Background: The integration of artificial intelligence (AI) into clinical practice is contingent on public trust. This trust often depends on physician oversight, yet a significant gap exists between the need for AI-competent physicians and the current state of medical education. While the perspectives of students and experts on this gap are known, the views of the US general public remain largely unquantified. Objective: This study aimed to assess US public perceptions regarding AI in medicine and the corresponding emergent needs for medical education. We specifically sought to quantify public trust in different diagnostic scenarios, concerns about physician overreliance on AI, support for mandatory AI education, and priorities for the future focus of medical training. Methods: We conducted a cross-sectional, web-based survey of adults in the United States in November 2025. Participants (N=524) were recruited via SurveyMonkey Audience. We calculated descriptive statistics, frequencies, proportions (percentages), and 95% CIs for all main survey items. Results: A total of 524 participants completed the survey. Most (n=329, 62.8%; 95% CI 58.6%‐66.9%) placed the most trust in a physician’s diagnosis based on their expertise alone; only 7.8% (n=41; 95% CI 5.5%‐10.1%) trusted an AI-first diagnostic model. Trust was highly contingent on training: 93.9% (n=492) of participants rated formal physician training on AI limitations as “essential” or “very important.” Widespread concern about physician overreliance on AI was reported, with 81.1% (n=425) being “very concerned” or “extremely concerned.” Consequently, 85.1% (n=446) agreed or strongly agreed that training on AI use, ethics, and limitations should be mandatory in medical school. When asked about future educational priorities, 70.2% (n=368; 95% CI 66.3%‐74.1%) believed that medical education should focus on human-centered skills (eg, empathy and communication) over clinical skills. Conclusions: The US public expressed conditional trust in medical AI, strongly preferring physician-led and critically supervised models. These findings reveal a clear public mandate for medical education reform. The public expects future physicians to be mandatorily trained to appraise AI, understand its limitations, and refocus their professional development on the human-centered skills that technology cannot replace.
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Motoneurons Inhibitory Synapses Homeostatically Respond to Neuronal Activity and Modulate Amyotrophic Lateral Sclerosis Pathogenesis
Alterations in excitation/inhibition (E/I) balance and changes in motor neurons (MN) activity may contribute to MN vulnerability in ALS. The balance of pathogenic versus adaptive changes occurring in inhibitory synapses and affecting E/I balance remain unclear. Confocal microscopy of MN from P45 male SOD1G93A mice reveal downregulated GlyR but upregulated GABAR clusters at inhibitory synapses. GlyR and GABAR respond to PSAM and DREADD chemogenetic alterations of MN excitability, with increased activity driving increase in inhibitory clusters. An E3 ligase-conjugated intrabody (GFE3) degrades Gephyrin, decreases GABAR and GlyR clusters, increases net activity, and downregulates disease markers. However, simultaneous decrease of inhibition and increased activity by actPSAM and GFE3 shows no net beneficial effects on disease markers. Thus inhibitory synapses are involved in the early phases of ALS pathogenesis and respond to persistent homeostatic loops, and their suppression delivers a net activity increase, offering potential benefits on disease pathways.
ThermoCas9: Gene Editor Targets Cells with Disease-Related Hypomethylation
Research led by Wageningen University in the Netherlands and the Van Andel Institute (VAI) in Michigan has shown that ThermoCas9, a variant of CRISPR, can distinguish tumor DNA from healthy DNA and selectively cut only the former, marking a potential step toward a highly precise cancer therapy.
The method relies on DNA methylation, a process in which methyl groups are added to DNA to regulate whether genes are on or off. In cancer cells, DNA methylation is altered and can therefore act as a molecular “fingerprint” that differentiates tumor cells from healthy ones.
“ThermoCas9 is the first CRISPR-associated enzyme to respond to differences in the most abundant type of DNA methylation in human and other eukaryotic cells,” explained co-senior author John van der Oost, PhD, from Wageningen University. “This means we now have a system that we can target specifically toward tumor cells.”
The study, published in Nature, represents the first time a CRISPR-based method has relied on methylation to target human cancer cells.
“ThermoCas9 uses methylation like an address to precisely target cancer cells while leaving healthy cells untouched,” added co-senior author Hong Li, PhD, from VAI. “The findings could be a game changer.”
After analyzing ThermoCas9’s structure and finding that it can distinguish between unmethylated and methylated genes, Li and team introduced the enzyme into different types of healthy human cells with distinct methylation landscapes and into breast and colorectal cancer cells.
They found that ThermoCas9 cut DNA in the tumor cells while leaving healthy DNA intact, suggesting that the system can detect subtle chemical differences between healthy and tumor cells and act on them.
“ThermoCas9 is a perfect example of the value of fundamental research; you have to know how these individual pieces work together,” said Li. “We used biochemistry and structural biology to discover a mechanism that we one day hope will lead to more precise, effective cancer treatment.”
Although the study highlights the potential of ThermoCas9 as a cancer treatment, it does not show that the selective DNA damage it inflicts leads to tumor cell death. The researchers next steps will focus on damaging tumor DNA sufficiently to trigger cell death.
Of note, aberrant methylation patterns also play a role in diseases other than cancer, including autoimmune disorders. It is therefore possible that ThermoCas9 or a similar CRISPR tool could evolve into a versatile molecular strategy that recognizes diseased cells by their chemical “signature” and selectively disables them.
The post ThermoCas9: Gene Editor Targets Cells with Disease-Related Hypomethylation appeared first on Inside Precision Medicine.
Epigenetic Target Could Sensitize Pancreatic Cancer to Immunotherapy
Researchers have found an epigenetic switch that pancreatic cancer cells use to protect themselves against genomic instability. In a study published in Cancer Research, the team reports that blocking the epigenetic regulator DPY30 triggered immune cell infiltration into pancreatic tumors in mice, sensitizing them to immunotherapy.
Frequently diagnosed at advanced stages, pancreatic cancer is often resistant to conventional therapies and shows limited response to immunotherapy. This leaves patients with few effective treatment options.
“As cancer biologists, we are intrigued by the remarkable ability of pancreatic cancer cells to tolerate genomic instability and sustained replication stress while continuing to proliferate and evade immune surveillance,” said Francesca Citron, PharmD, PhD, instructor of genomic medicine at The University of Texas MD Anderson Cancer Center and lead author of the study. “This paradox led us to investigate the adaptive mechanisms that enable cancer cells to buffer genomic instability, particularly by protecting replication forks and preventing catastrophic DNA damage.”
The researchers were interested in finding out whether epigenetic regulators may play a direct role in safeguarding the integrity of replication forks, where DNA is copied as cells divide. Under stress, DNA replication is typically disrupted, for instance as cancer cells continue dividing and accumulating mutations that result in genomic instability. However, Citron’s team discovered that pancreatic cancer cells rely on DPY30 to protect DNA replication forks under stress and continue multiplying in spite of genomic instability.
DPY30 belongs to a group of proteins that together form the WRAD/COMPASS complex, which is involved in epigenetics regulation. The study found that this component was able to switch the entire complex from playing a global epigenetics function to a localized role at stressed replication forks, where DPY30 stabilized.
“Historically, WRAD core components, particularly DPY30, have been primarily studied in the context of histone methylation and transcriptional regulation,” said Citron. “Our findings significantly expand this paradigm by demonstrating that these factors play a direct role in maintaining replication fork stability under conditions of stress. Importantly, we also establish a link between this mechanism and modulation of the tumor immune microenvironment, providing a conceptual bridge between replication stress and immune response.”
In a mouse model of pancreatic cancer, DPY30 inhibition destabilized replication forks, leading to increased genomic instability and activating inflammatory signaling pathways. This then triggered the recruitment of tumor-infiltrating lymphocytes and turned previously immunologically “cold” tumors into “hot” tumors that responded to immunotherapy.
“Inhibiting DPY30 leads to increased replication-associated DNA damage, which in turn robustly enhances immune signaling pathways,” said Citron. “This dual effect, on genome stability and immune activation, opens new therapeutic opportunities to impair replication fork protection while simultaneously stimulating anti-tumor immune responses.”
Furthermore, biopsies from pancreatic cancer patients showed that higher levels of DPY30 expression were associated with higher tumor grades, a poorer prognosis and lower response rates to immunotherapy. Together, these findings point at DPY30 as both a therapeutic target and a biomarker to stratify patients who are most likely to benefit from immunotherapy.
Going forward, the researchers plan to dive deeper into how HPY30 influences immune cell recruitment and activation within the tumor microenvironment. In parallel, they will be exploring pharmacological strategies to inhibit DPY30 and testing their efficacy in preclinical studies. Citron added: “Ultimately, our goal is to develop rational combination therapies that drive more effective and durable responses in patients.”
The post Epigenetic Target Could Sensitize Pancreatic Cancer to Immunotherapy appeared first on Inside Precision Medicine.
Digital Twin Process Could Slash Microbial Protein Costs
A consortium of companies has developed what they call a digital twin of a microbial process to produce protein A.
Novasign, based in Vienna, hopes its participation through the ECOnti consortium will help manufacturers slash the costs of microbial proteins by improving experimental design.
According to the company, the digital twin can reduce the number of experiments needed to understand process behavior by 70% compared to Design of Experiments (DoE).
“Generally, the biggest problem in the industry right now is it’s not very efficient,” explains Mark Duerkop, PhD, CEO of Novasign.
“We need methods to learn more efficiently from experiments, design better experiments, and adapt process trajectories if something goes wrong.”
According to Duerkop, Novasign began developing an end-to-end digital twin of the full processing chain for microbial protein production as part of the ECOnti consortium three years ago.
Novasign says it develops digital twins spanning an entire process—from upstream to downstream—with the goal of improving both process development and manufacturing efficiency.
“The digital twin supports process development by systematically recommending the next set of experiments based on model-informed insights,” he says.
“During manufacturing, it can detect deviations from the intended process trajectory and support corrective actions.”
For example, if the digital twin is used to recover a process following disturbances, such as pH shifts or feed pump failure, manufacturers could significantly reduce product losses.
However, this remains for the future, he says, as the U.S. Food and Drug Administration (FDA) requires extensive validation before approving self-optimizing or autonomous manufacturing processes.
At the recent Bioprocessing Summit Europe, Duerkop presented a showcase on using the Novasign Studio software for full process control for 30 consecutive days.
He also showed how the software can use small-scale experimental data to inform scale-up and, in biosimilar development and viral vector manufacturing, can reduce experimental effort by up to 64%.
The post Digital Twin Process Could Slash Microbial Protein Costs appeared first on GEN – Genetic Engineering and Biotechnology News.
Ultra- and Diafiltration Clear Leachables Effectively
In the push to de-risk biologics manufacturing, downstream purification steps are increasingly under the microscope. Now, new research led by Jonathan Bones, PhD, principal investigator in the characterization and comparability group at the National Institute for Bioprocessing Research in Dublin, and his colleagues provided compelling evidence that ultrafiltration and diafiltration (UF/DF) deliver robust clearance of process-related leachables—while also offering a predictive framework to better understand that performance.
Although UF/DF has long been assumed to reduce small-molecule contaminants, systematic data have been scarce. To address this gap, the team evaluated 28 representative organic compounds spiked into three distinct protein systems. Using liquid chromatography–high resolution mass spectrometry, they tracked how effectively these compounds were removed during UF/DF operations.
The results were striking. Twenty-four of the compounds demonstrated greater than 98% clearance across all three protein processes. Notably, variations in protein characteristics and process parameters had minimal impact on removal efficiency. Instead, clearance behavior was remarkably consistent, as reflected in similar sieving coefficients across the systems.
The intrinsic physicochemical properties of the leachables impacted clearance. Among these, lipophilicity—expressed as the octanol-water partition coefficient (Log P)—emerged as the dominant factor. Compounds with Log P values below four exhibited near-ideal clearance, while even highly hydrophobic molecules (Log P above seven) still achieved removal rates exceeding 93%. Molecular weight, polarizability, and solvent-accessible surface area also contributed to clearance outcomes.
Beyond empirical findings, the study advances the field with predictive modeling. By applying orthogonal partial least squares (OPLS) regression, the researchers developed tools capable of estimating sieving coefficients based on compound properties. These models could prove invaluable for anticipating leachable behavior without exhaustive experimental testing.
The implications are significant. As regulatory scrutiny around extractables and leachables intensifies, demonstrating effective clearance becomes central to product safety. This work not only confirms that UF/DF is a powerful mitigation step but also equips developers with quantitative tools to support risk assessments.
In an industry where unseen contaminants can pose outsized risks, the ability to both measure and predict their removal marks a meaningful step forward.
The post Ultra- and Diafiltration Clear Leachables Effectively appeared first on GEN – Genetic Engineering and Biotechnology News.
iPSC-based Manufacture vs. Autologous Model Production Costs Examined via Financial Analysis
Autologous and allogeneic cell therapies are establishing viable clinical pathways but cannot be manufactured cost-effectively at scale. Manufacturing natural killer (NK) cell therapies, and possibly T-cell therapies, using induced pluripotent stem cells (iPSCs) is understood to be significantly more cost-effective. Now those cost advantages have been quantified.
Specifically, the cost of goods per treatment can be reduced as much as 95% when manufacturing via iPSCs rather than using traditional autologous or allogenic production methods. By decoupling production from the patient, manufacturers can benefit from large-scale batch production, standardized processes, less labor, and a less complex infrastructure than either autologous or allogeneic production. Details are spelled out in a white paper by Cellistic, based on an intense cost-of-goods analysis of NK cell therapy manufacturing performed by Astrid Van Damme, PhD, head of project management at Cellistic, for her MBA thesis.
In it, Van Damme advocates creating a universal master cell bank that feeds multiple working cell banks. Those working cell banks, in turn, generate intermediate hematopoietic stem cells that are differentiated into the final therapeutic product. “This cascade creates an essentially inexhaustible, standardized source material for the entire commercial lifecycle of the product,” she asserts.
Cellistic’s internal review compared seven economic drivers for each of the three cell therapy manufacturing options. Notable advantages for an iPSC manufacturing strategy include:
- Commercial scale production
- Exponential scale-up or scale-out
- Industrial-scale reproducibility
- Use of standard cold-chain logistics
- Minimal patient interactions
- Reduced patient attrition
- Potentially global market reach
An iPSC manufacturing strategy for cell therapies drops the cost of goods sold to about $5,000 per dose, down from $115,000 per dose for autologous therapeutics and $40,000 per dose for allogeneic therapeutics, Van Damme reports.
Autologous and allogeneic manufacturing, in contrast, both have severe constraints that increase costs for manufacturers and payers alike. Materials and labor alone account for 50% to 70% of autologous cell therapies—roughly $80,000 to $150,000. That’s a huge driver for U.S. list prices that, for the oncology therapeutics Kymriah® and Carvykti®, are at or above an adoption-limiting $475,000 per dose. Even after factoring in regional pricing differences and payer discounts, the net per-dose costs to payers are still extremely high.
Compared to iPSC manufacturing at clinical scale (150 vials and 200 M cells per vial) and at commercial scale (450 vials with 400 M cells per vial), Van Damme indicates:
- Labor costs constituted about 13% of the costs of goods (vs. about 70% for autologous methods)
- Costs per vial drop approximately 40%
- Fixed costs were diluted by a factor of three
“Once a minimum threshold of operational maturity and throughput is achieved, iPSC-based manufacturing economics become comparatively robust to routine operational variability,” the paper concludes.
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Biopharma Adopting AI Despite Remaining GMP Compliance Questions
The biopharma industry is embracing artificial intelligence (AI) in manufacturing, even though questions remain about how best to use the technology in a GMP environment.
At least, so says Sanjay Konagurthu, PhD, senior director, science and innovation, pharma services, at Thermo Fisher Scientific, who argues that the key to successful AI adoption is a clear use case.
“As is true in most industries, adoption of AI and machine learning (ML) is real and accelerating across the biopharma industry. However, most use cases center around applications that augment teams without completely redefining a validated process, such as smarter quality and inspection workflows.
“We’re seeing that the hesitation isn’t so much reluctance to adopt AI as it is the practical constraints of operating in a good manufacturing practices-regulated environment. To adopt AI and ML in regulated environments, you need clear intended use, strong data foundations, traceable governance, and set parameters for disciplined control and monitoring,” he tells GEN.
Data foundations
In addition to establishing a strong use case, drug companies need an IT infrastructure that facilitates the flow of process data, according to Konagurthu, who says data silos are a persistent problem in biopharma.
“As with most scientific endeavors, vast quantities of data are generated across the biopharma industry, among labs, organizations, consortia, and nations, and much of this data is stored in a singular system. So, there’s a connectivity challenge, but that’s not the only reason why processing technologies struggle to exchange information.
“The data is often captured according to different standards and exists in a variety of formats, which means context is easily lost. Also, the data exists in a wide variety of structured and unstructured formats, which compounds the challenge in effective curation and analysis,” he says.
Failure to establish an effective infrastructure or standardize data has multiple negative consequences, Konagurthu adds.
“When data from early development can’t be connected through to commercialization, teams end up re-running experiments and analysis. They may even miss early signals that could impact downstream manufacturing or risk quality.
“When companies look to scale or implement new technologies like AI and ML, fragmented data can become prohibitive. Ultimately, for biopharma, this could extend the time it takes to bring a promising molecule to market,” he says.
Formul-AI-tion development
Beyond process development and control, formulation is another area where more and more biopharmaceutical companies are making a use case for AI.
Konagurthu says, “Biopharma scientists have historically used trial-and-error approaches to determine the right solubility and bioavailability of OSD [oral solid dose] therapies. With AI and ML models, teams can make earlier, better-informed decisions on formulation pathways.
“Early-stage acceleration in discovery and formulation echoes all the way into manufacturing and clinical supply, so improving the front end can compress timelines across the entire pipeline,” he adds.
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Novel Targets for Complex Cancer Revealed by Genetic Regulatory Node Mapping
In a new study published in Nature titled, “Mapping convergent regulators of melanoma drug resistance by PerturbFate,” researchers from The Rockefeller University have developed a platform called PerturbFate that can systematically map how diverse disease-associated genetic variations reshape cells. By tracking gene regulation in single cells over time, the team identified regulatory nodes common to diverse variations. Using melanoma drug resistance as a proof-of-concept, results showed that these shared points of control offer a path toward combination therapies that can target disease across many genetic causes.
“Once you know that a disease is associated with hundreds of genes, how do you design one therapy to target it?” posed Junyue Cao, PhD, head of the Laboratory of Single-Cell Genomics and Population Dynamics at Rockefeller. “We wondered whether all these different genes may be mediated by some shared downstream signaling that we can discover and target instead.”
Advances in genomic sequencing and genetic screening have allowed researchers to identify hundreds of genetic mutations linked to disease. Yet these genes often span diverse pathways with broad functionalities, from gene regulation to cell signaling, making them difficult to target collectively.
Cao proposed that if these mutations converge on shared downstream programs, the key challenge is not to target each mutation individually, but to identify the common control points known as regulatory nodes.
PerturbFate allows researchers to observe how different genetic changes reshape a cell in real time by tracking DNA accessibility, and RNA production and processing. By capturing these changes in the same single cell, the system reveals the networks of genes that control cell behavior and how different genetic variations can have the same effect.
“This technology lets us perturb hundreds to thousands of genes in parallel and then measure the detailed molecular changes in each individual cell,” says Cao. “That allows us to link many different genetic perturbations to their downstream effects and identify regulatory nodes.”
To test the platform, the authors focused on melanoma drug resistance. Using PerturbFate, they selected 143 genes linked to resistance to the common melanoma drug, Vemurafenib. PerturbFate then tracked how deactivating each of these genes reshaped the cell. Cao explains the platform captures gene expression, RNA dynamics and chromatin state, all critical components when identifying upstream regulators that drive these disease states.
After analyzing more than 300,000 cells, the researchers found that diverse genetic perturbations pushed melanoma cells into the same drug-resistant state. Drug resistance dropped significantly when these common control points were targeted, pointing to a promising strategy for combination therapies.
The platform also revealed an important nuance involving the transcriptional coactivator, Mediator Complex. Disrupting different parts of this same complex could trigger drug resistance through routes that ultimately converged on the same survival signal in melanoma cells, called VEGFC. Resistant cells could no longer proliferate after blocking that signal.
The team has made both the experimental and computational tools behind PerturbFate openly available, and plans to extend the approach from cultured cells to living systems. Cao and colleagues are currently applying PerturbFate to conditions, such as aging and Alzheimer’s disease, to uncover shared vulnerabilities that can guide more effective treatments.
“This is just a starting point,” says Cao. “Now that we’ve demonstrated the approach in a simple model, we’re working to extend it into living systems to study even more complex diseases.”
The post Novel Targets for Complex Cancer Revealed by Genetic Regulatory Node Mapping appeared first on GEN – Genetic Engineering and Biotechnology News.
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