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The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.

As a critical mechanical component, the operational integrity of rolling bearings is essential for equipment safety. However, under strong noise interference, the weak fault features in vibration signals are difficult to extract. To address this issue, a novel fault diagnosis method is proposed in this paper, which integrates an improved speed time-varying filtering empirical mode decomposition (ISTVF-EMD) with an adaptive sine–cosine optimization algorithm (A-SCA), enabling precise and efficient extraction of fault features. The core of the proposed method lies in improving the conventional time-varying filtering empirical mode decomposition (TVF-EMD) by setting a maximum decomposition layer limit, effectively addressing issues of excessive components and low computational efficiency during the decomposition of low signal-to-noise ratio (SNR) signals. Furthermore, a multi-characteristic frequency energy concentration centrality (MCFECC) index is employed as a fitness function to guide A-SCA in adaptively searching for the optimal bandwidth threshold and fitting order parameters of ISTVF-EMD, thereby extracting components with the most enriched fault information. Validated through simulation and multiple test bench cases, the results indicate that the proposed method can not only significantly enhance the fault characteristic frequencies and their harmonics in the envelope spectrum, successfully diagnosing outer race, inner race, and rolling element faults, but also, compared with the original method, ISTVF-EMD substantially reduces the computational time while ensuring or even improving the decomposition quality. The method presented in this paper provides an effective solution for achieving precise and adaptive fault diagnosis of rolling bearings under strong noise interference.

Background: Deferred cord clamping (DCC) is beneficial for preterm infants, but there are concerns about the safety of DCC during Cesarean deliveries (CD) under general anesthesia (GA). We evaluated maternal and neonatal outcomes in preterm CD under GA vs. regional anesthesia (RA) after implementing 180 s of DCC. Methods: This retrospective single-center observational study included CD at <33 weeks gestation, delivered between January 2018 and December 2023. The cord was clamped before 180 s for concerns of maternal bleeding or infant apnea after 30–45 s stimulation. Data was collected from reports from electronic medical records, neonatal intensive care unit database, and manually from the medical records of the patient. Multivariable regression analysis was used to assess the effect of anesthesia type and DCC on outcomes, adjusting for confounders. Results: This study included 170 mothers and 194 infants, and 84.9% of the infants received DCC ≥ 60 s. The GA group had a higher percentage of emergency CD and a lower median duration of DCC (105 s vs. 180 s, p ≤ 0.001) compared to RA. In multivariate regression analysis, GA was associated with lower odds (95% CI) of umbilical artery pH < 7 [0.1, (0.0, 0.6)], base deficit ≥ 16 [0.0, (0.0, 0.5)], and higher odds of necrotizing enterocolitis [28.2, (1.4, 560.0)]. GA was not associated with maternal hemorrhage, delivery room (DR) resuscitation, or other major neonatal morbidities or mortality. DCC ≥ 60 s was associated with lower maternal blood loss [Regression coefficient −698, (−1193, −202)], lower odds of transfusion [0.4, (0.1, 1.0)], DR resuscitation [0.4, (0.2, 0.8)], and chronic lung disease [0.4, (0.2, 0.9)], and higher survival without major morbidities [2.8, (1.2, 6.8)]. Conclusions: DCC was performed in a majority of CD under GA by adhering to protocols to shorten DCC in cases where maternal or fetal safety was threatened. GA with DCC was not associated with increased neonatal resuscitation or major neonatal morbidities and was associated with lower maternal hemorrhage and transfusion.

Acute coronary syndrome (ACS) has a high incidence of adverse cardiovascular events, even after early invasive treatment. Patients may still have a poor prognosis after discharge. The keys to the long-term survival of patients with ACS include effective treatment in a timely manner and identification of those patients who are at higher risk for long-term adverse events. Therefore, several nations have now devised a range of risk assessment models to provide data for accurately formulating treatment plans for patients with various risk levels following an ACS to prevent short and long-term cardiovascular events. The purpose of this article is to review the risk scores associated with mortality and ischemic events in patients with ACS. By using the clinical risk prediction score, we can accurately and effectively judge the prognosis of patients, so as to take a more reasonable treatment.

The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system.

An axle box bearing is one of the most important components of high-speed EMUs (electric multiple units), which runs at a very fast speed, suffers a heavy load, and operates under various complex working conditions. Once a bearing fault occurs, it not only has an enormous impact on the railway system, but also poses a threat to personal safety. Therefore, there is significant value in studying a real-time fault early warning of a high-speed EMU axle box bearing. However, to our best knowledge, there are three obvious defects in the existing fault early warning methods used for high-speed EMU axle box bearings: (1) these methods based on vibration are extremely mature, but there are no vibration sensors installed in high-speed EMU axle box because it will greatly increase the manufacturing cost; (2) a TADS (trackside acoustic device system) can effectively detect early failures, but only a portion of railways are equipped with such a facility; and (3) an EMU-ODS (electric multiple unit onboard detection system) has reported numerous untimely warnings, along with warnings of frequent occurrence being missed. Whereupon, a method is proposed to realize the fault early warning of an axle box bearing without installing a vibration sensor on the high-speed EMU in service, namely a MLSTM-iForest (multilayer long short-term memory–isolation forest). First, the time-series data of the temperature-related variables of the axle box bearing is used as the input of MLSTM to predict the axle box bearing temperature in the future. Then, the deviation index of the predicted axle box bearing temperature is calculated. Finally, the deviation index is input into an iForest algorithm for unsupervised classification to realize the fault early warning of an axle box bearing. Experimental results on high-speed EMU operation data sets demonstrated the availability and feasibility of the presented method toward achieving early fault warnings of a high-speed EMU axle box bearing.

Background Interferon gamma (IFNγ) plays an important role in the development of chronic lung diseases via the production of inflammatory mediators, although the exact mechanism remains unclear. The present study aimed at investigating the potential mechanisms by which IFNγ induced over-production of interleukins through the interaction between carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) and phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) pathway. Methods IFN-γ induced over-production of interleukin (IL) 6 and IL8, and RNA expression of CEACAM1 and its subtypes or PI3K and its subtypes in human bronchial epithelial cells (HBE). The production of IL6 and IL8 or cell proliferation and movement were also evaluated in cell CEACAM1− or cell CEACAM1+ after the induction of IFN-γ. Roles of PI3K subtype proteins, e.g. PI3Kp110α/δ, Akt, p110α/γ/δ/β/mTOR, PI3Kp110α/δ/β, PI3Kp110δ, or pan-PI3K in IFN-γ-induced CEACAM1 subtype alterations were furthermore validated using those proteins of PI3K subtypes. Results CEACAM1, especially CEACAM1-S isoforms, was significantly up-regulated in HBE cells after treatment with IFN-γ. CEACAM1 played roles in expression of IL-6 and IL-8, and facilitated cellular proliferation and migration. IFN-γ up-regulated the expression of CEACAM1 in airway epithelial cells, especially CEACAM1-S isoforms, promoting cellular proliferation, migration, and the production of inflammatory factors. PI3K (p110δ)/Akt/mTOR pathway was involved in the process of IFN-γ-upregulated CEACAM1, especially CEACAM1-S. On the other hand, CEACAM1 could promote the activation of PI3K/Akt/mTOR pathway. Conclusion IFN-γ could induce inflammatory responses, cellular growth and proliferation through the interaction of CEACAM1 (especially CEACAM1-S isoforms) and PI3K(p110δ)/Akt/mTOR in airway epithelial cells, which might be new alternative of future therapies against epithelial transition from inflammation to cancer.

Background Cell proliferation and death are key components of wound healing and tissue repair. Telocytes (TCs) represent a newly discovered cell type that can protect tissue from acute injury via cell–cell communication with adjacent cells. The aim of this study was to use a mouse model of skin wound healing and lipopolysaccharide (LPS)-induced cell injury to evaluate the effects of TCs on skin wound healing in vivo and in vitro. Material/methods Immunohistochemical staining was performed to evaluate the alteration of TCs in tissues from normal and chronic wound patients. Then, a male C57BL/6 mouse wound model of the back was established. The mice were divided randomly into three groups, and wound healing was estimated according to the wound healing rate and histology. An LPS-induced co-culture model of a mouse lung telocyte cell line (TCs) with human keratinocyte (HaCaT), human dermal microvascular endothelial cell (HDMEC) or murine fibroblast (L929) cell lines was established to analyse the effects of TCs on constitutive cell types of the skin. Cell proliferation, migration and apoptosis were examined, and reactive oxygen species (ROS) and inflammatory factors in HaCaT cells, HDMECs, and L929 cells were detected to study the mechanisms involved in TC protection in skin wounds. Results TCs were significantly increased in tissues from chronic wound patients compared with healthy controls. Wound healing was significantly improved in wound mouse models treated with exogenous TCs compared with LPS-induced models. TCs reversed the LPS-induced inhibition of HaCaT cells and HDMECs and reduced the LPS-induced apoptosis of HaCaT cells and the death ratios of HDMECs and L929 cells. TCs reversed LPS-induced ROS in HDMECs and L929 cells and decreased inflammatory factor mRNA levels in HaCaT cells, HDMECs and L929 cells. Conclusions TCs reduce wound healing delay, and inflammatory responses caused by LPS might be mediated by inflammatory inhibition, thus restricting apoptosis and promoting migration of the main component cell types in the skin.

Delayed cord clamping (DCC, ≥30 s) increases blood volume in newborns and is associated with fewer blood transfusions and short-term neonatal complications. The optimal timing of cord clamping for very preterm infants should maximize placental transfusion without interfering with stabilization and resuscitation. We compared the effect of different durations of DCC, 30-45 s vs. 60-75 s, on delivery room (DR) and neonatal outcomes in preterm infants <32 weeks gestational age (GA). This is a single-center prospective observational study. Data were collected prospectively from eligible infants from two groups: 30-45 s DCC group (January 2008 to February 2011, n = 187) and 60-75 s DCC group (March 2011 to April 2014, n = 166). The 60-75 s DCC group compared to the 30-45 s DCC group had higher hematocrits at <2 hours (49.2% vs. 47.4%, p = 0.02). In infants <28 weeks GA, the 12-36 hours hematocrit was higher in the 60-75 s DCC group compared to the 30-45 s DCC group (47.9% vs. 42.1%, p = 0.002). The 60-75 s DCC group had reductions in DR intubation (11% vs. 22%, p = 0.004), hypothermia on admission (1% vs. 5%, p = 0.01), surfactant therapy (13% vs. 28%, p = 0.001), intubation in the first 24 hours (20% vs. 34%, p = 0.004), any intubation (27% vs. 40%, p = 0.007), and any red blood cell transfusion (20% vs. 33%, p = 0.008) during the hospitalization compared to the 30-45 s DCC group. These reductions remained significant after adjusting for GA, gender and >48 hours of antenatal steroid exposure. There was no difference between the two groups in neonatal death, intraventricular hemorrhage, chronic lung disease, late onset sepsis, necrotizing enterocolitis and severe retinopathy of prematurity. In this study cohort increasing DCC duration from 30-45 s to 60-75 s is associated with decreased hypothermia on admission, neonatal respiratory interventions and red blood cell transfusions without increase in neonatal mortality and morbidities.

Bearings of rail vehicles bear various dynamic forces. Any fault of the bearing seriously threatens running safety. For fault diagnosis, vibration and temperature measured from the bogie and acoustic signals measured from trackside are often used. However, installing additional sensing devices on the bogie increases manufacturing cost while trackside monitoring is susceptible to ambient noise. For other application, structural displacement based on computer vision is widely applied for deflection measurement and damage identification of bridges. This article proposes to monitor the health condition of the rail vehicle bearings by detecting the displacement of bolts on the end cap of the bearing box. This study is performed based on an experimental platform of bearing systems. The displacement is monitored by computer vision, which can image real-time displacement of the bolts. The health condition of bearings is reflected by the amplitude of the detected displacement by phase correlation method which is separately studied by simulation. To improve the calculation rate, the computer vision only locally focuses on three bolts rather than the whole image. The displacement amplitudes of the bearing system in the vertical direction are derived by comparing the correlations of the image’s gray-level co-occurrence matrix (GLCM). For verification, the measured displacement is checked against the measurement from laser displacement sensors, which shows that the displacement accuracy is 0.05 mm while improving calculation rate by 68%. This study also found that the displacement of the bearing system increases with the increase in rotational speed while decreasing with static load.