Real-world prediction of preclinical Alzheimer's disease with a deep generative model.

Amyloid positivity is an early indicator of Alzheimer's disease and is necessary to determine the disease. In this study, a deep generative model is utilized to predict the amyloid positivity of cognitively normal individuals using proxy measures, such as structural MRI scans, demographic variables, and cognitive scores, instead of invasive direct measurements. Through its remarkable efficacy in handling imperfect datasets caused by missing data or labels, and imbalanced classes, the model outperforms previous studies and widely used machine learning approaches with an AUROC of 0.8609. Furthermore, this study illuminates the model's adaptability to diverse clinical scenarios, even when feature sets or diagnostic criteria differ from the training data. We identify the brain regions and variables that contribute most to classification, including the lateral occipital lobes, posterior temporal lobe, and APOE ϵ4 allele. Taking advantage of deep generative models, our approach can not only provide inexpensive, non-invasive, and accurate diagnostics for preclinical Alzheimer's disease, but also meet real-world requirements for clinical translation of a deep learning model, including transferability and interpretability.

Different effects of cardiometabolic syndrome on brain age in relation to gender and ethnicity.

BACKGROUND: A growing body of evidence shows differences in the prevalence of cardiometabolic syndrome (CMS) and dementia based on gender and ethnicity. However, there is a paucity of information about ethnic- and gender-specific CMS effects on brain age. We investigated the different effects of CMS on brain age by gender in Korean and British cognitively unimpaired (CU) populations. We also determined whether the gender-specific difference in the effects of CMS on brain age changes depending on ethnicity. METHODS: These analyses used de-identified, cross-sectional data on CU populations from Korea and United Kingdom (UK) that underwent brain MRI. After propensity score matching to balance the age and gender between the Korean and UK populations, 5759 Korean individuals (3042 males and 2717 females) and 9903 individuals from the UK (4736 males and 5167 females) were included in this study. Brain age index (BAI), calculated by the difference between the predicted brain age by the algorithm and the chronological age, was considered as main outcome and presence of CMS, including type 2 diabetes mellitus (T2DM), hypertension, obesity, and underweight was considered as a predictor. Gender (males and females) and ethnicity (Korean and UK) were considered as effect modifiers. RESULTS: The presence of T2DM and hypertension was associated with a higher BAI regardless of gender and ethnicity (p < 0.001), except for hypertension in Korean males (p = 0.309). Among Koreans, there were interaction effects of gender and the presence of T2DM (p for T2DM*gender = 0.035) and hypertension (p for hypertension*gender = 0.046) on BAI in Koreans, suggesting that T2DM and hypertension are each associated with a higher BAI in females than in males. In contrast, among individuals from the UK, there were no differences in the effects of T2DM (p for T2DM*gender = 0.098) and hypertension (p for hypertension*gender = 0.203) on BAI between males and females. CONCLUSIONS: Our results highlight gender and ethnic differences as important factors in mediating the effects of CMS on brain age. Furthermore, these results suggest that ethnic- and gender-specific prevention strategies may be needed to protect against accelerated brain aging.

Predicting conversion of brain ß-amyloid positivity in amyloid-negative individuals.

BACKGROUND: Cortical deposition of ß-amyloid (Aß) plaque is one of the main hallmarks of Alzheimer's disease (AD). While Aß positivity has been the main concern so far, predicting whether Aß (-) individuals will convert to Aß (+) has become crucial in clinical and research aspects. In this study, we aimed to develop a classifier that predicts the conversion from Aß (-) to Aß (+) using artificial intelligence. METHODS: Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort regarding patients who were initially Aß (-). We developed an artificial neural network-based classifier with baseline age, gender, APOE ε4 genotype, and global and regional standardized uptake value ratios (SUVRs) from positron emission tomography. Ten times repeated 10-fold cross-validation was performed for model measurement, and the feature importance was assessed. To validate the prediction model, we recruited subjects at the Samsung Medical Center (SMC). RESULTS: A total of 229 participants (53 converters) from the ADNI dataset and a total of 40 subjects (10 converters) from the SMC dataset were included. The average area under the receiver operating characteristic values of three developed models are as follows: Model 1 (age, gender, APOE ε4) of 0.674, Model 2 (age, gender, APOE ε4, global SUVR) of 0.814, and Model 3 (age, gender, APOE ε4, global and regional SUVR) of 0.841. External validation result showed an AUROC of 0.900. CONCLUSION: We developed prediction models regarding Aß positivity conversion. With the growing recognition of the need for earlier intervention in AD, the results of this study are expected to contribute to the screening of early treatment candidates.

A study on the changes in life habits, mental health, and sleep quality of college students due to COVID-19.

BACKGROUND: In early 2020, the coronavirus 2019 (COVID-19) pandemic necessitated changes in social behavior to prevent its spread, including holding online classes, implementing social distancing, and allowing employees to telecommute. However, these changes have had a negative impact on people's sleep patterns and mental health, particularly for college students. OBJECTIVE: This study investigated the relationship between mental health and sleep quality according to the changes in lifestyle of college students in the periods before and after COVID-19. METHODS: The study subjects were 164 college students from Korea who had both face-to-face and non-face-to-face college experiences before and after COVID-19. The experiment was conducted using a Google survey, and the participants were recruited from the college community. The general features and lifestyle habits for the individuals were assessed using the AUDIT-K, Delphi method, KGHQ (General Mental Health Scale), and PSQI-K (Pittsburg Sleep Quality Index). RESULTS: The KGHQ and PSQI scores increased with the spread of COVID-19, which means that the mental health and sleep quality of college students deteriorated. 11 categories of variables were further investigated to evaluate changes in lifestyle, and the results indicate significant changes in the number of private meetings per week, monthly drinking, outdoor activity time, electronic device usage time, weekly food delivery, weekly late-night snacks, daily snacks, and daily coffee intake and no significant changes in exercise, smoking, and fast food intake. CONCLUSION: COVID-19 caused many changes in the lifestyle of college students, which adversely affected mental health and sleep.

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.

Clinical trials for Alzheimer's disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

Investigation of SiO2 Etch Characteristics by C6F6/Ar/O2 Plasmas Generated Using Inductively Coupled Plasma and Capacitively Coupled Plasma.

The etching properties of C6F6/Ar/O2 in both an inductively coupled plasma (ICP) system and a capacitively coupled plasma (CCP) system were evaluated to investigate the effects of high C/F ratio of perfluorocarbon (PFC) gas on the etch characteristics of SiO2. When the SiO2 masked with ACL was etched with C6F6, for the CCP system, even though the etch selectivity was very high (20 ~ infinite), due to the heavy-ion bombardment possibly caused by the less dissociated high-mass ions from C6F6, tapered SiO2 etch profiles were observed. In the case of the ICP system, due to the higher dissociation of C6F6 and O2 compared to the CCP system, the etching of SiO2 required a much lower ratio of O2/C6F6 (~1.0) while showing a higher maximum SiO2 etch rate (~400 nm/min) and a lower etch selectivity (~6.5) compared with the CCP system. For the ICP etching, even though the etch selectivity was much lower than that by the CCP etching, due to less heavy-mass-ion bombardment in addition to an adequate fluorocarbon layer formation on the substrate caused by heavily dissociated species, highly anisotropic SiO2 etch profiles could be obtained at the optimized condition of the O2/C6F6 ratio (~1.0).

Amyloid Positivity in the Alzheimer/Subcortical-Vascular Spectrum.

OBJECTIVE: We investigated the frequency of ß-amyloid (Aß) positivity in 9 groups classified according to a combination of 3 different cognition states and 3 distinct levels of white matter hyperintensities (WMH) (minimal, moderate, and severe) and aimed to determine which factors were associated with Aß after controlling for WMH and vice versa. METHODS: A total of 1,047 individuals with subjective cognitive decline (SCD, n = 294), mild cognitive impairment (MCI, n = 237), or dementia (n = 516) who underwent Aß PET scans were recruited from the memory clinic at Samsung Medical Center in Seoul, Korea. We investigated the following: (1) Aß positivity in the 9 groups, (2) the relationship between Aß positivity and WMH severity, and (3) clinical and genetic factors independently associated with Aß or WMH. RESULTS: Aß positivity increased as the severity of cognitive impairment increased (SCD [15.7%], MCI [43.5%], and dementia [76.2%]), whereas it decreased as the severity of WMH increased (minimal [54.5%], moderate [53.9%], and severe [41.0%]) or the number of lacunes (0 [59.0%], 1-3 [42.0%], and >3 [23.4%]) increased. Aß positivity was associated with higher education, absence of diabetes, and presence of APOE ε4 after controlling for cognitive and WMH status. CONCLUSION: Our analysis of Aß positivity involving a large sample classified according to the stratified cognitive states and WMH severity indicates that Alzheimer and cerebral small vessel diseases lie on a continuum. Our results offer clinicians insightful information about the association among Aß, WMH, and cognition.

Prediction of tau accumulation in prodromal Alzheimer's disease using an ensemble machine learning approach.

We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. We recruited 64 patients with prodromal AD using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Supervised ML approaches based on the random forest (RF) and a gradient boosting machine (GBM) were used. The GBM resulted in an AUC of 0.61 (95% confidence interval [CI] 0.579-0.647) with clinical data (age, sex, years of education) and a higher AUC of 0.817 (95% CI 0.804-0.830) with clinical and neuropsychological data. The highest AUC was 0.86 (95% CI 0.839-0.885) achieved with additional information such as cortical thickness in clinical data and neuropsychological results. Through the analysis of the impact order of the variables in each ML classifier, cortical thickness of the parietal lobe and occipital lobe and neuropsychological tests of memory domain were found to be more important features for each classifier. Our ML algorithms predicting tau burden may provide important information for the recruitment of participants in potential clinical trials of tau targeting therapies.

Comparison of Longitudinal Changes of Cerebral Small Vessel Disease Markers and Cognitive Function Between Subcortical Vascular Mild Cognitive Impairment With and Without NOTCH3 Variant: A 5-Year Follow-Up Study.

No study yet has compared the longitudinal course and prognosis between subcortical vascular cognitive impairment patients with and without genetic component. In this study, we compared the longitudinal changes in cerebral small vessel disease markers and cognitive function between subcortical vascular mild cognitive impairment (svMCI) patients with and without NOTCH3 variant [NOTCH3(+) svMCI vs. NOTCH3(-) svMCI]. We prospectively recruited patients with svMCI and screened for NOTCH3 variants by sequence analysis for mutational hotspots in the NOTCH3 gene. Patients were annually followed-up for 5 years through clinical interviews, neuropsychological tests, and brain magnetic resonance imaging. Among 63 svMCI patients, 9 (14.3%) had either known mutations or possible pathogenic variants. The linear mixed effect models showed that the NOTCH3(+) svMCI group had much greater increases in the lacune and cerebral microbleed counts than the NOTCH3(-) svMCI group. However, there were no significant differences between the two groups regarding dementia conversion rate and neuropsychological score changes over 5 years.

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment.

BACKGROUND: Amyloid-ß (Aß) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aß evaluation through Aß positron emission tomography (PET) is limited due to high cost and safety issues. OBJECTIVE: We therefore aimed to develop and validate prediction models of Aß positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers. METHODS: We recruited 529 aMCI patients from multiple centers who underwent Aß PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers). RESULTS: Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aß positivity. CONCLUSION: Our results suggest that ML models are effective in predicting Aß positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.

Prediction of amyloid ß PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers.

Amyloid-ß(Aß) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, and it provides insights into the underlying vascular pathology (CAA vs. hypertensive angiopathy) to facilitate prognostic prediction and appropriate treatment decisions. In this study, we applied two interpretable machine learning algorithms, gradient boosting machine (GBM) and random forest (RF), to predict Aß PET positivity in patients with CAA MRI markers. In the GBM algorithm, the number of lobar cerebral microbleeds (CMBs), deep CMBs, lacunes, CMBs in dentate nuclei, and age were ranked as the most influential to predict Aß positivity. In the RF algorithm, the absence of diabetes was additionally chosen. Cut-off values of the above variables predictive of Aß positivity were as follows: (1) the number of lobar CMBs > 16.4(GBM)/14.3(RF), (2) no deep CMBs(GBM/RF), (3) the number of lacunes > 7.4(GBM/RF), (4) age > 74.3(GBM)/64(RF), (5) no CMBs in dentate nucleus(GBM/RF). The classification performances based on the area under the receiver operating characteristic curve were 0.83 in GBM and 0.80 in RF. Our study demonstrates the utility of interpretable machine learning in the clinical setting by quantifying the relative importance and cutoff values of predictive variables for Aß positivity in patients with suspected CAA markers.

A Validation Study of the Inbrain CST: a Tablet Computer-based Cognitive Screening Test for Elderly People with Cognitive Impairment.

BACKGROUND: Computerized versions of cognitive screening test could have advantages over pencil-and-paper versions by eliminating rater-dependent factors and saving the time required to score the tests and report the results. We developed a computerized cognitive screening test (Inbrain Cognitive Screening Test [Inbrain CST]) that takes about 30 minutes to administer on a touchscreen computer and is composed of neuropsychological tests already shown to be sensitive in detecting early cognitive decline in Alzheimer's disease (AD). The aims of this study were to 1) introduce normative data for Inbrain CST, 2) verify its reliability and validity, 3) assess clinical usefulness, and 4) identify neuroanatomical correlates of Inbrain CST. METHODS: The Inbrain CST runs on the Microsoft Windows 10 operating system and comprises 7 subtests that encompass 5 cognitive domains: attention, language, visuospatial, memory, and executive functions. First, we recruited 480 cognitively normal elderly people (age 50-90) from communities nationwide to establish normative data for Inbrain CST. Second, we enrolled 97 patients from our dementia clinic (26 with subjective cognitive decline [SCD], 42 with amnestic mild cognitive impairment [aMCI], and 29 with dementia due to AD) and investigated sensitivity and specificity of Inbrain CST for discriminating cognitively impaired patients from those with SCD using receiver operating characteristic (ROC) curve analyses. Third, we compared the Inbrain CST scores with those from another neuropsychological test battery to obtain concurrent validity and assessed test-retest reliability. Finally, magnetic resonance imaging (MRI)-based cortical thickness analyses were performed to provide anatomical substrates for performances on the Inbrain CST. RESULTS: First, in the normative sample, the total score on the Inbrain CST was significantly affected by age, years of education, and gender. Second, Inbrain CST scores among the three patient groups decreased in the order of SCD, aMCI, and AD dementia, and the ROC curve analysis revealed that Inbrain CST had good discriminative power for differentiating cognitively impaired patients from those with SCD. Third, the Inbrain CST subtests had high concurrent validity and test-retest reliability. Finally, in the cortical thickness analysis, each cognitive domain score and the total score of Inbrain CST showed distinct patterns of anatomical correlates that fit into the previously known brain-behavior relationship. CONCLUSION: Inbrain CST had good validity, reliability, and clinical usefulness in detecting cognitive impairment in the elderly. Furthermore, it showed neuroanatomical validity through MRI cortical thinning patterns. These results suggest that Inbrain CST is a useful cognitive screening tool with efficiency and validity to detect mild impairments in cognition in clinical settings.

Association between APOE ε2 and Aß burden in patients with Alzheimer- and vascular-type cognitive impairment.

OBJECTIVE: To investigate the association between APOE genotype and ß-amyloid (Aß) burden, as measured by PET in patients with subcortical vascular cognitive impairment (SVCI) and those with Alzheimer disease-related cognitive impairment (ADCI). METHODS: This was a cross-sectional study of 310 patients with SVCI and 999 with ADCI. To evaluate the effects of APOE genotype or diagnostic group on Aß positivity, we performed multivariate logistic regression analyses. Further distinctive underlying features of latent subgroups were examined by employing a latent class cluster analysis approach. RESULTS: In comparison with ε3 homozygotes, in the ADCI group, ε2 carriers showed a lower frequency of Aß positivity (odds ratio [OR] 0.43, 95% confidence interval [CI] 0.23-0.79), while in the SVCI group, ε2 carriers showed a higher frequency of Aß positivity (OR 2.26, 95% CI 1.02-5.01). In particular, we observed an interaction effect of ε2 carrier status and diagnostic group on Aß positivity (OR 5.12, 95% CI 1.93-13.56), in that relative to ε3 homozygotes, there were more Aß-positive ε2 carriers in the SVCI group than in the ADCI group. We also identified latent subgroups of Aß-positive APOE ε2 carriers with SVCI and Aß-positive APOE ε4 carriers with ADCI. CONCLUSIONS: Our findings suggest that APOE ε2 is distinctly associated with Aß deposition in patients with SVCI and those with ADCI. Our findings further suggest that there is a distinctive subgroup of Aß-positive APOE ε2 carriers with SVCI among patients with cognitive impairment.

The Cortical Neuroanatomy Related to Specific Neuropsychological Deficits in Alzheimer's Continuum.

BACKGROUND AND PURPOSE: In Alzheimer's continuum (a comprehensive of preclinical Alzheimer's disease [AD], mild cognitive impairment [MCI] due to AD, and AD dementia), cognitive dysfunctions are often related to cortical atrophy in specific brain regions. The purpose of this study was to investigate the association between anatomical pattern of cortical atrophy and specific neuropsychological deficits. METHODS: A total of 249 participants with Alzheimer's continuum (125 AD dementia, 103 MCI due to AD, and 21 preclinical AD) who were confirmed to be positive for amyloid deposits were collected from the memory disorder clinic in the department of neurology at Samsung Medical Center in Korea between September 2013 and March 2018. To analyze neuropsychological test-specific neural correlates representing the relationship between cortical atrophy measured by cortical thickness and performance in specific neuropsychological tests, a linear regression analysis was performed. Two neural correlates acquired by 2 different standardized scores in neuropsychological tests were also compared. RESULTS: Cortical atrophy in several specific brain regions was associated with most neuropsychological deficits, including digit span backward, naming, drawing-copying, verbal and visual recall, semantic fluency, phonemic fluency, and response inhibition. There were a few differences between 2 neural correlates obtained by different z-scores. CONCLUSIONS: The poor performance of most neuropsychological tests is closely related to cortical thinning in specific brain areas in Alzheimer's continuum. Therefore, the brain atrophy pattern in patients with Alzheimer's continuum can be predict by an accurate analysis of neuropsychological tests in clinical practice.

Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (n = 60), schizoaffective disorder (n = 19), bipolar disorder (n = 20), unipolar depression (n = 14), and healthy controls (n = 51) into psychiatric diagnostic categories. The Random Forest machine learning algorithm, which showed best efficacy (92.9% SD: 0.06), was used to generate variable importance ranking of positive, negative, and general psychopathology symptoms, cognitive indexes, global assessment of function (GAF), and parental ages at birth for sorting participants into diagnostic categories. Symptoms were ranked most influential for separating cases from healthy controls, followed by cognition and maternal age. To separate schizophrenia/schizoaffective disorder from bipolar/unipolar depression, GAF was most influential, followed by cognition and paternal age. For classifying schizophrenia from all other psychiatric disorders, low GAF and paternal age were similarly important, followed by cognition, psychopathology and maternal age. Controls misclassified as schizophrenia cases showed lower nonverbal abilities, mild negative and general psychopathology symptoms, and younger maternal or older paternal age. The importance of symptoms for classification of cases and lower GAF for diagnosing schizophrenia, notably more important and distinct from cognition and symptoms, concurs with current practices. The high importance of parental ages is noteworthy and merits further study.

High-Responsivity Multilayer MoSe2 Phototransistors with Fast Response Time.

There is a great interest in phototransistors based on transition metal dichalcogenides because of their interesting optoelectronic properties. However, most emphasis has been put on MoS2 and little attention has been given to MoSe2, which has higher optical absorbance. Here, we present a compelling case for multilayer MoSe2 phototransistors fabricated in a bottom-gate thin-film transistor configuration on SiO2/Si substrates. Under 650-nm-laser, our MoSe2 phototransistor exhibited the best performance among MoSe2 phototransistors in literature, including the highest responsivity (1.4 × 105 AW-1), the highest specific detectivity (5.5 × 1013 jones), and the fastest response time (1.7 ms). We also present a qualitative model to describe the device operation based on the combination of photoconductive and photogating effects. These results demonstrate the feasibility of achieving high performance in multilayer MoSe2 phototransistors, suggesting the possibility of further enhancement in the performance of MoSe2 phototransistors with proper device engineering.

Social Event Memory Test (SEMT): A Video-based Memory Test for Predicting Amyloid Positivity for Alzheimer's Disease.

Recent improvements in neuroimaging and molecular markers of Alzheimer's disease (AD) have aided diagnosis in the early stage of the disease, which greatly increases the chance for successful prevention and treatment. However, the expanding resources for AD diagnosis are unlikely to benefit all elderly due to economic burden. Here, we aimed to develop an inexpensive and sensitive method to detect early-stage AD. A scenario for real-world social event memory test (SEMT) was created and filmed in 360° video. Participants watched the 7-min video through head-mounted display (HMD) and then answered questionnaire about the video. We categorized the SEMT score into recall, recognition, and place-matching scores and compared them to scores on the Mini-Mental State Examination and Seoul Verbal Learning Test. Using the SEMT scores, we built a logistic regression model that discriminated between amyloid positivity and negativity of the participants, with a cross-validation AUC. Furthermore, a classifier was created using support vector machine, which produced 93.8-95.1% sensitivity in classifying individuals into four groups of normal, mild cognitive impairment with or without amyloid, and AD elderly. The high correlation between the SEMT score and amyloid positivity in individuals who experienced virtual social gathering through an HMD opens a new possibility for early diagnosis of AD.

Clinical characteristics and outcomes of placental site trophoblastic tumor: experience of single institution in Korea.

OBJECTIVE: Placental site trophoblastic tumor (PSTT) is the rarest form of gestational trophoblastic disease (GTD) and the optimum management is still controversial. In this study, we analyzed the clinical features, treatment, and outcomes of 6 consecutive patients with PSTT treated in our institution. METHODS: The electronic medical record database of Samsung Medical Center was screened to identify patients with PSTT from 1994 to 2017. Medical records for the details of each patient's clinical features and treatment were extracted and reviewed. This study was approved Institutional Review Board of our hospital. RESULTS: A total of 418 cases of GTD, 6 (1.4%) patients with PSTT were identified. The median age of the patients was 31 years. The antecedent pregnancy was term in all 5 cases with available antecedent pregnancy information and the median interval from pregnancy to diagnosis of PSTT was 8 months. The median titer of serum beta human chorionic gonadotropin (ß-hCG) at diagnosis was 190.9 mIU/mL. Five (83.3%) patients presented with irregular vaginal bleeding and one (16.7%) had amenorrhea. All patients had disease confined to the uterus without metastasis at diagnosis and were successfully treated by hysterectomy alone. All of them were alive without disease during the follow-up period. CONCLUSION: In this study, we observed low level serum ß-hCG titer and irregular vaginal bleeding with varying interval after antecedent term pregnancy were most common presenting features of PSTT. In addition, we demonstrated hysterectomy alone was successful for the treatment of stage I disease of PSTT.

Retrospective study of combination chemotherapy with etoposide and ifosfamide in patients with heavily pretreated recurrent or persistent epithelial ovarian cancer.

OBJECTIVE: This retrospective study is to evaluate the efficacy and toxicity of combination chemotherapy with etoposide and ifosfamide (ETI) in the management of pretreated recurrent or persistent epithelial ovarian cancer (EOC). METHODS: Patients with recurrent or persistent EOC who had measurable disease and at least one chemotherapy regimen were to receive etoposide at a dose of 100 mg/m2/day intravenous (IV) on days 1 to 3 in combination with ifosfamide 1 g/m2/day IV on days 1 to 5, every 21 days. RESULTS: From August 2008 to August 2016, 66 patients were treated with ETI regimen. Most patients were heavily pretreated prior to ETI: 53 (80.3%) patients had received 3 or more chemotherapy regimens. The response rate (RR) of ETI chemotherapy was 18.2% and median duration of response was 6.8 months (range, 0-30). Median survival of all patients was 5 months at a median follow up of 7.2 months. Platinum-free interval (PFI) more than 6 months prior to ETI has statistically significant correlation with overall survival (OS; 9.2 vs. 5.6 months; P=0.029) and RR (34.5% vs. 5.4%; P<0.010). However, treatment free interval before ETI, number of prior chemotherapy regimen, and optimality of primary surgery did not show significant difference for RR or OS. Grade 3 or 4 hematologic toxicities were observed in 7 cases (3%) of the 232 cycles of ETI. CONCLUSION: The ETI combination regimen shows comparatively low toxicity and modest activity in heavily pretreated recurrent or persistent EOC patients with more than 6 months of PFI after last platinum treatment.

An extremely rare case of hand prolapse with preterm premature rupture in the membrane of one twin.

The latency in preterm premature rupture of membranes (PPROM) can last for weeks. We describe an extremely rare case of hand prolapse with PPROM that was exposed for 23 days before delivery. The patient had spontaneous PPROM of twin A at 21.4 weeks of gestation with shoulder presentation. The right arm of the fetus eventually protruded out the vagina and the hand was exposed for extended period of time of 23 days until delivery. Daily dressing by applying collagen to dry skin and silicone to keep moisture was done to the protruding hand to prevent dehydration and desquamation of the skin. Prophylactic antibiotics were used and the patient underwent emergent cesarean section due to uncontrolled preterm labor at 25.2 weeks. To the best of our knowledge, this is the first case of hand prolapse of one twin with extended period of latency before delivery.