Sumin Bian

Metal–organic frameworks (MOFs) are emerging as versatile nanomaterials for biomedical applications owing to their high surface area, tunable porosity, and structural flexibility. However, concerns regarding their biocompatibility and hepatotoxicity, particularly as applications expand from drug delivery toward emerging bioengineering systems, necessitate physiologically relevant platforms for nanotoxicity evaluation. Here, we establish a liver organoid platform for controlled assessment of MOF biocompatibility. Liver organoids derived from hepatic progenitor cells exhibited hepatic characteristics, including hepatocyte marker expression and robust metabolic activity. Using this platform, we systematically evaluated zeolitic imidazolate framework-8 (ZIF-8), one of the most extensively studied MOFs for drug delivery, under defined exposure conditions using a Matrigel-embedded approach across multiple concentration gradients. Across tested concentrations, ZIF-8 caused no detectable changes in organoid morphology and viability. Histological architecture and hepatic function at representative concentrations were confirmed by hematoxylin and eosin staining, and alanine aminotransferase, aspartate aminotransferase, urea, and albumin analyses. Collectively, these findings demonstrate no detectable toxicity at concentrations up to 300 μg/mL under the investigated conditions over 96 h in a physiologically relevant organoid model. Importantly, this proof-of-concept study demonstrates the feasibility of using liver organoids as physiologically relevant platforms for ZIF-8 biocompatibility assessment, supporting multimodal functional readouts and controlled exposure conditions. The proposed organoid-based framework may be extended to systematic evaluation of additional MOF systems with diverse physicochemical properties, compositions, and biomedical applications, while facilitating future exploration of MOFs as programmable nano–bio interfaces for organoid and organoid-on-chips bioengineering. The platform may also support future integration with biosensing, artificial intelligence, and closed-loop bioengineering applications.

Introduction: Menopausal women commonly experience psychological symptoms. These symptoms reduce their quality of life. Pimpinella anisum (anise) is an Unani remedy traditionally used for such problems. This study aimed to test the effects of anise on psychological and menopausal symptoms, along with appraising machine learning models in classifying treatment response between the anise and control groups. Methods: A total of 60 menopausal women received either 4 g of anise per day or a matched placebo, administered in two divided doses over an eight-week period. Primary outcomes included the Depression, Anxiety, and Stress Scale 21 (DASS-21) in menopausal women. Secondary outcomes included overall Modified Kuppermen Index (MKI), Vaginal Health Index (VHI), and treatment satisfaction (MS-TSQ). Machine learning classifiers, including Gradient Boosting (GB), AdaBoost (AB), K-Nearest Neighbours (KNN), Naive Bayes (NB), and Random Forest (RF), were utilised. Safety was monitored weekly through interviews. Hepatic and renal function were evaluated at baseline and after 12 weeks. Results: Baseline variables were similar between the two groups. Anise significantly reduced the DASS-21 scores compared to placebo at 8 weeks (p < 0.0001). At 8 weeks, participants receiving anise demonstrated significant improvements across multiple measures. DASS‑21 scores declined markedly compared with placebo (p < 0.0001), with more than 80% reporting no symptoms of depression, anxiety, or stress. MKI and VHI scores also improved significantly in the anise group (p < 0.0001), while the control group showed no notable change. Satisfaction ratings on the MS‑TSQ were high among anise recipients but low in the placebo arm. No adverse effects were observed. In addition, the KNN model achieved outstanding performance, correctly classifying group membership with 99.20% accuracy. Discussion: Anise demonstrated significant benefits in reducing psychological and menopausal symptoms, with no adverse effects reported, supporting its potential as a safe non‑hormonal therapy. The strong performance of the KNN model additionally exemplifies how machine learning can improve menopausal research by precisely distinguishing treatment responses. Upcoming studies with larger and more varied populations will be important to endorse these findings and to discover long‑term outcomes. Conclusion: This research indicates that anise is a safe and effective alternative for relieving psychological symptoms in menopausal women. The KNN model reliably classified treatment outcomes, signifying that the integration of anise treatment with AI‑based assessment methods could enrich research on menopause care.

The complexity and intricacy of the brain, which is composed of billions of neurons, pose significant challenges to its study. Understanding neural connections and communication at the single-cell level is crucial for unraveling the brain’s functions. This study presents a novel strategy that utilizes magnetic nanoparticles (MNPs) and magnetic fields to manipulate neurons, thereby creating customized small-scale neural circuits for studying neural connections. To establish the feasibility of this approach, the effects of MNPs on neurons were initially investigated, demonstrating their low toxicity. Subsequently, a micromagnet array (MMA) chip was employed to manipulate the neurons, facilitating their precise arrangement on the electrodes. Over several days, the neurons extended their axons and established connections with neighboring cells, forming small-scale circular neural circuits. These artificially engineered circuits offer a simplified and controlled environment for studying neural networks in contrast to naturally occurring biological networks. Furthermore, electrophysiological recordings were conducted to investigate the connections between the manipulated neurons. This study introduces a customized small-scale neural circuit platform with electrode-specific recording and stimulating capabilities, enabling the study of neuron-to-neuron interactions at the single-cell level. By leveraging MNPs and an MMA chip, this research offers a powerful tool for studying neural connections and advancing our understanding of the brain’s intricate workings.

Three-dimensional (3D) neural cultures are increasingly recognized for their complexity and resemblance to in vivo neural microenvironments. In this paper, we present a novel 3D cell culturing and noninvasive characterization technique of neural spheroids. Based on embedded platinum wires, the cultured cells are lollipop-shaped spheroids where axons are extended and integrated around the embedded wires. Electrical microstimulation enhanced the connectivity between spheroids and demonstrated signal propagation among them. The resultant axonal elongation facilitated the formation of robust neural tracts interconnecting the neural spheroids. Variation of cells’ density allows to adjust the spheroid’s diameter, identifying 1 million cells as good number of cells for robust spheroid formation. Recordings of spheroid activities reveal higher-quality neural signal measurement from interior cells compared to those obtained from exterior cells. Viability assays confirmed the efficacy of the proposed culturing technique for sustained growth of neural spheroids over a 1-month period. The proposed spheroid culturing technique holds potential applications in various fields, such as development of brain organoids, which enables real-time interconnection characterization and sensing of environment conditions.

High sensitivity and high quality‐factor are crucial for achieving outstanding sensing performance in photonic biosensors. However, strong optical field confinement and high light–biomolecule interactions on photonic surfaces are usually contradictory and challenging to satisfy simultaneously. Here, a distinctive configuration for addressing this issue is reported: embedding a nanophotonic metasurface inside a micro vertical cavity as a meta‐channel (metacavity) biosensor. The analyte solution serves as the cavity medium, thereby maximizing the light–analyte interaction. Simulation validation is conducted to optimize the metacavity with high structural robustness and remarkable optical and sensing properties. Large‐scale low‐cost metacavity biosensors are realized by combining anodic aluminum oxide template technique and wafer bonding. Experimentally, the metacavity biosensor demonstrates a notable quality‐factor (maximum 4140) and high bulk refractometric sensitivity (450 nm RIU⁻¹), resulting in an unprecedented figure‐of‐merit (1670 RIU⁻¹). Moreover, the metacavity biosensor achieves high surface sensitivity, together with a detection‐limit of 119 viral copies mL⁻¹ for label‐free severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pseudovirus sensing, revealing remarkable performance in both bulk and surface sensing.