Recently, the development of the Metaverse has become a frontier spotlight, which is an important demonstration of the integration innovation of advanced technologies in the Internet. Moreover, artificial intelligence (AI) and 6G communications will be widely used in our daily lives. However, the effective interactions with the representations of multimodal data among users via 6G communications is the main challenge in the Metaverse. In this work, we introduce an intelligent cross-modal graph semantic communication approach based on generative AI and 3-dimensional (3D) point clouds to improve the diversity of multimodal representations in the Metaverse. Using a graph neural network, multimodal data can be recorded by key semantic features related to the real scenarios. Then, we compress the semantic features using a graph transformer encoder at the transmitter, which can extract the semantic representations through the cross-modal attention mechanisms. Next, we leverage a graph semantic validation mechanism to guarantee the exactness of the overall data at the receiver. Furthermore, we adopt generative AI to regenerate multimodal data in virtual scenarios. Simultaneously, a novel 3D generative reconstruction network is constructed from the 3D point clouds, which can transfer the data from images to 3D models, and we infer the multimodal data into the 3D models to increase realism in virtual scenarios. Finally, the experiment results demonstrate that cross-modal graph semantic communication, assisted by generative AI, has substantial potential for enhancing user interactions in the 6G communications and Metaverse.

Procalcitonin (PCT) serves as a crucial biomarker utilized in diverse clinical contexts, including sepsis diagnosis and emergency departments. Its applications extend to identifying pathogens, assessing infection severity, guiding drug administration, and implementing theranostic strategies. However, current clinical deployed methods cannot meet the needs for accurate or real-time quantitative monitoring of PCT. This review aims to introduce these emerging PCT immunoassay technologies, focusing on analyzing their advantages in improving detection performances, such as easy operation and high precision. The fundamental principles and characteristics of state-of-the-art methods are first introduced, including chemiluminescence, immunofluorescence, latex-enhanced turbidity, enzyme-linked immunosorbent, colloidal gold immunochromatography, and radioimmunoassay. Then, improved methods using new materials and new technologies are briefly described, for instance, the combination with responsive nanomaterials, Raman spectroscopy, and digital microfluidics. Finally, the detection performance parameters of these methods and the clinical importance of PCT detection are also discussed.

Utilizing renewable lignocellulosic resources for wastewater remediation is crucial to achieving sustainable social development. However, the resulting by-products and the synthetic process characterized by complexity, high cost, and environmental pollution limit the further development of lignocellulose-based materials. Here, we developed a sustainable strategy that involved a new functional deep eutectic solvent (DES) to deconstruct industrial xylose residue into cellulose-rich residue with carboxyl groups, lignin with carboxyl and quaternary ammonium salt groups, and DES effluent rich in lignin fragments. Subsequently, these fractions equipped with customized functionality were used to produce efficient wastewater remediation materials in cost-effective and environmentally sound manners, namely, photocatalyst prepared by carboxyl-modified cellulose residue, biochar-based adsorbent originated from modified lignin, and flocculant synthesized by self-catalytic in situ copolymerization of residual DES effluent at room temperature. Under the no-waste principle, this strategy upgraded the whole components of waste lignocellulose into high-value-added wastewater remediation materials with excellent universality. These materials in coordination with each other can stepwise purify high-hazardous mineral processing wastewater into drinkable water, including the removal of 99.81% of suspended solids, almost all various heavy metal ions, and 97.09% chemical oxygen demand, respectively. This work provided promising solutions and blueprints for lignocellulosic resources to alleviate water shortages while also advancing the global goal of carbon neutrality.

The thalamus and its cortical connections play a pivotal role in pain information processing, yet the exploration of its electrophysiological responses to nociceptive stimuli has been limited. Here, in 2 experiments we recorded neural responses to nociceptive laser stimuli in the thalamic (ventral posterior lateral nucleus and medial dorsal nucleus) and cortical regions (primary somatosensory cortex [S1] and anterior cingulate cortex) within the lateral and medial pain pathways. We found remarkable similarities in laser-evoked brain responses that encoded pain intensity within thalamic and cortical regions. Contrary to the expected temporal sequence of ascending information flow, the recorded thalamic response (N1) was temporally later than its cortical counterparts, suggesting that it may not be a genuine thalamus-generated response. Importantly, we also identified a distinctive component in the thalamus, i.e., the early negativity (EN) occurring around 100 ms after the onset of nociceptive stimuli. This EN component represents an authentic nociceptive thalamic response and closely synchronizes with the directional information flow from the thalamus to the cortex. These findings underscore the importance of isolating genuine thalamic neural responses, thereby contributing to a more comprehensive understanding of the thalamic function in pain processing. Additionally, these findings hold potential clinical implications, particularly in the advancement of closed-loop neuromodulation treatments for neurological diseases targeting this vital brain region.

Intervertebral disc degeneration (IVDD) is a prevalent cause of low back pain and a leading contributor to disability. IVDD progression involves pathological shifts marked by low-grade inflammation, extracellular matrix remodeling, and metabolic disruptions characterized by heightened glycolytic pathways, mitochondrial dysfunction, and cellular senescence. Extensive posttranslational modifications of proteins within nucleus pulposus cells and chondrocytes play crucial roles in reshaping the intervertebral disc phenotype and orchestrating metabolism and inflammation in diverse contexts. This review focuses on the pivotal roles of phosphorylation, ubiquitination, acetylation, glycosylation, methylation, and lactylation in IVDD pathogenesis. It integrates the latest insights into various posttranslational modification-mediated metabolic and inflammatory signaling networks, laying the groundwork for targeted proteomics and metabolomics for IVDD treatment. The discussion also highlights unexplored territories, emphasizing the need for future research, particularly in understanding the role of lactylation in intervertebral disc health, an area currently shrouded in mystery.

In this study, we systematically investigated the interactions between Cu²⁺ and various biomolecules, including double-stranded DNA, Y-shaped DNA nanospheres, the double strand of the hybridization chain reaction (HCR), the network structure of cross-linked HCR (cHCR), and small molecules (PPi and His), using Cu²⁺ as an illustrative example. Our research demonstrated that the coordination between Cu²⁺ and these biomolecules not only is suitable for modulating luminescent material signals through complexation reactions with Cu²⁺ but also enhances signal intensities in materials based on chemical reactions by increasing spatial site resistance and local concentration. Building upon these findings, we harnessed the potential for signal amplification in self-assembled DNA nanospheres and the selective complexation modulation of calcein in conjunction with the aptamer targeting mucin 1 as a recognition probe. We applied this approach to the analysis of circulating tumor cells, with the lung cancer cell line A549 serving as a representative model. Our assay, utilizing both a fluorometer and a handheld detector, achieved impressive detection limits of ag/ml and single-cell levels for mucin 1 and A549 cells, and this approach was successfully validated using 46 clinical samples, yielding 100% specificity and 86.5% sensitivity. Consequently, our strategy has paved the way for more portable and precise disease diagnosis.

Middle infrared stimulation (MIRS) and vibrational strong coupling (VSC) have been separately applied to physically regulate biological systems but scarcely compared with each other, especially at identical vibrational frequencies, though they both involve resonant mechanism. Taking cell proliferation and migration as typical cell-level models, herein, we comparatively studied the nonthermal bioeffects of MIRS and VSC with selecting the identical frequency (53.5 THz) of the carbonyl vibration. We found that both MIRS and VSC can notably increase the proliferation rate and migration capacity of fibroblasts. Transcriptome sequencing results reflected the differential expression of genes related to the corresponding cellular pathways. This work not only sheds light on the synergistic nonthermal bioeffects from the molecular level to the cell level but also provides new evidence and insights for modifying bioreactions, further applying MIRS and VSC to the future medicine of frequencies.

To explore the complementary relationship between magnetic resonance imaging (MRI) radiomic and plasma biomarkers in the early diagnosis and conversion prediction of Alzheimer’s disease (AD), our study aims to develop an innovative multivariable prediction model that integrates those two for predicting conversion results in AD. This longitudinal multicentric cohort study included 2 independent cohorts: the Sino Longitudinal Study on Cognitive Decline (SILCODE) project and the Alzheimer Disease Neuroimaging Initiative (ADNI). We collected comprehensive assessments, MRI, plasma samples, and amyloid positron emission tomography data. A multivariable logistic regression analysis was applied to combine plasma and MRI radiomics biomarkers and generate a new composite indicator. The optimal model’s performance and generalizability were assessed across populations in 2 cross-racial cohorts. A total of 897 subjects were included, including 635 from the SILCODE cohort (mean [SD] age, 64.93 [6.78] years; 343 [63%] female) and 262 from the ADNI cohort (mean [SD] age, 73.96 [7.06] years; 140 [53%] female). The area under the receiver operating characteristic curve of the optimal model was 0.9414 and 0.8979 in the training and validation dataset, respectively. A calibration analysis displayed excellent consistency between the prognosis and actual observation. The findings of the present study provide a valuable diagnostic tool for identifying at-risk individuals for AD and highlight the pivotal role of the radiomic biomarker. Importantly, built upon data-driven analyses commonly seen in previous radiomics studies, our research delves into AD pathology to further elucidate the underlying reasons behind the robust predictive performance of the MRI radiomic predictor.

Proper timing of vigilance states serves fundamental brain functions. Although disturbance of sleep onset rapid eye movement (SOREM) sleep is frequently reported after orexin deficiency, their causal relationship still remains elusive. Here, we further study a specific subgroup of orexin neurons with convergent projection to the REM sleep promoting sublaterodorsal tegmental nucleus (OX^SLD neurons). Intriguingly, although OX^SLD and other projection-labeled orexin neurons exhibit similar activity dynamics during REM sleep, only the activation level of OX^SLD neurons exhibits a significant positive correlation with the post-inter-REM sleep interval duration, revealing an essential role for the orexin-sublaterodorsal tegmental nucleus (SLD) neural pathway in relieving REM sleep pressure. Monosynaptic tracing reveals that multiple inputs may help shape this REM sleep-related dynamics of OX^SLD neurons. Genetic ablation further shows that the homeostatic architecture of sleep/wakefulness cycles, especially avoidance of SOREM sleep-like transition, is dependent on this activity. A positive correlation between the SOREM sleep occurrence probability and depression states of narcoleptic patients further demonstrates the possible significance of the orexin-SLD pathway on REM sleep homeostasis.

Porous substrates act as open “interfacial reactors” during the synthesis of polyamide composite membranes via interfacial polymerization. However, achieving a thin and dense polyamide nanofilm with high permeance and selectivity is challenging when using a conventional substrate with uniform wettability. To overcome this limitation, we propose the use of Janus porous substrates as confined interfacial reactors to decouple the local monomer concentration from the total monomer amount during interfacial polymerization. By manipulating the location of the hydrophilic/hydrophobic interface in a Janus porous substrate, we can precisely control the monomer solution confined within the hydrophilic layer without compromising its concentration. The hydrophilic surface ensures the uniform distribution of monomers, preventing the formation of defects. By employing Janus substrates fabricated through single-sided deposition of polydopamine/polyethyleneimine, we significantly reduce the thickness of the polyamide nanofilms from 88.4 to 3.8 nm by decreasing the thickness of the hydrophilic layer. This reduction leads to a remarkable enhancement in water permeance from 7.2 to 52.0 l/m²·h·bar while still maintaining ~96% Na₂SO₄ rejection. The overall performance of this membrane surpasses that of most reported membranes, including state-of-the-art commercial products. The presented strategy is both simple and effective, bringing ultrapermeable polyamide nanofilms one step closer to practical separation applications.