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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.

Novel alloy withstands extreme conditions, could replace metals used in aircraft engines and gas turbines

A new material might contribute to a reduction of the fossil fuels consumed by aircraft engines and gas turbines in the future. A research team from Karlsruhe Institute of Technology (KIT) has developed a refractory metal-based alloy with properties unparalleled to date.

The novel combination of chromium, molybdenum, and silicon is ductile at . With its of about 2,000°C, it remains stable even at high temperatures and is at the same time oxidation resistant. These results are published in Nature.

High-temperature-resistant metallic materials are required for , , X-ray units, and many other technical applications. Refractory metals such as tungsten, molybdenum, and chromium, whose melting points are around or higher than 2,000°C, can be most resistant to high temperatures.

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” | >

AI2 Incubator launches $80M fund as it doubles down on real-world AI applications in Seattle and beyond

The Seattle-based startup organization — known for spinning out companies at the intersection of AI and real-world applications — has closed an $80 million third fund to support about 70 new tech ventures over the next four years.

Programmable proteins use logic to improve targeted drug delivery

Targeted drug delivery is a powerful and promising area of medicine. Therapies that pinpoint the exact areas of the body where they’re needed—and nowhere they’re not—can reduce the medicine dosage and avoid potentially harmful off-target effects elsewhere in the body. A targeted immunotherapy, for example, might seek out cancerous tissues and activate immune cells to fight the disease only in those tissues.

The tricky part is making a therapy truly “smart,” where the medicine can move freely through the body and decide which areas to target.

Researchers at the University of Washington have taken a significant step toward that goal by designing proteins with autonomous decision-making capabilities. In a proof-of-principles study published in Nature Chemical Biology, researchers demonstrated that by adding smart tail structures to , they could control the proteins’ localization based on the presence of specific environmental cues.

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