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Abstract Diabetic retinopathy (DR) is a leading cause of irreversible vision loss in working-age populations. Fat mass and obesity-associated protein (FTO) is an N6-methyladenosine (m6A) demethylase that demethylates RNAs involved in energy homeostasis, though its influence on DR is not well studied. Herein, we detected elevated FTO expression in vitreous fibrovascular membranes of patients with proliferative DR. FTO promoted cell cycle progression and tip cell formation of endothelial cells (ECs) to facilitate angiogenesis in vitro, in mice, and in zebrafish. FTO also regulated EC-pericyte crosstalk to trigger diabetic microvascular leakage, and mediated EC–microglia interactions to induce retinal inflammation and neurodegeneration in vivo and in vitro. Mechanistically, FTO affected EC features via modulating CDK2 mRNA stability in an m6A-YTHDF2-dependent manner. FTO up-regulation under diabetic conditions was driven by lactate-mediated histone lactylation. FB23-2, an inhibitor to FTO’s m6A demethylase activity, suppressed angiogenic phenotypes in vitro. To allow for systemic administration, we developed a nanoplatform encapsulating FB23-2 and confirmed its targeting and therapeutic efficiency in mice. Collectively, our study demonstrates that FTO is important for EC function and retinal homeostasis in DR, and warrants further investigation as a therapeutic target for DR patients.

Abstract Prenatal diagnosis of congenital heart disease (CHD) relies primarily on fetal echocardiography conducted at mid‐gestational age—the sensitivity of which varies among centers and practitioners. An objective method for early diagnosis is needed. Here, we conducted a case–control study recruiting 103 pregnant women with healthy offspring and 104 cases with CHD offspring, including VSD (42/104), ASD (20/104), and other CHD phenotypes. Plasma was collected during the first trimester and proteomic analysis was performed. Principal component analysis revealed considerable differences between the controls and the CHDs. Among the significantly altered proteins, 25 upregulated proteins in CHDs were enriched in amino acid metabolism, extracellular matrix receptor, and actin skeleton regulation, whereas 49 downregulated proteins were enriched in carbohydrate metabolism, cardiac muscle contraction, and cardiomyopathy. The machine learning model reached an area under the curve of 0.964 and was highly accurate in recognizing CHDs. This study provides a highly valuable proteomics resource to better recognize the cause of CHD and has developed a reliable objective method for the early recognition of CHD, facilitating early intervention and better prognosis.

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F 1 scores (average F 1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors. The authors developed a computational method, HEARTSVG, which accurately identifies spatially variable genes in large-scale datasets, enhancing our understanding of tissue organization and disease mechanisms. [ABSTRACT FROM AUTHOR]

This study examined the impact of current solution treatment on the microstructure and mechanical properties of the Co-28Cr-6Mo-0.22C alloy investment castings. The findings reveal that the current solution treatment significantly promotes the dissolution of carbides at a lower temperature. The optimal conditions for solution treatment are determined as a solution temperature of 1,125 °C and a holding time of 5.0 min. Under these parameters, the size and volume fraction of precipitated phases in the investment castings are measured as 6.2 µm and 1.1vol.%. The yield strength, ultimate tensile strength, and total elongation of the Co-28Cr-6Mo-0.22C investment castings are 535 MPa, 760 MPa, and 12.6%, respectively. These values exceed those obtained with the conventional solution treatment at 1,200 °C for 4.0 h. The findings suggest a phase transformation of M23C6s+C following the current solution treatment at 1,125 °C for 5.0 min. In comparison, the traditional solution treatment at 1,200 °C for 4.0 h leads to the formation of M23C6 and M6C carbides. It is noteworthy that the non-thermal effect of the current during the solution treatment modifies the free energy of both the matrix and precipitation phase. This modification lowers the phase transition temperature of the M23C6s+C reaction, thereby facilitating the dissolution of carbides. As a result, the current solution treatment approach achieves carbide dissolution at a lower temperature and within a significantly shorter time when compared to the traditional solution treatment methods. [ABSTRACT FROM AUTHOR]

Rosacea is a chronic facial inflammatory skin disease that occurs with dysfunction of the immune system. Colchicine was reported to have anti-inflammatory properties. However, the impact of colchicine on rosacea remains unclear. In the present study, the phenotype of rosacea lesions was evaluated by the redness score, inflammatory biomarkers were analyzed by reverse transcription PCR (RT‒PCR), and the infiltration of inflammatory cells was assessed by IHC analysis and immunofluorescence in a rosacea-like mouse model. In vitro, RT‒PCR was used to identify the inflammatory factors that Toll-like receptor 2 (TLR2) agonist caused neutrophils to produce, and immunofluorescence and coimmunoprecipitation were used to identify putative signalling pathways. We found that skin erythema and histopathological alterations, as well as elevated proinflammatory factors (IL-1β, IL-6, TNFα, CXCL2) and CAMP, were significantly ameliorated by colchicine treatment in LL37-induced rosacea-like mice. In addition, colchicine reduced the colocalization of TLR2 and neutrophils and the formation of neutrophil extracellular trap networks (NET) in mouse lesions. In neutrophils, colchicine markedly reduced TLR2 agonist-induced inflammatory biomarker expression, NET formation, and ROS production. Moreover, we found that LL37 could bind to TLR2 upon activation of TLR2 in neutrophils. Importantly, colchicine could repress the combination of TLR2 and LL37 in vivo. Finally, bioinformatics methods further validated the key molecules of neutrophil-related inflammation in rosacea, which is consistent with our experimental findings. Collectively, colchicine ameliorated rosacea-like dermatitis by regulating the neutrophil immune response activated by the TLR2 pathway, indicating that it could be an effective therapeutic option for patients with rosacea. [ABSTRACT FROM AUTHOR]

It can provide a basis for decision making for the conservation and sustainable use of forest ecosystems in mountains to understand the stoichiometric properties and nutrient allocation strategies of major tree species. However, the plant nutrient allocation strategies under different environmental gradients in forest systems of arid and semi-arid mountains are not fully understand. Therefore, three typical regions in the Qilian Mountains on the eastern edge of the Qinghai-Tibet Plateau were selected based on precipitation and temperature gradients, and the stoichiometric characteristics and nutrient allocation strategies of Qinghai spruce (Picea crassifolia) of the dominant tree species under different environmental gradients were investigated. The results showed that (1) the stoichiometric characteristics of plant tissues were different in the three regions. (2) The importance of each tissue in the plant nutrient allocation varied in different regions, showing that the plant roots are more important in the warm-wet region, while the plant leaves, branches and trunks are more important in the transition and hot-dry regions. (3) The influencing factors affecting plant nutrient allocation strategies were inconsistent across regions, which showed that plant nutrient allocation strategies in the warm-wet and transition region were mainly influenced by soil factors, while they were more influenced by climatic factors in the hot-dry region. The patterns of plant nutrient allocation strategies and drivers under different environmental gradients could help us better understand the ecological adaptation mechanism and physiological adjustment mechanism of forest ecosystem in mountains. [ABSTRACT FROM AUTHOR]

Oxidative stress-induced lipid accumulation is mediated by lipid droplets (LDs) homeostasis, which sequester vulnerable unsaturated triglycerides into LDs to prevent further peroxidation. Here we identify the upregulation of lipopolysaccharide-binding protein (LBP) and its trafficking through LDs as a mechanism for modulating LD homeostasis in response to oxidative stress. Our results suggest that LBP induces lipid accumulation by controlling lipid-redox homeostasis through its lipid-capture activity, sorting unsaturated triglycerides into LDs. N-acetyl-L-cysteine treatment reduces LBP-mediated triglycerides accumulation by phospholipid/triglycerides competition and Peroxiredoxin 4, a redox state sensor of LBP that regulates the shuttle of LBP from LDs. Furthermore, chronic stress upregulates LBP expression, leading to insulin resistance and obesity. Our findings contribute to the understanding of the role of LBP in regulating LD homeostasis and against cellular peroxidative injury. These insights could inform the development of redox-based therapies for alleviating oxidative stress-induced metabolic dysfunction. Oxidative stress triggers lipid accumulation in cells by sequestering triglycerides in lipid droplets. Here, the authors show that lipopolysaccharide-binding protein interacts with redox sensor PRDX4 to control lipid-redox balance and promotes triglyceride accumulation in droplets by capturing unsaturated lipids. [ABSTRACT FROM AUTHOR]

The circadian clock regulates animal physiological activities. How temperature reorganizes circadian-dependent physiological activities remains elusive. Here, using in-vivo two-photon imaging with the temperature control device, we investigated the response of the Drosophila central circadian circuit to temperature variation and identified that DN1as serves as the most sensitive temperature-sensing neurons. The circadian clock gate DN1a's diurnal temperature response. Trans-synaptic tracing, connectome analysis, and functional imaging data reveal that DN1as bidirectionally targets two circadian neuronal subsets: activity-related E cells and sleep-promoting DN3s. Specifically, behavioral data demonstrate that the DN1a-E cell circuit modulates the evening locomotion peak in response to cold temperature, while the DN1a-DN3 circuit controls the warm temperature-induced nocturnal sleep reduction. Our findings systematically and comprehensively illustrate how the central circadian circuit dynamically integrates temperature and light signals to effectively coordinate wakefulness and sleep at different times of the day, shedding light on the conserved neural mechanisms underlying temperature-regulated circadian physiology in animals. The central circadian circuit's role in integrating temperature changes is not fully understood. Here, the authors demonstrate that temperature-sensitive DN1a circadian neurons in the Drosophila brain bidirectionally influence downstream circadian neurons, regulating temperature-dependent physiological activities. [ABSTRACT FROM AUTHOR]

At present, human translation is characterized by low efficiency, high cost, high consumption of resources and time, so it is urgent to study automatic translation technology to meet the needs of information interaction and language communication in various countries under the background of economic globalization. In order to improve the performance of Chinese-English translation system, this study aims to use optical network transmission technology to optimize the translation process. This research is based on deep learning model and uses optical network transmission technology to implement Chinese-English translation system. The neural network model is used to train the Chinese-English translation, and then the data is transmitted to the cloud for translation using optical network transmission technology. Finally, the translation result is returned to the user. The system can learn by itself on the basis of analyzing source documents, and predict target translation output by extracting features. The system is equipped with a translation engine based on the data source, which can realize interactive translation, and the final output can be achieved by processing the predicted translation output and based on the translator's understanding. Through the design of simulation experiments for system detection, the results show that the Chinese English translation model system has a high translation quality. By comparing it with the baseline system, it can be seen that both of them can translate sentences normally in 8 PR cycles (PR * 8, about 18% KSMR), and the data set improvement rate is about + 35 BLEU, that is, 76 BLEU is finally achieved. This proves that users can maintain the translation accuracy while reducing the number of effective interactions by using the optimized translation model to translate the target text, showing the effectiveness. In this paper, an interactive Chinese English translation system is designed by combining deep learning with cloud computing. [ABSTRACT FROM AUTHOR]

Purpose: To report the incidence and risk factors of adjacent vertebral fracture (AVF) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs). We focused to investigate effect of radiological or surgical features on AVF. Methods: All patients with OVCFs who were treated with PVP between January 2016 and December 2019 were retrospectively reviewed. Patients were followed up at least 12 months after procedure according to treatment protocol. AVF was defined as postoperatively recurrent intractable back pain and subsequently presence of fracture on magnetic resonance imaging (MRI) in adjacent levels. Clinical, radiological, and surgical factors potentially affecting occurrence of AVF were recorded and analyzed using univariate and multivariate analysis. Results: Totally, 1077 patients with 1077 fractured vertebrae who underwent PVP were enrolled in the study, after inclusion and exclusion criteria were met. Mean follow-up time was 24.3 ± 11.9 months (range, 12–59 months). AVF was identified in 98 (9.1%) patients. Univariate analysis showed that seven significant factors related to AVF were older age, non-traumatic fracture, cortical disruption on anterior wall, cortical disruption on lateral wall, basivertebral foramen, type-B leakage and type-C leakage. In multivariate analysis, two clinical factors, older age (P = 0.031) and non-traumatic fracture (P = 0.002), were significantly associated with AVF. However, any radiological or surgical factor did not reach significance in final model analysis. Conclusions: Incidence of AVF after PVP in patients with OVCFs was 9.1% (98/1077). Older age and non-traumatic fracture were two clinical risk factors for AVF. Neither radiological nor surgical feature was significantly correlated with AVF. [ABSTRACT FROM AUTHOR]

Safe and efficient cooperative planning of multiple robots in pedestrian participation environments is promising for applications. In this paper, a novel multi-robot social-aware efficient cooperative planner on the basis of off-policy multi-agent reinforcement learning (MARL) under partial dimension-varying observation and imperfect perception conditions is proposed. We adopt a temporal-spatial graph (TSG)-based social encoder to better extract the importance of social relations between each robot and the pedestrians in its field of view (FOV). Also, we introduce a K-step lookahead reward setting in the multi-robot RL framework to avoid aggressive, intrusive, short-sighted, and unnatural motion decisions generated by robots. Moreover, we improve the traditional centralized critic network with a multi-head global attention module to better aggregate local observation information among different robots to guide the process of the individual policy update. Finally, multi-group experimental results verify the effectiveness of the proposed cooperative motion planner. [ABSTRACT FROM AUTHOR]

Purposes: The paper aims to investigative the cacuses and impacts of In- and Vacancy-doped to 6H-SiC, expecting that improving optical properties of materials. Design-Using the first-principles calculations, we discuss the electronic structure and optical properties of different doped 6H-SiC systems. Findings: The results show that In-doped 6H-SiC becomes a direct bandgap p-type semiconductor and the energy bandgap is reduced from the intrinsic 2.059 to 1.515 eV. We demonstrate the stability of the systems through the formation energy analysis, meanwhile identify their physical origins and discuss applications of all structures in electronic devices within optical analysis. Find the energy beginning values of the VSi-doped and VC-doped systems' optical absorption spectrums and extend to 0.4 2 eV and 0.11 eV respectively compared with the original 3.23 eV. In the visible light region, the reflectivity images of the VC/VSi and (In, VSi)-codoped systems rise obviously. Conclusions: The optical properties of all doping systems were analyzed to be improved compared with the intrinsic, all above mentioned provide a theoretical basis for the fabrication of spintronic and optical devices. [ABSTRACT FROM AUTHOR]

Insufficient observations near the origin of the Kuroshio have led to incomplete understanding of the intraseasonal variability (ISV) of the Kuroshio. Direct measurements of the Kuroshio velocity were performed with an array of three profiler moorings (122.7°E, 123°E, and 123.3°E) along 18°N from January 2018 to February 2020. The ISV of the Kuroshio at 18°N was investigated based on a combination of mooring observations and global high-resolution HYbrid Coordinate Ocean Model reanalysis data. The estimated time-averaged transport in the upper 350 m across the observation transect was 6.5±2.6 Sv (1.0 Sv=106 m3/s). Two significant ISV peaks at 50–60 and ∼100 d were recognized in the power spectra of the meridional velocity and transport. Further analysis indicated that the ISV at 50–60 d was caused by westward-propagating eddies at average propagation speed of ∼13 cm/s and wavelength of ∼635 km. Another ISV peak at ∼100 d was mainly caused by northward-propagating eddies generated in the North Equatorial Current region. Further investigation indicated that the ISV of the Kuroshio at 18°N is dominated by meridional transport, rather than by the zonal migration of the Kuroshio main axis. Our findings provide a better understanding of the ISV of the Kuroshio east of Luzon Island. [ABSTRACT FROM AUTHOR]

The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC. [ABSTRACT FROM AUTHOR]

Ketogenic diet (KD) alleviates refractory epilepsy and reduces seizures in children. However, the metabolic/cell biologic mechanisms by which the KD exerts its antiepileptic efficacy remain elusive. Herein, we report that KD-produced β-hydroxybutyric acid (BHB) augments brain gamma-aminobutyric acid (GABA) and the GABA/glutamate ratio to inhibit epilepsy. The KD ameliorated pentetrazol-induced epilepsy in mice. Mechanistically, KD-produced BHB, but not other ketone bodies, inhibited HDAC1/HDAC2, increased H3K27 acetylation, and transcriptionally upregulated SIRT4 and glutamate decarboxylase 1 (GAD1). BHB-induced SIRT4 de-carbamylated and inactivated glutamate dehydrogenase to preserve glutamate for GABA synthesis, and GAD1 upregulation increased mouse brain GABA/glutamate ratio to inhibit neuron excitation. BHB administration in mice inhibited epilepsy induced by pentetrazol. BHB-mediated relief of epilepsy required high GABA level and GABA/glutamate ratio. These results identified BHB as the major antiepileptic metabolite of the KD and suggested that BHB may serve as an alternative and less toxic antiepileptic agent than KD. [ABSTRACT FROM AUTHOR]

This paper presents a study on the finite-time tracking control problem for a class of nonstrict-feedback nonlinear systems with immeasurable states and unknown nonlinearities. To model the unknown functions, fuzzy logic system technique is employed and a state observer is proposed. Using this observer, an event-triggered finite-time output feedback control scheme is designed based on the backstepping method. The proposed controller guarantees finite-time convergence of the tracking error and boundedness of all states in the closed-loop system. Additionally, it reduces the computing burden and communication resources. A C 1 switching function is introduced to overcome the singularity problem, and the Zeno behavior caused by the event-triggered strategy is avoided. Simulation results are presented to validate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The T-box family transcription factor 18 (Tbx18) has been found to play a critical role in regulating the development of the mammalian heart during the primary stages of embryonic development while the cellular heterogeneity and landscape of Tbx18-positive (Tbx18+) cardiac cells remain incompletely characterized. Here, we analyzed prior published single-cell RNA sequencing (scRNA-seq) mouse heart data to explore the heterogeneity of Tbx18+ cardiac cell subpopulations and provide a comprehensive transcriptional landscape of Tbx18+ cardiac cells during their development. Bioinformatic analysis methods were utilized to identify the heterogeneity between cell groups. Based on the gene expression characteristics, Tbx18+ cardiac cells can be classified into a minimum of two distinct cell populations, namely fibroblast-like cells and cardiomyocytes. In terms of temporal heterogeneity, these cells exhibit three developmental stages, namely the MEM stage, ML_P0 stage, and P stage Tbx18+ cardiac cells. Furthermore, Tbx18+ cardiac cells encompass several cell types, including cardiac progenitor-like cells, cardiomyocytes, and epicardial/stromal cells, as determined by specific transcriptional regulatory networks. The scRNA-seq results revealed the involvement of extracellular matrix (ECM) signals and epicardial epithelial-to-mesenchymal transition (EMT) in the development of Tbx18+ cardiac cells. The utilization of a lineage-tracing model served to validate the crucial function of Tbx18 in the differentiation of cardiac cells. Consequently, these findings offer a comprehensive depiction of the cellular heterogeneity within Tbx18+ cardiac cells. [ABSTRACT FROM AUTHOR]

Low-carbon design that reduces the life cycle carbon emissions of products at source is increasingly becoming an important direction in product design to mitigate global warming further. An important design principle should be minimizing the product's weight and movement efficiency while minimizing its life cycle carbon emissions. This paper proposed a product kinematic analysis based on carbon footprint. After that, it developed a flexible picking actuator for tomato fruit based on the Bricard mechanism. Through kinematic analysis of the picking actuator, the optimized design of the mechanism is followed by simulation tests to reduce the carbon footprint over its life cycle. A systematic carbon footprint calculation model for the Bricard mechanism-based picking actuator is also developed and applied in the product design process to verify the design mechanism's feasibility, method and technology. [ABSTRACT FROM AUTHOR]

Due to the limited transmission capabilities of terrestrial intelligent devices within the Internet of Remote Things (IoRT), this paper proposes an optimization scheme aimed at enhancing data transmission rate while ensuring communication reliability. This scheme focuses on multi-unmanned aerial vehicle (UAV)-assisted IoRT data communication within the satellite–aerial–terrestrial integrated network (SATIN), which is one of the key technologies for the sixth generation (6G) networks. To optimize the system's data transmission rate, we introduce a multi-dimensional coverage and power optimization (CPO) algorithm, rooted in the block coordinate descent (BCD) method. This algorithm concurrently optimizes various parameters, including the number and deployment of UAVs, the correlation between IoRT devices and UAVs, and the transmission power of both devices and UAVs. To ensure comprehensive coverage of a large-scale randomly distributed array of terrestrial devices, combined with machine learning algorithm, we present the Dynamic Deployment based on K-means (DDK) algorithm. Additionally, we address the non-convexity challenge in resource allocation for transmission power through variable substitution and the successive convex approximation technique (SCA). Simulation results substantiate the remarkable efficacy of our CPO algorithm, showcasing a maximum 240% improvement in the uplink transmission rate of IoRT data compared to conventional methods. [ABSTRACT FROM AUTHOR]

The sparse reward problem has long been one of the most challenging topics in the application of reinforcement learning (RL), especially in complex multi-agent systems. In this paper, a hierarchical multi-agent RL architecture is developed to address the sparse reward problem of cooperative tasks in continuous domain. The proposed architecture is divided into two levels: the higher-level meta-agent implements state transitions on a larger time scale to alleviate the sparse reward problem, which receives global observation as spatial information and formulates sub-goals for the lower-level agents; the lower-level agent receives local observation and sub-goal and completes the cooperative tasks. In addition, to improve the stability of the higher-level policy, a channel is built to transmit the lower-level policy to the meta-agent as temporal information, and then a two-stream structure is adopted in the actor-critic networks of the meta-agent to process spatial and temporal information. Simulation experiments on different tasks demonstrate that the proposed algorithm effectively alleviates the sparse reward problem, so as to learn desired cooperative policies. [ABSTRACT FROM AUTHOR]

Introduction: Tumor immunotherapy has recently emerged as a crucial focal point in oncology treatment research. Among tumor immunotherapy approaches, tumor immune checkpoint inhibitors (ICIs) have attracted substantial attention in clinical research. However, this treatment modality has benefitted only a limited number of patients. We conducted a meta-analysis of various biomarkers to decipher their prognostic implications in patients with head and neck squamous cell carcinoma (HNSCC) who are treated with ICIs, and thus identify predictive markers with practical clinical relevance. Methods: A systematic search of electronic databases was conducted to identify clinical studies that examined the correlation between biomarkers and treatment outcomes in the HNSCC patients. The included articles were screened and analyzed to extract data regarding overall survival (OS) and progression-free survival (PFS). Results: The relationship between the biomarkers included in the summary and prognosis was as follows: HPV positivity was associated with improved OS (HR = 0.76, 95% CI = 0.58–1.99), PFS (HR = 1.16, 95% CI = 0.81–1.67), and response (OR = 1.67, 95% CI = 1.37–2.99). PD-L1 positivity was associated with OS (HR = 0.71, 95% CI = 0.59–0.85), PFS (HR = 0.56 95% CI = 0.43–0.73), and response (OR = 2.16, 95% CI = 1.51–3.10). Neither HPV positivity nor PD-L1 positivity was associated with DCR. The following markers were collected for OS and PFS data and were associated with longer OS: lower Glasgow prognostic score (GPS/mGPS) grading, lower PS grading, high body mass index (BMI), low neutrophil-to-lymphocyte ratio (NLR), low platelet-to-lymphocyte ratio (PLR), high albumin (Alb), low lactate dehydrogenase (LDH). Factors associated with better PFS were lower GPS/mGPS grading, lower PS grading, high BMI, low NLR, high absolute lymphocyte count, and low LDH. Hyperprogressive disease was associated with worse OS and PFS. Fewer clinical studies have been completed on the tumor microenvironment and hypoxia, microsatellite instability/DNA mismatch repair, and microbiome and systematic analysis is difficult. Conclusion: In our meta-analysis, different immune checkpoint factors were associated with different prognoses in HNSCC patients receiving immunotherapy. HPV, PD-L1, BMI, Alb, HPD, PS, GPS/mGPS, LDH, NLR, and PLR predicted the ICI outcome in HNSCC patients. [ABSTRACT FROM AUTHOR]

Background: Acupuncture therapy is believed to have therapeutic potential for patients suffering from chemotherapy-induced peripheral neuropathy (CIPN). This umbrella review aims to summarize and evaluate the evidence from current systematic reviews/meta-analyses (SRs/MAs) on the effectiveness of acupuncture treatment for CIPN. Methods: We conducted a comprehensive search in eight electronic databases for SRs/MAs that included RCTs on acupuncture treatment for CIPN. Two separate researchers independently evaluated the methodological quality, reporting quality, and evidence quality of the SRs/MAs that were included in the study. Additionally, we examined the extent of overlap among the included RCTs by calculating the corrected covered area (CCA). Furthermore, we assessed the dependability of the effect sizes by conducting excess significance tests. We conducted a quantitative synthesis of all RCTs included in the SRs/MAs to obtain objective and updated conclusions. Furthermore, we also conducted an analysis of the acupuncture points used in RCTs. Results: This umbrella review includes 9 SRs/MAs, and their methodological quality, risk of bias, reporting quality, and evidence quality were all deemed unsatisfactory. Out of the 9 SRs/MAs, 28 RCTs were included, with a total CCA of 25.4%, indicating a high degree of overlap. The test of super-significance did not yield any significant results. Our updated meta-analysis suggests that CIPN patients can benefit from acupuncture therapy, as indicated by effectiveness in measures including BPI-SF, VAS, FACT-NTX, NRS, SCV, and NCI-CTCAE. Egger's test and sensitivity analysis demonstrate the reliability and stability of this conclusion. The commonly used acupuncture points in the current RCTs include ST36, LI11, LI4, LR3, and SP6. Conclusion: Based on the existing evidence, acupuncture is effective and safe for patients with CIPN, as it can significantly improve effective rate, pain symptoms, quality of life, and nerve conduction velocity. However, given the low quality of current evidence, we should be cautious in interpreting this conclusion. [ABSTRACT FROM AUTHOR]

Objective: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC). Methods: Based on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models. A logistic regression method was used to construct the predictive models for LNM in PTCs. Results: Calcifications detected by the network model and experienced radiologists had an agreement degree of above 90%. The novel quantitative parameters of US calcification defined in this study showed a significant difference between PTC patients with and without cervical LNM (p < 0.05). The calcification parameters were beneficial to predicting the LNM risk in PTC patients. The LNM prediction model using these calcification parameters combined with patient age and other US nodular features showed a higher specificity and accuracy than the calcification parameters alone. Conclusions: Our models not only detect the calcifications automatically, but also have value in predicting cervical LNM risk of PTC patients, thereby making it possible to investigate the relationship between calcifications and highly invasive PTC in detail. Clinical relevance statement: Due to the high association of US microcalcifications with thyroid cancers, our model will contribute to the differential diagnosis of thyroid nodules in daily practice. Key Points: • We developed an ML-based network model for automatically detecting and quantifying calcifications within thyroid nodules in US images. • Three novel parameters for quantifying US calcifications were defined and verified. • These US calcification parameters showed value in predicting the risk of cervical LNM in PTC patients. [ABSTRACT FROM AUTHOR]

Gamma radiation is progressively emerging as an effective method to enhance the sterilization efficiency of medical plastics including Polyvinyl chloride (PVC). The parameters of the radiation facility will affect the efficiency of radiation sterilization. To investigate these effects, we simulate the gamma radiation sterilization performance of PVC material sample using Monte Carlo Method. The simulation results indicated that compared with the sterilization time of 20–90 min from high-temperature steam sterilization of medical waste, by optimizing the parameters of the model radiation facility, the radiation sterilization time can be reduced to 6.61 min. The optimized model facility parameters are as follows: the gamma photon energy is 1.25 MeV, the model space is 300 × 300 × 300 cm3, the reflective layer material is concrete and its thickness is 8 cm, the PVC sample layer area is 100 × 100 cm2, the distance between the radiation source and the PVC sample layer is 150 cm, the energy deposition in the bottom layer of the PVC sample layer is 1.31315 × 10–6 MeV/g. This study offers a potentially feasible way for PVC sterilization, while also providing a crucial reference for the further promotion and application of radiation sterilization technology. [ABSTRACT FROM AUTHOR]

Snapshot compressive imaging (SCI) systems aim to capture high-dimensional ( ≥ 3 D) images in a single shot using 2D detectors. SCI devices consist of two main parts: a hardware encoder and a software decoder. The hardware encoder typically consists of an (optical) imaging system designed to capture compressed measurements. The software decoder, on the other hand, refers to a reconstruction algorithm that retrieves the desired high-dimensional signal from those measurements. In this paper, leveraging the idea of deep unrolling, we propose an SCI recovery algorithm, namely GAP-net, which unfolds the generalized alternating projection (GAP) algorithm. At each stage, GAP-net passes its current estimate of the desired signal through a trained convolutional neural network (CNN). The CNN operates as a denoiser projecting the estimate back to the desired signal space. For the GAP-net that employs trained auto-encoder-based denoisers, we prove a probabilistic global convergence result. Finally, we investigate the performance of GAP-net in solving video SCI and spectral SCI problems. In both cases, GAP-net demonstrates competitive performance on both synthetic and real data. In addition to its high accuracy and speed, we show that GAP-net is flexible with respect to signal modulation implying that a trained GAP-net decoder can be applied in different systems. Our code is available at https://github.com/mengziyi64/GAP-net. [ABSTRACT FROM AUTHOR]

Experiences with stigma and discrimination in healthcare settings are associated with negative health outcome for persons with HIV (PWH). PWH may experience discrimination due to the intersection of multiple marginalized social identities. Describing these experiences is important for informing interventions and strategies to reduce stigma and discrimination. We report experiences with discrimination in HIV healthcare settings attributed to multiple characteristics, e.g., sexual orientation, race/ethnicity, income, or social class, and/or injection drug use, among a nationally representative sample of persons with diagnosed HIV in the United States using data from the Medical Monitoring Project (MMP). We calculated weighted prevalences and associated 95% confidence intervals for any discrimination and discrimination attributed to multiple characteristics (intersectional discrimination). Among those experiencing discrimination, nearly 1 in 4 persons reported intersectional discrimination, with a higher burden among key populations of focus for HIV prevention and treatment. Discrimination was attributed to HIV status (62.5%), sexual orientation (60.4%), and race/ethnicity (54.3%). Persons who experienced intersectional discrimination were less likely to have a regular HIV care provider, have trust in HIV care or treatment information from healthcare providers, and be antiretroviral treatment or HIV care visit adherent. Future studies should explore methods to operationalize and assess experiences with intersectional stigma and discrimination and use the outcomes to inform qualitative research that provides more context and a deeper understanding of experiences with intersectional discrimination among PWH. [ABSTRACT FROM AUTHOR]

Objectives: The accurate prediction of osteoarthritis (OA) severity in patients can be helpful to make the proper decision of intervention. This study aims to build up a powerful model to assess predictive risk factors and severity of knee osteoarthritis (KOA) in the clinical scenario. Methods: A total of 4796 KOA cases and 1205 features were selected by feature selections from the public OA database, Osteoarthritis Initiative (OAI). Six machine learning-based models were constructed and compared for the accuracy of OA prediction. The gradient-boosting decision tree was used to identify important prediction features in the extreme gradient boosting (XGBoost) model. The performance of models was evaluated by F1-score. Results: Twenty features were determined as predictors for KOA risk and severity, including the subject characteristics, knee symptoms/risk factors and physical exam. The XGBoost model demonstrated 100% prediction accuracy for 54.7% of examined samples, and the remaining 45.3% of samples showed Kellgren and Lawrence (KL) gradings very close to the actual levels. It showed the highest prediction accuracy with an F1-score of 0.553 among the tested six models. Conclusions: We demonstrate that the XGBoost is the best model for the prediction of KOA severity in the six examined models. In addition, 20 risk features were determined as the essential predictors of KOA, including the physical exam, knee symptoms/risk factors and subject characteristics, which may be useful for the identification of high-risk KOA cases and for making appropriate treatment decisions as well. [ABSTRACT FROM AUTHOR]

The mechanism of deleted in lymphocytic leukemia 2 (DLEU2)-long non-coding RNA in tumors has become a major point of interest in recent research related to the occurrence and development of a variety of tumors. Recent studies have shown that the long non-coding RNA DLEU2 (lncRNA-DLEU2) can cause abnormal gene or protein expression by acting on downstream targets in cancers. At present, most lncRNA-DLEU2 play the role of oncogenes in different tumors, which are mostly associated with tumor characteristics, such as proliferation, migration, invasion, and apoptosis. The data thus far show that because lncRNA-DLEU2 plays an important role in most tumors, targeting abnormal lncRNA-DLEU2 may be an effective treatment strategy for early diagnosis and improving the prognosis of patients. In this review, we integrated lncRNA-DLEU2 expression in tumors, its biological functions, molecular mechanisms, and the utility of DLEU2 as an effective diagnostic and prognostic marker of tumors. This study aimed to provide a potential direction for the diagnosis, prognosis, and treatment of tumors using lncRNA-DLEU2 as a biomarker and therapeutic target. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multi-spectral imaging is a fundamental tool characterizing the constituent energy of scene radiation. However, current multi-spectral video cameras cannot scale up beyond megapixel resolution due to optical constraints and the complexity of the reconstruction algorithms. To circumvent the above issues, we propose a tens-of-megapixel handheld multi-spectral videography approach (THETA), with a proof-of-concept camera achieving 65-megapixel videography of 12 wavebands within visible light range. The high performance is brought by multiple designs: We propose an imaging scheme to fabricate a thin mask for encoding spatio-spectral data using a conventional film camera. Afterwards, a fiber optic plate is introduced for building a compact prototype supporting pixel-wise encoding with a large space-bandwidth product. Finally, a deep-network-based algorithm is adopted for large-scale multi-spectral data decoding, with the coding pattern specially designed to facilitate efficient coarse-to-fine model training. Experimentally, we demonstrate THETA's advantageous and wide applications in outdoor imaging of large macroscopic scenes. Current multispectral video cameras can hardly scale up beyond megapixel resolution and do not apply to large-scale scenes. The authors design a tens-ofmegapixel handheld multi-spectral imaging scheme, build a setup achieving 65-megapixel videography of 12 wavebands, and show its wide applications. [ABSTRACT FROM AUTHOR]

The aberrantly up-regulated CDK9 can be targeted for cancer therapy. The CDK inhibitor dinaciclib (Dina) has been found to drastically sensitizes cancer response to TRAIL-expressing extracellular vesicle (EV-T). However, the low selectivity of Dina has limited its application for cancer. We propose that CDK9-targeted siRNA (siCDK9) may be a good alternative to Dina. The siCDK9 molecules were encapsulated into EV-Ts to prepare a complexed nanodrug (siEV-T). It was shown to efficiently suppress CDK9 expression and overcome TRAIL resistance to induce strikingly augmented apoptosis in lung cancer both in vitro and in vivo, with a mechanism related to suppression of both anti-apoptotic factors and nuclear factor-kappa B pathway. Therefore, siEV-T potentially constitutes a novel, highly effective and safe therapy for cancers. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) have been widely concerned as an important precursor of haze. The petrochemical industry not only has many VOC emission sources, but also produces a large amount of VOCs. Although some scholars have analyzed the emission sources of VOCs in the oilfield industry, there still exists the gap in quantitative accounting the emission of organic volatiles from oilfield sewage pools. Moreover, such studies lack certain standardization, accuracy and validity in China. At present, most of the sewage pools in oilfield stations are open-ended. A large amount of VOCs are released into the surrounding environment during the storage of oily wastewater, which has caused serious environmental pollution. In this work, considering six significant influencing factors, a fast VOC emission accounting method from sewage pool in oilfield has been established. To the best of our knowledge, this is the first time to conduct VOC emission quantitative accounting study based on multi-field coupling numerical simulation technology, method of experimental design, and statistical analysis of the response surface methodology. Through comparing the results of numerical simulation with COMSOL, laboratory experiment, and the published literature, the results show that the error is in the range of reasonable, which can satisfy the rapid accounting of VOC emissions from sewage pool in oilfield. [ABSTRACT FROM AUTHOR]

Dual-specificity tyrosine phosphorylation regulated kinase 1A (DYRK1A) signaling is involved in the dynamic balance of catabolism and anabolism in articular chondrocytes. This study aimed to investigate the roles and mechanism of DYRK1A in the pathogenesis of osteoarthritis (OA). The expressions of DYRK1A and its downstream signal epidermal growth factor receptor (EGFR) were detected in the cartilage of adult wild-type mice with destabilized medial meniscus (DMM) and articular cartilage of patients with OA. We measured the progression of osteoarthritis in chondrocyte-specific knockout DYRK1A(DYRK1A-cKO) mice after DMM surgery. Knee cartilage was histologically scored and assessed the effects of DYRK1A deletion on chondrocyte catabolism and anabolism. The effect of inhibiting EGFR signaling in chondrocytes from DYRK1A-cKO mice was analyzed. Trauma-induced OA mice and OA patients showed downregulation of DYRK1A and EGFR signaling pathways. Conditional DYRK1A deletion aggravates DMM-induced cartilage degeneration, reduces the thickness of the superficial cartilage, and increases the number of hypertrophic chondrocytes. The expression of collagen type II, p-ERK, and aggrecan was also downregulated, and the expression of collagen type X was upregulated in the articular cartilage of these mice. Our findings suggest that DYRK1A delays the progression of knee osteoarthritis in mice, at least in part, by maintaining EGFR-ERK signaling in articular chondrocytes. [ABSTRACT FROM AUTHOR]

Video Snapshot compressive imaging (SCI) is a promising technique to capture high-speed videos, which transforms the imaging speed from the detector to mask modulating and only needs a single measurement to capture multiple frames. The algorithm to reconstruct high-speed frames from the measurement plays a vital role in SCI. In this paper, we consider the promising reconstruction algorithm framework, namely plug-and-play (PnP), which is flexible to the encoding process comparing with other deep learning networks. One drawback of existing PnP algorithms is that they use a pre-trained denoising network as a plugged prior while the training data of the network might be different from the task in real applications. Towards this end, in this work, we propose the online PnP algorithm which can adaptively update the network's parameters within the PnP iteration; this makes the denoising network more applicable to the desired data in the SCI reconstruction. Furthermore, for color video imaging, RGB frames need to be recovered from Bayer pattern or named demosaicing in the camera pipeline. To address this challenge, we design a two-stage reconstruction framework to optimize these two coupled ill-posed problems and introduce a deep demosaicing prior specifically for video demosaicing in SCI. Extensive results on both simulation and real datasets verify the superiority of our adaptive deep PnP algorithm. Code is available at https://github.com/xyvirtualgroup/AdaptivePnP_SCI. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study focuses on a novel event-triggered boundary control design of an uncertain flexible manipulator subject to input constraints and external disturbances. To this end, a novel boundary control law is developed based on the Lyapunov stability theory to suppress elastic deflection and regulate the manipulator to track the desired angular position. Moreover, to approximate the unknown functions and compensate for the effect of input constraints, the neural network technique is adopted with an arbitrarily adjustable approximating error. Simultaneously, the event-triggering mechanism is incorporated with a controller design to alleviate the communication load and the execution rate of the actuator. Lastly, numerical simulations are performed to demonstrate the effectiveness of the derived scheme. [ABSTRACT FROM AUTHOR]

Enzymes achieve high catalytic activity with their elaborate arrangements of amino acid residues in confined optimized spaces. Nevertheless, when exposed to complicated environmental implementation scenarios, including high acidity, organic solvent and high ionic strength, enzymes exhibit low operational stability and poor activity. Here, we report a metal-organic frameworks (MOFs)-based artificial enzyme system via second coordination sphere engineering to achieve high hydrolytic activity under mild conditions. Experiments and theoretical calculations reveal that amide cleavage catalyzed by MOFs follows two distinct catalytic mechanisms, Lewis acid- and hydrogen bonding-mediated hydrolytic processes. The hydrogen bond formed in the secondary coordination sphere exhibits 11-fold higher hydrolytic activity than the Lewis acidic zinc ions. The MOFs exhibit satisfactory degradation performance of toxins and high stability under extreme working conditions, including complicated fermentation broth and high ethanol environments, and display broad substrate specificity. These findings hold great promise for designing artificial enzymes for environmental remediation.Enzymatic degradation of pollutants under mild conditions is promising for efficient degradation of environmental contaminants under facile operation conditions, but natural enzymes tend to lose enzymatic activity in environmental application scenarios. Here, the authors report a metal-organic frameworks-based artificial enzyme system to achieve high hydrolytic activity under mild conditions. [ABSTRACT FROM AUTHOR]

Manganese dioxide (MnO2) is promising cathode materials for aqueous rechargeable zinc ion batteries (ARZIBs) owing to their diverse polymorphy, high operating voltage and environmental benignity. However, the sluggish electrochemical kinetics and poor cycling stability are major issues to binder their practical applications. Herein, potassium pre-intercalated manganese dioxide (KMO) nanoflakes were fabricated via simple hydrothermal method. The KMO nanoflakes are assigned to be layered birnessite phase with layer spacing of 7.2 Å and the atomic ratio between K and Mn is around 1:3. As cathode for ARZIBs, the KMO electrode delivered specific capacity of 252 mAh g−1 at current density of 0.1 A g−1 and gained specific capacity of 288 mAh g−1 after 100 cycles, being with the retention of 114%. The KMO cathode displayed outstanding cycling stability that under large current density of 2.0 A g−1 it could retain 84.3% of initial capacity after 4000 cycles. By analyzing the electrochemical dynamics and phase evolution of KMO electrode during discharge/charge process, it was validated that the potassium pre-intercalation in MnO2 framework not only provides enlarged spacing for Zn2+/H+ ion transferring but also is served as strong support to stabilize the layer structure, resulting in high energy density and better cycling stability. Our work provides rational way for designing high-performance manganese-based cathode materials for ARZIBs. [ABSTRACT FROM AUTHOR]

This paper presents a novel model for flow angles at off-design conditions. The model is based on energy conservation in turbomachinery. By the mean of average process, statistical properties of the wake profile are related to the flow angle and mainstream parameters; then by adopting the classic flow topology of behind-blade wakes, the effect of operating conditions is included in the plot and a model for flow angle is thus developed. The advantage of the model is that it can achieve off-design flow angle estimations with design flow characteristics and it is compatible with CFD-based throughflow methods. This model is then implemented into a standard CFD-based throughflow solver from the previous research to estimate off-design characteristics of blade rows with proper input-output strategies. Furthermore, the effect of loss upon flow angle is also modeled as corrections to the proposed model. The model is validated on a compressor stage and the results show promising accuracy for off-design flow angle estimations. [ABSTRACT FROM AUTHOR]

This paper studies the event-based decentralized adaptive finite-time tracking control problem of the interconnected nonlinear time-varying systems. A novel tracking control strategy associating event-triggered techniques, dynamic surface control, and finite-time control is presented. Correspondingly, the newly designed controller not only ensures finite-time convergence but also decreases the communication burden between the controller and the actuator. Moreover, the complexity explosion problem caused by the backstepping design procedure can be excluded. In addition, the difficulty caused by the system uncertainty is solved by utilizing bound estimation methods and constructing a suitable smooth function. Simulation results verify the effectiveness of our proposed control strategy. [ABSTRACT FROM AUTHOR]

Tailoring the band structure of photocatalysts by elemental doping to enhance redox ability and carrier separation kinetics is a promising strategy for designing high-efficiency catalysts for uranium (U(VI)) photoreduction. Herein, we report enhanced visible-light spectral absorption by tuning the energy bandgap of flower-like ZnO microspheres by doping Co. Accordingly, Co doping narrows the bandgap and increases the photogenerated carrier concentration, thereby promoting the U(VI) photocatalytic reduction. Especially, the removal ratio of U(VI) by Co-doped ZnO microspheres reaches 98.4% with a remarkable adsorption capacity of 1645 mg/g for U(VI) uptake in 400 ppm U(VI) solution. In addition, the Co-doped ZnO microspheres present desired U(VI) reduction ratio in a wide range of U(VI) concentrations, as well as excellent reusability, and resistance to ion interference during photocatalysis. This paper provides a feasible strategy for enhancing the photocatalytic reduction of uranium by introducing metal element doping on the metal oxide surface. [ABSTRACT FROM AUTHOR]

Background and aim: Prospective cohort studies have suggested that sugar-sweetened beverages (SSBs) intake is significantly associated with the risk of colorectal cancer (CRC). However, it remains unclear whether this observed association was susceptible to potential confounding factors due to the long-term development process of CRC, and the risk of CRC associated with sweet beverages has rarely been reported. We aimed to investigate the association between SSBs/sweet beverages and CRC risk. Methods: We performed two-sample Mendelian randomization (MR) analysis using independent genetic variants for SSBs and sweet beverages from a published genome-wide association study (GWAS). Summary statistics for instrument-outcome associations from two databases for malignant neoplasms of the colon and the rectum (FinnGen and UK Biobank). The inverse weighted method (IVW) meta-analysis was the main method used to estimate the relationship, and sensitivity analyses were performed with Cochran's Q test, leave-one-out analysis, MR–Egger regression, Steiger filtering, and the MR PRESSO test. Results: Genetically predicted SSBs intake was associated with a higher colonic malignant neoplasms risk (odds ratio (OR): 1.013; 95% confidence interval (CI) 1.001, 1.026; P = 0.036) in a combined sample size of 579,986 individuals (4029 cases). Such a significant causal effect of SSBs on rectal malignant neoplasms or sweet beverages on CRC was not observed. Conclusion: Our findings corroborated a causal association between SSBs and colonic malignant neoplasms risk but did not support such a relationship in the analysis of the rectal malignant neoplasms nor the sweet beverage intake, which might be interpreted with caution and further confirmed. [ABSTRACT FROM AUTHOR]

Purpose: To compare the effectiveness of TiRobot-assisted kyphoplasty with that of the traditional fluoroscopy-assisted approach in treating multilevel osteoporotic vertebral compression fractures. Methods: In this retrospective study, we collected data from 71 patients (TiRobot-assisted group, n = 39; fluoroscopy-assisted group, n = 32) with multilevel osteoporotic vertebral compression fracture treated with unilateral traditional TiRobot-assisted or fluoroscopy-assisted percutaneous kyphoplasty. The operative time, infusion volume, length of stay (LOS), hospital expenses, visual analog scale (VAS), Oswestry Disability Index (ODI), radiation exposure, puncture deviation, anterior height of diseased vertebrae, local kyphotic angle, bone cement distribution, and bone cement leakage were compared between the TiRobot- and fluoroscopy-assisted groups. Results: Of the 257 treated vertebrae, the average amount of bone cement injected in the TiRobot-assisted (142 vertebrae) and fluoroscopy-assisted (115 vertebrae) groups was 4.6 mL and 4.5 mL, respectively. The VAS score was significantly lower in the TiRobot-assisted group at 24 hours post-operatively (p = 0.006). The X-ray frequency was 34.7 times in the TiRobot-assisted group and 51.7 times in the fluoroscopy-assisted group (p < 0.001). In addition to the operative time, cumulative radiation dose for the surgeon and patient was significantly lower in the TiRobot-assisted group. The hospital expenses of the TiRobot-assisted group were significantly higher (p < 0.001). The puncture deviation and bone cement distribution were better in the TiRobot-assisted group (p < 0.001). Bone cement leakage was found in 18 and 29 cases in the TiRobot- and fluoroscopy-assisted groups, respectively (p = 0.010). One patient in the fluoroscopy-assisted group experienced radiculopathy due to a misplaced puncture but recovered in three months. No radiculopathy was observed in the TiRobot-assisted group. Conclusions: TiRobot-assisted percutaneous multilevel kyphoplasty is more accurate and has smaller radiometry, a more uniform bone cement distribution, and lower bone cement leakage. This method was therefore accurate and safe. [ABSTRACT FROM AUTHOR]

Hair loss is a common skin disease that causes intense emotional suffering. Hair regeneration in a personalized area is highly desirable for patients with different balding conditions. However, the existing pharmaceutical treatments have difficulty precisely regenerating hair in a desired area. Here, we show a method to precisely control the hair regeneration using customized microneedle arrays (MNAs). The MNA with a customized shape is fast fabricated by a static optical projection lithography process in seconds, which is a 3D printing technology developed by our group. In the mouse model, MNA treatment could induce hair regrowth in a defined area corresponding to the customized shape of MNA. And the regenerated hair promoted by MNAs had improved quality. Cellular and molecular analysis indicated that MNA treatment could recruit macrophages in situ and then initiate the proliferation of hair follicle stem cells, thereby improving hair regeneration. Meanwhile, the activation of the Wnt/β-catenin signaling pathway was observed in hair follicles. The expressions of Hgf, Igf 1 and Tnf-α were also upregulated in the treated skin, which may also be beneficial for the MNA-induced hair regeneration. This study provides a strategy to precisely control hair regeneration using customized microneedle arrays by recruiting macrophages in situ, which holds the promise for the personalized treatment of hair loss. [ABSTRACT FROM AUTHOR]