Journal of Advanced in Medicinal, Pharmaceutical and Biomedical Research (JAMPBR)

Abstract The advent of artificial intelligence (AI) in aesthetic dermatology has introduced novel methods for the objective quantification of facial features following filler injections. Traditional assessments of filler outcomes rely heavily on subjective clinician evaluations and patient feedback, which may lead to variability in treatment results and hinder standardized outcome measurements. This study explores the use of AI-driven facial feature quantification to provide accurate, reproducible, and automated analyses of post-injection changes in facial volume, symmetry, and contour. Using advanced machine learning algorithms combined with three-dimensional (3D) imaging and high-resolution photography, AI systems can detect subtle volumetric alterations and soft tissue shifts with high precision. These technologies analyze multiple facial landmarks and compare pre- and post-treatment images to quantify changes in surface topology and structural enhancement. The AI application facilitates objective monitoring of filler distribution, volume retention over time, and asymmetry correction, thereby enabling clinicians to optimize treatment plans and enhance patient satisfaction. Furthermore, AI-based quantification supports longitudinal studies by standardizing data collection and enabling large-scale analyses of filler efficacy across diverse populations. Challenges remain in integrating AI seamlessly into clinical workflows and ensuring data privacy and algorithm transparency. However, the potential for AI to improve clinical decision-making, reduce inter-observer variability, and provide personalized treatment feedback is significant. In conclusion, artificial intelligence-driven facial feature quantification represents a promising advancement in aesthetic medicine, offering precise, objective, and scalable tools for evaluating filler injection outcomes.

Abstract Background: For early-stage primary breast cancer, breast-conserving surgery (BCS) followed by radiotherapy and mastectomy have historically yielded similar long-term overall survival (OS) in randomized trials.

Methods: We performed a focused systematic search for randomized trials, pooled individual-patient meta-analyses, large population cohort studies, and systematic reviews on OS after mastectomy versus BCS + radiotherapy. We also reviewed literature on preoperative and intraoperative imaging (MRI, mammography, tomosynthesis, specimen radiography) and their influence on surgical choice and local control. Key randomized controlled trials (RCTs) and the Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) meta-analysis were prioritized, as well as recent large registry analyses and imaging meta-analyses.

Results: Landmark RCTs and the EBCTCG pooled analyses show no significant difference in long-term overall survival between mastectomy and BCS + radiotherapy for early invasive breast cancer when appropriate multidisciplinary treatment is given. Recent large population studies and some meta-analyses suggest non-inferiority and, in several cohorts, a small OS advantage for BCS + radiotherapy—often attributed to improved systemic therapy, breast-conserving approaches selecting lower-risk tumors, and survival benefits related to adjuvant radiotherapy. Registry data also show higher rates of mastectomy (including bilateral) in some groups without OS benefit; double mastectomy for unilateral disease does not appear to improve OS for average-risk patients.

Conclusions: The highest-level randomized evidence supports equivalent OS for BCS + radiotherapy and mastectomy in appropriately selected early breast cancer patients. Radiological tools are critical for accurate staging and margin assessment but must be applied judiciously to avoid overtreatment. Individualized multidisciplinary decision-making remains paramount.

Abstract Introduction: Pregnancy blood pressure disorders are related to adverse outcomes and even maternal death. In order to better manage this problem, it is important to evaluate the effective social factors in the self-care status of women with these disorders so this study was conducted with the aim of determining the relationship between cognitive social support and self-care of women with preeclampsia referred to a selected hospital in Ilam.

Materials and methods: In this "descriptive-correlation" study, 233 women with preeclampsia referred to the emergency department of Taleghani Hospital in Ilam in 2024 participated based on available sampling method. The data collection tools of this study include demographic and disease information questionnaire, Zimmet Perceptual Social Support Scale (MSPSS) and self-care questionnaire in patients with hypertension (HTN-SCP). These data were analyzed using SPSS version 23 software and based on descriptive tests and Pearson test.

Findings: The average score of perceived social support among the studied samples was equal to 71.05±6.04 out of the total score of 84 which shows a high level of perceived social support. The average self-care score among the studied samples was 39.82±4.07 out of a total score of 54 which shows the average level of self-care in the samples. Also there was a negative and significant correlation between perceived social support and self-care (p<0.001, r=-0.29).

Conclusion: According to the results of this study, officials active in the field of pregnant women's health and nurses working in medical centers should consider the measures and interventions necessary to promote cognitive social support and increase the amount of self-care in women with preeclampsia.

Abstract Introduction: Early recognition of patient deterioration and timely escalation of care are central responsibilities within nursing departments. With the increasing adoption of electronic health records and continuous monitoring, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as tools to support nurses in predicting critical outcomes such as hospital admission, intensive care unit (ICU) transfer, and mortality. However, the overall diagnostic accuracy of these models in nursing-driven settings remains unclear.

Objective: This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of AI-based models in predicting hospital admission status, ICU transfer, and mortality, focusing on studies that utilized nursing assessments, observations, or ward-based data as primary inputs.

Methodology: A comprehensive literature search was conducted across MEDLINE, Embase, Cochrane CENTRAL, Scopus, and IEEE Xplore from January 2000 to July 2025. Eligible studies reported the performance of AI or ML algorithms predicting at least one of the three outcomes of interest. Two independent reviewers performed study selection, data extraction, and quality assessment using the PROBAST tool. Pooled area under the receiver operating characteristic curve (AUROC) values were calculated using a random-effects model. Heterogeneity was quantified using the I² statistic, and meta-regression was used to explore potential moderators such as model type, validation method, and inclusion of nursing data.

Findings: From 6,842 records, 83 studies met inclusion criteria, covering approximately 12.4 million patient encounters. The pooled AUROC was 0.81 (95% CI: 0.77–0.85) for admission prediction, 0.84 (95% CI: 0.80–0.88) for ICU transfer, and 0.86 (95% CI: 0.83–0.89) for mortality. Models incorporating nursing-reported concerns and temporal vital-sign patterns demonstrated superior calibration and clinical utility.

Conclusion

AI-based models exhibit strong diagnostic accuracy for predicting patient deterioration outcomes relevant to nursing practice. Nonetheless, further research emphasizing external validation, interpretability, and real-world clinical integration is essential before routine deployment in nursing departments.

Abstract Anticoagulant therapy is a cornerstone in the management of cardiovascular diseases, including atrial fibrillation, venous thromboembolism, and mechanical heart valve replacement. While these therapies significantly reduce thromboembolic events, they are associated with an inherent risk of bleeding, making precise monitoring essential for optimizing clinical outcomes. This systematic review evaluates the current laboratory assays used to monitor anticoagulant therapy, their clinical relevance, limitations, and emerging methodologies. Traditional monitoring methods, including prothrombin time (PT), activated partial thromboplastin time (aPTT), and international normalized ratio (INR), remain widely utilized. PT and INR are standard for warfarin therapy; however, variations in thromboplastin reagents and laboratory practices contribute to inter-laboratory variability, potentially affecting dosing accuracy. aPTT is commonly employed for unfractionated heparin monitoring, yet its sensitivity is influenced by reagents and instrumentation, prompting recommendations for institution-specific therapeutic ranges and anti-Xa assay utilization for more precise assessment. Direct oral anticoagulants (DOACs), such as dabigatran, rivaroxaban, apixaban, and edoxaban, offer predictable pharmacokinetics and reduce the need for routine monitoring. Nonetheless, clinical situations including renal or hepatic impairment, bleeding complications, and urgent surgical interventions may necessitate evaluation of anticoagulant effects. Emerging assays, including anti-Xa activity measurement, thrombin generation assays, and point-of-care testing, demonstrate improved accuracy and patient-centered monitoring potential, though limited accessibility and standardization remain challenges. Overall, integrating these advanced methods with conventional assays can enhance anticoagulation management, minimize adverse events, and improve patient safety. Standardization of laboratory protocols, adherence to guidelines, and ongoing research are critical to achieving reliable monitoring across diverse clinical settings.

Abstract Background: Nursing practice in the Intensive Care Unit (ICU) is inherently complex due to the critical condition of patients, multiple comorbidities, and rapidly changing clinical states. While many protocols and guidelines exist, there remains inconsistency in how evidence-based nursing practices are implemented, adapted, and evaluated in real-world ICU settings.

Objective: This review aims to synthesize existing literature on ICU nursing practices grounded in clinical considerations, focusing on interventions, barriers, facilitators, and outcomes relevant to patient safety, care quality, and nursing workflow.

Methods: A systematic search was conducted in PubMed, CINAHL, Embase, Cochrane Library, and Web of Science for articles published from 2010 to 2025, using combinations of keywords such as “ICU nursing practices,” “intensive care,” “clinical nursing,” “interventions,” and “patient outcomes.” Inclusion criteria encompassed peer-reviewed empirical studies (qualitative, quantitative, or mixed-methods) that described specific nursing practices, strategies, or protocols within adult ICU settings.

Results: From an initial 2,420 records, 42 articles met criteria. Practices clustered in areas such as sedation management and daily interruption, early mobilization, pressure injury prevention, mechanical ventilation weaning protocols, and nurse-driven weaning. Key facilitators included strong leadership, interprofessional collaboration, simulation-based training, and institutional support. Common barriers were high nurse-to-patient ratios, resistance to change, variability in guideline adoption, and documentation burden. Many studies reported improvements in ICU length of stay, ventilator days, complication rates, and patient safety indicators.

Conclusion: The review underscores that nursing practices in the ICU must be tailored to dynamic clinical realities—protocols cannot be rigid. Successful implementation depends on organizational support, adequate resources, ongoing education, and adaptive modification. Future research should pursue rigorous controlled trials and standardized outcome measures to validate the most effective nursing practices across different ICU contexts.