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Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

Macrophages can exhibit pro-inflammatory or pro-reparatory functions, contingent upon their specific activation state. This dynamic behavior empowers macrophages to engage in immune reactions and contribute to tissue homeostasis. Understanding the intricate interplay between macrophage motility and activation status provides valuable insights into the complex mechanisms that govern their diverse functions. In a recent study, we developed a classification method based on morphology, which demonstrated that movement characteristics, including speed and displacement, can serve as distinguishing factors for macrophage subtypes. In this study, we develop a deep learning model to explore the potential of classifying macrophage subtypes based solely on raw trajectory patterns. The classification model relies on the time series of x-y coordinates, as well as the distance traveled and net displacement. We begin by investigating the migratory patterns of macrophages to gain a deeper understanding of their behavior. Although this analysis does not directly inform the deep learning model, it serves to highlight the intricate and distinct dynamics exhibited by different macrophage subtypes, which cannot be easily captured by a finite set of motility metrics. Our study uses cell trajectories to classify three macrophage subtypes: M0, M1, and M2. This advancement holds promising implications for the future, as it suggests the possibility of identifying macrophage subtypes without relying on shape analysis. Consequently, it could potentially eliminate the necessity for high-quality imaging techniques and provide more robust methods for analyzing inherently blurry images.

The composite material-like extracellular matrix (ECM) in the sinoatrial node (SAN) supports the native pacemaking cardiomyocytes (PCMs). To test the roles of SAN ECM in the PCM phenotype and function, we engineered reconstructed-SAN heart tissues (rSANHTs) by recellularizing porcine SAN ECMs with hiPSC-derived PCMs. The hiPSC-PCMs in rSANHTs self-organized into clusters resembling the native SAN and displayed higher expression of pacemaker-specific genes and a faster automaticity compared with PCMs in reconstructed-left ventricular heart tissues (rLVHTs). To test the protective nature of SAN ECMs under strain, rSANHTs and rLVHTs were transplanted onto the murine thoracic diaphragm to undergo constant cyclic strain. All strained-rSANHTs preserved automaticity, whereas 66% of strained-rLVHTs lost their automaticity. In contrast to the strained-rLVHTs, PCMs in strained-rSANHTs maintained high expression of key pacemaker genes (HCN4, TBX3, and TBX18). These findings highlight the promotive and protective roles of the composite SAN ECM and provide valuable insights for pacemaking tissue engineering.

For the problem of low accuracy and applicability of the model prediction in the study of mine water inflow, a method of mine water inflow prediction based on bidirectional short and long memory network(BiLSTM) and deep residual network (DRN) is proposed. First, the data of mine water inflow is processed by wavelet decomposition and normalization to obtain trend item data and detail item data. Secondly, the trend item data was predicted by DRN network method, and the detail item data was predicted by BiLSTM network method. Finally, the two parts of the prediction results will be combined to get the mine water inflow prediction results. The results show that the DRN-BiLSTM model has higher prediction accuracy than a single model, indicating that the model has better generalization.

Digital intraoral scanning is a hot topic in the field of oral digital technology. In recent years, digital intraoral scanning has gradually become the mainstream technology in orthodontics, prosthodontics, and implant dentistry. The precision of digital intraoral scanning and the accuracy and stitching of data collection are the keys to the success of the impression. However, the operators are less familiar with the intraoral scanning characteristics, imaging processing, operator scanning method, oral tissue specificity of the scanned object, and restoration design. Thus far, no unified standard and consensus on digital intraoral scanning technology has been achieved at home or abroad. To deal with the problems encountered in oral scanning and improve the quality of digital scanning, we collected common expert opinions and sought to expound the causes of scanning errors and countermeasures by summarizing the existing evidence. We also describe the scanning strategies under different oral impression requirements. The expert consensus is that due to various factors affecting the accuracy of digital intraoral scanning and the reproducibility of scanned images, adopting the correct scanning trajectory can shorten clinical operation time and improve scanning accuracy. The scanning trajectories mainly include the E-shaped, segmented, and S-shaped methods. When performing fixed denture restoration, it is recommended to first scan the abutment and adjacent teeth. When performing fixed denture restoration, it is recommended to scan the abutment and adjacent teeth first. Then the cavity in the abutment area is excavated. Lastly, the cavity gap was scanned after completing the abutment preparation. This method not only meets clinical needs but also achieves the most reliable accuracy. When performing full denture restoration in edentulous jaws, setting markers on the mucosal tissue at the bottom of the alveolar ridge, simultaneously capturing images of the vestibular area, using different types of scanning paths such as Z-shaped, S-shaped, buccal-palatal and palatal-buccal pathways, segmented scanning of dental arches, and other strategies can reduce scanning errors and improve image stitching and overlap. For implant restoration, when a single crown restoration is supported by implants and a small span upper structure restoration, it is recommended to first pre-scan the required dental arch. Then the cavity in the abutment area is excavated. Lastly, scanning the cavity gap after installing the implant scanning rod. When repairing a bone level implant crown, an improved indirect scanning method can be used. The scanning process includes three steps: First, the temporary restoration, adjacent teeth, and gingival tissue in the mouth are scanned; second, the entire dental arch is scanned after installing a standard scanning rod on the implant; and third, the temporary restoration outside the mouth is scanned to obtain the three-dimensional shape of the gingival contour of the implant neck, thereby increasing the stability of soft tissue scanning around the implant and improving scanning restoration. For dental implant fixed bridge repair with missing teeth, the mobility of the mucosa increases the difficulty of scanning, making it difficult for scanners to distinguish scanning rods of the same shape and size, which can easily cause image stacking errors. Higher accuracy of digital implant impressions can be achieved by changing the geometric shape of the scanning rods to change the optical curvature radius. The consensus confirms that as the range of scanned dental arches and the number of data concatenations increases, the scanning accuracy decreases accordingly, especially when performing full mouth implant restoration impressions. The difficulty of image stitching processing can easily be increased by the presence of unstable and uneven mucosal morphology inside the mouth and the lack of relatively obvious and fixed reference objects, which results in insufficient accuracy. When designing restorations of this type, it is advisable to carefully choose digital intraoral scanning methods to obtain model data. It is not recommended to use digital impressions when there are more than five missing teeth.

Highlights In situ Raman spectroscopy study of the cation regulation for interfacial water on Au@Pd core–shell nanoparticle surface during hydrogen evolution reaction process. Direct spectral evidence reveals new strategies to optimize the structure of interfacial water by the adjusting of radius and valence of the cation in the electrolyte. The optimized structure of interfacial water will improve the charge transfer efficiency between the water and electrode increasing the performance of the hydrogen evolution reaction.

Macrophages can exhibit pro-inflammatory or pro-reparatory functions, contingent upon their specific activation state. This dynamic behavior empowers macrophages to engage in immune reactions and contribute to tissue homeostasis. Understanding the intricate interplay between macrophage motility and activation status provides valuable insights into the complex mechanisms that govern their diverse functions. In a recent study, we developed a classification method based on morphology, which demonstrated that movement characteristics, including speed and displacement, can serve as distinguishing factors for macrophage subtypes. In this study, we develop a deep learning model to explore the potential of classifying macrophage subtypes based solely on raw trajectory patterns. The classification model relies on the time series of x-y coordinates, as well as the distance traveled and net displacement. We begin by investigating the migratory patterns of macrophages to gain a deeper understanding of their behavior. Although this analysis does not directly inform the deep learning model, it serves to highlight the intricate and distinct dynamics exhibited by different macrophage subtypes, which cannot be easily captured by a finite set of motility metrics. Our study uses cell trajectories to classify three macrophage subtypes: M0, M1, and M2. This advancement holds promising implications for the future, as it suggests the possibility of identifying macrophage subtypes without relying on shape analysis. Consequently, it could potentially eliminate the necessity for high-quality imaging techniques and provide more robust methods for analyzing inherently blurry images.

Many cell types migrate in response to naturally generated electric fields. Furthermore, it has been suggested that the external application of an electric field may be used to intervene in and optimize natural processes such as wound healing. Precise cell guidance suitable for such optimization may rely on predictive models of cell migration, which do not generalize. Here, we present a machine learning model that can forecast directedness of cell migration given a timeseries of previous directedness and electric field values. This model is trained using time series galvanotaxis data of mammalian cranial neural crest cells obtained through time-lapse microscopy of cells cultured at 37 °C in a galvanotaxis chamber at ambient pressure. Next, we show that our modeling approach can be used for a variety of cell types and experimental conditions with very limited training data using transfer learning methods. We adapt the model to predict cell behavior for keratocytes (room temperature, ~ 18-20 °C) and keratinocytes (37 °C) under similar experimental conditions with a small dataset (~ 2-5 cells). Finally, this model can be used to perform in silico studies by simulating cell migration lines under time-varying and unseen electric fields. We demonstrate this by simulating feedback control on cell migration using a proportional-integral-derivative (PID) controller. This data-driven approach provides predictive models of cell migration that may be suitable for designing electric field based cellular control mechanisms for applications in precision medicine such as wound healing.

Malignant melanoma (MM) frequently occurs in the skin or mucosa, whereas malignant melanoma of unknown primary (MUP) is diagnosed in patients with lymph nodes or visceral organs as the site of origin, where it is challenging to detect the primary lesion by comprehensive examination. MUP is possibly related to the spontaneous regression of the primary lesion. In addition, primary hepatic melanoma (PHM) usually refers to the primary MM occurring in the liver, with no typical primary lesions and no manifestations of tumor metastasis. A 61-year-old male patient with liver as the site of origin was diagnosed with MM by Melan-A, HMB-45, and S-100 immunohistochemistry staining of liver biopsy tissue. Based on a comprehensive examination, no basis was found for melanoma in sites such as the skin, mucosa, five sense organs, brain, digestive tract, respiratory tract, or genitalia, and the patient was subsequently diagnosed with MUP. MMs require a comprehensive inspection, beginning with the liver, to search for the primary lesion; if the primary lesion is not found, the possibility of PHM or MUP should be considered.

Abstract Ruthenium exhibits comparable or even better alkaline hydrogen evolution reaction activity than platinum, however, the mechanistic aspects are yet to be settled, which are elucidated by combining in situ Raman spectroscopy and theoretical calculations herein. We simultaneously capture dynamic spectral evidence of Ru surfaces, interfacial water, *H and *OH intermediates. Ru surfaces exist in different valence states in the reaction potential range, dissociating interfacial water differently and generating two distinct *H, resulting in different activities. The local cation tuning effect of hydrated Na+ ion water and the large work function of high-valence Ru(n+) surfaces promote interfacial water dissociation. Moreover, compared to low-valence Ru(0) surfaces, high-valence Ru(n+) surfaces have more moderate adsorption energies for interfacial water, *H, and *OH. They, therefore, facilitate the activity. Our findings demonstrate the regulation of valence state on interfacial water, intermediates, and finally the catalytic activity, which provide guidelines for the rational design of high-efficiency catalysts.

In this study, a novel fluidized-bed homogeneous granulation (FBHo-G) process was developed to recover lithium (Li) from industrial Li-impacted wastewater. Five important operational variables (i.e., temperatures, pH, [P]0/[Li]0 molar ratios, surface loadings, and up-flow velocities (Umf)) were selected to optimize the Li recovery (TR%) and granulation ratio (GR%) efficiencies of the process. The optimal operational conditions were determined as the following: a temperature of 75 °C, pH of 11.5, [P]0/[Li]0 of 0.5, surface loading of 2.5 kg/m2·h, and Umf of 35.7 m/h). The TR% and GR% at optimal condition could be as much as 90%. The material characterization of the recovery pellet products showed that they were highly crystallized Li3PO4 (purity ~88.2%). The pellets had a round shape and smooth surface with an average size of 0.65 mm, so could easily be stored and transported. The high purity enables them to be further directly reused as raw materials for a wide range of industrial applications (e.g., in the synthesis of cathode materials). Our calculation shows that the FBHo-G process could recover up to 0.1845 kg of lithium per cubic meter of Li-containing wastewater, at a recovery rate of ~90%. A brief technoeconomic analysis shows that FBHG process had economic viability, with an estimate production cost of USD 26/kg Li removed, while the potential gained profit for selling lithium phosphate pellets could be up to USD 48 per the same volume of wastewater and the net profit up to USD 22/m3 Li treated. In all, fluidized-bed homogeneous granulation, a seedless one-step recovery process, opens a promising pathway toward a green and sustainable recycling industry for the recovery and application of the resource-limited lithium element from nonconventional water sources.

Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

Despite having similar histologic features, patients with high-grade serous ovarian carcinoma (HGSC) often experience highly variable outcomes. The underlying determinants for long-term survival (LTS, ≥10 years) versus short-term survival (STS, 2; false discovery rate < 0.01). In the subsequent validation cohort, transmembrane protein 62 (TMEM62) was found to be related to LTS. CIBERSORT analysis showed that T cells (follicular helper) were found at higher levels in tumors from LTS than STS groups. In vitro data using OVCAR5 and OVCAR8 cells showed decreased proliferation with TMEM62 overexpression and positive correlation with a longevity-regulating pathway (KEGG HSA04213) at the RNA level. In vivo analysis using the OVCAR8-TMEM62-TetON model showed decreased tumor burden in mice with high- vs. low-expressing TMEM62 tumors. Our results demonstrate that restoring TMEM62 may be a novel approach for treatment of HGSC. These findings may have implications for biomarker and intervention strategies to help improve patient outcomes