Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: biotech/medical – Page 337

Our Future in Imaging Comes Into Focus

Pushing the bounds of imaging isn’t new for the San Francisco Biohub and Imaging Institute. Both organizations have already taken down barriers to research by building imaging tools that don’t exist anywhere else, as well as creating pioneering cell atlases that have redefined how we understand health and disease.

One example is the San Francisco Biohub’s research on how zebrafish embryos develop over time. In order to capture video images of whole zebrafish embryos through various developmental stages, Biohub scientists built a custom microscope, along with novel software that can find and track the movement of each cell in the videos. The “Google Earth” of embryology resulting from this research is Zebrahub, which brings a new vision to developmental biology and helps us understand our own cellular origins.

Projects like Zebrahub require scientists from a host of different disciplines. Teams across the Biohub, along with interdisciplinary partners, worked to build the microscope, develop the cell tracking software and interpret the resulting images. The collaborative nature of this project isn’t unique to our research on zebrafish — it’s part of our philosophy, and we believe collaboration is critical to drive scientific advancement in all of our work.

Scientists just found a molecule that could stop Parkinson’s in its tracks

Researchers have designed a peptide that prevents the deadly misfolding of alpha-synuclein, the protein behind Parkinson’s and some dementias. In lab and animal tests, it stabilized the protein and improved motor function. The work demonstrates the power of rational drug design in tackling brain diseases that have long lacked effective treatments.

A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques

In this section, the authors reveal the findings of this review. The findings are categorized based on data modalities, showcasing the effectiveness of AI models in terms of evaluation metrics. Figure 1 summarizes the extraction process, providing a clear representation of the progression from article identification to final selection of studies. The initial search yielded a substantial total of 1,175 articles. Based on the inclusion and exclusion criteria, the subsequent screening process excluded irrelevant articles. By meticulously filtering the literature, 25 studies were deemed suitable for inclusion into this review.

A chronology of research studies on the uses of AI in DS diagnosis is shown in Figure 2. This timeline highlights a considerable growth in academic interest over the course of the years. A single study was published per year between the years 2013 and 2017. Technical restrictions and the availability of datasets restricted the early attempts to integrate AI into DS diagnoses. Advancements in deep learning and machine learning technologies have been driven by continuous growth in research, representing a milestone in 2021. These developments are signs of increasing confidence in the ability of artificial intelligence to identify and resolve challenging diagnostic problems. The year 2021 reaches a high with four studies, indicating a surge of innovation. This may result from improved computing tools and a more extensive understanding of the usefulness of artificial intelligence in the medical field. However, the minor decline in 2022 and 2023, with three studies, may indicate difficulties in maintaining the rapid pace of research. These challenges may include restricted access to different datasets or limitations to clinical adoption.

In 2024, there was a significant increase in DS diagnostics approaches, achieving a total of seven studies. This increase is a result of developments in AI algorithms, collaborations across diverse fields, and the significant role of AI in medical diagnosis. It demonstrates the increased academic and multidisciplinary interest in developing effective AI-powered DS detection models. In addition, an increasing trajectory highlights the importance of maintaining research efforts in order to overcome current challenges in implementing AI applications in the healthcare sector.

Pan-disease atlas maps molecular fingerprints of health, disease and aging

A new study has mapped the distinct molecular “fingerprints” that 59 diseases leave in an individual’s blood protein, which could enable blood tests to discern troubling signs from those that are more common.

As now published in Science, an international team of researchers mapped how thousands of proteins in human blood shift as a result of aging and serious diseases, such as cancer and cardiovascular and .

The Human Disease Blood Atlas also reveals that each individual’s blood profile has a unique molecular fingerprint, which changes through childhood and stabilizes in adulthood. This provides a baseline for comparison that could one day use to flag early deviations.

Forget hair transplants — a laser that reactivates follicles is the latest trend in hair-loss care

Hair transplants have exploded in popularity in recent years — so much so that flights returning from Turkey packed with men sporting freshly transplanted hairlines have become a meme. And the stigma surrounding the once-taboo procedure is lessening.

In August, John Cena said his recent hair transplant “completely changed the course of my life.” While effective, transplants are still surgeries that require thousands of dollars, time off work, and a multi-week recovery process.

However, FoLix, the first FDA-cleared fractional laser of its kind, administered in-office by a provider, offers men and women a surgery-free way to help with their hair loss. Dermatologists say that the new treatment, which began appearing in clinics over the past year, could be a game changer for patients who may not like the daily regimen of pills or the invasiveness of hair transplant surgery.

Dr. Aliza Apple, Ph.D. — VP, Catalyze360 AI/ML and Global Head, Lilly TuneLab, Eli Lilly

Accelerating Promising Biotech Innovation — Dr. Aliza Apple, Ph.D. — Vice President, Catalyze360 AI/ML and Global Head, Lilly TuneLab, Eli Lilly and Company.

Dr. Aliza Apple, Ph.D. is a Vice President of Catalyze360 AI (https://www.lilly.com/science/partners/catalyze-360 and Global Head of Lilly TuneLab (https://tunelab.lilly.com/) at Eli Lilly where she leads the strategy, build and launch of Lilly’s external-facing AI/ML efforts for drug discovery.

Lilly Catalyze360 represents a comprehensive approach to enabling the early-stage biotech ecosystem, agnostic of the therapeutic area, designed to accelerate emerging and promising science, strategically removing barriers to support biotech innovation.

In her previous role at Lilly, Dr. Apple served as the COO and head of Lilly Gateway Labs West Coast, where she supported the local biotech ecosystem through early engagement and providing tailored offerings to meet their needs.

Prior to Lilly, Dr. Apple served as a co-founder at Santa Ana Bio, a venture-backed precision biologics company focused on autoimmune disease, and as an advisor to the founders of Firefly Biologics.

Continue reading “Dr. Aliza Apple, Ph.D. — VP, Catalyze360 AI/ML and Global Head, Lilly TuneLab, Eli Lilly” | >

/* */