Latest news with #EastAsians
Miami Herald
02-06-2025
- Science
- Miami Herald
7,100-year-old skeleton reveals origins of ‘ghost' lineage in Tibetan Plateau
For tens of thousands of years, the people and cultures of southeast Asia have intertwined, moved around and diversified through time. The region, primarily encompassing modern-day China, is brought together today by shared language and culture, reflected now in their genetic makeup. But one group remains genetically isolated, separated geographically and culturally. The people of the Tibetan Plateau, an area also known as the Xizang Autonomous Region by the Chinese government and under the control of China, have what archaeologists have called a 'ghost ancestry,' researchers said in a study published May 29 in the peer-reviewed journal Science. Their genetic code varies 'significantly from modern East Asians,' researchers said in a May 29 news release from the Institute of Vertebrate Paleontology and Paleoanthropology of the Chinese Academy of Sciences. Where and how this divergence occurred was a mystery, until the remains of a person from 7,100 years ago were found in Yunnan, China, according to the study. 'Nestled between the Tibetan Plateau, Southeast Asia, and southern China, the region known today as the Chinese province of Yunnan is home to the highest ethnic and linguistic diversity in China today. Ancient humans that lived in this region may be key to addressing several remaining questions on the prehistoric populations of East and Southeast Asia,' researchers said. The remains of a woman were found at the Xingyi archaeological site in central Yunnan, a site that dates to between 7,158 and 6,888 years ago, according to the study. The 7,100-year-old remains were included in an analysis of genomes from 127 ancient bodies found in the region, researchers said, but were distinct among the findings. The woman was found below all other layers of other burials, researchers said, and was dated to about 1,500 years older than the other remains. 'Although the individual genetically differed significantly from modern East Asians, the researchers noted that the individual's ancestry shared qualities similar to those of populations indigenous to the Qinghai-Tibet Plateau,' researchers said. 'This aligns with previous observations that these plateau populations have certain genetic characteristics that make them distinct from other present-day human groups.' Researchers called the finding a 'large piece of the puzzle,' because it explained that after an early Asian population separated in southwestern China around 40,000 years ago, it persisted in the region. Then, when humans migrated westward and north, the lineage went on to start the native Tibetan populations, according to the release. The lineage was named the 'Basal Asian Xingyi ancestry,' according to the study, and will help researchers learn more about the history of the Tibetan Plateau. Tibet is an autonomous region controlled by China, which is on Tibet's northern border. Yunnan Province in south-central China. The research team includes Tianyi Wang, Melinda A. Yang, Zhonghua Zhu, Minmin Ma, Han Shi, Leo Speidel, Rui Min, Haibing Yuan, Zhilong Jiang, Changcheng Hu, Xiaorui Li, Dongyue Zhao, Fan Bai, Peng Cao, Feng Liu, Qingyan Dai, Xiaotian Feng, Ruowei Yang, Xiaohong Wu, Xu Liu, Ming Zhang, Wanjing Ping, Yichen Liu, Yang Wan, Fan Yang, Ranchao Zhou, Lihong Kang, Guanghui Dong, Mark Stoneking and Qiaomei Fu.
Nikkei Asia
23-05-2025
- Politics
- Nikkei Asia
Asian students flock to Malaysian international schools
In Nikkei Asia News Roundup's latest episode, Jada Nagumo and Brian Chapman discuss why parents across East Asia are increasingly sending their children to international schools in Malaysia. This week's featured story Malaysia wins over East Asians seeking safer, cheaper international schools The episode consists of: Please subscribe to us wherever you get your podcasts, and leave us a review to let us know what you thought about this episode.
Miami Herald
21-05-2025
- Health
- Miami Herald
Lung cancer risk in never-smokers predicted by AI tool ‘Sybil'
ST. PAUL, Minn., May 19 (UPI) -- With lung cancer rates among non-smokers rising, especially young East Asian women, a new study released Monday is touting the promise of an artificial intelligence tool to "strongly" predict who's most at risk. Lung cancer has long been associated with smoking. But even as overall rates steadily drop and smoking decreases around the world, a unique population of young East Asians are seeing a 2% annual increase in lung cancer cases -- even though half of them have never smoked. The cause of this remains unknown, but suspicion is centered on genetic mutations developed during a person's lifetime rather than inherited, such as damage to a gene that codes for a protein known as EGFR, which prevents cells from growing too quickly. This genetic damage is believed to be caused by environmental toxins including second-hand smoke and even fumes produced by high-temperature stir-fry cooking in rooms that lack proper ventilation. Globally, more than 50% of women diagnosed with lung cancer are non-smokers, compared to 15% to 20% of men. Meanwhile, an estimated 57% of Asian-American women diagnosed with lung cancer have never smoked, compared to only about 15% of all other women, according to a recent University of California-San Francisco study. Against this backdrop of rising cancer cases among seemingly low-risk women, the potential of AI to accurately predict who may be most suspectable to a surprise lung cancer diagnosis has generated considerable interest around the world. In a paper presented Monday at the American Thoracic Society's medical conference in San Francisco, Dr. Yeon Wook Kim of the Seoul National University Bundang Hospital reported a new AI tool dubbed "Sybil" has proven to be accurate in identifying which "true low-risk individuals" are more likely to develop lung cancer -- all foretold from a single low-dose chest CT scan, or LDCT. Sybil, named after the female seers of ancient Greek mythology, was developed in 2023 by researchers at the Massachusetts Institute of Technology's Abdul Latif Jameel Clinic for Machine Learning in Health, the Mass General Cancer Center and Chang Gung Memorial Hospital in Taiwan. It was trained first by feeding it LDCT images largely absent of any signs of cancer, since early-stage lung cancer occupies only tiny portions of the lung and is invisible to the human eye. Then, researchers gave Sybil hundreds of scans with visible cancerous tumors. In its first run, Sybil was able to deliver "C-indices" of up to 0.81 in predicted future occurrences of lung cancer from analyzing one LDCT. Models achieving predictive C-index scores of over 0.7 are considered "good" and those over 0.8 are "strong." This week's Korean study validated those results. Kim and his colleagues evaluated 21,087 people ages 50 to 80 who underwent self-initiated LDCT screening between January 2009 and December 2021 in a tertiary hospital-affiliated screening center in South Korea. These subjects were followed up until June 2024. Baseline LDCTs were analyzed with Sybil to calculate the risk of lung cancer diagnosis within one to six years. Analyses were performed for individuals with various smoking histories, ranging from more than 20 "pack-years" to never-smokers, who comprised 11,098 of the participants. Among all participants, 257 (including 115 never-smokers) were diagnosed with lung cancer within six years from the baseline LDCT. Sybil achieved a C-index for lung cancer prediction at one year of 0.86 and 6 years of 0.74 for all the participants, while among never-smokers, one-year and six-year C-indices were 0.86 and 0.79, respectively. Kim told UPI the results hold the promise of helping to regularize lung cancer screening in Asia, where those efforts are inconsistent and, due to differing demographics, sometimes are at a "disconnect" with international screening criteria. "Asia bears the highest burden of lung cancer, accounting for over 60% of new cases and related deaths worldwide," he said in emailed comments. "A growing proportion of this burden is observed among individuals who have never smoked, particularly among women. "In Korea, more than 85% of female lung cancer patients are non-smokers. As a result, increasing attention has been given to evaluating the effectiveness of lung cancer screening, or LCS, in traditionally low-risk populations in Asia." Government-led programs and initiatives have expanded to include never-smokers into their LCS efforts, while other efforts varying from international guidelines due to their inclusion of such never-smokers have "gained traction in East Asian countries, including South Korea, Taiwan and China," Kim said. AI tools like Sybil could be used to develop "personalized strategies" for patients who have already undergone LDCT screening, but have not yet had follow-ups, he added, while cautioning that further validation will be needed "to confirm the model's potential for clinical use. "While the need for screening low-risk groups may be justified in certain settings, the lack of evidence from randomized trials limits the development of long-term LCS strategies for these populations." Researchers, meanwhile, are "actively" working on expanding Sybil's uses into other personalized health applications, said Adam Yala, an assistant professor at the UCSF/UC-Berkeley Joint Program in Computational Precision Health and one of the AI model's developers. "One, this is broadly applicable across many different types of cancers," he told UPI. "We've got processes ongoing for breast cancer, and we're also working on prostate and pancreas cancers. "And there's also evidence that from CT scans you could predict sudden deaths from cardiovascular disease. This would provide early detection, giving you a better opportunity for early intervention to provide better outcomes. So it's not uniquely about cancer. ... There's a version of this for cardiovascular health, and there could be other areas of medicine, as well." AI's potential to provide health benefits, Yala added, "is totally untapped. For instance, now we're only looking at a patient's CT scan once, but over time, you could look at multiple CTs. Mammograms, as well. There's a lot of data available there. It's a field at its infancy." Copyright 2025 UPI News Corporation. All Rights Reserved.
Yahoo
19-05-2025
- Health
- Yahoo
Lung cancer risk in never-smokers predicted by AI tool 'Sybil'
ST. PAUL, Minn., May 19 (UPI) -- With lung cancer rates among non-smokers rising, especially young East Asian women, a new study released Monday is touting the promise of an artificial intelligence tool to "strongly" predict who's most at risk. Lung cancer has long been associated with smoking. But even as overall rates steadily drop and smoking decreases around the world, a unique population of young East Asians are seeing a 2% annual increase in lung cancer cases -- even though half of them have never smoked. The cause of this remains unknown, but suspicion is centered on genetic mutations developed during a person's lifetime rather than inherited, such as damage to a gene that codes for a protein known as EGFR, which prevents cells from growing too quickly. This genetic damage is believed to be caused by environmental toxins including second-hand smoke and even fumes produced by high-temperature stir-fry cooking in rooms that lack proper ventilation. Globally, more than 50% of women diagnosed with lung cancer are non-smokers, compared to 15% to 20% of men. Meanwhile, an estimated 57% of Asian-American women diagnosed with lung cancer have never smoked, compared to only about 15% of all other women, according to a recent University of California-San Francisco study. Against this backdrop of rising cancer cases among seemingly low-risk women, the potential of AI to accurately predict who may be most suspectable to a surprise lung cancer diagnosis has generated considerable interest around the world. In a paper presented Monday at the American Thoracic Society's medical conference in San Francisco, Dr. Yeon Wook Kim of the Seoul National University Bundang Hospital reported a new AI tool dubbed "Sybil" has proven to be accurate in identifying which "true low-risk individuals" are more likely to develop lung cancer -- all foretold from a single low-dose chest CT scan, or LDCT. Sybil, named after the female seers of ancient Greek mythology, was developed in 2023 by researchers at the Massachusetts Institute of Technology's Abdul Latif Jameel Clinic for Machine Learning in Health, the Mass General Cancer Center and Chang Gung Memorial Hospital in Taiwan. It was trained first by feeding it LDCT images largely absent of any signs of cancer, since early-stage lung cancer occupies only tiny portions of the lung and is invisible to the human eye. Then, researchers gave Sybil hundreds of scans with visible cancerous tumors. In its first run, Sybil was able to deliver "C-indices" of up to 0.81 in predicted future occurrences of lung cancer from analyzing one LDCT. Models achieving predictive C-index scores of over 0.7 are considered "good" and those over 0.8 are "strong." This week's Korean study validated those results. Kim and his colleagues evaluated 21,087 people ages 50 to 80 who underwent self-initiated LDCT screening between January 2009 and December 2021 in a tertiary hospital-affiliated screening center in South Korea. These subjects were followed up until June 2024. Baseline LDCTs were analyzed with Sybil to calculate the risk of lung cancer diagnosis within one to six years. Analyses were performed for individuals with various smoking histories, ranging from more than 20 "pack-years" to never-smokers, who comprised 11,098 of the participants. Among all participants, 257 (including 115 never-smokers) were diagnosed with lung cancer within six years from the baseline LDCT. Sybil achieved a C-index for lung cancer prediction at one year of 0.86 and 6 years of 0.74 for all the participants, while among never-smokers, one-year and six-year C-indices were 0.86 and 0.79, respectively. Kim told UPI the results hold the promise of helping to regularize lung cancer screening in Asia, where those efforts are inconsistent and, due to differing demographics, sometimes are at a "disconnect" with international screening criteria. "Asia bears the highest burden of lung cancer, accounting for over 60% of new cases and related deaths worldwide," he said in emailed comments. "A growing proportion of this burden is observed among individuals who have never smoked, particularly among women. "In Korea, more than 85% of female lung cancer patients are non-smokers. As a result, increasing attention has been given to evaluating the effectiveness of lung cancer screening, or LCS, in traditionally low-risk populations in Asia." Government-led programs and initiatives have expanded to include never-smokers into their LCS efforts, while other efforts varying from international guidelines due to their inclusion of such never-smokers have "gained traction in East Asian countries, including South Korea, Taiwan and China," Kim said. AI tools like Sybil could be used to develop "personalized strategies" for patients who have already undergone LDCT screening, but have not yet had follow-ups, he added, while cautioning that further validation will be needed "to confirm the model's potential for clinical use. "While the need for screening low-risk groups may be justified in certain settings, the lack of evidence from randomized trials limits the development of long-term LCS strategies for these populations." Researchers, meanwhile, are "actively" working on expanding Sybil's uses into other personalized health applications, said Adam Yala, an assistant professor at the UCSF/UC-Berkeley Joint Program in Computational Precision Health and one of the AI model's developers. "One, this is broadly applicable across many different types of cancers," he told UPI. "We've got processes ongoing for breast cancer, and we're also working on prostate and pancreas cancers. "And there's also evidence that from CT scans you could predict sudden deaths from cardiovascular disease. This would provide early detection, giving you a better opportunity for early intervention to provide better outcomes. So it's not uniquely about cancer. ... There's a version of this for cardiovascular health, and there could be other areas of medicine, as well." AI's potential to provide health benefits, Yala added, "is totally untapped. For instance, now we're only looking at a patient's CT scan once, but over time, you could look at multiple CTs. Mammograms, as well. There's a lot of data available there. It's a field at its infancy."
NDTV
01-05-2025
- Health
- NDTV
Here's What Your Earwax Reveals About Health
Earwax, technically known as cerumen, might seem like an unpleasant byproduct of the body, but it serves several important functions. It traps dust, bacteria, and foreign particles, keeping the inner ear clean and protected. Surprisingly, its colour, texture, and amount can offer insights into your overall health. From infections to genetic markers, earwax may act as an early warning sign for certain conditions. According to the American Academy of Otolaryngology, earwax is a natural self-cleaning agent and a key indicator of ear health. Paying attention to changes in earwax could help detect potential issues before they become serious. What changes in earwax might signal Earwax isn't just about hygiene, it reflects what's happening inside your body. Variations in its colour, texture, or odour can point toward infections, stress, metabolic conditions, and even inherited traits. Understanding these subtle shifts can help you better monitor your ear and general health. Let's take a closer look at what different types of earwax may reveal. 1. Dry vs wet earwax One of the most basic distinctions in earwax is whether it's dry (grey and flaky) or wet (yellow to brown and sticky). This is determined by a single gene, ABCC11. Most East Asians have dry earwax, while people of African or European descent generally have the wet type. While this difference is harmless, it may also be associated with body odour tendencies. 2. Yellow or light brown This is the most common colour and texture of earwax in children and young adults. It's sticky and traps debris efficiently. This type usually indicates a healthy, functioning ear canal. If you notice this and there are no symptoms like pain or hearing loss, there's likely no cause for concern. 3. Dark brown or black Dark-coloured earwax is often a sign of older cerumen that has collected dust or debris. However, very dark or black earwax can also be linked to oxidative stress, which may result from anxiety or environmental factors. Unless accompanied by discomfort or odour, it's generally harmless, but worth monitoring if it happens frequently. 4. White, dry, or flaky Earwax that appears white and flaky may suggest an underlying skin condition such as eczema or psoriasis, especially if there is associated itching or scaling near the ear canal. People with these conditions are also more prone to wax build-up and blockages. 5. Greenish or foul-smelling wax If your earwax is green or has a bad odour, it could indicate a bacterial infection in the ear canal. This may be accompanied by symptoms like pain, fluid discharge, or hearing issues. According to the National Institutes of Health (NIH), foul-smelling earwax is often an early sign of otitis externa or 'swimmer's ear.' 6. Watery or runny earwax Thin, watery wax, especially if accompanied by popping sounds or ear pressure, may suggest fluid behind the eardrum, a symptom common in ear infections or sinus issues. This is particularly frequent in children and can impact hearing if untreated. 7. Bloody earwax If your earwax is tinged with blood, it might signal a scratch, ruptured eardrum, or even more serious issues like a tumour or chronic infection. The Mayo Clinic recommends consulting an ENT specialist if you notice blood in earwax, especially if paired with hearing loss or dizziness. Earwax is more than just an ear-cleaning mechanism, it can be a useful indicator of your inner health. By observing changes in texture, colour, and smell, you can detect early warning signs of infections, skin disorders, or even genetic traits. If in doubt, consult a medical professional rather than self-cleaning with cotton buds, which may worsen the issue. Disclaimer: This content including advice provides generic information only. It is in no way a substitute for a qualified medical opinion. Always consult a specialist or your own doctor for more information. NDTV does not claim responsibility for this information.
