Radically different
Adam Zamora, PsyD, discusses how decoding engages the same part of the brain, whether we’re reading or listening to an audiobook.
The post TODAY: Does Listening to an Audiobook Count As Reading? Experts Answer Once and for All appeared first on Child Mind Institute.
Precision oncology has transformed lung cancer care, but its reach remains uneven. Targeted therapies and immunotherapies have improved outcomes for selected patients, yet many people with lung cancer still receive systemic treatment without a reliable way to know whether their tumor will respond.
For most patients, treatment decisions are still guided by tumor type, stage, standard-of-care guidelines, and a limited set of biomarkers. That works for some, but not enough. Lung cancer is biologically heterogeneous, and tumors with similar clinical features can respond very differently to the same drug.
A new study in npj Precision Oncology describes a rapid ex vivo tumor platform designed to address that gap. Instead of relying only on genomic markers, the method tests drug response directly on patient-derived 3D lung tumor replicas generated from routine clinical samples.
Genomic profiling has become central to lung cancer treatment, especially for patients with actionable alterations such as EGFR mutations. But many tumors do not carry targetable drivers, and even when they do, genomic information does not always predict resistance, chemotherapy response, or sensitivity to combination regimens.
That is where functional precision oncology is gaining attention. The idea is straightforward: take a patient’s tumor cells, grow them in a clinically relevant model, expose them to candidate drugs, and measure whether the tumor responds.
The challenge is making this fast, reliable, and feasible from the small biopsies that are actually available in routine lung cancer care. Many patient-derived organoid approaches require surgical specimens, have low establishment rates, or take longer than the treatment decision window.
The authors frame the problem clearly: “Effective prediction models are needed” to enable more personalized lung cancer care.
The new platform was developed in a prospective multicenter cohort of 129 treatment-naïve lung cancer patients. Researchers generated 3D lung tumor replicas from resection material as well as small diagnostic samples obtained through EUS-FNA and EBUS-TBNA biopsy procedures.
The overall establishment success rate was 65%. Success was highest from surgical resection material, at 100%, and remained strong for EUS-FNA biopsy material, at 82.7%. EBUS-TBNA samples had a lower but still clinically relevant success rate of 47.1%.
This is important because most patients who need systemic treatment do not undergo upfront surgery. A platform intended for real-world treatment selection must work from limited biopsy material, not only from large resection specimens.
The tumor replicas formed compact 3D clusters and retained key features of the original tumor. Histology and immunohistochemistry showed that replicas preserved subtype-specific characteristics of lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer. In selected samples, genetic features such as KRAS mutations were also retained.
A central strength of the platform is speed. The researchers report that ex vivo drug response results were available within a median of 12 days from biopsy acquisition. In 80.4% of patients, results were generated within two weeks.
That timing matters. A functional drug test has limited clinical value if results arrive after treatment has already started. In this cohort, patients receiving standard systemic therapy began treatment after a median of 21 days, suggesting that the platform could produce results early enough to inform decision-making.
The researchers tested standard lung cancer therapies, including platinum-based chemotherapy backbones and other agents such as etoposide, pemetrexed, paclitaxel, afatinib, and sotorasib. The tumor replicas showed heterogeneous drug responses, mirroring the variability seen clinically.
In patient-derived xenograft models, ex vivo responses were consistent with in vivo treatment responses or expected mutation-drug relationships. For example, tumor replicas harboring EGFR L858R responded to the EGFR inhibitor afatinib, while KRAS G12C-mutant replicas showed sensitivity to sotorasib.
The study also compared ex vivo drug responses with patient outcomes. In stage 3 lung cancer patients treated with chemoradiation, ex vivo chemotherapy sensitivity was significantly associated with overall survival.
In another group of 20 patients treated with platinum-doublet chemotherapy, with or without immunotherapy, the platform showed a sensitivity of 73% and a positive predictive value of 92% for predicting treatment response in the biopsy lesion.
That high positive predictive value is clinically meaningful. It suggests that when the platform classified a tumor as sensitive, the patient was likely to experience clinical benefit. However, the negative predictive value was lower, meaning the test was less reliable at identifying tumors that would not respond.
This distinction matters for clinical use. At this stage, the platform may be better suited to helping identify promising treatment options than to ruling therapies out definitively.
The study signals a broader shift in precision oncology. Molecular testing asks what alterations a tumor carries. Functional testing asks what the tumor actually does when exposed to therapy. Both approaches are valuable, but they answer different questions.
For lung cancer, this could be especially important because chemotherapy remains a backbone of treatment across multiple stages and subtypes. Yet chemotherapy selection is rarely personalized in the same way targeted therapy is. A rapid ex vivo assay could help distinguish patients likely to benefit from a specific chemotherapy combination from those who may need an alternative approach.
The authors describe the platform as following the same concept as an antibiogram: testing patient-specific tumor material to identify effective treatment options before therapy begins.
If validated in larger studies, this type of approach could reduce ineffective treatment, avoid unnecessary toxicity, and support more rational selection of systemic therapies.
The results are promising, but still early. The clinical validation cohort was small, and the authors describe the patient-response data as proof-of-concept. Larger prospective trials will be needed to determine whether using the platform to guide treatment improves outcomes compared with standard care.
The system also has biological limitations. The current tumor replicas primarily capture intrinsic tumor cell drug sensitivity. They do not fully reproduce the tumor microenvironment, including immune cells, stromal cells, endothelial cells, or extracellular matrix components. That means the platform may be less suited, in its current form, to predicting responses to therapies where the immune microenvironment is central, such as immune checkpoint inhibitors.
The short-term 72-hour drug readout also cannot capture delayed effects, acquired resistance, or long-term tumor evolution. Future versions may need to incorporate immune co-cultures, repeated sampling at progression, or more complex microenvironmental features.
The promise of this platform lies in its practicality. It uses routine biopsy material, produces results quickly, and tests actual drug response rather than inferring sensitivity from biomarkers alone.
As the authors conclude, the platform enables upfront screening of anti-cancer drug responses “within a clinically relevant timeframe of two weeks.”
That is the key translational point. For functional precision oncology to become clinically useful, it must fit the pace and constraints of real cancer care. This study suggests that, at least for lung cancer, rapid patient-derived tumor replicas may bring that goal closer.
The post Predicting Drug Response with Lung Cancer Tumor Replica Platform appeared first on Inside Precision Medicine.
Background: Heart failure is a chronic condition that significantly impacts patients’ quality of life and increases health care burden. Effective self-monitoring and lifestyle modification are essential components of heart failure management and can support improved health outcomes. Mobile health technologies, such as smartphone apps, are increasingly used to assist patients with heart failure in self-management. However, evidence regarding patient engagement, user experience, and the effectiveness of these mobile health tools remains limited and continues to evolve. Objective: This study aimed to explore the feasibility of a mobile health platform, MoTER-HF, which incorporates a smartphone app and a web-based clinical portal to support self-management in patients with heart failure. Methods: The feasibility study used a single-group pretest-posttest mixed methods design. A total of 23 participants diagnosed with heart failure were recruited to use the app and 2 Bluetooth-enabled measurement devices (a blood pressure monitor and a digital weight scale) over a 12-week period. Participants’ engagement and acceptance were assessed using a satisfaction questionnaire, semistructured interviews, and platform usage logs. Potential health and behavioral outcomes were explored using validated instruments administered at baseline and week 12. Results: Most participants found the MoTER-HF app easy to use and aligned with their routine self-monitoring practices. Daily monitoring features such as blood pressure and weight tracking were used frequently. However, features such as symptom tracking and exercise logging were used less often, reflecting individual preferences and perceived relevance. Participants reported improved self-monitoring practices and valued the ability to visualize and track their data, and the reassurance provided through nurses’ oversight in the satisfaction questionnaire and interviews. Changes in health and behavioral outcome measures were not statistically significant, although exploratory changes were observed in the scores of self-care, quality of life, and psychological well-being. Conclusions: The MoTER-HF platform has demonstrated potential in supporting self-management among individuals with heart failure, particularly when it incorporates features that participants find engaging. Further research is needed to better understand the platform’s impact on health outcomes and the implementation challenges, and to involve clinicians in developing a scalable digital model of care.
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By using tiny particles made from the membranes of human immune cells, scientists from the University of California, San Diego and the University of Missouri have created antifungal nanoparticles that target Candida albicans, a fungus responsible for oral and vaginal yeast infections as well as bloodstream infections. Tests in mice with severe Candida infections show that the macrophage-derived nanoparticles reduced the amount of fungus in major organs, including heart, kidneys, lungs, and spleen. The mice also had improved survival rates.
Full details are published in Cell Biomaterials in a paper titled “Cell membrane-derived nanotherapeutic for combating Candida albicans infections.” In it, the scientists write that “this bioinspired nanodisc not only disrupts fungal membranes directly but also enhances host immune clearance, achieving potent antifungal activity.”
Current treatment options for fungal infections are limited and there are growing concerns about drug resistance. Existing medications typically target specific parts of a fungal cell and can lose effectiveness as fungi evolve resistance. The nanoparticles described in the current paper have a more potent strategy. Besides damaging fungal membranes, they also boost the body’s natural immune defenses to better fight infections.
According to the scientists, each nanodisc measures about 10-20 nanometers, about 1,000 times smaller than a normal macrophage. Their tiny size is an advantage as it allows them to fuse directly with fungal cell membranes and destabilize them, which is harder for full-sized macrophages.
To create the nanodiscs, the scientists isolated the outer membranes of the macrophages and broke up them up into tiny pieces. They then fused them onto disc-shaped nanoparticles made from a biodegradable polymer. Since the nanodiscs are built from macrophage cell membranes, they retain the same receptor proteins that the immune cells use to recognize and attack Candida. This means that the nanodiscs can identify and attach to fungal cells more effectively than those made from other cell types such as red blood cells.
Once attached, the nanodiscs weaken the fungal cell’s protective outer membrane until tiny openings form. As the membrane breaks down, the cell’s contents leak out while external substances seep in ultimately killing the fungus. Because this treatment strategy physically damages the fungal cell rather than targeting a specific molecule, the developers believe that it may be harder for the fungus to evolve resistance.
The nanodiscs also provide other countermeasures. They reverse the suppression of antifungal chemicals produced by macrophages during infection, and prevent Candida from forming biofilms that help to shield fungal cells from drugs and the immune system. Testing also revealed that the treatment was effective when administered both before and after infection suggesting that it could also be used as a preventative.
For their next steps, the scientists will further evaluate the antifungal potency of the nanodiscs against a broader range of pathogenic fungal species.
The post Macrophage Membrane-Derived Nanoparticles Shows Potential Against <i>Candida</i> Infections appeared first on GEN – Genetic Engineering and Biotechnology News.
Background: Understanding how medical students learn is critical for improving teaching strategies in clinical education. Despite the widespread use of learning style frameworks, such as visual, aural, read/write, and kinesthetic (VARK), evidence from sub-Saharan Africa remains limited, and the use of learning style approaches is debated in the literature. In clinical and health sciences education, aligning teaching with learners’ preferences can enhance knowledge retention, procedural competence, and ultimately the quality of patient care. Objective: This study aimed to determine the predominant learning style preferences of clinical students at a Nigerian medical school and to examine how demographic and academic factors influence these preferences, with explicit attention to implications for clinical pedagogy. Methods: A cross-sectional survey was conducted among 200 clinical students (400-600 level) at Niger Delta University between October 2021 and December 2021, using the validated VARK inventory (version 7.8). Descriptive statistics summarized distributions, and the Pearson chi-square tests or Fisher exact tests assessed bivariate associations with sex, age group, and year of study. A multivariable modeling strategy was prespecified but not performed due to the categorical structure of the primary outcomes, sparse cells for some modality categories, and the sample size limitations for multinomial modeling. Results: Of 200 participants (mean age 25.1, SD 3.9 y; n=107, 53.5% male), 105 (52.5%) preferred unimodal learning, and 95 (47.5%) preferred multimodal learning. Kinesthetic (n=121, 60.5%) and auditory (n=110, 55%) were the most common dominant preferences, followed by read/write (n=68, 34%) and visual (n=36, 18%). Visual preference was significantly higher among male participants (χ=4.49; =.03). Read/write preference varied by year of study (=8.29; =.02). No significant associations were found with age. The pedagogical implications for clinical teaching were discussed, including bedside instruction, skills laboratory, simulation, small-group teaching, and audio-visual learning resources. Conclusions: Clinical students in this Nigerian setting predominantly favored kinesthetic and auditory learning, with nearly half reporting multimodal preferences. Medical educators should adopt blended instructional designs that include hands-on, discussion-based, and audio-visual elements to better prepare students for clinical practice. These insights can inform faculty development, curriculum design, and national medical education policies to foster adaptive, learner-centered training that improves clinical competency and readiness for professional service.
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ARPA-H has unveiled a $160 million effort to speed bespoke gene-editing therapies for rare diseases into the clinic. Meanwhile, Roche has abandoned two Huntington’s gene-silencing drugs after disappointing data, and drugmakers have stepped in to promote Medicare’s new obesity drug discount program.
I got coffee with my cousin this morning here in SF. Before leaving for his job at an AI behemoth, he said the bone-chilling July gloom is perfect “working weather.”
Good morning, everyone. Damian Garde here, filling in for Ed Silverman at Pharmalot’s satellite campus along the East River, where today’s cup of stimulation is filled not with coffee but rather a smoothie of curious color and questionable contents (what exactly is an “adaptogen”?). Anyway it’s Friday, as you’re almost certainly aware, and here are some tidbits to help you through the waning hours of another working week. …
German lawmakers passed a bill that would more than double the discount on branded medicines drugmakers must provide to the government, Reuters reports. The policy, part of an effort to plug a sizable budget gap in the country’s health insurance system, would increase the mandatory rebate from 7% to 15.5%. Industry groups have said the bill, if it clears Germany’s upper chamber, would deter investment and imperil the country’s access to new medicines.
The rapid rise of China’s biotech industry has led some American drug developers to do their work in near total secrecy, the Wall Street Journal observes. U.S. startups are increasingly loath to publish early data, disclose their scientific ambitions, or even publicize which diseases they hope to treat, all in fear that nimble Chinese firms will use that information to whip up competing drugs and beat them to the punch of starting clinical trials.