Friday, August 15, 2025

Unusual case of rare ovarian tumor mimicking pregnancy with successful treatment outcome

“Our case illustrates the classic features of NGOC, including significant bleeding, markedly elevated β-hCG levels, and a unilateral adnexal mass on imaging”

Impact Journals LLC

A rare case of pure non-gestational ovarian choriocarcinoma: Diagnostic mimicry and management strategies — image:
Figure 1. Transabdominal sonography image revealing a well-defined, predominantly solid-cystic lesion (10.2 × 7.8 × 7.8 cm) with vascularized solid components in the right adnexa with areas of hemorrhage.
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Credit: Copyright: © 2025 Kumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

“Our case illustrates the classic features of NGOC, including significant bleeding, markedly elevated β-hCG levels, and a unilateral adnexal mass on imaging.”

BUFFALO, NY — August 14, 2025 — A new case report was published in Volume 12 of Oncoscience on July 28, 2025, titled “A rare case of pure non-gestational ovarian choriocarcinoma: Diagnostic mimicry and management strategies.”

This report, led by Naina Kumar from the All India Institute of Medical Sciences, Bibinagar, details the case of a 36-year-old woman diagnosed with a rare pure form of ovarian cancer called non-gestational ovarian choriocarcinoma (NGOC). This is an extremely rare tumor, affecting less than 0.6% of malignant ovarian germ cell tumors. It usually appears in young women and is difficult to diagnose because it shares symptoms with pregnancy-related conditions, such as vaginal bleeding and high levels of the pregnancy hormone β-hCG.

“Non-gestational ovarian choriocarcinomas (NGOC) are rare, distinct, highly aggressive tumors, primarily affecting young women.”

In this case, the patient had been experiencing abnormal bleeding for several months. A positive pregnancy test and imaging studies led doctors to initially suspect an ectopic pregnancy. Advanced imaging and blood tests revealed a large mass in the right ovary. Surgery was performed to remove it along with the uterus, ovaries, and nearby lymph nodes. Genetic testing of the tumor tissue showed that it contained only maternal DNA, confirming it as non-gestational. This confirmation is important because non-gestational tumors are more aggressive and respond differently to treatment compared to tumors linked to pregnancy.

The patient received a chemotherapy regimen that included Bleomycin, Etoposide, and Cisplatin. After two cycles, her β-hCG levels returned to normal, indicating a complete response to treatment. She remains under regular follow-up with hormone monitoring and imaging scans to evaluate for any recurrence.

This case highlights the challenge of diagnosing pure NGOC, especially when the symptoms closely resemble more common conditions. It also shows how genetic testing and imaging can help guide accurate diagnosis and appropriate treatment. Early detection and timely intervention can lead to favorable outcomes, even in aggressive cancers like NGOC.

As one of the few documented cases of pure NGOC, this report adds valuable knowledge to the limited literature on this rare tumor type. It emphasizes the need for clinicians to consider rare diagnoses when common conditions do not fully explain a patient’s symptoms.

Continue reading: DOI: https://doi.org/10.18632/oncoscience.622

Correspondence to: Naina Kumar – naina.obg@aiimsbibinagar.edu.in

Keywords: cancer, chemotherapy, ectopic pregnancy, germ cell tumor, gestational ovarian choriocarcinoma, non-gestational ovarian choriocarcinoma

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About Oncoscience:

Oncoscience is a peer-reviewed, open-access, traditional journal covering the rapidly growing field of cancer research, especially emergent topics not currently covered by other journals. This journal has a special mission: Freeing oncology from publication cost. It is free for the readers and the authors.

Oncoscience is indexed and archived by PubMed, PubMed Central, Scopus, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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Journal

Oncoscience

DOI

10.18632/oncoscience.622

Method of Research

Case study

Subject of Research

People

Article Title

A rare case of pure non-gestational ovarian choriocarcinoma: Diagnostic mimicry and management strategies

Ensemble AI unlocks hidden tree crown structures in dense forests

Nanjing Agricultural University The Academy of Science

The method not only overcomes limitations of traditional LiDAR scanning but also enables the extraction of other hard-to-measure crown parameters, promising to advance forest management, breeding, and ecological modeling.

Tree crown architecture reflects a tree’s competitive environment and influences growth, productivity, and photosynthesis. Among these traits, HMCW—marking the transition between the lower and upper crown—is a critical but often overlooked parameter. Its uneven distribution is shaped by directional competition, shading, and branch dieback, making it difficult to measure in dense canopies. While UAV and ground-based LiDAR have improved forest phenotyping, crown overlap still hampers accuracy. Traditional spatial structure analysis methods, such as the nearest four trees (NFT) or Voronoi diagrams, lack the directional precision needed to capture localized competition effects on crown shape. Based on these challenges, the research team sought a more realistic way to map crown interactions and couple them with advanced regression models.

A study (DOI: 10.1016/j.plaphe.2025.100018) published in Plant Phenomics on 28 February 2025 by Huaiqing Zhang’s team, Chinese Academy of Forestry, improves the accuracy of predicting hard-to-measure tree crown traits like HMCW, offering a scalable tool for forest structure analysis and management.

In this study, spatial structure units for 1,943 sample trees were first constructed using the proposed BSETC method, which accounts for crown distribution in four cardinal directions, and compared with the traditional nearest four trees (NFT) and Voronoi methods. The BSETC approach identified 2–8 neighboring trees per unit, versus a fixed four in NFT and 2–10 in Voronoi. Comparative analyses of neighbor selection showed that while all methods rely on inter-tree distances, BSETC achieved higher realism by incorporating crown width, distance, and shading effects, yielding neighbors with genuine spatial interaction. Overlap analysis indicated moderate-to-high similarity between BSETC and NFT (0.4–0.7 range) and lower overlap with Voronoi (0.1–0.4 range), while unique neighbor counts were lowest for BSETC (median=0), moderate for NFT (median=2), and highest for Voronoi (median=5). Similarity and dissimilarity metrics confirmed that BSETC aligns with forestry principles but captures directional competition more precisely. Using these spatial units, 11 machine learning algorithms were trained to couple HMCW with phenotype and competition parameters, with features including tree height, directional crown width, and vertical/horizontal competition indices. Hyperparameters were optimized via GridSearchCV, and evaluation across R², RMSE, MAE, MAPE, EVS, and MedAE identified Random Forest (RF) as the best-performing single model on test data (R² = 0.8186). To further enhance accuracy, five ensemble learning methods (Bagging, Boosting, Voting, Stacking, Blending) generated 10,180 model combinations; 398 exceeded RF’s performance. The top ensemble, a Bagging regressor integrating XGBoost, RF, SVR, GB, and Ridge, achieved R² = 0.8346, RMSE reduced by 6.66%, and EVS improved by 1.63% over RF. This confirmed that ensemble learning, combined with refined spatial structure mapping, provides a more accurate, generalizable solution for predicting HMCW from easily measured parameters.

The approach provides a scalable, non-destructive way to estimate HMCW and other challenging crown traits across species with similar architectures. By enabling more precise canopy morphology simulations, it supports studies on photosynthesis distribution, forest growth modeling, and selective breeding. In practical forestry, it can inform thinning strategies, optimize stand density, and improve timber yield predictions. The methodology also strengthens ecological research by allowing finer-scale coupling between environmental conditions and tree phenotypes, critical for climate change adaptation and biodiversity assessments.

References

DOI

10.1016/j.plaphe.2025.100018

Original Source URL

https://doi.org/10.1016/j.plaphe.2025.100018

Funding information

This work was funded by Fundamental Research Funds of CAF (CAFYBB2023PA003), Science and Technology Innovation 2030-Major Projects (2023ZD0406103) and National Natural Science Foundation of China (32271877).

About Plant Phenomics

Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.

Journal

Plant Phenomics

DOI

10.1016/j.plaphe.2025.100018

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Fitting maximum crown width height of Chinese fir through ensemble learning combined with fine spatial competition

Texas A&M researchers map America’s power outage hot spots using AI

New nationwide vulnerability index reveals 20% annual increase in outage severity since 2019, with East and West coasts, Great Lakes and Gulf regions facing the highest risk of weather-induced blackouts

Texas A&M University

Hurricane Beryl, Winter Storm Uri and other severe weather events have increased long-term power outages for Texas residents in recent years. But this issue does not just affect Texans.

Researchers from the Urban Resilience AI Lab at Texas A&M University have used machine learning to create a nationwide Power System Vulnerability Index (PSVI) that identifies areas at increased risk of power outages.

“Using data from Oak Ridge National Laboratory, we were able to study the effect of weather events on the frequency and duration of nationwide power outages over the past 10 years,” said Dr. Junwei Ma, a postdoctoral researcher in the Zachry Department of Civil and Environmental Engineering. “The dataset included over 179 million data points sorted by time and location, allowing us to create the PSVI.”

The study’s results show an increase in the extent of weather-induced power outages. Trends show an increase in the length and frequency of power outages, with more customers being affected annually.

Authors of this paper — including Ma, his fellow postdoctoral researcher Dr. Bo Li, Dr. Olufemi A. Omitaomu from Oak Ridge National Laboratory, and Dr. Ali Mostafavi, a professor in the Zachry Department of Civil and Environmental Engineering — identified several regions they would consider hot spots, facing the highest levels of power system vulnerability. Hot spots include the East and West Coasts and the Great Lakes and Gulf regions, indicating areas of dense development face higher vulnerability for power outages.

The research team identified the hot spots and annual increase rate trends thanks to their novel and publicly available PSVI map.

“This is an interactive tool that can showcase the overall PSVI ratings and scores of individual U.S. counties over the past decade, and how vulnerability shifts year by year,” said Ma.

Researchers also observed that many AI data centers — like the ones used to store this study’s data — are located in the hot spots, showing the need for increased investments in infrastructure to protect these resources.

By using a type of machine learning called explainable AI, this software goes beyond just sorting data. It can identify trends. This innovation is central to a series of studies on power outage vulnerability from the Urban Resilience AI Lab. Previous studies have revealed growing and disparate vulnerability in the U.S. power system.

“We knew that the state of power system vulnerability nationwide is exacerbating. But the magnitude of that was shocking, and greater than we hypothesized,” said Mostafavi, who also serves as the director of the Urban Resilience AI Lab. “After 2019, we see a 20% annual increase in outage duration, frequency and magnitude.”

Knowing an area is at-risk allows policymakers to prioritize preparation for long and frequent power outages, reducing associated socioeconomic impacts, such as limited access to food and inability to travel to work. Understanding power system vulnerability is key for stakeholders making decisions that impact community resilience.

By Alyssa Schaechinger, Texas A&M University College of Engineering

Journal

Applied Energy

DOI

10.1016/j.apenergy.2025.126360

Method of Research

Data/statistical analysis

Subject of Research

Not applicable

Article Title

Establishing nationwide power system vulnerability index across US counties using interpretable machine learning

UH researchers to develop AI to aid in emergency food distribution

University part of $1.2 million investment to improve disaster response

University of Houston

Families, particularly those already vulnerable to food insecurity, can face difficulties obtaining food in the aftermath of natural disasters. University of Houston researchers will utilize artificial intelligence to develop an online resource for food pantries, aiming to streamline stakeholder collaboration and distribute resources to families in need.

The U.S. Department of Agriculture’s National Institute of Food and Agriculture announced June 18 that it awarded UH and three other institutions nearly $1.2 million to address disaster response concerns in the aftermath of hurricanes Helene and Milton hitting Florida last fall.

With $300,000 of the funding, UH researchers will create a website featuring an AI-powered dashboard for Florida food pantries, which could reduce communication bottlenecks and later support regions like Houston.

“Our team is building directly on experience gained from Hurricane Harvey in 2017, and we expect the lessons learned in Florida will strengthen our disaster response capabilities back home in Houston,” said Ioannis Kakadiaris, principal investigator and professor of computer science in UH’s College of Natural Sciences and Mathematics.

Developing the system

The tool will allow food pantry leaders to send SMS text messages of their needs following disasters, which the AI system will prioritize on the dashboard.

“Our AI system can automatically and efficiently process large volumes of status reports, enabling emergency coordinators to respond more quickly when demand spikes,” Kakadiaris said.

Support can range from food and water to first aid and other emergency supplies. The system will also be adaptable to various disaster scenarios.

“We want the tool to be completely flexible, so if there are fires or some other type of damage or disaster, it can handle the new situation,” said Marcus Sammer, application developer for the project at Kakadiaris’ Computational Biomedicine Lab.

Why it matters

A USDA report shows 13.5% of U.S. households were food insecure at least one time during 2023 — a rise from 12.8% in 2022.

This tool is crucial to Kakadiaris’ team because disasters often worsen food insecurity. Power outages, floods, wildfires or tornadoes can displace families and deprive individuals of access to essential resources such as food.

The project builds on two previous U.S. National Science Foundation grants awarded to Kakadiaris. He has received more than $2.2 million since 2021 to develop AI-based food security systems.

Throughout the one-year study period, the UH research team will conduct surveys with stakeholders to gain a deeper understanding of current communication systems. A pilot version of the system will be available to Florida food pantries by September for testing and feedback.

“Our job is to explore what the challenges are that need to be addressed using the technology, and then we hope that we or somebody else will run with this technology,” Kakadiaris said.

Monell Center researchers present latest findings at International Meeting on Consumer Sensory Science

Coinciding with the 2025 Philadelphia-based conference, Monell - the first independent nonprofit dedicated to smell and taste research - hosts academic, industry partners for visits, collaborations

Monell Chemical Senses Center

Monell Center Researchers Present Latest Findings at International Meeting On Consumer Sensory Science

Coinciding with the 2025 Philadelphia-based conference, Monell - the first independent nonprofit dedicated to smell and taste research - hosts academic, industry partners for visits, collaborations

PHILADELPHIA (Aug 14, 2025) – Scientists from the Monell Chemical Senses Center will present their research at the 16th Pangborn Sensory Science Symposium, “Connecting Senses and Minds,” August 17-21, 2025 in Philadelphia.

This conference draws more than 1,000 academic and flavor and food industry experts from around the world. It broadly covers research at the intersection of sensory and consumer science to better understand human food and beverage perception and consumption to gain consumer insights, as well as improve health and well-being.

Monell researchers will share their investigations on added sugars, smell and taste function while taking GLP-1 weight loss medications, odor mixtures, oral sensitivity to sucrose and dairy fat, and sensory mechanisms of tasting complex carbohydrates.

These are highlights of Monell sensory science being delivered in oral and poster sessions:

Monday August 18

Oral presentation
10:00-10:15am

Smell and taste dysfunction with GLP-1 RAs in the FDA adverse event reporting system: a pharmacovigilance assessment
Ryann Kolb*1, Emmanuel Nartey*2, Alicia Lozano2, Alexandra Hanlon2, Vicente Ramirez**1, Valentina Parma**1

*Co-first authors

**Co-last authors

1Parma Lab, Monell Chemical Senses Center

2Center for Biostatistics and Health Data Science, Virginia Tech

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are increasingly prescribed for type 2 diabetes and weight management, yet their impact on taste and smell remains under characterized. We analyzed reports from the U.S. FDA Adverse Event Reporting System (FAERS) from 2005–2024 to assess associations between GLP-1 RAs and chemosensory adverse events (CSAEs). Our findings suggest that chemosensory disturbances, especially altered taste perception, may contribute to the appetite-suppressing effects of GLP-1 RAs. Given the scale of GLP-1 RA use and the importance of taste and smell in nutrition and quality of life, prospective studies using psychophysical sensory testing are warranted to clarify prevalence, mechanisms, and reversibility. Recognizing chemosensory changes as part of the sensory and behavioral profile of GLP-1 RAs will advance understanding of how pharmacological treatments shape flavor perception, food choice, and eating behavior in real-world settings.

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Friday, August 15, 2025

Unusual case of rare ovarian tumor mimicking pregnancy with successful treatment outcome

Journal

DOI

Method of Research

Subject of Research

Article Title

Ensemble AI unlocks hidden tree crown structures in dense forests

Journal

DOI

Method of Research

Subject of Research

Article Title

Texas A&M researchers map America’s power outage hot spots using AI

Journal

DOI

Method of Research

Subject of Research

Article Title

UH researchers to develop AI to aid in emergency food distribution

Monell Center researchers present latest findings at International Meeting on Consumer Sensory Science

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