Background: Sleep staging is essential for diagnosing sleep disorders and managing sleep health. Traditional methods require time-consuming manual scoring. Recent PPG-based deep learning models perform well on local datasets but struggle with external generalization due to data drift. Methods: This study evaluates multi-source domain training for improving out-of-distribution generalization in four-class sleep staging (wake, light, deep, REM) from raw PPG time-series. The trained deep learning model is denoted SleepPPG-Net2. Additionally, we examined the impact of demographic factors, ethnicity, and obstructive sleep apnea on performance. SleepPPG-Net2 was benchmarked against two state-of-the-art models. Results: SleepPPG-Net2 outperformed benchmark models, improving generalization performance (Cohen’s kappa) by up to 21%. Performance disparities were observed in relation to age, sex, and obstructive sleep apnea severity. Conclusion: SleepPPG-Net2 enhances PPG-based sleep staging and provides insights into demographic and clinical influences on model performance.

Synthetic and native switchable genetic expression systems are extensively used in basic research and a wide range of applications in biomedicine and biotechnology. Leakiness, which represents promoter activity prior to the presence of an appropriate instigator, could have negative consequences ranging from ineffective biological sensor systems to toxic side effects in medical applications. Here, we construct an auxiliary RNA-based augmentation system rooted in mutual inhibition that can be added to existing systems without modifying the involved transcription factors, promoters, or output genes. This system utilizes a constitutively expressed small hairpin RNA to reduce the expression level of a target gene, which is governed by a responsive promoter. Additionally, a second promoter, identical to the output promoter, drives the expression of a binding-site sponge specifically designed to sequester the shRNA, thereby mitigating its inhibitory effect. Following mathematical modeling, we examine the experimental effectiveness of the system in reducing the leakiness and enhancing the fold change of the doxycycline-inducible Tet-On system tested in a two- and three-dimensional spheroid setting, a GAL4-inducible system, and a native cancer-specific promoter–pH2A1.

Osteosarcoma (OS) remains a challenging malignancy, particularly for metastatic cases, due to its heterogeneous genetic landscape characterized by lack of actionable oncogenic drivers and thus lack of personalized therapy. Though personalization of therapy based on functional assays using patient derived cells is emerging as a promising approach, it was not explored as a workflow for identifying patient specific drug cocktails. In this study we investigated a sequential drug screening approach for single and pair drugs which can be rationally used to identify 4 drug cocktails. We started with a systematic screening of 17 drugs from multiple classes across four osteosarcoma cell lines (U2OS, MG-63, SaOS-2, and K7M2), and observed differential drug responses among the cell lines. Interestingly, leading large language models (LLMs) failed to predict cell specific efficacy in OS cells while they were successful in KRAS mutation driven cells. Both SaOS-2 and K7M2 showed sensitivity to kinase inhibitors, particularly ponatinib, and we further explored their combinatorial therapeutic potential. We identified promising non-chemotherapeutic drug pairs, including trametinib-ponatinib and rapamycin-ganetespib and demonstrated potent synergistic effects. To mitigate dose limiting toxicities, drug pairs were formulated in polydopamine-stabilized nanoparticles and K7M2 murine models in vivo studies revealed that they preferably accumulated in tumors and were highly superior to the chemotherapeutic standard of care. Surprisingly, alternating administration of nanoparticle drug pairs was superior to concomitant regimen in both efficacy, survival and toxicity profiles. These findings strengthen a functional approach for combinatorial personalized treatment, potentially overcoming the limitations of current therapeutic strategies.

Abstract. The pulmonary capillary network (PCN) is a highly complex and dynamic structure essential for gas exchange and systemic homeostasis. Beyond its primary role in oxygen transport, the PCN also mediates a range of transport processes relevant to both health and disease. Scientific interest in the PCN has evolved considerably over time, from early efforts characterizing its anatomy and architecture to modern investigations of its in situ microcirculatory dynamics using advanced imaging technologies. More recently, research has shifted toward developing biomimetic models - such as in vitro microfluidic systems and tissue-engineered constructs—that replicate the PCN's intricate structure and cellular function. These platforms now play a crucial role in disease modeling, drug delivery research, and the study of pulmonary microvascular biology under both physiological and pathological conditions. In this review, we first outline the anatomical and physiological characteristics of the PCN and discuss its roles in homeostasis and disease. We then examine classical biomechanical models of blood flow in the PCN, followed by an overview of recent advances in in vitro modeling approaches, with an emphasis on microfluidic platforms. Finally, we highlight emerging next-generation models designed to better replicate PCN complexity and discuss how they can accelerate the development of new therapeutic strategies.

Recent advances in single-molecule technologies are transforming the field of protein analysis. Solid-state nanopores provide an effective method to linearize and thread full-length proteins in a single file. However, slowing their rapid translocation remains a challenge for accurate, time-resolved ion-current-based fingerprinting. In this work, we present a click-chemistry-based strategy for covalently attaching short oligonucleotides to cysteine residues on denatured proteins across a broad range of molecular weights. The negatively charged oligonucleotides increase the capture rate by a factor of ten compared with native proteins and induce a distinct ‘stick–slip’ motion that slows protein passage through the nanopore by more than 20-fold. These oligonucleotide tags also produce characteristic, time-resolved ion current pulses that serve as unique protein-specific signatures. To uncover the physical mechanism responsible for the protein translocation dynamics, we model our system using all-atom molecular dynamics and finite element simulations. By leveraging a supervised machine learning classifier, we demonstrate that a small number of translocation events is sufficient to identify individual proteins, achieving near-perfect classification accuracy. To demonstrate the robustness of the method, we successfully distinguish between VEGF-A isoforms (VEGF-165 and VEGF-121), which are relevant to cancer diagnostics, within a mixed protein sample. Our nanopore-based fingerprinting technique eliminates the need for affinity reagents, such as protein-specific antibodies, or motor proteins, offering a rapid, direct and cost-effective approach for single-molecule protein identification and classification.

Inflammation drives various diseases, including cardiovascular, neurodegenerative, and oncological disorders, by altering immune cell dynamics in hematopoietic niches. The bone marrow is the primary site for hematopoietic stem and progenitor cell activity. Here, we present a novel, noninvasive approach using multispectral optoacoustic tomography (MSOT) to track oxygenation dynamics in the murine calvarial bone marrow during acute systemic inflammation induced by lipopolysaccharide (LPS). Our MSOT system provided real-time, label-free imaging of hemoglobin oxygen saturation (sO₂), revealing significant reductions in sO₂ levels in lipopolysaccharide-treated mice, indicative of increased oxygen consumption. Co-registration with microCT enabled precise vascular mapping. Hypoxia was confirmed by ex vivo Pimonidazole staining and optical imaging and was associated with elevated neutrophil counts and enhanced hematopoietic activation. These findings demonstrate MSOT’s potential for noninvasive imaging of marrow oxygenation, offering insights into inflammation-driven hematopoietic activation and supporting the development of therapies targeting oxygen-sensitive pathways.

Biomedical hydrogels often use a photopolymerization strategy to cross-link the polymer network. There are only a few cyto-compatible photoinitiators (PIs) that are commonly used for cross-linking biomedical hydrogels, including Irgacure 2959, lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP), and Eosin Y. Herein, we tested these PIs to optimize the cross-linking efficiency while minimizing cell death. Testing was performed on three types of hydrogels, including a synthetic material (poly(ethylene glycol)-diacrylate, PEG-DA), a semisynthetic material, PEG-fibrinogen (PF), and a modified biological material, methacrylated fibrinogen (FibMA). The results showed that PI concentration and illumination intensity had a significant impact on cross-linking efficiency, as measured by the shear storage modulus, with each material demonstrating different responses to the photopolymerization parameters. Optimal photo-cross-linking conditions were not the same for the modified protein hydrogels as compared to synthetic and semisynthetic materials. These findings may have consequential implications when applying photopolymerization to cross-link various types of cell-compatible hydrogels for biomedical applications.

Magnetic resonance imaging (MRI) is vital for diagnosing abdominal and neurological conditions, yet conventional sequential slice acquisitions favor in-plane over through-plane resolution to minimize scan time and motion artifacts, leading to anisotropic data and reduced volumetric accuracy. Existing super-resolution (SR) techniques reconstruct isotropic images from anisotropic scans but often rely on simulated downsampling or limited 3D isotropic data, emphasizing through-plane interpolation rather than preserving full anatomy. We introduce SIMPLE, a Simultaneous Multi-Plane Self-Supervised Learning approach that directly restores isotropic MRI from real-world multi-plane acquisitions via adversarial training. Testing on OASIS-1 brain (n = 416) and Crohn’s disease abdominal (n = 115) MRI datasets demonstrates SIMPLE’s superiority in image fidelity and anatomical detail over state-of-the-art methods. Notably, SIMPLE achieved lower averaged Kernel Inception Distance (KID) scores than SMORE4 in both brain MRI (28.709 vs. 29.295) and abdominal MRI (17.435 vs. 20.724), retained higher-frequency details as confirmed by Fourier analysis, and was rated 1.5 points higher in the axial plane by radiologists. By improving volumetric analysis and 3D reconstructions, SIMPLE shows promise for enhancing diagnostic accuracy in pathologies demanding precise structural visualization. Our source code is publicly available at https://github.com/TechnionComputationalMRILab/SIMPLE.

Obstructive pulmonary diseases, including asthma and chronic obstructive pulmonary disease (COPD), are widespread and represent a major global health burden. Despite their impact, effective therapeutic delivery to the small airways using inhaled aerosols remains suboptimal. In this study, we present a novel in vitro airway-on-chip platform that mimics both normal and constricted small bronchial geometries to quantify the deposition charged and neutral polystyrene latex (PSL) aerosol particles ranging from 0.2 to 2 µm. Analytical and numerical solutions were derived from dimensionless scaling laws to further support the experiments and predict deposition location. Our experiments showcase how electrostatic forces significantly alter deposition patterns across particle sizes in these small airways. For submicron particles, we observe the enhancement of proximal airway deposition due to the coupling of electrostatic-diffusive screening effects. For larger particles, which typically deposit only in the direction of gravity, the inclusion of electrostatic forces significantly extends their deposition footprint, enabling deposition even in orientations where gravitational sedimentation is not feasible. Constricted regions consistently exhibit lower deposition across all cases, the presence of electrostatic forces enhanced overall deposition, offering a potential strategy for targeting bronchioles. Together, these findings suggest that electrostatic attraction may be strategically leveraged to enhance aerosol targeting in the small airways, providing new opportunities for optimizing inhaled drug delivery in obstructive lung diseases.

Air quality monitoring currently relies mostly on a combination of epidemiological data and classic experimental data. Our objective was to design an alternative approach for assessing air pollutant risk potential using a specialized platform capable of detecting long-term and indirect effects of exposure via the inhaled route. We used a Bronchial Airways-On-Chip (BOC) offering some of the physiological complexity of the human lung based on our previously developed device coupled with in-vitro differentiated bronchial epithelium derived from induced Pluripotent Stem Cells (iPSCs). This setup is capable of replicating bronchial epithelia exposure to irritants at air-liquid interface, in controlled and reproducible conditions. It comprises the first proof-of-concept design combining a BOC with iPSCs-derived bronchial epithelium as an an alternative approach towards potential risk assessmnet of inhaled pollutants. As a representative pollutant we use Benzene, a Volatile Organic Compound (VOC). At low concentrations and short-term exposure it is not considered acutely harmful, but long-term exposure can result in mutagenic and carcinogenic effects. As air pollutant toxicity is known to be mediated by the respiratory epithelial lining and secretion of cytokines, we demonstrate our system to be sufficiently sensitive to capture increased cytokine secretion corresponding to increasing concentrations of Benzene. Of utmost relevance is our finding that an accumulative effect could be detected, only caused by prolonged exposure at low concentrations of Benzene, previously shown to be non-toxic in classic short-term in-vitro studies. Finally, the accumulative effect could be reversed using a commonly-used asthma medication (Montelukast), further supporting the relevance of the setup.