Bosnian Journal of Basic Medical Sciences (BJBMS)
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A pragmatic approach to teaching physician assistant students basic pain management
Get PDFChronic pain is increasing in prevalence, with new cases now outnumbering those of diabetes, depression, or hypertension. Advanced practice providers have reported that their training in pain management inadequately prepared them to care for patients suffering from painful conditions. In response, the authors of this work developed a basic pain management conceptual framework to provide physician assistant (PA) students with the foundational knowledge necessary to manage and treat patients suffering from a wide variety of painful conditions. The devised framework activity includes categories of pain management such as conservative therapies, medications, injections, minimally invasive procedures, and moderately to highly invasive procedures. This framework can be incorporated into the existing PA educational curriculum and presented alongside a realistic pain patient case study. Furthermore, other health science educational programs, such as nurse practitioner or pharmacy programs, could adopt this framework to increase student knowledge in pain management
Hormone replacement therapy in surgical menopause after gynecological malignancies
Get PDFThis review examines hormone replacement therapy (HRT) in cases of surgical menopause following gynecological malignancies. It aims to capture current knowledge, summarize recent findings, and provide recommendations for clinical settings. Unlike natural menopause, surgical menopause occurs abruptly, without an adjustment period, and is associated with a notably higher risk of fractures, arthritis, cognitive decline, dementia, Parkinson’s disease, and various metabolic disorders affecting glucose and lipid levels—all of which contribute to an increased risk of major cardiovascular events. In 2017, The North American Menopause Society recommended that, barring contraindications, HRT should be initiated in women who enter surgical menopause before age 45. If these women do not experience vasomotor symptoms or other issues, HRT should be maintained consistently at least until age 52. This guideline reflects contemporary knowledge and is the result of a multidisciplinary consensus, based on a review of existing literature and several randomized clinical trials focusing on women who have survived gynecological cancers and whose quality of life is significantly impacted by surgical or early menopause. Estrogen supplementation is particularly beneficial, as it is linked to marked improvements in quality of life, including delayed onset of chronic cardiovascular issues, reduced fracture risk, enhanced cognitive function, reduced inflammation, and improved self-esteem, as well as better social and work performance. Clinical implementation of HRT, however, requires a highly individualized approach. This approach must consider the type and stage of malignancy, histopathological characteristics, risk factors for recurrence (such as diet, concurrent medications, medical history, and genetic predispositions), and a thorough assessment of the potential benefits and risks of HRT, as well as the patient’s personal wishes and expectations
Clinical features and demographic characteristics of gestational trophoblastic neoplasia: Single center experience and the SEER database
Get PDFThe aim of this study was to analyze the clinical features and demographic characteristics of gestational trophoblastic neoplasia (GTN) patients, specifically choriocarcinoma (CC), placental site trophoblastic tumour (PSTT), and epithelioid trophoblastic tumor (ETT). We utilized data from a local hospital and the SEER database, as well as survival outcomes of CC in SEER database. Additionally, we used multiple risk factors to create a prognostic nomogram model for CC patients. The study included GTN patients from the SEER database between 1975 and 2016 as well as those from the First Affiliated Hospital of Xi \u27an Jiaotong University between January 2005 and May 2022. Related factors of patients were compared using the chi-square (χ2) or Fisher\u27s exact test. For assessing overall survival we employed the Kaplan-Meier method and log-rank test. To construct the nomogram, we used Cox regression. Statistically significant differences were found between CC and PSTT/ETT patients in terms of surgery in local hospital, as well as age and year of diagnosis in the SEER database. Moreover, significant differences were observed between low and high (HR) /ultra-high risk (UHR) groups regarding FIGO stage, surgery and chief complaint at the local hospital, and FIGO stage, surgery and unemployment in the SEER database. The Cox regression analysis confirmed that age, race, surgery, marital status, FIGO stage, and unemployment were correlated with CC prognosis. Furthermore, the analysis showed that patients aged 40 years or older and those with FIGO Ⅲ/Ⅳ were independent prognostic factors of CC. The study indicates that atypical symptoms or signs may be the main reasons for HR /UHR patients to seek medical treatment. Therefore, providing multidisciplinary care is recommended for CC patients experiencing psychological distress due to unfavorable marital status or unemployment
Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review
Get PDFAnti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients\u27 responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes
MicroRNAs expression profile in chemotherapy-induced cardiotoxicity in non-small cell lung cancer using a co-culture model
Get PDFClinical application of chemotherapy in lung cancer is constrained by side effects, notably cardiotoxicity, the mechanisms of which remain elusive. This study assessed the potential of specific microRNAs (miRNAs) as biomarkers for chemotherapy-induced cardiotoxicity in lung cancer. We employed two lung adenocarcinoma cell lines (Calu6 and H1792) and ventricular normal human cardiac fibroblasts (NHCF-V) in single and co-culture experiments. Functional tests were conducted using 100 µM carboplatin and 1µM vinorelbine doses. The effects of carboplatin and vinorelbine, both individually and in combination, were evaluated at cellular and molecular levels 48h post-therapy for both mono- and co-cultures. miR-205-5p, miR-21-5p, and miR-30a-5p, modulated by anticancer treatments and influencing cardiotoxicity, were analyzed. Vinorelbine and carboplatin treatment promoted apoptosis and autophagy in lung cancer cells and cardiac fibroblasts more than in controls. Western blot analyses revealed BCL2 and p53 protein upregulation. Using qRT-PCR, we investigated the expression dynamics of miR-21-5p, miR-30c-5p, and miR-205-5p in co-cultured cardiomyocytes and lung cancer cells, revealing altered miRNA patterns from vinorelbine and carboplatin treatment. Our findings underscore the intricate relationship between chemotherapy, miRNA regulation, and cardiotoxicity, highlighting the importance of cardiac health in lung cancer treatment decisions
Meta-analysis of changes in thiol-disulfide homeostasis during preeclampsia
Get PDFThe present study systematically assessed alterations in thiol-disulfide homeostasis among women with preeclampsia (PE) through meta-analysis. This was conducted as such changes are believed to be associated with the oxidative stress underlying this condition. A comprehensive search of Medline, Web of Science, and Embase databases was conducted from their inception until 22 March 2023, to identify studies comparing levels of native thiol, total thiol, and disulfide between pregnant women with PE and those without PE. Results were pooled using a random-effects model to account for study heterogeneity. The analysis included a total of 631 women diagnosed with PE and 668 healthy pregnant women, encompassing 13 case-control studies and 1 prospective study. Pooled outcomes revealed that women with PE had significantly lower blood levels of native thiol, (mean difference [MD] -51.42 umol/L; 95% confidence interval [CI] -79.75 to -23.10 umol/L; P < 0.001; I2 = 0%) and total thiol (MD -65.56 umol/L; 95% CI -104.97 to -26.15 umol/L; P = 0.001; I2 = 0%) compared to the control group. In contrast, no significant difference was observed in blood disulfide levels between the two groups (MD -1.10 umol/L; 95% CI -4.41 to -2.21 umol/L; P = 0.51; I2 = 0%). Subgroup analyses indicated that the results were consistent across studies matched by gestational age and body mass index, as well as those with varying quality scores (P for subgroup differences all > 0.05). In conclusion, women with PE are associated with significantly reduced blood levels of native and total thiols but show no change in blood disulfide levels, suggesting a state of reduced antioxidants in PE
Beyond hot flashes: Exploring the role of estrogen therapy in postmenopausal women for myocardial infarction prevention and recovery
Get PDFMyocardial infarction (MI) commonly known as "heart attack" results from the blockage of blood flow to the heart. Postmenopausal women face an elevated risk of MI due to declining estrogen levels, a hormone pivotal in maintaining cardiovascular health. It promotes vasodilation, reduces inflammation, and improves lipid profiles. While estrogen therapy shows promise in mitigating MI risk for postmenopausal women, its efficacy in prevention and recovery remains a subject of debate. This review provides a critical assessment of existing evidence on estrogen therapy\u27s cardioprotective effects for postmenopausal women. It delves into estrogen\u27s role in vascular function enhancement, inflammation reduction, and lipid metabolism modulation. Additionally, it addresses the various forms of estrogen therapy, administration methods, dosage considerations, safety implications, and associated risks. The review highlights the existing controversies and knowledge gaps related to estrogen therapy for MI prevention. It underscores the urgency for in-depth research to decipher the nexus between estrogen therapy and MI risk, especially concerning primary prevention and specific postmenopausal subgroups. Future studies should investigate optimal formulations, doses, and administration routes of estrogen therapy as well as assess treatment timing and duration. Comparative studies and long-term follow-up are necessary to inform clinical decision-making and improve patient care. Addressing these research gaps will empower clinicians to make more judicious choices about estrogen therapy for MI prevention and recovery in postmenopausal women, aiming for enhanced patient outcomes.
The laboratory parameters in predicting the severity and death of COVID-19 patients: Future pandemic readiness strategies
Get PDFThe range of clinical manifestations associated with the infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encompasses a broad spectrum, ranging from flu-like symptoms to the occurrence of multiple organ failure and death. The severity of the coronavirus disease 2019 (COVID-19) is categorized based on clinical presentation and is divided into three distinct levels of severity identified as non-severe, severe, and critical. Although individuals of all age groups are susceptible to SARS-CoV-2 infection, middle-aged and older adults are more frequently impacted, with the latter being more likely to develop severe illness. Various laboratory characteristics observed in hospitalized COVID-19 patients have been correlated with adverse outcomes. These include elevated levels of D-dimer, liver enzymes, lactate dehydrogenase, C-reactive protein, ferritin, prothrombin time, and troponin, as well as decreased lymphocyte and platelets counts. This review investigated the relationship between baseline clinical characteristics, initial laboratory parameters upon hospital admission, and the severity of illness and mortality rates among COVID-19 patients. Although the COVID-19 pandemic has concluded, understanding the laboratory predictors of virus severity and mortality remains crucial, and examining these predictors can have long-term effects. Such insights can help healthcare systems manage resources more effectively and deliver timely and appropriate care by identifying and targeting high-risk individuals. This knowledge can also help us better prepare for future pandemics. By examining these predictors, we can take steps to protect public health and mitigate the impact of future pandemics
A novel machine learning-derived four-gene signature predicts STEMI and post-STEMI heart failure
Get PDFHigh mortality and morbidity rates associated with ST-elevation myocardial infarction (STEMI) and post-STEMI heart failure (HF) necessitate proper risk stratification for coronary artery disease (CAD). A prediction model that combines specificity and convenience is highly required. This study aimed to design a monocyte-based gene assay for predicting STEMI and post-STEMI HF. A total of 1,956 monocyte expression profiles and corresponding clinical data were integrated from multiple sources. Meta-results were obtained through the weighted gene co-expression network analysis (WGCNA) and differential analysis to identify characteristic genes for STEMI. Machine learning models based on the decision tree (DT), support vector machine (SVM), and random forest (RF) algorithms were trained and validated. Five genes overlapped and were subjected to the model proposal. The discriminative performance of the DT model outperformed the other two methods. The established four-gene panel (HLA-J, CFP, STX11, and NFYC) could discriminate STEMI and HF with an area under the curve (AUC) of 0.86 or above. In the gene set enrichment analysis (GSEA), several cardiac pathogenesis pathways and cardiovascular disorder signatures showed statistically significant, concordant differences between subjects with high and low expression levels of the four-gene panel, affirming the validity of the established model. In conclusion, we have developed and validated a model that offers the hope for accurately predicting the risk of STEMI and HF, leading to optimal risk stratification and personalized management of CAD, thereby improving individual outcomes
Early machine learning prediction of hospitalized patients at low risk of respiratory deterioration or mortality in community-acquired pneumonia: Derivation and validation of a multivariable model
Get PDFCurrent prognostic tools for pneumonia predominantly focus on mortality, often neglecting other crucial outcomes such as the need for advanced respiratory support. The objective of this study was to develop and validate a tool that predicts the early risk of non-occurrence of respiratory deterioration or mortality. We conducted a single-center, retrospective cohort study involving hospitalized adult patients with community-acquired pneumonia (CAP) and acute hypoxic respiratory failure from January 2009 to December 2019 (n = 4379). We employed the gradient boosting machine (GBM) learning to create a model that estimates the likelihood of patients requiring advanced respiratory support (high flow nasal cannula [HFNC], non-invasive mechanical ventilation [NIMV], and invasive mechanical ventilation [IMV]) or facing mortality during hospitalization. This model utilized readily available data including demographic, physiologic, and laboratory data, sourced from electronic health records and obtained within the first six hours of admission. Out of the cohort, 890 patients (25.2%) either required advanced respiratory support or died during their hospital stay. Our predictive model displayed superior discrimination and higher sensitivity (cross-validation C-statistic = 0.71; specificity = 0.56; sensitivity = 0.72) compared to the pneumonia severity index (PSI) (C-statistic = 0.65; specificity = 0.91; sensitivity = 0.24; P value < 0.001), while maintaining a negative predictive value (NPV) of approximately 0.85. These data demonstrate that our machine learning model predicted the non-occurrence of respiratory deterioration or mortality among hospitalized CAP patients more accurately than the PSI. The enhanced sensitivity of this model holds potential for reliably excluding low-risk patients from pneumonia clinical trials