Blood is a force-rich system. Beyond biochemistry, blood cells continually sense shear, stretch, and substrate rigidity, encode these inputs into biochemical and structural state changes, and adjust future behavior. We term this mechanical intelligence: an operational framework encompassing mechanosense, learning and memory, decision-making, and adaptive evolution. We synthesize evidence across red blood cells (RBCs), leukocytes, and platelets showing how mechanosensitive receptors (Piezo-type mechanosensitive ion channel component 1, glycoprotein Ib-IX-V complex, and integrins), cytoskeletal feedback, and nuclear effectors (Yes-associated protein/transcriptional coactivator with PDZ-binding motif and nuclear factor κB) couple mechanical cues to cellular states and functions (oxygen delivery, immune surveillance, and hemostasis). Repeated mechanical exposures produce mechanical memory (e.g., RBC fatigue and shape memory, shear-primed platelet hyperreactivity, and stiffness-modulated leukocyte transmigration) that improves performance in physiological contexts but contributes to pathology when dysregulated (vaso-occlusion, thrombosis, and inflammaging). We outline mechanodiagnostics (single-cell deformability cytometry, atomic force microscopy, and microfluidic thrombus profiling) and mechanotherapy (ion channel modulators, engineering cells, and mechanocompatible devices) and propose reporting standards, readiness levels, and validation pathways.
| • | Scope: Not cognition; an engineering-style control loop in cells: (a) sensors for mechanical sensing (PIEZO1, integrins, GPIb/IX/V, and selectins), (b) encoders (Ca2+ pulses, Rho GTPase signaling pathway, and SFK signaling pathways), (c) learning and memory (shape recovery, antigen recognition, and repairing vascular), (d) decision (adhere versus roll, spread versus rest, and contract versus stabilize), and (e) adaptation evolution (cytoskeletal connectivity and chromatin marks). |
| • | Testable predictions: Prior mechanical exposures shift thresholds (e.g., activation, adhesion, and transmigration) in quantifiable ways. |
| • | Falsifiability: If prior exposures do not measurably change thresholds or policies under controlled force histories, the “memory” claim is rejected. |
| • | Shear profile characteristics, including steady, pulsatile, and oscillatory patterns, along with peak and root mean square values; exposure duration and duty cycle. |
| • | Cell source information, including donor age, sex, and comorbidities. |
| • | Device calibration parameters, such as cantilever spring constants, microchannel dimensions, and flow wverification procedures. |
| • | Measured end points, including deformability indices, stiffness, adhesion lifetime, contractile force, and calcium ion (Ca2+) dynamics. |
| • | Interventions involving pharmacologic inhibitors (e.g., PIEZO1 and ROCK blockers), as well as cell-scale and device-scale engineering modifications. |
| • | TRL 2 and 3: Ex vivo mechanophenotyping correlates with disease (e.g., RBC deformability in sickle cell disease and shear-primed platelet assays). |
| • | TRL 4 and 5: Prospective observational studies demonstrate prognostic value (e.g., microfluidic thrombus profiling in hypertension/aging). |
| • | TRL 6 and 7: Interventional studies showing mechanotarget modulation changes clinical end points (e.g., integrin/GPVI axis and PIEZO1 modulators). |
| • | TRL 8 and 9: Device/drug integrated into clinical workflow with demonstrated non-inferiority to standard care plus unique benefit on mechanical end points. |
| • | Resetting memory: Which mechanical memories are reversible, on what timescales, and by which interventions (Ca2+ buffering, histone deacetylase inhibitors, and cytoskeletal drugs)? |
| • | Causal stacks: Can we phenotype from sensor engagement, state variables, and policy with causal interventions (optogenetic adhesion and mechanopharmacology)? |
| • | Generalization versus overfitting: Do cells overspecialize to pathological shear/stiffness and lose adaptability (e.g., device-exposed platelets)? |
| • | Clinical end points: Which mechanical end points (e.g., thrombus contractile force and RBC deformability variance) are prognostic and actionable? |
| • | Mechano-omics: How do mechanical phenotypes align with transcriptomic/epigenomic states in vivo (paired single-cell assays in patients)? |
| • | Device ecology: What shear spectra are tolerable in ECMO/VAD without priming maladaptive memories? |
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |