Neural networks excel at capturing local spatial patterns through convolutional modules, but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals. In this work, we propose a novel network named filtering module fully convolutional network (FM-FCN), which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise. First, instead of using a fully connected layer, we use an FCN to preserve the time-dimensional correlation information of physiological signals, enabling multiple cycles of signals in the network and providing a basis for signal processing. Second, we introduce the FM as a network module that adapts to eliminate unwanted interference, leveraging the structure of the filter. This approach builds a bridge between deep learning and signal processing methodologies. Finally, we evaluate the performance of FM-FCN using remote photoplethysmography. Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse (BVP) signal and heart rate (HR) accuracy. It substantially improves the quality of BVP waveform reconstruction, with a decrease of 20.23% in mean absolute error (MAE) and an increase of 79.95% in signal-to-noise ratio (SNR). Regarding HR estimation accuracy, FM-FCN achieves a decrease of 35.85% in MAE, 29.65% in error standard deviation, and 32.88% decrease in 95% limits of agreement width, meeting clinical standards for HR accuracy requirements. The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction. The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.

Recent work demonstrated stimulated Raman adiabatic passage-type transfer of energy along 3 acoustic cavities. After brief comments on the stimulated Raman adiabatic passage method, remarks on the scientific and technological relevance of this work are presented, followed by noting other recent important applications of the process.

The intestinal and intratumoral microbiota are closely associated with tumor progression and response to antitumor treatments. The antibacterial or tumor microenvironment (TME)-modulating approaches have been shown to markedly improve antitumor efficacy, strategies focused on normalizing the microbial environment are rarely reported. Here, we reported the development of an orally administered inulin-based hydrogel with colon-targeting and retention effects, containing hollow MnO₂ nanocarrier loaded with the chemotherapeutic drug Oxa (Oxa@HMI). On the one hand, beneficial bacteria in the colon specifically metabolized Oxa@HMI, resulting in the degradation of inulin and the generation of short-chain fatty acids (SCFAs). These SCFAs play a crucial role in modulating microbiota and stimulating immune responses. On the other hand, the hydrogel matrix underwent colon microbiota-specific degradation, enabling the targeted release of Oxa and production of reactive oxygen species in the acidic TME. In this study, we have established, for the first time, a microbiota-targeted drug delivery system Oxa@HMI that exhibited high efficiency in colorectal cancer targeting and colon retention. Oxa@HMI promoted chemotherapy efficiency and activated antitumor immune responses by intervening in the microbial environment within the tumor tissue, providing a crucial clinical approach for the treatment of colorectal cancer that susceptible to microbial invasion.

Muscle strength (MS) is related to our neural and muscle systems, essential for clinical diagnosis and rehabilitation evaluation. Although emerging wearable technology seems promising for MS assessment, problems still exist, including inaccuracy, spatiotemporal differences, and analyzing methods. In this study, we propose a wearable device consisting of myoelectric and strain sensors, synchronously acquiring surface electromyography and mechanical signals at the same spot during muscle activities, and then employ a deep learning model based on temporal convolutional network (TCN) + Transformer (Tcnformer), achieving accurate grading and prediction of MS. Moreover, by combining with deep clustering, named Tcnformer deep cluster (TDC), we further obtain a 25-level classification for MS assessment, refining the conventional 5 levels. Quantification and validation showcase a patient’s postoperative recovery from level 3.2 to level 3.6 in the first few days after surgery. We anticipate that this system will importantly advance precise MS assessment, potentially improving relevant clinical diagnosis and rehabilitation outcomes.

The flexible and conformal interconnects for electronic systems as a potential signal transmission device have great prospects in body-worn or wearable applications. High-efficiency wave propagation and conformal structure deformation around human body at radio communication are still confronted with huge challenges due to the lack of methods to control the wave propagation and achieve the deformable structure simultaneously. Here, inspired by the kirigami technology, a new paradigm to construct spoof plasmonic interconnects (SPIs) that support radiofrequency (RF) surface plasmonic transmission is proposed, together with high elasticity, strong robustness, and multifunction performance. Leveraging the strong field-confinement characteristic of spoof surface plasmons polaritons, the Type-I SPI opens its high-efficiency transmission band after stretching from a simply connected metallic surface. Meanwhile, the broadband transmission of the kirigami-based SPI exhibits strong robustness and excellent stability undergoing complex deformations, i.e., bending, twisting, and stretching. In addition, the prepared Type-II SPI consisting of 2 different subunit cells can achieve band-stop transmission characteristics, with its center frequency dynamically tunable by stretching the buckled structure. Experimental measurements verify the on-off switching performance in kirigami interconnects triggered by stretching. Overcoming the mechanical limitation of rigid structure with kirigami technology, the designer SPIs exhibit high stretchability through out-of-plane structure deformation. Such kirigami-based interconnects can improve the elastic functionality of wearable RF electronics and offer high compatibility to large body motion in future body network systems.

Complex diseases do not always follow gradual progressions. Instead, they may experience sudden shifts known as critical states or tipping points, where a marked qualitative change occurs. Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration. Nevertheless, the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle, especially in scenarios involving high-dimensional data with limited samples, where conventional statistical methods frequently prove inadequate. In this study, we introduce an innovative quantitative approach termed sample-specific causality network entropy (SCNE), which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules, thereby capturing critical points or pre-deterioration states of complex diseases. We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets, including single-cell data of epithelial cell deterioration (EPCD) in colorectal cancer, influenza infection data, and three different tumor cases from The Cancer Genome Atlas (TCGA) repositories. Compared to other existing six single-sample methods, our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states. Additionally, the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.

Despite the recognized influence of natural factors on groundwater, the impact of human activities remains less explored because of the challenges in measuring such effects. To address this gap, our study proposes an approach that considers carbon emissions as an indicator of human activity intensity and quantifies their impact on groundwater storage. The combination of carbon emission data and groundwater storage data for 17,152 grid cells over 16 years in 4 typical basins shows that they were generally negatively correlated, whereas both agriculture and aviation had positive impacts on groundwater storage. The longest impact from aviation and agriculture can even persist for 7 years. Furthermore, an increase of 1 Yg CO₂/km² per second in emissions from petroleum processing demonstrates the most pronounced loss of groundwater storage in the Yangtze River Basin (approximately 4.1 mm). Moreover, regions characterized by high-quality economic development tend to have favorable conditions for groundwater storage. Overall, our findings revealed the substantial role of human activities in influencing groundwater dynamics from both temporal and spatial aspects. This study fills a crucial gap by exploring the relationship between human activities and groundwater storage through the introduction of a quantitative modeling framework based on carbon emissions. It also provides insights for facilitating empirical groundwater management planning and achieving optimal emission reduction levels.

Poly (adenosine 5′-diphosphate-ribose) polymerase inhibitors (PARPi) are increasingly important in the treatment of ovarian cancer. However, more than 40% of BRCA1/2-deficient patients do not respond to PARPi, and BRCA wild-type cases do not show obvious benefit. In this study, we demonstrated that progesterone acted synergistically with niraparib in ovarian cancer cells by enhancing niraparib-mediated DNA damage and death regardless of BRCA status. This synergy was validated in an ovarian cancer organoid model and in vivo experiments. Furthermore, we found that progesterone enhances the activity of niraparib in ovarian cancer through inducing ferroptosis by up-regulating palmitoleic acid and causing mitochondrial damage. In clinical cohort, it was observed that progesterone prolonged the survival of patients with ovarian cancer receiving PARPi as second-line maintenance therapy, and high progesterone receptor expression combined with low glutathione peroxidase 4 (GPX4) expression predicted better efficacy of PARPi in patients with ovarian cancer. These findings not only offer new therapeutic strategies for PARPi poor response ovarian cancer but also provide potential molecular markers for predicting the PARPi efficacy.

Circular aptamers are promising candidates for analytical and therapeutic applications due to their enhanced biological and structural stability. However, the process of circular aptamer selection remains a great challenge, as it requires multiple rounds of binding–separation–amplification that involves issues with nonspecific binding and amplification bias. Here, we develop a highly practical solution for reliable selection of circular aptamers in a single round based on magnetosome-like magnetic chain cross-linked graphene oxide (separation efficiency ≈ 10⁵). High-affinity aptamer candidates can be rapidly selected from a preenriched circular DNA library, while low-affinity candidates are effectively adsorbed and separated by magnetosome-like magnetic chain cross-linked graphene oxide. With lipopolysaccharide as a representative model, the single-round selected lipopolysaccharide circular aptamer has been identified to have a high binding affinity with a K_d value of low to nanomolar range. Using this method, circular aptamers for protein and small-molecule targets were also successfully generated. We envision that this approach will accelerate the discovery of various new circular aptamers and open up a new avenue for analytical and therapeutic studies.

Gout, a common form of arthritis, is characterized by the deposition of monosodium urate (MSU) crystals in joints. MSU deposition in synovial tissues would initiate arthritis flares and recurrence, causing irreversible joint damage. However, the dynamic deposition of MSU crystals in tissues lacks experimental observation. In this study, we used chemical-specific, label-free stimulated Raman scattering (SRS) microscopy to investigate the spatiotemporal deposition and morphological characteristics of MSU crystals in human synovial organoids. Our findings revealed a critical 12-h window for MSU deposition in the lining layer of gouty synovium. Moreover, distinctive inflammatory reactions of the lining and sublining synovial layers in gout using SRS microscopy were further verified by immunofluorescence. Importantly, we identified a crucial proinflammatory role of sublining fibroblast-like synoviocytes, indicating a need for targeted medication treatment on these cells. Our work contributes to the fundamental understanding of MSU-based diseases and offers valuable insights for the future development of targeted gout therapies.