By combining bioorthogonal metabolic labelling and resolution enhancement through sequential imaging of DNA barcodes, the molecular organization of individual sugars in the native glycocalyx has been resolved at a spatial resolution of 9 ångström.

“Cancer and other complex diseases arise from the interplay of various biological factors, for example, at the DNA, RNA, and protein levels,” explains the author. Characteristic changes at these levels — such as the amount of HER2 protein produced in breast or stomach cancer — are often recorded, but typically not yet analyzed in conjunction with all other therapy-relevant factors.
This is where Flexynesis comes in. “Comparable tools so far have often been either difficult to use, or only useful for answering certain questions,” says the author. “Flexynesis, by contrast, can answer various medical questions at the same time: for example, what type of cancer is involved, what drugs are particularly effective in this case, and how these will affect the patient’s chances of survival.” The tool also helps identify suitable biomarkers for diagnosis and prognosis, or — if metastases of unknown origin are discovered — to identify the primary tumor. “This makes it easier to develop comprehensive and personalized treatment strategies for all kinds of cancer patients,” says the author.
Nearly 50 new cancer therapies are approved every year. This is good news. “But for patients and their treating physicians, it is becoming increasingly difficult to keep track and to select the treatment methods from which the people affected — each with their very individual tumor characteristics — will benefit the most,” says the senior author. The researcher has been working for some time on developing tools that use artificial intelligence to make more precise diagnoses and that also determine the best form of therapy tailored to individual patients.
The team has now developed a toolkit called Flexynesis, which does not rely solely on classical machine learning but also uses deep learning to evaluate very different types of data simultaneously — for example, multi-omics data as well as specially processed texts and images, such as CT or MRI scans. “In this way, it enables doctors to make better diagnoses, prognoses, and develop more precise treatment strategies for their patients,” says the author. Flexynesis is described in detail in a paper published in “Nature Communications.”
“We are running multiple translational projects with medical doctors who want to identify biomarkers from multi-omics data that align with disease outcomes,” says the first and co-corresponding author of the publication. “Although many deep-learning based methods have been published for this purpose, most have turned out to be inflexible, tied to specific modeling tasks, or difficult to install and reuse. That gap motivated us to build Flexynesis as a proper toolkit, which is flexible for different modeling tasks and packaged on PyPI, Guix, Docker, Bioconda, and Galaxy, so others can readily apply it in their own pipelines.”
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Cannabis use is linked to an almost quadrupling in the risk of developing diabetes, according to an analysis of real-world data from over 4 million adults, being presented at the Annual Meeting of the European Association for the Study of Diabetes (EASD) held in Vienna, Austria (15–19 Sept).
Cannabis use is increasing globally with an estimated 219 million users (4.3% of the global adult population) in 2021, but its long-term metabolic effects remain unknown. While some studies have suggested potential anti-inflammatory or weight management properties, others have raised concerns regarding glucose metabolism and insulin resistance, and the magnitude of the risk of developing diabetes hasn’t been clear.
To strengthen the evidence base, Dr. Ibrahim Kamel from the Boston Medical Center, Massachusetts, U.S. and colleagues analyzed electronic health records from 54 health care organizations (TriNetX Research Network, with centers from across U.S. and Europe) to identify 96,795 outpatients (aged between 18 and 50 years, 52.5% female) with cannabis-related diagnoses (ranging from occasional use to dependence, including cases of intoxication and withdrawal) between 2010 and 2018.
Psilocybin, the active ingredient in magic mushrooms, might just revolutionize how depression and anxiety are treated in cancer patients. In a groundbreaking trial, a single dose combined with therapy significantly reduced emotional suffering, and these effects often lasted over two years. As follow-up studies expand the research to multiple doses and larger samples, scientists are eyeing a possible new standard of care that merges psychedelics with psychological support.
Psychological problems such as depression and anxiety increase the risk of cognitive impairment in older adults. But mechanisms on the effect of psychological disorder on cognitive function is inconclusive. Repetitive negative thinking (RNT) is a core symptom of a number of common psychological disorders and may be a modifiable process shared by many psychological risk factors that contribute to the development of cognitive impairment. RNT may increase the risk of cognitive impairment. However, there are fewer studies related to RNT and cognitive function, and there is a lack of epidemiological studies to explore the relationship between RNT and cognitive function.
A cross-sectional study of 424 older adults aged 60 years or over was performed form May to November 2023 in hospital. To investigate the RNT level by using the Perseverative Thinking Questionnaire (PTQ), and investigate the cognitive function level by using the Montreal Cognitive Assessment Scale (MoCA). Multivariable linear regression and subgroup analyses were used to explore the relationship between RNT and cognitive function.
We categorized the total RNT scores into quartiles. The multivariable linear regression analysis showed that after adjusting for all covariates, the participants in the Q3 and Q4 groups exhibited lower cognition scores (Q3:β =-0.180, 95%CI-2.849~-0.860; Q4:β =-0.164, 95%-2.611~-0.666) compared to the Q1 group. The results of the subgroup analyses showed that individuals aged 60 ~ 79 years, junior high school and above are more prone to suffer from cognitive impairment with a high RNT score.
A joint team from the University of Stuttgart in Germany and the University of Melbourne in Australia has developed a new method for the straightforward analysis of tiny nanoplastic particles in environmental samples. One needs only an ordinary optical microscope and a newly developed test strip—the optical sieve. The research results have now been published in Nature Photonics.
“The test strip can serve as a simple analysis tool in environmental and health research,” explains Prof. Harald Giessen, Head of the 4th Physics Institute of the University of Stuttgart. “In the near future, we will be working toward analyzing nanoplastic concentrations directly on site. But our new method could also be used to test blood or tissue for nanoplastic particles.”
Plastic waste is one of the central and acute global problems of the 21st century. It not only pollutes oceans, rivers, and beaches but has also been detected in living organisms in the form of microplastics. Until now, environmental scientists have focused their attention on larger plastic residues.