Scientists have identified a new therapeutic strategy for neurodegenerative diseases by boosting a protein called PI31, which ensures proteasomes reach synapses to clear protein waste.
Category: biotech/medical – Page 3
Engineers develop technology that stimulates heart cells with light
In a new study, University of California, Irvine chemical and biomolecular engineering researchers report the creation of biomolecules that can help grow light-sensitive heart muscle cells in the laboratory. The development enables a biotechnology that could deliver light-triggered signals to the heart, improving its function, without requiring genetic modifications or invasive procedures.
“We show for the first time that light can be converted into cardiac stimulatory cues, with synthetic materials made of biomolecules,” said Herdeline Ann Ardoña, assistant professor of chemical and biomolecular engineering. “This can be beneficial for downstream medical applications, such as in cardiac pacemaking technologies, or helping direct therapeutic patient-derived stem cells to better mimic adult heart cell features.”
The findings are reported in the Proceedings of the National Academy of Sciences. The paper’s co-first authors are recent Ph.D. graduate Sujeung Lim, and Ze-Fan Yao, previous postdoctoral scholar in the Ardoña Research Group.
The Role of Bioelectrical Patterns in Regulative Morphogenesis: An Evolutionary Simulation and Validation in Planarian Regeneration
Endogenous bioelectrical patterns are an important regulator of anatomical pattern during embryogenesis, regeneration, and cancer. While there are three known classes of instructive bioelectric patterns: directly encoding, indirectly encoding, and binary trigger, it is not known how these design principles could be exploited by evolution and what their relative advantages might be. To better understand the evolutionary role of bioelectricity in anatomical homeostasis, we developed a neural cellular automaton (NCA). We used evolutionary algorithms to optimize these models to achieve reliable morphogenetic patterns driven by the different ways in which tissues can interpret their bioelectrical pattern for downstream anatomical outcomes. We found that: All three types of bioelectrical codes allow the reaching of target morphologies; Resetting of the bioelectrical pattern and the change in duration of the binary trigger alter morphogenesis; Direct pattern organisms show an emergent robustness to changes in initial anatomical configurations; Indirect pattern organisms show an emergent robustness to bioelectrical perturbation; Direct and indirect pattern organisms show a emergent generalizability competency to new (rotated) bioelectrical patterns; Direct pattern organisms show an emergent repatterning competency in post-developmental-phase. Because our simulation was fundamentally a homeostatic system seeking to achieve specific goals in anatomical state space (the space of possible morphologies), we sought to determine how the system would react when we abrogated the incentive loop driving anatomical homeostasis. To abrogate the stress/reward system that drives error minimization, we used anxiolytic neuromodulators. Simulating the effects of selective serotonin reuptake inhibitors diminished the ability of artificial embryos to reduce error between anatomical state and bioelectric prepattern, leading to higher variance of developmental outcomes, global morphological degradation, and induced in some organisms a bistability with respect to possible anatomical outcomes. These computational findings were validated by data collected from in vivo experiments in SSRI exposure in planarian flatworm regeneration.
AI to integrate bulk multi-omics data for precision oncology
“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 associated with quadrupled risk of developing type 2 diabetes, finds study of over 4 million adults
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.
