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Targeted Healing Before Birth: Placenta-Derived Stem Cells in Prenatal Repair of Spinal Defects

Antoinette Konski — Mon, 09 Mar 2026 14:02:40 +0000

Neural tube defects are among the most challenging congenital conditions in medicine, with a devastating burden on patients, families, and health care systems. These defects arise when the neural tube, the embryonic structure that becomes the brain and spinal cord, fails to close properly during early fetal development. This failure can range in severity from minor disabilities to lethal malformations. Globally, nearly half a million babies are born each year with such defects. Spina bifida, one of the most recognized forms, continues to occur despite preventive measures such as folate supplementation. In the United States, annual hospital charges related to spina bifida were estimated at two billion dollars in 2024, underscoring the economic weight of lifelong multidisciplinary care for affected children.

Myelomeningocele is the most severe form of spina bifida. In this condition, the spinal cord and surrounding tissues remain exposed due to failure of closure, leading to leakage of cerebrospinal fluid. The developing spinal cord is then subjected to chemical damage from amniotic fluid and mechanical trauma from contact with the uterine wall. This dual insult results in a host of developmental abnormalities. Patients may suffer loss of motor function below the lesion level, causing paralysis, as well as bowel and bladder dysfunction. A common associated problem is hindbrain herniation, in which the lower portions of the brain — specifically the cerebellum and brainstem — are pulled downward from their normal position into the upper spinal canal. This can disrupt normal brain and spinal fluid flow, cause pressure on delicate brain structures, and lead to neurological symptoms.

The newly reported phase 1 trial explored a groundbreaking regenerative approach to myelomeningocele repair before birth — “.” The therapy uses placenta-derived mesenchymal stem cells, known as PMSCs, obtained from early-gestation placentas. These cells are uniquely suited for fetal applications due to their fetal origin, lack of tumor-forming potential, neuroprotective properties, and ability to reduce cell death (apoptosis). Importantly, when cultured in a specialized neurogenic medium, PMSCs adapt to secrete higher concentrations of neurotrophic growth factors and can rescue dying neurons in laboratory settings.

In this trial, PMSCs were seeded onto an FDA-approved extracellular matrix graft and placed directly onto the exposed fetal spinal cord during standard prenatal surgery for myelomeningocele. The aim was to augment the mechanical effects of surgical closure with a biologically active therapy capable of protecting and regenerating neural tissue during a crucial window of fetal neurodevelopment. Investigators designed the study to rigorously monitor surgical feasibility and early safety, given the unknowns of placing living allogeneic stem cells into the developing central nervous system.

The results are highly encouraging. All six patients in the initial cohort successfully received the therapy, and no stem cell-related adverse events were observed. Every newborn had reversal of hindbrain herniation on postnatal MRI, with intact and fully healed surgical repair sites. There was no evidence of abnormal tissue growth or tumor formation. The therapy did not interfere with the known benefits of fetal surgery, nor did it cause complications in wound healing. These findings suggest that PMSCs can be safely integrated into prenatal surgery without compromising established surgical gains.

This pioneering work also highlights the growing intersection between regenerative medicine and personalized medicine. Personalized medicine is not only about genetic diagnostics and targeted drugs, it increasingly encompasses designing and delivering the right biologic or regenerative therapy at the right developmental moment for maximum impact. In the case of myelomeningocele, early, targeted intervention with a biologically active repair tailored to the unique characteristics of the patient’s lesion could represent the next evolution in precision perinatal care.

The implications go beyond this single condition. This trial demonstrates a scalable and clinically feasible platform for delivering biologically active therapeutics directly to the fetus. By intervening early in development, such therapies may alter lifelong health trajectories, potentially preventing paralysis, preserving bladder and bowel function, and reducing the societal and economic impact of chronic disability. While longer-term follow-up is needed to confirm sustained benefits, this first-in-human study provides hope for addressing a serious unmet medical need for which no curative alternatives exist. It also adds to the very short list of regenerative medicine therapies reaching clinical application, particularly in the prenatal setting.

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Single‑Reference Disclosures and the Motivation‑to‑Combine Requirement

Holly Kane — Mon, 09 Feb 2026 19:21:44 +0000

In January 2026, the Federal Circuit issued a nonprecedential opinion in Guardant Health, Inc. v. University of Washington (Slip Op. 2024-1129, Jan. 23, 2026) that, while not binding precedent, is nevertheless highly relevant to patent practice in any technology. The patent at issue, U.S. Patent No. 10,760,127, claims methods for reducing sequencing errors using Duplex Consensus Sequencing (DCS), a technique that pairs double-stranded DNA with unique molecular identifiers before amplification and sequencing. This approach is designed to achieve unprecedented sensitivity in detecting rare variants, a capability critical to applications like early cancer detection and other high-stakes medical diagnostics.

Guardant had challenged the patent before the Patent Trial and Appeal Board (PTAB), arguing that the claims were obvious under 35 U.S.C. § 103, over a combination of references. The elements at issue were:

b) amplifying original strands of at least a portion of the double-stranded adapter-DNA molecules to produce first and second strand copies;

c) sequencing a plurality of first and second strand copies to obtain first and second strand sequence reads for at least a portion of the adapter-DNA molecules;

Slip Op. at 4.

Guardant argued that these two claim elements were disclosed together in a single embodiment in a prior art reference, U.S. Patent Application No. 2009/0298075 A1[1] (Travers ’075). Travers ’075, while primarily directed to PacBio’s SMRT sequencing, expressly described in a single paragraph performing rolling circle replication (RCR) to produce repeated sense and antisense strands, followed by sequencing those amplified products. Washington countered with Travers 2010[2], another publication from PacBio scientists. Travers 2010 described the SMRTbell template, a topologically circular DNA structure with a double-stranded region and single-stranded hairpin loops, which can be sequenced using SMRT technology without any amplification step. In Washington’s view, the general teaching in Travers 2010 that SMRT sequencing does not depend on amplification reinforced their argument that a skilled artisan would need additional motivation to incorporate RCR amplification before SMRT sequencing and might doubt the feasibility of such a combination.

The PTAB agreed with Washington, concluding Guardant had not shown a motivation to combine the amplification concept from Travers ’075 with the sequencing process of SMRTbell templates as taught in Travers 2010, nor a reasonable expectation of success in doing so.[3] But the Federal Circuit disagreed, explaining that when claim elements appear together in one embodiment of a single reference, here, amplification and sequencing in Travers ’075, no separate “motivation to combine”[4] or “reasonable expectation of success”[5] analysis is required as if the elements originated from disparate sources. Because Guardant’s petition relied on a single paragraph of Travers ’075 itself, and not on modifying the SMRT sequencing process described in Travers 2010, the PTAB’s approach was legally erroneous. The court vacated the PTAB’s ruling and remanded for consideration of whether Travers ’075’s RCR-plus-sequencing disclosure was enabled, a separate requirement for relying on it in an obviousness analysis.

Two key takeaways emerge from the opinion. First, in the context of obviousness, if all disputed claim limitations are disclosed together in one embodiment in a single prior art reference, the analysis does not require a separate finding of motivation to combine those elements. Treating co-disclosed steps as if they came from isolated references improperly burdens the challenger and can distort the obviousness inquiry. Second, even when a prior art reference makes only brief mention of a process or embodiment, in this case, rolling circle replication followed by SMRT sequencing, that disclosure remains prior art “for all that it teaches.” Whether that disclosure is enabled is a separate question that must be addressed before relying on it to establish obviousness.

Now is the perfect time to reassess ongoing patent challenges and diligence frameworks to ensure internal strategies fully reflect this clarified standard. To ensure your patent strategy is ahead of the game, contact your �鶹��ý attorney for guidance and to proactively review your portfolio.

[1] U.S. Patent Application No. 2009/0298075 A1

[2] Travers et al., A Flexible and Efficient Template Format for Circular Consensus Sequencing and SNP Detection, 38 Nucleic Acids Research e159 (2010)

[3] Slip Op. at 8.

[4] Slip Op. at 9.

[5] Slip Op. at 13.

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From Abstract to Applied: How Desjardins Can Reframe Patent Protection for AI in Health Care

Antoinette Konski — Wed, 08 Oct 2025 14:58:09 +0000

For a decade, innovators at the intersection of artificial intelligence (AI) and precision medicine have faced a stubborn paradox: the very breakthroughs in software and machine learning that enable early cancer detection and personalized therapy recommendations are often denied U.S. patent protection. Under the unpredictable Alice/Mayo framework, patent examiners and courts frequently categorize adaptive AI models as “abstract ideas,” equating them to mathematical exercises rather than technological advances deserving protection.

The result has been a chilling effect on investment and disclosure in one of health care’s most promising frontiers and, potentially, a threat to the United States’ leadership in biomedical AI.

The USPTO’s September 25, 2025, Ex parte Desjardins,[i] rehearing marks the clearest acknowledgment that AI innovations, including those with health care applications, can be patent-eligible. The Appeals Review Panel (“ARP”) vacated a § 101 rejection against DeepMind’s continual learning framework, holding that it integrated a mathematical concept into a practical application by improving the model’s own functionality. Notably, not only did the ARP reverse the Board and find the claims patent-eligible,[ii] but the decision was also authored by John A. Squires, the new Director of the US Patent and Trademark Office.

The Rejected Claims

The claims under consideration relate to a computer-implemented method of training a machine learning model. Representative independent claim 1[iii] recites:

1. A computer-implemented method of training a machine learning model,

wherein the machine learning model has at least a plurality of parameters and has been trained on a first machine learning task using first training data to determine first values of the plurality of parameters of the machine learning model, and wherein the method comprises:

determining, for each of the plurality of parameters, a respective measure of an importance of the parameter to the first machine learning task, comprising:

computing, based on the first values of the plurality of parameters determined by training the machine learning model on the first machine learning task, an approximation of a posterior distribution over possible values of the plurality of parameters, assigning, using the approximation, a value to each of the plurality of parameters, the value being the respective measure of the importance of the parameter to the first machine learning task and approximating a probability that the first value of the parameter after the training on the first machine learning task is a correct value of the parameter given the first training data used to train the machine learning model on the first machine learning task;

obtaining second training data for training the machine learning model on a second different machine learning task; and training the machine learning model on the second machine learning task by training the machine learning model on the second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task;

wherein adjusting the first values of the plurality of parameters comprises adjusting the first values of the plurality of parameters to optimize an objective function that depends in part on a penalty term that is based on the determined measures of importance of the plurality of parameters to the first machine learning task.

Legal Analysis

The ARP followed the Alice/Mayo two-step test and the MPEP § 2106 analytical framework.[iv] The panel confined its analysis to Step 2A (Alice Step 1) because the issue was dispositive. Under Step 2A, Prong 1, the inquiry focuses on whether the claim recites an abstract idea. Here, the ARP did not dispute the Board’s position that computing an approximation over parameters constitutes a mathematical calculation, and thus an abstract idea.[v]

The panel then proceeded to Step 2A, Prong 2, where the inquiry focuses on whether the abstract idea is integrated into a practical application. This is where the ARP disagreed with the Board on a number of grounds. First, the ARP found that the claims provided technical improvements in how the learning model itself operates by preserving earlier knowledge while reducing storage needs and system complexity.[vi] Second, these improvements are technical, not merely field of use limitations.[vii]

The ARP cited to Federal Circuit case law to ground the decision, notably Enfish, LLC. V. Microsoft Corp., 822 F.3D 1327, 1339 (Fed. Cir. 2016) and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016), that found that software-based structural or logical improvements can be patent-eligible.[viii] The ARP specifically emphasized Enfish, stating the case is “among the Federal Circuit’s leading cases on the eligibility of technological improvements” and quoted the decision in Enfish stating “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can.” The ARP proceeded to point to language in the specification describing how the claimed invention uses less storage capacity and enables reduced system complexity as reflecting a patent eligible technical improvement. Each of the points the ARP raised in view of Step 2A, Prong 2 of the Alice/Mayo test appears to establish that improvements to machine learning models or algorithms themselves are improvements to technology and are therefore patent-eligible.

In summary, Desjardins sets a new tone for the application of current Federal Circuit § 101 jurisprudence and highlights and recognizes the importance of AI to the United States’ technological innovation:

Categorically excluding AI innovations from patent protection in the United States jeopardizes America’s leadership in this critical emerging technology. Yet, under the panel’s reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an patentable “algorithm” and the remaining additional elements as “generic computer components,” without adequate explanation. Examiners and panels should not evaluate claims at such a high level of generality.[ix]

In addition to vacating the rejection under 35 U.S.C. 101, and making a bold statement as to the importance of AI-related technologies, Director Squires places 35 U.S.C. § 101 in its proper role in patentability analysis, noting that “[t]his case demonstrates that §§ 102, 103 and 112 are the traditional and appropriate tools to limit patent protection to its proper scope. These statutory provisions should be the focus of examination.”[x]

Closing Thoughts and Recommendations[xi]

AI in personalized medicine often integrates multi-omics, imaging and clinical data and learns from prior patients while adapting to new ones. Under Desjardins, if one ties those methods to technical improvements in the model’s architecture or training, one can argue that continual learning improves how the model functions. In addition, AI tools that improve model generalization, interpretation or training efficiency, or use hybrid architectures, or reduce drift or overfitting across patient populations, are not mere abstract ideas. In sum, personalized-medicine AI that changes how the computer learns, not just what it learns, may be a new safe harbor for patent-eligibility for some AI health care inventions.

[i] .

[ii] The ARP did not review or reverse the Board’s rejection of the claims as obvious over 35 U.S.C. § 103.

[iii]Ex parte Desjardins, at 2-3.

[iv]Id. at 4-6.

[v]Id. at 6-7.

[vi]Id. at 8-9.

[vii]Id.

[viii]Id.

[ix]Id .at 9.

[x]Id., internal citations omitted.

[xi] While Desjardins is not precedential in the same way as a decision issued by the courts, ARP decisions are binding on the USPTO under Director authority. Thus, examiners and PTAB panels must follow this reasoning unless overruled by the Federal Circuit or a future ARP decision.

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Rethinking University Research: Innovating the Innovation Ecosystem to Support Life Sciences and Personalized Medicine

Antoinette Konski — Wed, 28 May 2025 13:52:09 +0000

Personalized medicine—tailoring treatments to individual patients based on their genetic makeup, lifestyle, and environment—is transforming healthcare. But this revolution didn’t begin in the private sector. It was sparked and shaped by decades of strategic investment from the U.S. government, especially the National Institutes of Health (NIH).

Genomics as the Foundation

The , completed in 2003 with major NIH support, provided the genetic blueprint of human life. Follow-on initiatives like Program (TCGA), the (ENCODE), and the (GTEx) linked DNA variants to disease risk.

All of Us: A New Era of Data

��’s research program, aiming to enroll over one million participants, is creating one of the world’s most diverse and comprehensive health datasets. It is enabling insights into how genes, the environment, and behavior intersect to shape health.

Pharmacogenomics in Practice

��’s seeks to understand how individual genetic differences affect drug response, which can improve dosing precision and reduce adverse effects.

The Evolving Landscape of Federally Funded Research

While federal grants and investments have helped translate genomic insights into clinical impact, much of that foundational work begins in academic labs. Many fundamental discoveries take place in universities—long before they appear in clinical trials or investor pitch decks. Yet, the sustainability of this engine of innovation depends heavily on continued federal support. As funding pressures grow, especially in areas that do not promise immediate commercial return, the need to protect and strengthen university research funding has never been more urgent.

The evolving financial landscape of university-led academic research was addressed by at the . Overall, Dr. Frenk painted a hopeful picture of the future of academic research, encouraging universities to embrace change and seize opportunities for growth and success. His insights provide valuable guidance for navigating the evolving financial landscape and ensuring the continued advancement of knowledge and discovery.

A powerful idea was presented that challenges traditional notions of university research: the need to “innovate the innovation.” This concept, also described as meta-innovation, calls on research institutions to not only produce groundbreaking discoveries but to reimagine the entire process by which those discoveries are made, translated, and applied.

From Research to Real-World Impact

Universities have long been hubs of knowledge creation, but Dr. Frenk emphasized that in today’s complex world, that’s no longer enough. Academic institutions should drive research to link innovation with societal benefits.

A few of Dr. Frenk’s solutions include:

Dissolve the divide between basic and applied research. Universities should foster a more integrated approach that allows ideas to move more fluidly from theory to practical application.
Partner earlier and more intentionally with industry and philanthropic investors. Diversifying funding sources and collaborating sooner in the research cycle can speed up the path from concept to impact.
Redefine the university’s mission beyond knowledge creation to include translation—turning discoveries into technologies and evidence that inform policy and improve lives.
Build new physical and intellectual spaces, like the UCLA Research Park, that support interdisciplinary collaboration, entrepreneurship, and commercialization.
Embrace innovation in education and governance, ensuring the way the United States and academic institutions teach, organize, and evaluate research evolves alongside science itself.

The Future of Innovation is Integrated

This call to action urges universities to adopt agile, impact-focused systems instead of traditional academic models. By aligning excellence with relevance, and forging new types of collaboration, universities can remain at the forefront of solving humanity’s most urgent challenges.

In sum, the question is not just what we discover—but how we innovate to make that discovery matter.

About 2025 LABEST Bioscience Conference

The 7th Annual LABEST Bioscience Conference included keynotes from UCLA Chancellor Julio Frenk, Gilead Sciences CEO Daniel O’Day, and the University of Pennsylvania’s Jonathan A. Epstein. Sessions addressed Los Angeles’ growth as a bioscience hub, with panels on topics like “Space Medicine” and “The Business of Bioscience.” The Pearl Cohen Poster Competition showcased over 100 entries from institutions including UCLA, USC, and Caltech. The conference highlighted three core themes: current scientific excitement, market challenges, and future predictions in biotech. �鶹��ý was a proud sponsor of LABEST Bioscience Conference.

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