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Background Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. Methods From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared. Results The sensitivity of mNGS in mixed pulmonary infection diagnosis was much higher than that of conventional test (97.2% vs 13.9%; P <  0.01), but the specificity was the opposite (63.2% vs 94.7%; P =  0.07). The positive predictive value of mNGS was 83.3% (95% CI, 68.0–92.5%), and the negative predictive value was 92.3% (95% CI, 62.1–99.6%). A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1 (1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1 (1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected by mNGS was Human cytomegalovirus in our study, which was identified in 19 (34.5%) cases of mixed pulmonary infection . Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7 (12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection. Conclusions mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity. Trial registration ( retrospectively registered): ChiCTR1900023727 . Registrated 9 JUNE 2019.

Electrostatic interaction is strong but usually diminishes in high ionic-strength environments. Biosystems can use this interaction through adjacent cationic–aromatic amino acids sequence of proteins even in a saline medium. Application of such specific sequence to the development of cationic polymer materials adhesive to negatively charged surfaces in saline environments is challenging due to the difficulty in controlling the copolymer sequences. Here, we discover that copolymers with adjacent cation–aromatic sequences can be synthesized through cation–π complex-aided free-radical polymerization. Sequence controlled hydrogels from diverse cation/aromatic monomers exhibit fast, strong but reversible adhesion to negatively charged surfaces in seawater. Aromatics on copolymers are found to enhance the electrostatic interactions of their adjacent cationic residues to the counter surfaces, even in a high ionic-strength medium that screens the electrostatic interaction for common polyelectrolytes. This work opens a pathway to develop adhesives using saline water. Specific sequences are essential for the development of cationic polymers that can adhere to negatively charged surfaces in saline environments. Here, the authors show that copolymers with adjacent cation–aromatic sequences can be synthesized through cation–π complex-aided free-radical polymerization, which exhibit fast, strong, but reversible adhesion.

Background and objectives Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes. Results The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases. Conclusion MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.

To comprehensively investigate risk factors for proliferative vitreoretinopathy (PVR) after retinal detachment (RD) surgery. PubMed, Embase, Cochrane Library, and Web of Science were systematically searched until May 22, 2023. Risk factors included demographic and disease-related risk factors. Odds ratios (ORs) and weighted mean differences (WMDs) were used as the effect sizes, and shown with 95% confidence intervals (CIs). Sensitivity analysis was conducted. The protocol was registered with PROSPERO (CRD42022378652). Twenty-two studies of 13,875 subjects were included in this systematic review and meta-analysis. Increased age was associated with a higher risk of postoperative PVR (pooled WMD = 3.98, 95%CI: 0.21, 7.75, P = 0.038). Smokers had a higher risk of postoperative PVR than non-smokers (pooled OR = 5.07, 95%CI: 2.21-11.61, P<0.001). Presence of preoperative PVR was associated with a greater risk of postoperative PVR (pooled OR = 22.28, 95%CI: 2.54, 195.31, P = 0.005). Presence of vitreous hemorrhage was associated with a greater risk of postoperative PVR (pooled OR = 4.12, 95%CI: 1.62, 10.50, P = 0.003). Individuals with aphakia or pseudophakia had an increased risk of postoperative PVR in contrast to those without (pooled OR = 1.41, 95%CI: 1.02, 1.95, P = 0.040). The risk of postoperative PVR was higher among patients with macula off versus those with macula on (pooled OR = 1.85, 95%CI: 1.24, 2.74, P = 0.002). Extent of RD in patients with postoperative PVR was larger than that in patients without (pooled WMD = 0.31, 95%CI: 0.02, 0.59, P = 0.036). Patients with postoperative PVR had longer duration of RD symptoms than those without (pooled WMD = 10.36, 95%CI: 2.29, 18.43, P = 0.012). Age, smoking, preoperative PVR, vitreous hemorrhage, aphakia or pseudophakia, macula off, extent of RD, and duration of RD symptoms were risk factors for postoperative PVR in patients undergoing RD surgery, which may help better identify high-risk patients, and provide timely interventions.

The three performance indexes of the space robot, travel time, energy consumption, and smoothness, are the key to its important role in space exploration. Therefore, this paper proposes a multi-objective trajectory planning method for robots. Firstly, the kinematics and dynamics of the Puma560 robot are analyzed to lay the foundation for trajectory planning. Secondly, the joint space trajectory of the robot is constructed with fifth-order B-spline functions, realizing the continuous position, velocity, acceleration, and jerk of each joint. Then, the improved multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the trajectory, and the distribution uniformity, convergence, and diversity of the obtained Pareto front are good. The improved MOPSO algorithm can realize the optimization between multiple objectives and obtain the trajectory that meets the actual engineering requirements. Finally, this paper implements the visualization of the robot’s joints moving according to the optimal trajectory.

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Background In 2021, nationwide Diagnosis-Related Groups (DRG) reform was initiated and the DRG-based medical insurance payment method has been widely applied in China. However, few studies focused on pediatric leukemia patients where the inherent tension between cost control and nursing quality is particularly pronounced and systematically explored whether DRG has controlled unnecessary expenditures, optimized cost structures, while maintaining the quality, which warrants urgent evaluation. Method This study analyzed hospitalization data for pediatric leukemia patients treated at the Children’s Specialized Hospital in City U between October 2022 and July 2024. Patients were categorized into two groups based on whether their hospitalizations were settled using the DRG payment system. Quantile regression, beta regression with a logit link function, and logistic regression were employed to evaluate the effect of DRG reform on hospitalization costs, the proportion of medical expenses, 30-day readmission rates and improvement, respectively. Propensity Score Matching is used to balance the characteristic differences between samples in DRG group and the Non-DRG group. Result Both before and after matching, patients under the DRG payment model consistently exhibited a significantly lower proportion of medication expenses (OR = 0.774–0.806), a higher proportion of diagnostic testing expenses (OR = 1.135–1.157) and a higher proportion of consumables expenses (OR = 1.127–1.151 ) compared to those under the non-DRG model. No significant effects were observed on outcomes variables of hospitalization expense and medical quality. Conclusion As an early situational evaluation, this study indicates that while DRG reform has not yet reduced total expenditures for pediatric leukemia due to rigid treatment protocols, it has significantly altered the cost structure by shifting resource allocation from medications to diagnostics and consumables. This transformation reflects an initial optimization of cost structure but also highlights the need to monitor the rationality of diagnostic expenditures to prevent compensatory cost-shifting. Furthermore, no effect on proportion of treatment expenses suggests that the valuation of medical staff labor requires further improvement. DRG reform is limited in its ability to reduce overall expenditures, efforts should continue to control medical costs by optimizing DRG payment methods, enhancing medical security for children with leukemia, and strengthening expense monitoring.

Wearable electronics presage a future in which healthcare monitoring and rehabilitation are enabled beyond the limitation of hospitals, and self‐powered sensors and energy generators are key prerequisites for a self‐sustainable wearable system. A triboelectric nanogenerator (TENG) based on textiles can be an optimal option for scavenging low‐frequency and irregular waste energy from body motions as a power source for self‐sustainable systems. However, the low output of most textile‐based TENGs (T‐TENGs) has hindered its way toward practical applications. In this work, a facile and universal strategy to enhance the triboelectric output is proposed by integration of a narrow‐gap TENG textile with a high‐voltage diode and a textile‐based switch. The closed‐loop current of the diode‐enhanced textile‐based TENG (D‐T‐TENG) can be increased by 25 times. The soft, flexible, and thin characteristics of the D‐T‐TENG enable a moderate output even as it is randomly scrunched. Furthermore, the enhanced current can directly stimulate rat muscle and nerve. In addition, the capability of the D‐T‐TENG as a practical power source for wearable sensors is demonstrated by powering Bluetooth sensors embedded to clothes for humidity and temperature sensing. Looking forward, the D‐T‐TENG renders an effective approach toward a self‐sustainable wearable textile nano‐energy nano‐system for next‐generation healthcare applications. A universal strategy for enhancing the current and charging speed of textile‐based triboelectric nanogenerator (TENG) toward a wearable textile Nano‐energy nano‐system for next‐generation healthcare applications is proposed. The soft, flexible, and thin TENG textile is capable of scavenging energy from various body motions effectively, and the enhanced current can directly stimulate both nerves and muscles.

Studies have shown that diabetes can cause cognitive dysfunction, and cognitive dysfunction in patients with diabetes combined with obstructive sleep apnoea (OSA) is more severe. LncRNAs are known to be associated with type 2 diabetes mellitus (T2DM) with OSA. This study aimed to investigate the role and underlying mechanism of the lncRNA MALAT1/miR-224-5p/NLRP3 axis in T2DM with OSA. qRT-PCR was used to quantify the expression of MALAT1, miR-224-5p and NLRP3 in brain tissues. NLRP3 expression was assessed by immunohistochemistry (IHC) and immunofluorescent labelling. The interaction involving MALAT1, miR-224-5p and NLRP3 was evaluated by transfection. Western blotting was utilized to evaluate the expression levels of the pathway-related proteins NLRP3, caspase 1, TNFα and IL-1β both in vitro and in vivo. qRT-PCR was used to assess the mRNA expression levels of NLRP3, caspase 1, TNFα and IL-1β both in vitro and in vivo. In brain tissues of T2DM with OSA, MALAT1 and NLRP3 were overexpressed, while miR-224-5p was downregulated, which was consistent with subsequent cell experiments. We screened the miRNAs that could bind to MALAT1 and NLRP3 by the StarBase database and the TargetScanMouse7.2 website. Our research showed that among these miRNAs, the level of miR-224-5p was most significantly negatively correlated with the levels of MALAT1 and NLRP3. In addition, a firefly luciferase assay showed that miR-224-5p, which is a target of MALAT1, directly reduced the expression of the downstream protein NLRP3. Overexpression of miR-224-5p significantly inhibited the expression levels of NLRP3, caspase 1, TNFα and IL-1β in vitro. MALAT1 promoted NLRP3 expression by acting as a competing endogenous RNA and sponging miR-224-5p. MiR-224-5p reduces microglial inflammation activation through the regulation of NLRP3 expression, which ultimately affected the NLRP3/IL-1β pathway in the hippocampus. This suggests that miR-224-5p may serve as a potential target for T2DM and OSA therapy.

Material properties of phase-separated biomolecular condensates, enriched with disordered proteins, dictate many cellular functions. Contrary to the progress made in understanding the sequence-dependent phase separation of proteins, little is known about the sequence determinants of condensate material properties. Using the hydropathy scale and Martini models, we computationally decipher these relationships for charge-rich disordered protein condensates. Our computations yield dynamical, rheological, and interfacial properties of condensates that are quantitatively comparable with experimentally characterized condensates. Interestingly, we find that the material properties of model and natural proteins respond similarly to charge segregation, despite different sequence compositions. Molecular interactions within the condensates closely resemble those within the single-chain ensembles. Consequently, the material properties strongly correlate with molecular contact dynamics and single-chain structural properties. We demonstrate the potential to harness the sequence characteristics of disordered proteins for predicting and engineering the material properties of functional condensates, with insights from the dilute phase properties. How material properties affect the functional state of biocondensates is a long-standing question. Here the authors demonstrate how such properties are encoded in a disordered protein sequence and can be predicted from dilute phase conformations.