Lifeboat Foundation

Tuberculosis (TB) is a potentially serious infectious disease caused by a type of bacterium called Mycobacterium tuberculosis. The bacteria usually affect the lungs, but also can invade other organs.

In 2018, tuberculosis bacteria infected 1.7 billion people — roughly 23% of the world’s population, according to the Centers for Disease Control and Prevention (CDC). In 2020, the CDC reported 7,174 TB cases and 13 million people living with a latent tuberculosis infection (the germs are in the body but do not cause sickness) in the United States.

Even after successful therapy for tuberculosis, survivors of the disease have an increased risk of recurrent infection and death. A new study published recently by researchers at Baylor College of Medicine found that the cells of humans and animals who have recovered from tuberculosis had prematurely aged up to 12 to 14 years.

Science_Hightech — operanewsapp.

DARPA, the innovation arm of the U.S. military, wants artificial intelligence to make battlefield medical decisions, raising red flags from some experts and ethicists.

𝐍𝐞𝐰 𝐀𝐭𝐥𝐚𝐬:

The Neuro-Network.

𝐄𝐧𝐳𝐲𝐦𝐞 𝐛𝐥𝐨𝐜𝐤𝐞𝐫 𝐜𝐨𝐮𝐥𝐝 𝐨𝐩𝐞𝐧 𝐧𝐞𝐰 𝐭𝐫𝐞𝐚𝐭𝐦𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐧𝐞𝐮𝐫𝐨𝐝𝐞𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐝𝐢𝐬𝐞𝐚𝐬𝐞𝐬

Continue reading “Enzyme blocker could open new treatments for neurodegenerative diseases” »

Circa 2017 😀

As the most common subtype of Leber congenital amaurosis (LCA), LCA10 is a severe retinal dystrophy caused by mutations in the CEP290 gene. The most frequent mutation found in patients with LCA10 is a deep intronic mutation in CEP290 that generates a cryptic splice donor site. The large size of the CEP290 gene prevents its use in adeno-associated virus (AAV)-mediated gene augmentation therapy. Here, we show that targeted genomic deletion using the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system represents a promising therapeutic approach for the treatment of patients with LCA10 bearing the CEP290 splice mutation. We generated a cellular model of LCA10 by introducing the CEP290 splice mutation into 293FT cells and we showed that guide RNA pairs coupled with SpCas9 were highly efficient at removing the intronic splice mutation and restoring the expression of wild-type CEP290. In addition, we demonstrated that a dual AAV system could effectively delete an intronic fragment of the Cep290 gene in the mouse retina. To minimize the immune response to prolonged expression of SpCas9, we developed a self-limiting CRISPR/Cas9 system that minimizes the duration of SpCas9 expression. These results support further studies to determine the therapeutic potential of CRISPR/Cas9-based strategies for the treatment of patients with LCA10.

Keywords: CEP290; CRISPR/Cas9; LCA10.

Continue reading “CRISPR/Cas9-Mediated Genome Editing as a Therapeutic Approach for Leber Congenital Amaurosis 10” »

Introducing a novel visual tool for explaining the results of classification algorithms, with examples in R and Python.

Classification algorithms aim to identify to which groups a set of observations belong. A machine learning practitioner typically builds multiple models and selects a final classifier to be one that optimizes a set of accuracy metrics on a held-out test set. Sometimes, practitioners and stakeholders want more from the classification model than just predictions. They may wish to know the reasons behind a classifier’s decisions, especially when it is built for high-stakes applications. For instance, consider a medical setting, where a classifier determines a patient to be at high risk for developing an illness. If medical experts can learn the contributing factors to this prediction, they could use this information to help determine suitable treatments.

Some models, such as single decision trees, are transparent, meaning that they show the mechanism for how they make decisions. More complex models, however, tend to be the opposite — they are often referred to as “black boxes”, as they provide no explanation for how they arrive at their decisions. Unfortunately, opting for transparent models over black boxes does not always solve the explainability problem. The relationship between a set of observations and its labels is often too complex for a simple model to suffice; transparency can come at the cost of accuracy [1].

Continue reading “Explainable AI (XAI) with Class Maps” »

This conference will take place at EMBL Heidelberg, with a live streaming option for virtual participants free of charge. Proof of COVID-19 vaccination or recovery is required for on-site attendance. Please see EMBL’s COVID-19 terms and conditions.

Workshop registration is available only to EIROforum members. Please note the workshop is an on-site-only event and contact Iva Gavran for more information or use this link for registration.

Hunter Biden DID help secure millions in funding for US contractor in Ukraine specializing in deadly pathogen research, laptop emails reveal, raising more questions about the disgraced son of then vice president. This Biolabs Biden Jr.’s scandal is what the U.S. media try to hush up… but not for long.

Email emails and correspondence obtained by DailyMail.com from Hunter’s abandoned laptop show he helped secure millions for Metabiota.

Continue reading “Hunter Biden helped secure millions for biotech research Ukraine” »

University of Notre Dame researchers have discovered another way tumor cells transfer genetic material to other cells in their microenvironment, causing cancer to spread.

In their latest study, published in Cell Reports, Crislyn D’Souza-Schorey, the Morris Pollard Professor in the Department of Biological Sciences, and collaborators discovered that DNA “cargo” is transported in small informational sacs called extracellular microvesicles. Their study is a continuation of work her lab has undertaken to further understand the sharing of information between cells.

“We’ve shown that DNA present in these microvesicles is related to metastasis, so now we have a great platform to assess for genetic aberrations,” said D’Souza-Schorey, who is also affiliated with the Berthiaume Institute for Precision Health, the Boler-Parseghian Center for Rare and Neglected Diseases and the Harper Cancer Research Institute.