Ovarian cancer is the leading cause of death from gynecological cancers. In the early stages, it is generally asymptomatic, leading to delays in the early diagnosis of ovarian cancer. Inflammation, which is the body’s response to tissue damage, plays a crucial role in the pathogenesis of cancer. Inflammatory responses result in cellular changes, damaged tissue repair, and affected area proliferation. This research aims to determine the differences in LMR (Lymphocyte-to-Monocyte Ratio), PLR (Platelet-to-Lymphocyte Ratio), and SIRI (Systemic Immune-Inflammation Index), which were the parameters of inflammation between ovarian malignant tumors and benign ovarian tumors. This retrospective cohort study analyzed medical records of 327 patients treated at Dr. Hasan Sadikin General Hospital, Bandung, between 2020 and 2022, including 166 cases of malignant ovarian tumors and 161 benign ovarian tumors. Inflammatory markers were calculated from routine blood counts. The mean LMR was significantly lower in the malignant group than the benign group (3.32 vs. 3.83, P = 0.004). Conversely, PLR and SIRI values were significantly higher in malignant tumors (PLR: 355 vs. 213, P = 0.001; SIRI: 3.50 vs. 2.97, P = 0.042). These findings suggest that LMR, PLR, and SIRI may serve as supportive inflammatory biomarkers in distinguishing malignant from benign ovarian tumors.

Objective: Tumor markers such as CA125 are highly beneficial in predictive ovarian malignancy; however, this advanced test is not always available in remote areas. To address this issue, the author aimed to explore the use of systemic inflammatory biomarkers as complementary modalities for diagnosis of ovarian malignancy. Methods: This diagnostic study utilized a cross-sectional approach, with outcomes measured by AUC and sensitivity. A total of 132 patients with adnexal tumors were consecutively included and measured a complete blood count. From this, the MLR (Monocyte Lymphocyte Ratio), NLR (Neutrophil Lymphocyte Ratio), PLR (Platelet Lymphocyte Ratio), SII (Systemic Immune Inflammation Index), and SIRI (Systemic Inflammatory Response Index) biomarkers were calculated. After surgery, histopathological examination was performed as the gold standard and the biomarker predictions were then compared to it, followed by statistical analysis. Results: The AUC values for MLR, NLR, PLR, SII, and SIRI were 0.70, 0.731, 0.696, 0.743, and 0.722, respectively. The p-values were MLR (0.005), NLR (0.001), PLR (0.001), SII (< 0.001), and SIRI (< 0.001), respectively. In multivariate analysis, only SII was significant (p = 0.015). The Exp(B) and 95% CI were 5.472 (1.383– 21.655). The validity test for SII showed satisfactory results: sensitivity 71.64%, specificity 73.84%, PPV 73.84%, NPV 71.64%, accuracy 72.72%, LR+ 2.74%, and LR- 0.38%. Conclusion: Systemic inflammatory biomarkers, particularly SII may aid in the predictive markers of early ovarian with diagnostic values nearly as good as CA125 (sensitivity 71.64% vs 75.97%). These biomarkers can serve as complementary predictive markes modalities for ovarian malignancy, especially when advanced tumor marker tests like CA125 are not available in remote areas.

Hydatidiform mole (HM) is an aberrant pregnancy characterized by atypical trophoblastic hyperplasia, hydropic chorionic villi, and deprived fetal development. There are two types of HM, ie, complete (CHM) and partial (PHM). Both CHM and PHM can recur; however, the recurrence of PHM is very scarce compared to CHM. In this report, we present a case of a 33-year-old woman with recurrent PHM for 7 times without any normal pregnancy in-between. PHM was determined by histology examination. The patient underwent suction curettage and was followed up with serial β-hCG levels. Recurrent PHM, although rare, is associated with an increased incidence of malignancy. A series of clinical and β-hCG evaluation should be warranted because of the possibility of gestational trophoblastic neoplasia development.

Increasing demand for glutaminase (GLS) due to high rates of glutamine metabolism is considered one of the hallmarks of malignancy. In parallel, cancer antigen 125 (CA‐125) is a commonly used ovarian tumor marker. This study aimed to compare the roles of GLS and CA‐125 in distinguishing between benign and malignant ovarian tumors. The research was conducted as a comparative study, enrolling 156 patients with ovarian tumors. Preoperative serum CA‐125 and GLS levels were analyzed to evaluate their effectiveness in distinguishing between benign and malignant ovarian tumors. The results revealed that the mean levels of CA‐125 and GLS were significantly higher in malignant ovarian tumors compared with benign ones (389.54 ± 494.320 vs. 193.15 ± 529.932 (U/mL) and 17.37 ± 12.156 vs. 7.48 ± 4.095 (μg/mL), respectively). The CA‐125 and GLS cutoff points of 108.2 U/mL and 18.32 μg/mL, respectively, were associated with malignant ovarian tumors. Multivariate analyses showed that GLS had higher predictive capabilities compared with CA‐125 (odds ratio 9.4 vs. 2.1). The accuracy of using GLS combined with CA‐125 was higher than using CA‐125 alone (73.1% vs. 68.8%). In conclusion, higher levels of CA‐125 and GLS are associated with malignant ovarian tumors. GLS outperforms CA‐125 in distinguishing between benign and malignant ovarian tumors. The combination of GLS and CA‐125 demonstrated improved accuracy for distinguishing benign and malignant ovarian tumors when compared with using CA‐125 alone.

Introduction Preeclampsia is a syndrome that occurs in pregnancy, characterized by hypertension, proteinuria and edema. Zinc and copper are micronutrients that play a role in the performance of several important enzymes in the human body, such as CuZnSOD and ACE2 enzymes that play a role in the pathogenesis of severe preeclampsia. Zinc also plays a role in the kallikrein-kinin system in the formation of bradykinin which then acts as a vasodilator. This study plans to compare the level of copper (Cu), Zink (Zn), and Cu/Zn ratio in preeclamtic and normal pregnancy women. Method The study recruited 30 pregnant women with severe preeclampsia who were treated at Dr. Hasan Sadikin Bandung and 30 normal pregnant women at one of the Independent Practice Midwives in the Cibabat area, which was conducted in the period September 2021 - November 2021. This research was an analytical observational study with a cross sectional study design. Cu and Zn levels were examined using inductively coupleds plasma mass spectrometry (ICP-MS). Chi Square, student’s t test, Mann Whitney and multivariat analysis were used for statistical analysis. Results The results of this study showed that the average Cu levels were higher in severe preeclampsia (mean: 2.149 vs. 2.116 mol/L, p=0.728). The median Zn level in the subjects with severe preeclampsia was higher than in normal pregnancies (58 vs 49 g/dL, p<0.001). The median Cu/Zn ratio in severe preeclampsia subjects was lower than in normal pregnancies (0.034 vs 0.063 g/dL, p=0.021). Conclusion Zn levels was significantly increased in the preeclampsia group, Cu levels was not significantly increased in the preeclampsia group and the ratio of Cu/Zn levels was significantly decreased in preeclampsia group compared to normal pregnancies.

Objective: The modality to detect ovarian cancer at an early stage is very limited. Early diagnosis determines the prognosis. This study aimed to develop a risk assessment tool for early detection of ovarian cancer using artificial intelligence. To accomplish this, the presence of ten signs and symptoms reported by patients with ovarian cancer was assessed. Methods: This study was carried out as a cohort study of patients diagnosed with suspected ovarian tumors undergoing cytoreduction operation at Hasan Sadikin Hospital, Bandung, from December 2019 to September 2020. Compared to ovarian cancer self-assessment through questionnaire, postoperative histopathology in patients with suspected ovarian tumors. The questionnaire proceeded by artificial intelligence is grouped into risk and no risk. Statistical analyses were done using Chi-Square and Exact Fisher Test. Result: In total, 115 patients included in this study. The differences were statistically significant in terms of the six variables (abdominal bloating, nausea/vomiting, decreased of appetite, fullness, menstrual disturbance, and weight loss) ovarian cancer self-assessment compared to postoperative histopathology with a tendency towards benign ovarian tumors (p0.05). According to the artificial intelligence grouping, fifty-five patients were at risk, and sixty patients were not at risk. The Fifty-five risk patients were related with postoperative histopathology diagnosis (with RR 0.682 and CI 95% 0.519-0.895). Conclusion: Risk assessments based on ovarian cancer self-assessment unfortunately were not comparable to postoperative histopathology as a single predictor. Ten variables in ovarian cancer artificial intelligence self-assessment for early detection needs improvement in adding another variable like tumor marker and ultrasonography assessment.