Deep Learning Model Coupling Wearable Bioelectric and Mechanical Sensors for Refined Muscle Strength Assessment.

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.

A light-sensitive protein-based wearable pH biometer.

Bacteriorhodopsin is a biological material with excellent photosensitivity properties. It can directly convert optical signals into electrical signals and is widely used in various biosensors. Here, we present a bR-based wearable pH biometer that can be used to monitor wound infection. The mechanism of the pH-sensitive effect of the bR electrode is explained, which generates a transient photovoltage under light irradiation and a negative photovoltage when the lamp is turned off. Since the photoelectric signal of bR is affected by different pH values, the photovoltage is changed by adjusting the pH value. The ratio (Vn/Vp) of negative photovoltage (Vn) to positive photovoltage (Vp) has a good linear relationship (R2 = 0.9911) in the pH range of 4.0-10.0. In vitro experiments using rats as a model confirmed that this wearable pH biometer can monitor pH changes that occur in wound infection.

Direct Mapping of Cytomechanical Homeostasis Destruction in Osteoarthritis Based on Silicon Nanopillar Array.

Osteoarthritis (OA) is the most prevalent joint degenerative disease characterized by chronic joint inflammation. The pathogenesis of OA has not been fully elucidated yet. Cartilage erosion is the most significant pathological feature in OA, which is considered the result of cytomechanical homeostasis destruction. The cytomechanical homeostasis is maintained by the dynamic interaction between cells and the extracellular matrix, which can be reflected by cell traction force (CTF). It is critical to assess the CTF to provide a deeper understanding of the cytomechanical homeostasis destruction and progression in OA. In this study, a silicon nanopillar array (Si-NP) with high spatial resolution and aspect ratio is fabricated to investigate the CTF in response to OA. It is discovered that the CTF is degraded in OA, which is attributed to the F-actin reorganization induced by the activation of RhoA/ROCK signaling pathway. Si-NP also shows promising potential as a mechanopharmacological assessment platform for OA drug screening and evaluation.

Identification of a virulence tal gene in the cotton pathogen, Xanthomonas citri pv. malvacearum strain Xss-V2-18.

BACKGROUND: Bacterial blight of cotton (BBC), which is caused by the bacterium Xanthomonas citri pv. malvacearum (Xcm), is a destructive disease in cotton. Transcription activator-like effectors (TALEs), encoded by tal-genes, play critical roles in the pathogenesis of xanthomonads. Characterized strains of cotton pathogenic Xcm harbor 8-12 different tal genes and only one of them is functionally decoded. Further identification of novel tal genes in Xcm strains with virulence contributions are prerequisite to decipher the Xcm-cotton interactions. RESULTS: In this study, we identified six tal genes in Xss-V2-18, a highly-virulent strain of Xcm from China, and assessed their role in BBC. RFLP-based Southern hybridization assays indicated that Xss-V2-18 harbors the six tal genes on a plasmid. The plasmid-encoded tal genes were isolated by cloning BamHI fragments and screening clones by colony hybridization. The tal genes were sequenced by inserting a Tn5 transposon in the DNA encoding the central repeat region (CRR) of each tal gene. Xcm TALome evolutionary relationship based on TALEs CRR revealed relatedness of Xss-V2-18 to MSCT1 and MS14003 from the United States. However, Tal2 of Xss-V2-18 differs at two repeat variable diresidues (RVDs) from Tal6 and Tal26 in MSCT1 and MS14003, respectively, inferred functional dissimilarity. The suicide vector pKMS1 was then used to construct tal deletion mutants in Xcm Xss-V2-18. The mutants were evaluated for pathogenicity in cotton based on symptomology and growth in planta. Four mutants showed attenuated virulence and all contained mutations in tal2. One tal2 mutant designated M2 was further investigated in complementation assays. When tal2 was introduced into Xcm M2 and expressed in trans, the mutant was complemented for both symptoms and growth in planta, thus indicating that tal2 functions as a virulence factor in Xcm Xss-V2-18. CONCLUSIONS: Overall, the results demonstrated that Tal2 is a major pathogenicity factor in Xcm strain Xss-V2-18 that contributes significantly in BBC. This study provides a foundation for future efforts aimed at identifying susceptibility genes in cotton that are targeted by Tal2.