Fertility
Non-invasive wearable patch could monitor oestrogen in sweat
The sensor could make it easier for women to monitor their estradiol levels at home, say researchers

Researchers from the California Institute of Technology have developed a wearable sensor that could reliably monitor estradiol by detecting its presence in sweat.
The sex hormone commonly known as oestrogen plays an important role in multiple aspects of women’s health and fertility.
High levels of oestrogen in the body are associated with breast and ovarian cancers, while low levels of estradiol can result in osteoporosis, heart disease, and even depression.
Oestrogen is a class of hormones that includes estradiol as the most potent form. Estradiol is also necessary for the development of secondary sexual characteristics in women and regulates the reproductive cycle.
Because of its many functions, estradiol is often specifically monitored by doctors, but this usually requires the patient to visit a clinic to have blood drawn for analysis in a lab.
The new wearable sensor, scientists argue, could monitor estradiol by detecting its presence in sweat, making it easier for women to monitor their estradiol levels at home and in real time.
The research, published in Nature Nanotechnology, was conducted in the lab of Wei Gao, assistant professor of medical engineering, investigator with the Heritage Medical Research Institute, and Ronald and JoAnne Willens Scholar.
Gao has previously developed sweat sensors that detect cortisol, the presence of the COVID-19 virus and a whole slew of other nutrients and biological compounds.
He says the development of the estradiol sensor was spurred in part by requests from people who were unsatisfied with the options they had for monitoring their oestrogen levels.
“People often ask me if I could make the same kind of sweat sensor for female hormones, because we know how much those hormones impact women’s health,” he explains.
The primary challenge, and what dictated changes in the sensor’s design, is that estradiol, which already is present at fairly low levels in the blood, is roughly 50 times less concentrated in sweat.
“Since it’s such a low concentration, it’s very challenging to detect estradiol automatically in sweat,” Gao says.
Despite this, testing in the laboratory has shown that the sensor could reliably and accurately track the changing levels of estradiol in sweat over the course of the reproductive cycle, from the lowest level during menstruation to its highest level (10 times greater) during ovulation.
Who could benefit from estradiol monitoring?
One population of women who could benefit from estradiol monitoring are those who are attempting to conceive, either naturally or through IVF.
The success of either method, Gao says, is dependent on getting timing right with regards to ovulation, but not all women have a reproductive cycle that follows a regular schedule.
Some women have been able to track their ovulation by monitoring their body temperature, but the researcher points out that method has limited usefulness because it is not very accurate and body temperature does not increase until ovulation has begun.
He says: “Because oestrogen increases before ovulation, with this sweat sensor, we would be able to give people notice ahead of time.”
Women undergoing hormone replacement therapy (HRT) could also benefit from a wearable oestrogen sensor, Gao adds, as their bodies do not produce sufficient estradiol.
In these patients, however, estradiol levels will need to be carefully monitored to ensure they are taking the correct dosage.
Gao says he plans to continue working on this technology to allow it to monitor other female hormones, like luteinising hormone or progesterone, which are both involved in ovulation.
He and his team hope to “miniaturise” the sensors so they could all fit inside of small wearable devices.
Fertility
Gum disease may impair female fertility and egg quality – study
Fertility
AI could transform ovarian care through personalisation, study finds

AI could transform ovarian care by personalising cancer and fertility treatment, but more clinical validation is needed before routine use.
A systematic review and meta-analysis found AI models showed high diagnostic accuracy for ovarian cancer when combining data such as ultrasound scans and blood test results.
Across 81 studies, AI models correctly identified ovarian cancer in around nine out of 10 cases, with pooled rates of 89 to 94 per cent.
They were also highly accurate at ruling out ovarian cancer when it was not present, with specificity of 85 to 91 per cent.
The analysis also found that explainable AI tools could predict complete surgical cytoreduction in advanced ovarian cancer.
Complete surgical cytoreduction means removing all visible cancer during surgery, which can be an important goal in treatment planning.
The tools achieved a pooled AUC of 0.87. AUC is a measure of how well a model distinguishes between different outcomes, with higher scores showing stronger performance.
In reproductive medicine, AI algorithms helped physicians optimise ovarian stimulation protocols and predict follicular growth during IVF.
Ovarian stimulation is the use of hormones to encourage the ovaries to produce eggs, while follicles are the small sacs in the ovaries where eggs develop.
The review found AI could reliably model ovarian response in IVF with a pooled AUC of 0.81.
However, researchers said challenges remain in translating promising research findings into routine clinical practice.
They identified substantial variation across studies, driven by retrospective study designs, variable AI systems and a lack of standardised validation.
Only 22 per cent of analysed studies reported prospective, multicentre external validation, where models are tested forward in time across multiple healthcare settings.
The authors called for rigorous validation to help close the gap between research and routine clinical practice, alongside standardised methodological and reporting frameworks, smooth integration with clinical workflow and robust governance to support responsible and ethical AI use.
They concluded: “Artificial intelligence is a transformative force in the management of ovarian conditions.
“In gynaecologic oncology, AI enhances every phase of care, from early detection and accurate diagnosis to prognostic stratification and surgical planning.”
In reproductive medicine, AI personalises ovarian stimulation and refines the diagnosis of heterogenous endocrine disorders such as PCOS.
PCOS, or polycystic ovary syndrome, is a hormonal condition that can affect periods, skin, weight and fertility.
Fertility
Housing, work and fertility stop Britons having the families they want – research
Opinion4 weeks agoWhat Maternal Mental Health Month reveals about where postpartum support actually breaks down
Menopause7 days agoPerimenopause misinformation ‘putting women at risk’
Insight4 weeks agoNIH Grant terminations disproportionately impact minority scientists, research finds
Adolescent health4 weeks agoWUKA brings Period-Positive Pool Party to London Aquatics Centre to keep girls swimming through puberty
Insight3 weeks agoPCOS renamed after decade-long campaign to end ‘cyst’ misconception
Events4 weeks agoWHIS 2026 unveils agenda and first speakers for the leading women’s health summit
Menopause4 weeks agoCBT shows promise for menopause insomnia and hot flashes
Insight4 weeks agoOnline abuse and deepfakes ‘pushing women out of public life’















Pingback: URL
Pingback: บาคาร่าเกาหลี
Pingback: สล็อตเกาหลี
Pingback: ซื้อเหล้าออนไลน์
Pingback: นำเข้ามอเตอร์ โบลเวอร์
Pingback: mawinbet
Pingback: Book of Ra
Pingback: fear of god essentials
Pingback: VG98
Pingback: ปั้มไลค์
Pingback: Thai Massage Manhattan
Pingback: ตรายางออนไลน์
Pingback: โคมไฟ
Pingback: rkk42
Pingback: EA Forex
Pingback: toybf
Pingback: endoliftX ที่ไหนดี
Pingback: เว็บตรง บาคาร่า
Pingback: แทงหวย
Pingback: หา Influencer
Pingback: sultan games
Pingback: บริษัทรับสร้างบ้าน
Pingback: shisha shop
Pingback: קטגוריית גלוק 19
Pingback: รับงานเอง
Pingback: rubiscore.com
Pingback: cat888