Academic research – Page 136

May 13, 2026

Usage of the Tablet-Based “Keep On Keep Up” Digital Program and Resulting Changes in Physical Capacity and Real-World Walking in Community-Dwelling Older Adults: Process Evaluation

Background: “Keep On Keep Up” (KOKU) is a tablet-based digital program based on the well-validated Otago and Fitness and Mobility Exercise programs for older adults to decrease the risk of falling. Objective: This substudy involved a process evaluation in order to analyze the usage patterns of the KOKU digital program, specifically training frequency, volume, and intensity among older adults over a 3-month self-managed training period. Pre-post changes in physical capacity and real-world walking were examined. Methods: This study is a nested cohort study within the three-armed randomized controlled SMART-AGE trial conducted in Germany (German Clinical Trials Register ID: DRKS00034316). Participants aged 67 years or older with basic digital literacy were included. KOKU provided guided but unsupervised progressive strength and balance training for 3 months. The data on training adherence, engagement, and progression were collected. Instrumented assessments included the Timed Up and Go Test, the 30-Second Chair Rise Test, and real-world walking monitoring using wearable sensors. Results: A total of 113 participants (n=63, 56% female; mean age 74.02, SD 5.36 y) were included in the analysis. During the 3-month period, participants used KOKU for 24 (SD 15) days, that is, 2 to 3 times per week. Over the entire study period, no falls or other adverse events were reported due to KOKU usage. The number of exercises performed per participant ranged from 2 to 213, with a median value of 70. The instrumented Timed Up and Go Test results revealed a prolonged total duration (=0.26; =.009). In the instrumented 30-Second Chair Rise Test, improvements were observed in the number of completed repetitions (=0.21; =.04) and frequency of repetitions (=0.23; =.03). This was mainly due to a reduction in inactive time (=−0.60; <.001). Real-world walking parameters remained unchanged, except for a slower walking speed during walking bouts of less than 30 seconds (=0.49; <.001). All changes did not meet the criteria for minimally important differences. Conclusions: KOKU is a novel digital intervention for older adults, promoting balance and strength exercises. Physical capacity improvements were small. However, the use of instrumented assessments provided further insights into participants’ capacity and mobility that would not have been identifiable with conventional assessments. Future improvements to the program should focus on incorporating more challenging exercises for individuals with varying levels of physical capacity. Trial Registration: German Clinical Trials Register DRKS00034316; https://drks.de/search/en/trial/DRKS00034316

May 13, 2026

Equitable Digital Frailty Screening for Marginalized Older Adults Using Audio Computer-Assisted Self-Interview: Collaborative Development Guide and User Testing Study

Background: Older adults facing social or structural marginalization for reasons such as lower literacy, digital exclusion, financial constraints, restricted living environments, or complex health histories, face persistent barriers to much-needed health screening. Digital health tools, particularly those using audio computer-assisted self-interview (ACASI) technology, offer potential to overcome these barriers (audio-delivered and self-administrable), but their application to marginalized populations remains underexplored. Moreover, guidance is limited for developing such tools which require collaboration within cross-disciplinary teams. This paper presents development insights and user testing findings from the ASCAPE (Audio App-Delivered Screening for Cognition and Age-Related Health in Prisoners) project, which aimed to develop equitable digital frailty and cognition screening for older people in Australian prisons. Objective: This study aims to describe the collaborative development of the “ASCAPE-HS” prototype, a tablet-based ACASI-delivered Frailty Index and aging screener, and to synthesize key lessons from the project that can inform equitable digital health tool development in hard-to-reach older adults. Also, to present findings on the usability and acceptability of ASCAPE-HS in a diverse community sample. Methods: The ASCAPE-HS prototype was developed through an iterative process involving researchers, clinicians, software developers, and end users under a digital health equity framework. The prototype included a self-administered, audio-delivered Frailty Index, alongside other items relevant to aging. The design process prioritized accessibility, sociocultural relevance, and technical feasibility, with regular multidisciplinary consultation and iterative refinement. Exploratory user testing with 20 older adults (aged 47‐93 years, including n=5 who had not finished secondary schooling, n=3 people with previous imprisonment history, and n=9 with mild or moderate cognitive impairment) provided feedback on usability and acceptability. Results: A 50-item Frailty Index was developed, alongside an additional selection of holistic questions that could meaningfully capture age-related health, and transferred to an iOS app (Apple, Inc), with ACASI features. Key elements included lay wording, consistent interface, simple “tapping” response options with repeatable audio feedback, a tutorial, and artificial intelligence–generated audio guidance. Key development considerations were synthesized into a checklist for teams undertaking similar projects. Successful strategies for the collaborative design process included diverse teams abreast of emerging literature and policy with varying expectations for engagement during development, and dedicating time to flexible, iterative development processes. Acceptability (median scores ≥4 out of 5 across 6 constructs) and usability (mean System Usability Scale score 79.0, SD 8.8) were high. Conclusions: A collaborative approach can produce ACASI-based health screening tools that are well-received by older adults. We highlight the feasibility of integrating frailty and aging assessment into a usable and acceptable digital tool, and offer actionable principles for collaborative, evidence-based development of equitable health screening tools in diverse, hard-to-reach populations.
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May 13, 2026

Adopting Creative Chemistry to Optimize Bioprocessing Workflow

Taking a creative approach to chemistry can help developers of antibody-drug conjugates (ADCs) improve the stability and purity of their products. That’s the view of Sunny Zhou, PhD, professor of chemistry and chemical biology at Northeastern University. Zhou will be speaking at the Bioprocessing Summit in Boston in August.

According to Zhou, the structure of ADCs can make them vulnerable to bioprocessing issues that don’t affect traditional antibodies. As one example, he says, the payloads of antibody drug conjugates often significantly absorb above 280 nm, making them markedly more sensitive to light.

“There’ll be photochemistry induced by the payload that can damage both the antibodies and payloads, such as crosslinking that likely leads to aggregation,” he says. “We’ve already published some work showing light-induced protein modifications, crosslinking, and aggregation.”

According to Zhou, some initiatives are already underway to address this issue. For example, by engaging in antibody production and downstream processing in dim or safe light (e.g., yellow or red light) instead of the more commonly used bright white light.

Another issue, he says, is that the linker connecting the antibody and drug payload is designed to be cleaved by enzymes in human patients. On the other hand, it also means that similar enzymes in host cell proteins (HCPs) may prematurely cleave the linker during production and storage, thereby decomposing the drug and contaminating the final product.

“Many host cell proteins contain such enzymes, but they don’t cleave antibodies. With these ADC linkers, however, enzymes that didn’t create problems before might do so now,” he says.

Zhou explains that premature cleavage of ADC linkers has been observed in an industrial setting. Fortunately, he says, his research team, in collaboration with companies like Takeda, is already creating universal platforms and workflows to identify and effectively remove these potential HCP contaminants, as well as working to better understand the stability of the linkers.

“These drugs circulate in the body for maybe two to three weeks, and stability issues can be amplified during circulation,” he says. “So, making the linker more stable [during manufacturing] may also help improve stability during circulation, further down the line.”

Zhou’s team is now hoping to look at other creative chemistries in bioprocessing. Among these is, for example, removing reagents, by-products, and impurities by filtration, which may be faster than relying on chromatography, he says.

The post Adopting Creative Chemistry to Optimize Bioprocessing Workflow appeared first on GEN – Genetic Engineering and Biotechnology News.

May 13, 2026

Yeast We Can Cut Costs By Optimizing Cell-Free Expression Systems

Choosing the right additives could help “cell-free” expression systems finally fulfill their potential and provide biopharma with a low-cost way of making protein drugs, according to a recent research report.

The new study looked at how cell-free systems, in which biochemical reactions occur independently of cells, could be fine-tuned to provide drug makers with alternatives for large-scale protein production.

And the potential of the approach is significant, says Karen Polizzi, PhD, a professor from the department of chemical engineering at Imperial College London, who adds, “Cell-free protein synthesis (CFPS) is a flexible manufacturing technology. It can be used for on-demand synthesis in low-resource environments or to make difficult-to-express products, especially medicines that are toxic to the cell. Cell-free reactions scale well across microliter to liter scale without needing adjustments.”

The Imperial team’s research focused on expression systems based on the yeast species Pichia pastoris, which, as Polizzi explains, “has machinery capable of post-translational modifications of proteins that can be necessary for function.”

As an expression host, P. pastoris combines elements of both prokaryotic and eukaryotic systems, such as a rapid growth rate and the ability to perform post-translational modifications (PTMs).

The problem is that current commercially available Pichia systems are only able to produce low amounts of protein. According to Polizzi and her co-authors, the productivity of P. pastoris-based cell-free systems usually ranges from 6 to 100 µg/mL, which is only approximately five percent of that achieved by comparable E. coli systems. In addition, the additives required by Pichia-based systems are more expensive than those required by equivalent platforms.

Additives to improve yields

To address this, Polizzi and co-authors systematically evaluated a variety of chemical additive combinations to identify the most effective stabilizers and crowding agents to be incorporated in the reaction.

The researchers also used a machine learning model to predict translation initiation rates and optimized the Kozak sequence—the protein translation initiation site in most eukaryotic mRNA transcripts—to enhance expression.

In addition, the Imperial team evaluated lower-cost glycolytic intermediates as substrates for ATP regeneration to reduce the cost of goods.

Polizzi says, “We focused on how to improve the yields and reduce the cost of production. We identified some additional additives that boost the yield without substantially increasing the cost. We also identified a different energy source that can be used.”

She adds, “This work underscores the importance of protein-stabilizing additives and the role of rationally designed DNA sequences with minimized mRNA structural complexity to enhance yield in CFPS. Our demonstration of glycolytic intermediates as a potential secondary energy system additionally provides the foundation for the development of a cost-effective P. pastoris CFPS.”

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May 13, 2026

Technique Yields Uniform, High-Quality, EVs at Scale

Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) play an outsized role in intracellular communications, influencing such functions as inflammation and tissue repair. With the possible applications of these small, membrane-bound particles growing, an efficient, cost-effective production method has been on drug manufacturers’ wish lists for some time.

A novel, streamlined chromatographic production and isolation method developed by scientists at Satorius BIA Separations in Slovenia may fulfill that wish, yielding uniform, high-quality EVs at scale. The method concentrates MSC-EVs directly from conditioned media. It also removes 97% of protein impurities and 95% of double-stranded DNA-related impurities, increasing their potential as therapeutics or drug delivery vessels.

Microcarrier + suspension

The method relies upon preferential exclusion chromatography, Katja Vrabec, head of product application area (EVs) at Sartorius, notes in a recent paper. In it, Vrabec and colleagues explain the method “uses monolithic hydroxyl columns to purify and concentrate the MSC-EVs,” and biochromatography analytics to track EV-specific surface antigens.

First, the team expanded the MSCs in growth media, and then produced the EVs in a lean media formulation to limit production of protein and particle contaminants. That part is standard.

Here’s what’s different: The scientists used a microcarrier-based system rather than flask-based 2D cultivation to scale the MSC cultures and increase the ratio of EVs to contaminants in conditioned media. They also used a suspension culture to enhance cell growth surface-to-volume ratios, and thereby increase EV yield. Then, they used a monolithic hydroxyl column to capture and purify the EVs directly from harvest.

Increasing cell density and the cell-to-impurity ratio lowers buffer consumption downstream and lays the groundwork for biomanufacturers to transition to a scalable bioreactor system.

Because the main impurities in EV harvests don’t interact with the chromatographic column in high-salt-binding conditions, the team recommends choosing a low-salt buffer for elution to reduce the need for buffer exchange before the polishing step. The optimal binding condition, they report, is “sodium citrate of 0.75M at pH 7.0.”

This research highlights the need to consider upstream and downstream processing as a cohesive system, to design a simple, scalable, holistic process, and to apply reliable analytics. This all is particularly challenging, the team admits, given “the heterogeneous nature of EVs and the presence of similarly-sized components in biological samples.”

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May 13, 2026

Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review

Background: Major depressive disorder (MDD) affects approximately 1 in 6 adults during their lifetime, yet antidepressant selection relies predominantly on trial-and-error, with response rates of only 42% to 53%. While machine learning (ML) models have shown promise in predicting treatment outcomes, most focus on single treatments rather than comparative selection across therapeutic alternatives, limiting their clinical utility for the medication choice decisions that clinicians face in practice. Objective: This systematic review evaluates ML approaches that examine 2 or more pharmacological interventions for predicting treatment outcomes in MDD, with a focus on their capacity to facilitate comparative treatment selection between medications or medication classes for individual patients. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched PubMed, Scopus, and Web of Science for studies published from 2015 to 2025. We included studies involving adults with MDD that used ML models to predict treatment outcomes across 2 or more pharmacological treatments and reported medication-specific prediction outcomes. Risk of bias was assessed using PROBAST-AI (Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence). We conducted a narrative synthesis organized by modeling strategies, data integration approaches, validation methodologies, and performance patterns. Results: From 5370 initial records, 19 studies met the inclusion criteria, with dataset sample sizes ranging from 49 to 77,226 participants. Studies employed 3 distinct modeling strategies: drug-specific supervised models trained independently for each medication, subtype- or trajectory-based approaches using clustering methods to identify differential response patterns, and a unified differential prediction framework generating calibrated cross-treatment predictions. Performance varied substantially, with area under the curve values ranging from 0.59 to 0.95 and classification accuracies between 62% and 95.4%, though high performance was concentrated in studies with small samples, high-dimensional neurobiological features, and internal-only validation. Only 7 studies conducted external validation, which generally yielded more conservative performance estimates. Feature informativeness was more consistently associated with performance variation than algorithm complexity. Most studies did not formally distinguish between prognostic features predicting general outcomes and predictive features identifying differential medication responses, and none applied formal explainability techniques. Conclusions: ML for comparative antidepressant selection remains in an early stage of development. Only 1 study implemented a unified framework directly supporting patient-level treatment ranking. Key barriers to clinical translation include insufficient distinction between prognostic and predictive markers, limited cross-trial validation, near-absent calibration reporting, and absent explainability. Future research should prioritize unified comparative frameworks with calibrated predictions, rigorous external validation on diverse cohorts, explicit modeling of heterogeneous treatment effects, and integration of explainability into model development.
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May 13, 2026

Regional Virtual Acute Care Helpline in Singapore at a National University Health System Virtual Care Centre: Retrospective Study

Background: Emergency department (ED) overcrowding and delayed access to care are ongoing challenges in Singapore. The COVID-19 pandemic further underscored the need for scalable virtual care models that go beyond traditional hospital settings, allowing patients to access acute specialist care quickly and efficiently. Objective: This study describes the design, implementation, and early outcomes of the National University Health System (NUHS) Virtual Care Centre (VCC), a clinician-led helpline aimed at reducing unnecessary ED visits and supporting community-based acute care. Methods: In 2020, the NUHS launched the VCC, a helpline at Alexandra Hospital, as a prehospital triage model. The helpline functions as a nurse-led telephone triage with real-time escalation to doctors for urgent medical issues. It ensures the continuity of care for patients recently discharged and diverts nonemergency cases from the ED. A retrospective analysis of call data from 2020 to 2024 was conducted to evaluate utilization patterns, clinical outcomes, and safety. Results: Over 4 years, the VCC managed 4857 calls, of which 59.3% (n=2879) were clinical in nature. Nearly two-thirds (1834/2879, 63.7%) were resolved remotely, preventing in-person ED visits. Only 13.8% (397/2879) required redirection to an ED, and 3.3% (95/2879) were directly admitted to an acute hospital or hospital at home service. Within 72 hours of call resolution, 69.1% (1990/2879) of the callers avoided an ED visit. Undertriage was 4.9% (110/2232) at 72 hours post call resolution, with no high dependency or intensive care unit admissions during this period. Mortality rates were low (1.0% at 14 days; 2.3% at 30 days). Conclusions: The NUHS VCC provides a feasible and safe model for virtual acute care triage within the public health care system. It effectively diverted lower-acuity cases from the ED and ensured continuity of care, offering a scalable approach aligned with national efforts to extend health care beyond hospital walls.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/bbbab2870bb94f230cfd987cf69b3559" />

May 13, 2026

iCARE Self-Guided Digital Intervention for Postpartum Depression in Danish Mothers: Formative Research Using User-Centered Design

Background: Postpartum depression (PPD) is a major public health concern. Despite advancements in treatment, many barriers to accessing care remain. There has been a growing interest in digital interventions for the prevention and treatment of PPD. However, for mothers with mild and moderate symptoms of depression, there is a limited offer of self-guided internet-based interventions developed with user input and with considerations on how to integrate the intervention into stepped care models for PPD. Objective: The aim of this study was (1) to describe the process of the design and development of iCARE, a self-guided digital psychological intervention for mothers with mild and moderate symptoms of PPD in Denmark, (2) present the program’s theory illustrated by a logic model, and (3) explore its initial usability and prospective acceptability. Methods: Applying user-centered design methods, the intervention development followed six steps: (1) a literature review to identify evidence‑based therapeutic components of self‑guided interventions for PPD, (2) interviews with women with lived experience of PPD and group discussions with mental health experts and home‑visiting providers to identify user needs, (3) iterative design and content development with stakeholder feedback in collaboration with the Department of Digital Psychiatry, (4) prototype testing using think‑aloud usability sessions and interviews with 5 mothers, (5) a group cognitive walkthrough with mental health experts, and (6) final refinement and implementation of the iCARE program with developers and designers. Results: Initial interviews with mothers and maternal health care providers emphasized the importance of a digital intervention offering timely psychoeducation, coping strategies, and pathways to in-person care while addressing the diversity of expressions of PPD symptoms. Stakeholders recommended a flexible program, multimodal content, and integration into maternal care systems with community health nurses supporting engagement and participation. The prototype was designed to be user-centered, engaging, and with multiple interactive features. It included components on psychoeducation, cognitive exercises grounded in cognitive behavioral therapy, acceptance and commitment principles, and mood-monitoring. The prototype was designed to be user-centered and engaging, with interactive features and components on psychoeducation, cognitive exercises grounded in cognitive behavioral and acceptance and commitment principles, and mood-monitoring. Prototype testing indicated high prospective acceptability and led to refinements across 6 themes: appropriateness of content; motivation and engagement; inclusivity and gender representation; clarity of instructions and data use; understanding of therapeutic method; and usability, layout, and navigation. Conclusions: iCARE is a self-guided internet-based psychological intervention for mothers with mild and moderate symptoms of PPD in Denmark. It was developed with user input by using qualitative methods, user-centered design, and psychological theory. Further research is needed to evaluate the feasibility and effectiveness of the program in a randomized controlled trial and its integration into maternal health care models such as universal PPD screening and home-visiting.

May 13, 2026

Early Detection of Alzheimer’s Disease and Related Dementias From Spontaneous Speech Using Foundation Speech and Language Models: Comparative Evaluation

Background: Alzheimer’s disease and related dementias (ADRD) are progressive neurodegenerative conditions where early detection is critical for timely intervention and care planning. However, current diagnostic methods are often inaccessible, costly, and delayed, especially for underserved populations. There is a growing need for scalable, noninvasive tools that can support timely diagnosis. Spontaneous speech contains rich acoustic and linguistic markers that can serve as noninvasive behavioral markers for cognitive decline. Foundation models, pretrained on large-scale audio or text data, generate high-dimensional embeddings that encode rich contextual and acoustic information. Objective: This study benchmarks open-source foundation language and speech models to evaluate their effectiveness in detecting ADRD from spontaneous speech as a potential solution for early, noninvasive, and scalable ADRD detection. Methods: In this study, we used the Pioneering Research for Early Prediction of Alzheimer’s and Related Dementias EUREKA (PREPARE) Challenge dataset, which consists of audio recordings from over 1600 participants with 3 distinct categories of cognitive decline: healthy control (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). We further excluded samples that are non-English, nonspontaneous speech, or of poor quality. Our final samples included 703 (59.13%) HC, 81 (6.81%) MCI, and 405 (34.06%) AD cases. We systematically benchmarked 18 open-source foundation speech and language models to classify cognitive status into 3 categories (HC, MCI, or AD). Post hoc interpretability analysis was performed for the best-performing model using Shapley additive explanations linking high-dimensional embeddings with explainable acoustic and linguistic markers. Results: Whisper-medium model achieved the highest performance among speech models at 0.731 accuracy and 0.802 area under the curve, while Bidirectional Encoder Representations from Transformers with pause annotation achieved the top accuracy of 0.662 and 0.744 area under the curve among language models. Overall, ADRD detection based on state-of-the-art automatic speech recognition model-generated audio-embeddings outperformed other models, and the inclusion of nonsemantic information, such as pause patterns, consistently improved the classification performance of text-embedding–based models. Conclusions: Our work presents a comprehensive comparative evaluation of state-of-the-art speech and language models for AD and MCI detection on a large, clinically relevant dataset. Embeddings derived from acoustic models, which capture both semantic and acoustic information, show promising performance and highlight the potential for developing a more scalable, noninvasive, and cost-effective early detection tool for ADRD.

May 13, 2026

Roles of NRXN1 in neuropsychiatric disorders: from genetic lesion to molecular mechanism

Numerous neuropsychiatric disorders frequently exhibit overlapping genetic risk factors, implying the molecular basis for their comorbidity. Nevertheless, the pathogenesis of these disorders remains elusive, particularly regarding how genetic variations impair the physiological function of risk genes and contribute to disease phenotypes. Neurexin 1 protein, encoded by NRXN1 gene, belongs to the neurexin family of presynaptic adhesion molecules. And neurexin 1 is involved in synaptogenesis and the maintenance of synaptic action. Genetic variations of NRXN1 have been demonstrated to be associated with a spectrum of neuropsychiatric disorders. Herein, this review focuses on the most recent and relevant literature concerning the genetic and molecular mechanisms through which NRXN1 variants contribute to the pathogenesis of neuropsychiatric disorders, particularly schizophrenia and autism spectrum disorder. Among them, we propose the isoform-dependent excitation-inhibition imbalance hypothesis of NRXN1 in autism spectrum disorder. And this hypothesis may account for both the elevated and decreased excitation-inhibition ratios observed in diverse individuals with autism spectrum disorder. Moreover, both schizophrenia and autism spectrum disorder involve deletions and alternative splicing of NRXN1, offering molecular evidence for their comorbidity. Then, we analyzed and summarized the current research status of NRXN1 in other neuropsychiatric disorders, including attention-deficit hyperactivity disorder, insomnia, epilepsy, suicide, and depression. Additionally, available limited researches on NRXN1-targeted therapeutic strategies and associated pharmacological studies are also incorporated. Finally, we discussed existing challenges in NRXN1 research within the context of neuropsychiatric disorders and proposed potential avenues to overcome these obstacles.