A recent study has shed light on an alarming trend: among women who strongly desire to avoid becoming pregnant, those who engage in heavy drinking are more than 50% likelier to become pregnant compared to those who drink moderately or not at all. In contrast, participants who use cannabis do not appear to have a higher risk of undesired pregnancy.

Researchers from the University of California, San Francisco conducted this study among a subgroup of over 900 non-pregnant women aged 15-34 who reported a strong desire to avoid pregnancy. They found that those who drank heavily and those who used cannabis frequently had an even higher overall desire to avoid pregnancy compared to participants who drank moderately or did not use cannabis.

Over the course of one year, 71 out of the 936 women in this subgroup became pregnant unintentionally. A significant proportion (38) of these unwanted pregnancies occurred among heavy drinkers, far exceeding the combined number for those who drank moderately or not at all. This suggests that heavy drinking is associated with a higher risk of undesired pregnancy compared to lower levels of consumption.

On the other hand, less than half (28) of the 71 unintended pregnancies occurred among people who used cannabis, indicating that these individuals did not have an elevated risk of undesired pregnancy compared to those who did not use cannabis.

According to Dr. Sarah Raifman, lead author of this study, the findings imply two crucial points: first, non-pregnant women who drink heavily may have a higher desire to avoid pregnancy than those who drink moderately or do not drink at all; second, heavy drinking compared to moderate or no drinking appears to put those who most want to avoid pregnancy at a higher risk of becoming pregnant within one year.

Given the potentially life-altering effects of fetal alcohol spectrum disorders and the fact that the risk of FASD increases with the amount and duration of maternal drinking, healthcare professionals should support women who drink heavily in stopping their consumption as soon as they suspect an unintentional pregnancy.

In an era where manipulated videos can spread disinformation, bully people, and incite harm, researchers at the University of California, Riverside (UCR), have created a powerful new system to expose these fakes. Amit Roy-Chowdhury, a professor of electrical and computer engineering, and doctoral candidate Rohit Kundu, teamed up with Google scientists to develop an artificial intelligence model that detects video tampering – even when manipulations go far beyond face swaps and altered speech.

Their new system, called the Universal Network for Identifying Tampered and synthEtic videos (UNITE), detects forgeries by examining not just faces but full video frames, including backgrounds and motion patterns. This analysis makes it one of the first tools capable of identifying synthetic or doctored videos that do not rely on facial content.

“Deepfakes have evolved,” Kundu said. “They’re not just about face swaps anymore. People are now creating entirely fake videos – from faces to backgrounds – using powerful generative models. Our system is built to catch all of that.”

UNITE’s development comes as text-to-video and image-to-video generation have become widely available online. These AI platforms enable virtually anyone to fabricate highly convincing videos, posing serious risks to individuals, institutions, and democracy itself.

“It’s scary how accessible these tools have become,” Kundu said. “Anyone with moderate skills can bypass safety filters and generate realistic videos of public figures saying things they never said.”

Kundu explained that earlier deepfake detectors focused almost entirely on face cues. If there’s no face in the frame, many detectors simply don’t work. But disinformation can come in many forms. Altering a scene’s background can distort the truth just as easily.

To address this, UNITE uses a transformer-based deep learning model to analyze video clips. It detects subtle spatial and temporal inconsistencies – cues often missed by previous systems. The model draws on a foundational AI framework known as SigLIP, which extracts features not bound to a specific person or object. A novel training method, dubbed “attention-diversity loss,” prompts the system to monitor multiple visual regions in each frame, preventing it from focusing solely on faces.

The result is a universal detector capable of flagging a range of forgeries – from simple facial swaps to complex, fully synthetic videos generated without any real footage. It’s one model that handles all these scenarios,” Kundu said. “That’s what makes it universal.”

The researchers presented their findings at the high-ranking 2025 Conference on Computer Vision and Pattern Recognition (CVPR) in Nashville, Tenn. Their paper, led by Kundu, outlines UNITE’s architecture and training methodology.

While still in development, UNITE could soon play a vital role in defending against video disinformation. Potential users include social media platforms, fact-checkers, and newsrooms working to prevent manipulated videos from going viral.

“People deserve to know whether what they’re seeing is real,” Kundu said. “And as AI gets better at faking reality, we have to get better at revealing the truth.”

The COVID-19 pandemic has left an indelible mark on humanity, but its impact may go beyond the physical toll of the virus itself. A recent study suggests that even those who never contracted COVID-19 may have experienced accelerated brain aging due to the stresses and disruptions caused by the pandemic.

Led by experts at the University of Nottingham, the research team analyzed longitudinal brain scans from nearly 1,000 healthy adults, taken as part of the UK Biobank study. Some participants had scans before and after the pandemic, while others only had pre-pandemic scans. Using advanced imaging and machine learning, the researchers estimated each person’s “brain age” – how old their brain appeared to be compared to their actual age.

The findings were striking: people who lived through the pandemic showed signs of faster brain aging over time than those scanned entirely before it. The changes were most noticeable in older individuals, men, and those from more disadvantaged backgrounds. Notably, only participants who were infected with COVID-19 between their scans showed a drop in certain cognitive abilities, such as mental flexibility and processing speed.

“This study reminds us that brain health is shaped not only by illness but by our everyday environment,” said Dorothee Auer, Professor of Neuroimaging and senior author on the study. “The pandemic put a strain on people’s lives, especially those already facing disadvantage. We can’t yet test whether the changes we saw will reverse, but it’s certainly possible, and that’s an encouraging thought.”

Stress, isolation, and global disruption may have left their mark on people’s minds, leading to accelerated brain aging. However, the study highlights the possibility of reversibility, offering a glimmer of hope for those affected. The pandemic has taught us that even in the face of adversity, our brains can adapt and potentially recover – a testament to the resilience of the human spirit.