Hospital Practices and Research

Abstract Amacher and colleagues have assembled a dataset and posed exactly the right question: not merely whether women and men differ in how often life-sustaining therapy is curtailed, but when along the care pathway that divergence takes shape. Their headline finding is elegant in its symmetry. A pronounced female excess at admission (adjusted odds ratio 1.26, 95% CI 1.24–1.28) gives way, they report, to parity once treatment is under way, the during-stay rates standing at “5.5% versus 5.5%.” From this they infer that decisions grow more even-handed as objective clinical information accrues. We admire the study but believe two linked problems undermine that inference. The first is arithmetical and present on the face of Table 1; the second is structural and inheres in the staged design itself. Both deserve closer scrutiny than they have so far received.

Abstract We wish to highlight a growing concern in urological practice about the increasing rates of sepsis and systemic infections following transrectal ultrasound-guided prostate biopsy (TRUS-PB). Despite being the traditional gold standard for prostate cancer diagnosis, the transrectal method is progressively associated with severe infectious complications, primarily driven by the global rise in antimicrobial resistance.

Abstract Background: Artificial Intelligence (AI) and Machine Learning (ML) have transformed computer-aided drug design (CADD) by leveraging big data and advanced algorithms to accelerate drug design. These technologies enhance the exploration of chemical spaces, prediction of drug-target interactions, and development of personalized therapeutics, particularly for complex diseases like cancer.
Objectives: This review aims to evaluate the role of AI and ML in CADD, focusing on their applications in high-throughput screening (HTS), three-dimensional (3D) protein structure prediction, and drug-target identification, while addressing challenges and future prospects.
Methods: A comprehensive analysis of recent literature (2005–2024) was conducted using scientometric tools like VOSviewer to identify trends and keywords in CADD. AI-driven methods, including deep learning frameworks (e.g., TensorFlow, AlphaFold2) and computational techniques (e.g., molecular dynamics simulations), were reviewed for their contributions to drug design.
Results: AI and ML have streamlined HTS, improved 3D protein structure prediction, and enhanced drug-target identification, reducing development timelines and costs. Tools like AlphaFold 2 and QuoteTarget have identified novel drug targets with high accuracy. However, challenges such as data quality, model interpretability, and ethical concerns persist. Interdisciplinary collaboration has driven innovation in personalized therapeutics.
Conclusion: AI and ML have revolutionized CADD, offering efficient and precise solutions for drug design. Overcoming data and ethical challenges through interdisciplinary efforts and advanced algorithms will further enhance the development of targeted therapies, reshaping therapeutic paradigms for complex diseases.

Abstract Background: Klebsiella pneumoniae (K. pneumoniae) is a major cause of hospital-acquired infections, where treatment options are restricted due to drug-resistant strains, including those producing extended-spectrum beta-lactamases (ESBLs) and carbapenemases.
Objectives: This study focuses on the molecular identification of beta-lactamase and carbapenemase genes in K. pneumoniae strains from Valiasr Hospital, Zanjan, to enhance infection control and antimicrobial strategies.
Methods: In this cross-sectional study, 50 K. pneumoniae clinical isolates were obtained from blood, urine, and wound samples of ICU patients in 2024. Phenotypic identification and antimicrobial susceptibility testing were performed using the Kirby-Bauer disk diffusion method. Genomic DNA extraction was performed using the phenol-chloroform method, followed by polymerase chain reaction (PCR) to identify resistance genes (blaCTX-M, blaTEM, blaSHV, blaIMP, blaVIM). The PCR products were analyzed via agarose gel electrophoresis, with data processed using SPSS version 26.
Results: The mean age of the patients was 58.90 years, and the isolates exhibited high resistance rates to ampicillin (90%), piperacillin (78%), and cefotaxime (72%). Genotypic testing indicated ESBL gene frequencies: blaTEM (46%), blaSHV (44%), and blaCTX-M (40%), along with carbapenemase genes blaVIM and blaIMP in 26% and 22% of isolates, respectively. A significant link was found between the blaTEM gene and urinary isolates (P = 0.037). Furthermore, the presence of the blaTEM, blaSHV, and blaCTX-M genes was associated with MDR, XDR, and PDR phenotypes (P < 0.05), with 100% of MDR isolates containing all three genes.
Conclusion: The study found a high prevalence of ESBL genes linked to multidrug resistance patterns among K. pneumoniae isolates in ICUs. The presence of carbapenemase genes (blaIMP and blaVIM) indicates a shift toward carbapenem resistance, underscoring the urgent need for better antibiotic surveillance and infection control in these settings.

Abstract Background: Kidney stones significantly raise healthcare expenses due to the requirement for specialized treatments and frequent hospital visits. Kidney stones lower workforce productivity because affected individuals frequently miss work or are less efficient due to pain and the necessity of treatment. In society, kidney stones lead to reduced productivity and quality of life, affecting both individuals and the broader economy.
Objectives: The primary objective of this study is to analyze the influence of urine-based biochemical and physical parameters on kidney stone formation and to develop a high-accuracy machine learning model for early kidney stone prediction.
Methods: In this paper, data analytics methods are used to investigate kidney stones based on urea analysis and uncover valuable insights from the relationship between various factors. Additionally, a predictive model is developed by integrating deep learning with Particle swarm optimization (PSO) algorithms, where PSO is utilized to tune the model hyperparameters.
Results: This model boasts an impressive 98.1% accuracy in predicting kidney stones. Such a proactive approach can significantly enhance patient outcomes by preventing the onset of painful and debilitating kidney stones.
Conclusion: In this research, it was shown that early prediction allows for timely medical intervention, reducing symptom severity and preventing complications like urinary tract infections or kidney damage. Consequently, the need for emergency treatments and hospitalizations is minimized, lowering healthcare costs.

Abstract Background: Hospital nurses face psychological challenges and occupational burnout due to the demanding nature of their profession. Variables such as resilience, openness to experience, identity styles, and expansion of spiritual experiences have been shown to reduce these problems, but have mainly been studied in non-Muslim societies using non-Islamic approaches. This study addresses this gap by examining the effectiveness of Islamic spiritual therapy on these variables in hospital nurses.
Objectives: To investigate the effectiveness of Islamic spiritual therapy on resilience, openness to experience, identity styles, and expansion of spiritual experience in hospital nurses.
Methods: This was a semi-experimental study with a pre-test-post-test design and a control group. The sample consisted of 30 nurses working in public hospitals in Tehran, Iran, selected using convenience sampling and randomly assigned to experimental and control groups. The experimental group received 10 sessions of 90-minute Islamic spiritual therapy, while the control group received no intervention. Data were collected using the Connor and Davidson Resilience Scale (CD-RISC), the Revised NEO Personality Inventory (NEOPI-R) for openness to experience, the Berzonsky Identity Styles Questionnaire (ISI-6G), and the Underwood and Teresi Daily Spiritual Experiences Scale (DSES). Data were analyzed using analysis of covariance (ANCOVA).
Results: The results showed that Islamic spiritual therapy had a significant effect on resilience, openness to experience, identity styles (informational, normative, avoidant/confused, and commitment), and the expansion of spiritual experience in hospital nurses (P < 0.05).
Conclusion: Islamic spiritual therapy enhances resilience, openness to experience, identity styles, and spiritual experiences in hospital nurses. It can reduce burnout and promote holistic (physical-psychological-spiritual) care in hospital settings. Future research should include larger sample sizes and follow-up assessments.

Abstract Background: Mild cognitive impairment (MCI) represents a transitional stage between normal age-related cognitive decline and dementia. In this population, psychological factors such as hope and self-efficacy are frequently impaired, contributing to reduced quality of life and potential progression to dementia.
Objectives: This study aimed to compare the effects of cognitive rehabilitation (CR) and mindfulness-based stress reduction (MBSR) on self-efficacy and hope in individuals with MCI.
Methods: A quasi-experimental pretest-posttest design with a control group and a three-month follow-up was employed. The study population comprised patients with MCI attending memory and cognitive rehabilitation clinics in Tehran in 2024. Forty-five participants were selected through convenience sampling and randomly allocated to three groups: CR (n = 15), MBSR (n = 15), and control (n = 15). Each intervention group received 10 sessions, while the control group received no intervention. Outcomes were assessed using the General Self-Efficacy Scale and the Adult Hope Scale. Data were analyzed with repeated-measures analysis of variance (ANOVA).
Results: CR significantly improved self-efficacy at posttest (P < 0.01), with effects well maintained (and slightly numerically increased) at the three-month follow-up. MBSR showed no significant immediate effect on self-efficacy but was superior to control at follow-up. Both CR and MBSR significantly increased hope (P < 0.05), with CR showing greater improvements at posttest (P < 0.05). Direct comparisons indicated CR superiority over MBSR mainly at posttest for both outcomes.
Conclusion: Both interventions provide psychological benefits for individuals with MCI, but CR appears to be more effective in strengthening self-efficacy beliefs and future-oriented motivation. Clinically, this suggests CR as a valuable adjunct to support autonomy in MCI patients.

Abstract Background: Electrical injuries can be life-threatening, ranging from mild skin burns to serious organ damage. Low-voltage electric shocks (<1000V) are more common than high-voltage electric shocks. Phone chargers have been used quite frequently in recent years, especially at night, to charge portable phones. Cases of burns due to phone chargers have been reported.
Case Presentation: In this case report, a 30-year-old woman developed chest pain and symptomatic bradycardia after exposure to low-voltage electricity (5 V, 3 A) from the connector of a phone charger.
Conclusion: This case describes transient symptomatic sinus bradycardia temporally associated with contact with a mobile phone charger connector. However, causality cannot be established based on a single case. In symptomatic patients after such exposure, ECG assessment and short-term emergency department monitoring may be considered.