Background:Palliative care is an approach that focuses on improving a patient's quality of life. This research aimed to develop a path model of the relationships between the variables of nursing care (information, emotional support, technical support, and palliative care), patient coping, family support, patient spirituality, and patient comfort expressed through physical and emotional mediators.Method:This cross-sectional study involved 308 breast cancer patients from 3 referral hospitals in Jakarta, Indonesia. A structural equation model with Kolcaba's theory was used to develop a theoretical model estimating the path or relationships between the key variables.Results:The results showed that palliative care significantly improved breast cancer patient comfort by reducing anxiety and depression. Furthermore, the study demonstrated a significant positive relationship between spirituality and emotional well-being.Conclusion:Spirituality-focused palliative care is fundamentally importance for breast cancer patients. Nurses play an essential role in providing spirituality-focused palliative care to promote comfort in breast cancer patients in Indonesia.

Objectives Cancer stem cells are involved in radioresistant cancers. Transcription factors Sry-related HMG box (SOX2) and octamer binding transcription factor 4 (OCT4) can confer pluripotent cell characteristics and self-renewal ability and are involved in carcinogenesis, metastasis, tumor recurrence, and resistance to therapy. Apoptosis, DNA repair, and telomerase factors also contribute to radioresistance. We sought to identify the role of SOX2 and OCT4 as cancer stem cell markers and their effects on apoptosis (via caspase 3), DNA repair (Chk1) and telomerase (hTERT) in conferring resistance to radiotherapy. Methods We conducted a case-control study of 40 patients with stage IIIB cervical squamous cell carcinoma who completed radiation therapy at Cipto Mangunkusumo Hospital, Jakarta, Indonesia. The patients were classified according to their treatment response as having exhibited a complete or incomplete response. Clinical follow-up and Pap smears were performed between six and 12 months after therapy for those with a good initial response to determine the final response to therapy. Immunohistochemistry was used to analyze SOX2, OCT4, caspase-3, Chk1, and hTERT expression in paraffin sections of the initial biopsy. Results Strong expression of SOX2 ( p = 0.011, p = 0.001) and OCT4 ( p < 0.001, p < 0.001) was significantly associated with both an incomplete initial and final therapy response, respectively. Multivariate analysis showed that SOX2 and OCT4 expression levels were the strongest markers of an incomplete response to radiotherapy (odds ratio (OR) = 5.12, p = 0.034, and OR = 17.03, p = 0.004, respectively). Conclusions Strong expression of SOX2 and OCT4 may be a good indicator of incomplete radiotherapy outcome in patients with stage IIIB cervical cancer.

BACKGROUND Ovarian cancer is a heterogeneous disease, and most patients are diagnosed at an advanced stage. Epithelial ovarian cancer type II is characterized by rapid tumor growth and is genetically more labile than type I. This study was aimed to demonstrate the prognostic value of CSC by using the markers CD133, CD44, and ALDH1A1 in EOC.METHODS Clinicopathological and demographic data were collected from medical records. The markers CD133, CD44, and ALDH1A1 were examined with flow cytometry and immunohistochemistry. Cancer stem cell (CSC) marker expression in patients with ovarian cancer types I and II were related to chemotherapy and survival. In multivariate analysis, the prognosis model was tested for ten months.RESULTS The largest demographic consisted of patients aged ≥45 years, with stage I, poor differentiation, and type II, of which there were 40 samples (72.7%), 23 samples (41.8%), 30 samples (54.5%), and 16 samples (29.1%), respectively. There is a high correlation between the 10-month chemotherapy response and the 4 variables, i.e., age ≥45 years, type II, stage III–IV, and CD44, with an ROC of 80.75% and a post-test probability of 82.5%. Using the ROC curve, the highest chemoresistance score was 0.841, based on the combination of CSCs markers and clinicopathological factors, that is stage III–IV, age ≥45 years, poor differentiation, type II, negative CD133, high CD44, and high ALDH1A1.CONCLUSIONS CSC (CD133, CD44, and ALDH1A1) markers and clinicopathological factors are prognostic of epithelial ovarian cancer.

Background: Given the role of inflammation in carcinogenesis, this study investigated the utility of pre-treatment inflammatory markers as associative indicators for advanced-stage disease, lymph node metastasis (LNM), and distant metastasis (DM) in vulvar cancer (VC). Methods: A cross-sectional study was conducted on 86 women with VC in a single centre in Jakarta, Indonesia. The laboratory data was based on C-reactive protein (CRP), procalcitonin, the erythrocyte sedimentation rate (ESR) and fourteen derived, recorded and calculated ratios: leukocyte-to-platelet (LPR), neutrophil-to-lymphocyte (NLR), derived neutrophil-to-lymphocyte (dNLR), neutrophil-to-monocyte (NMR), platelet-to-monocyte (PLR), lymphocyte-to-monocyte (LMR), basophil-to-monocyte (BLR), systemic immune-inflammation index (SII), body mass index, albumin, and NLR (BAN) score, haemoglobin-to-platelet (HPR), prognostic nutritional index (PNI), modified Glasgow Prognostic Score (mGPS), CRP-to-albumin, and CRP-to-procalcitonin. The optimal cut-off for each marker was determined using receiver operating characteristic (ROC) curve analysis, and their diagnostic indicator performances were assessed. The utility of these ratios as associative factors for three endpoints was further evaluated in multivariate regression models. Results: Investigated inflammatory markers exhibited specific performances for individual adverse outcomes, proving a fair to excellent ability in case finding and screening. After adjustment, the BAN score ≤334.89 (OR 9.20, p = 0.001) and ESR ≥104 (OR 4.18, p = 0.048) become two advanced-stage associative factors with AUC: 0.769. LNM was solely determined by higher NLR ≥2.83 (OR 4.15, p = 0.014) with AUC: 0.615. Meanwhile, BLR ≥0.035 (OR 5.67, p = 0.001) and ESR ≥84 (OR 6.01, p = 0.003) were contributing factors for DM, with AUC: 0.765. Conclusions: Inflammatory markers are crucial for identifying the deleterious outcomes of VC. Accordingly, yielded models require external validation.