicians^{18, 21}.

Ultimately, physicians will not be replaced by AI, but physicians who use AI wisely may replace physicians who do not. The future of medicine lies in filling the gap between humans and machines, not in choosing one over the other.