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A challenge in developing informative neuroimaging biomarkers for early diagnosis of Alzheimer's disease is the need to identify biomarkers that are evident before the onset of clinical symptoms, and which have sufficient sensitivity and specificity on an individual patient basis. Recent literature suggests that spatial patterns of brain atrophy discriminate amongst Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN) older adults with high accuracy on an individual basis, thereby offering promise that subtle brain changes can be detected during prodromal Alzheimer's disease stages. Here, we investigate whether these spatial patterns of brain atrophy can be detected in CN and MCI individuals and whether they are associated with cognitive decline. Images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to construct a pattern classifier that recognizes spatial patterns of brain atrophy which best distinguish Alzheimer's disease patients from CN on an individual person basis. This classifier was subsequently applied to longitudinal magnetic resonance imaging scans of CN and MCI participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. The degree to which Alzheimer's disease-like patterns were present in CN and MCI subjects was evaluated longitudinally in relation to cognitive performance. The oldest BLSA CN individuals showed progressively increasing Alzheimer's disease-like patterns of atrophy, and individuals with these patterns had reduced cognitive performance. MCI was associated with steeper longitudinal increases of Alzheimer's disease-like patterns of atrophy, which separated them from CN (receiver operating characteristic area under the curve equal to 0.89). Our results suggest that imaging-based spatial patterns of brain atrophy of Alzheimer's disease, evaluated with sophisticated pattern analysis and recognition methods, may be useful in discriminating among CN individuals who are likely to be stable versus those who will show cognitive decline. Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms.

3D printing has attracted a lot of attention in recent years. Over the past three decades, various 3D printing technologies have been developed including photopolymerization-based, materials extrusion-based, sheet lamination-based, binder jetting-based, power bed fusion-based and direct energy deposition-based processes. 3D printing offers unparalleled flexibility and simplicity in the fabrication of highly complex 3D objects. Tactile sensors that emulate human tactile perceptions are used to translate mechanical signals such as force, pressure, strain, shear, torsion, bend, vibration, etc. into electrical signals and play a crucial role toward the realization of wearable electronics and electronic skin. To date, many types of 3D printing technologies have been applied in the manufacturing of various types of tactile sensors including piezoresistive, capacitive and piezoelectric sensors. This review attempts to summarize the current state-of-the-art 3D printing technologies and their applications in tactile sensors for wearable electronics and electronic skin. The applications are categorized into five aspects: 3D-printed molds for microstructuring substrate, electrodes and sensing element; 3D-printed flexible sensor substrate and sensor body for tactile sensors; 3D-printed sensing element; 3D-printed flexible and stretchable electrodes for tactile sensors; and fully 3D-printed tactile sensors. Latest advances in the fabrication of tactile sensors by 3D printing are reviewed and the advantages and limitations of various 3D printing technologies and printable materials are discussed. Finally, future development of 3D-printed tactile sensors is discussed.

To date, various stretchable conductors have been fabricated, but simultaneous realization of the transparency, high stretchability, electrical conductivity, self-healing capability, and sensing property through a simple, fast, cost-efficient approach is still challenging. Here, α-lipoic acid (LA), a naturally small biological molecule found in humans and animals, is used to fabricate transparent (>85%), electrical conductivity, highly stretchable (strain up to 1100%), and rehealable (mechanical healing efficiency of 86%, electrical healing efficiency of 96%) ionic conductor by solvent-free one-step polymerization. Furthermore, the ionic conductors with appealing sensitivity can be served as strain sensors to detect and distinguish various human activities. Notably, this ionic conductor can be fully recycled and reprocessed into new ionic conductors or adhesives by a direct heating process, which offers a promising prospect in great reduction of electronic wastes that have brought acute environmental pollution. In consideration of the extremely facile preparation process, biological available materials, satisfactory functionalities, and full recyclability, the emergence of LA-based ionic conductors is believed to open up a new avenue for developing sustainable and wearable electronic devices in the future.features. [1][2][3][4] These conductors provide huge opportunities for promising applications of artificial muscles, skin sensors, biological actuators, stretchable displays, electronic eye cameras, intelligent robot arms, and others. [5][6][7][8][9][10][11] It was well known that the conventional electronic conductors are normally prepared from waferbased materials, which possess several drawbacks including fragility, rigidity, and low conductivity under large-scale deformations. [12] They cannot satisfy the demands of high stretchability, flexibility, durability, and stability. To achieve these criteria, strain engineering and nanocomposites are the two most adoptable strategies to fabricate stretchable conductors. In the first strategy, nonstretchable inorganic materials, such as silicon and metals, are geometrically patterned into buckled, serpentine structures on elastomeric substrates that renders the conductors excellent sensitivity and larger workable range of strain. [10,13,14] Nonetheless, most resultant conductors still show narrow range of strain from 20% to 70%, [15] and presents out-of-plane patterns that is difficult to encapsulate. Meanwhile, this strategy usually involves expensive and very complicated techniques, which greatly limits the further development of these conductors. Integrating conductive fillers into polymer matrix to produce nanocomposites used as stretchable conductors is the second strategy. [16] So far, various nanomaterials, such as carbon nanotubes, [17][18][19][20] carbon black, [21] graphene-based materials, [22,23] metal nanowires, and nanoparticles, [24,25] have been used as conductive fillers because of their unique mechanical and electrical properties. Although the robu...

Metallic surface nanopatterns are prepared by a template-confi ned dewetting process with multiple structural controllabilities. The morphology of the building blocks is homogeneous throughout the surface nanopatterns, as the dewetting process proceeds separately in each bowl. The features of the building units in the surface patterns are highly dependent on the annealing temperature. Importantly, the size and composition of the nanoparticles in the surface nanopatterns can be pre-calculated and designed by manipulating the thickness of the evaporated metallic fi lms. The heating temperature and composition of the building units infl uence the surface-enhanced Raman scattering (SERS) and plasmonic properties, thus tuning the localized surface plasmon resonance peaks over a broad range (from visible to near infrared). The introduction of silver in the gold surface nanopatterns enhances the SERS performance dramatically. This work not only provides a powerful route to fabricate surface nanopatterns, but also supplies a platform to study the mechanism of the complicated dewetting processes of metals.