Quintom cosmology and modified gravity after DESI 2024.

We reconstruct the cosmological background evolution under the scenario of dynamical dark energy through the Gaussian process approach, using the latest Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillations (BAO) combined with other observations. Our results reveal that the reconstructed dark-energy equation-of-state (EoS) parameter w(z) exhibits the so-called quintom-B behavior, crossing -1 from phantom to quintessence regime as the universe expands. We investigate under what situation this type of evolution could be achieved from the perspectives of field theories and modified gravity. In particular, we reconstruct the corresponding actions for f(R),f(T), and f(Q) gravity, respectively. We explicitly show that, certain modified gravity can exhibit the quintom dynamics and fit the recent DESI data efficiently, and for all cases the quadratic deviation from the ΛCDM scenario is mildly favored.

Application of machine reading comprehension techniques for named entity recognition in materials science.

Materials science is an interdisciplinary field that studies the properties, structures, and behaviors of different materials. A large amount of scientific literature contains rich knowledge in the field of materials science, but manually analyzing these papers to find material-related data is a daunting task. In information processing, named entity recognition (NER) plays a crucial role as it can automatically extract entities in the field of materials science, which have significant value in tasks such as building knowledge graphs. The typically used sequence labeling methods for traditional named entity recognition in material science (MatNER) tasks often fail to fully utilize the semantic information in the dataset and cannot effectively extract nested entities. Herein, we proposed to convert the sequence labeling task into a machine reading comprehension (MRC) task. MRC method effectively can solve the challenge of extracting multiple overlapping entities by transforming it into the form of answering multiple independent questions. Moreover, the MRC framework allows for a more comprehensive understanding of the contextual information and semantic relationships within materials science literature, by integrating prior knowledge from queries. State-of-the-art (SOTA) performance was achieved on the Matscholar, BC4CHEMD, NLMChem, SOFC, and SOFC-Slot datasets, with F1-scores of 89.64%, 94.30%, 85.89%, 85.95%, and 71.73%, respectively in MRC approach. By effectively utilizing semantic information and extracting nested entities, this approach holds great significance for knowledge extraction and data analysis in the field of materials science, and thus accelerating the development of material science.Scientific contributionWe have developed an innovative NER method that enhances the efficiency and accuracy of automatic entity extraction in the field of materials science by transforming the sequence labeling task into a MRC task, this approach provides robust support for constructing knowledge graphs and other data analysis tasks.

Periplaneta americana extract promotes infectious diabetic ulcers wound healing by downregulation of LINC01133/SLAMF9.

Wound healing in diabetic ulcers remains a significant clinical challenge, primarily due to bacterial infection and impaired angiogenesis. Periplaneta americana extract (PAE) has been widely used to treat diabetic wounds, yet its underlying mechanisms are not fully understood. This study aimed to elucidate these mechanisms by analyzing long non-coding RNA (lncRNA) expressions in the wound tissues from diabetic anal fistula patients treated with or without PAE, using high-throughput sequencing. Peripheral blood monocytes from patients were differentiated into M0 macrophages with human macrophage colony-stimulating factor (hM-CSF) and subsequently polarized into M1 macrophages with lipopolysaccharide. The results indicated that LINC01133 and SLAMF9 were downregulated in wound tissues of patients treated with PAE. Furthermore, PAE suppressed M1 macrophage polarization and enhanced human umbilical vein endothelial cell (HUVEC) proliferation, migration, and angiogenesis. These effects were diminished when LINC01133 or SLAMF9 were overexpressed. Mechanistically, LINC01133 was shown to upregulate SLAMF9 through interaction with ELAVL1. Overexpression of SLAMF9 reversed the effects of LINC01133 silencing on macrophage polarization and HUVEC functions. In conclusion, PAE facilitates the healing of infected diabetic ulcers by downregulating the LINC01133/SLAMF9 pathway.

Enhanced Cr(VI) removal and stabilization from bioleached wastewater by zero-valent iron coupled with hetero and autotrophic bacteria.

Zero-valent iron (Fe0) usually suffers from organic acid complexation and ferrochrome layer passivation in Cr(VI) removal from bioleached wastewater of Cr slag. In this work, a synergetic system combined Fe0 and mixed hetero/autotrophic bacteria was established to reduce and stabilize Cr(VI) from bioleached wastewater. Due to bacterial consumption of organic acid and hydrogen, severe iron corrosion and structured-Fe(II) mineral generation (e.g., magnetite and green rust) occurred on biotic Fe0 surface in terms of solid-phase characterization, which was crucial for Cr(VI) adsorption and reduction. Therefore, compared with the abiotic Fe0 system, this integrated system exhibited a 6.1-fold increase in Cr(VI) removal, with heterotrophic reduction contributing 3.4-fold and abiotic part promoted by hydrogen-autotrophic bacteria enhancing 2.7-fold. After reaction, the Cr valence distribution and X-ray photoelectron spectroscopy indicated that most Cr(VI) was converted into immobilized products such as FexCr1-x(OH)3, Cr2O3, and FeCr2O4 by biotic Fe0. Reoxidation experiment revealed that these products exhibited superior stability to the immobilized products generated by Fe0 or bacteria. Additionally, organic acid concentration and Fe0 dosage showed significantly positive correlation with Cr(VI) removal within the range of biological adaptation, which emphasized that heterotrophic and autotrophic bacteria acted essential roles in Cr(VI) removal. This work highlighted the enhanced effect of heterotrophic and autotrophic activities on Cr(VI) reduction and stabilization by Fe0 and offered a promising approach for bioleached wastewater treatment.

Social participation classification and activities in association with health outcomes among older adults: Results from a scoping review.

AIMS: The aim of this study is to summarize the characteristics of social participation classification and examine the association between activities and health outcomes among older adults. DESIGN: Scoping review. DATA SOURCES: Eight databases (Cumulative Index to Nursing and Allied Health Literature, The Cochrane Library, Embase, ProQuest, Psychological Information Database, PubMed, Scopus and Web of Science) were searched. Reference lists of relevant social participation reviews were also considered. METHODS: This study applied a five-stage methodological framework. A narrative synthesis summarized social participation classification and activities and their associations with health outcomes among older adults (≥65 years) living at home, in the community or in nursing residences. RESULTS: Forty-two articles published between 1975 and 2022 were selected. Four classification criteria of social participation were extracted and summarized from these studies. Based on the depth and breadth of social interactions, this review proposed a four-level classification schema. A lower risk of mortality and less visual impairment were associated with participation in level-one, level-three or level-four activities, whereas less depression, less pain and better cognitive function were linked to participation in level-three or level-four activities. CONCLUSION: Future studies should provide a clear definition, establish classification criteria for participation and properly select activity forms while considering both subjective and objective dimensions. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The results could provide data for designing targeted social participation interventions to improve specific health outcomes among older adults. IMPACT: This review could help researchers examine the role of social participation activities in specific health outcomes. Moreover, a proposed classification of social participation activities would benefit researchers and community nurses in discerning the similarities and differences among activities. REPORTING METHOD: This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Extension for Scoping Reviews guideline. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Haplotype-resolved T2T genome assemblies and pangenome graph of pear reveal diverse patterns of allele-specific expression and the genomic basis of fruit quality traits.

Hybrid crops often exhibit increased yield and greater resilience, yet the genomic mechanism(s) underlying hybrid vigor or heterosis remain unclear, hindering our ability to predict the expression of phenotypic traits in hybrid breeding. Here, we generated haplotype-resolved T2T genome assemblies of two pear hybrid varieties, 'Yuluxiang' (YLX) and 'Hongxiangsu' (HXS), which share the same maternal parent but differ in their paternal parents. We then used these assemblies to explore the genome-scale landscape of allele-specific expression (ASE) and create a pangenome graph for pear. ASE was observed for close to 6000 genes in both hybrid cultivars. A subset of ASE genes related to aspects of fruit quality such as sugars, organic acids, and cuticular wax were identified, suggesting their important contributions to heterosis. Specifically, Ma1, a gene regulating fruit acidity, is absent in the paternal haplotypes of HXS and YLX. A pangenome graph was built based on our assemblies and seven published pear genomes. Resequencing data for 139 cultivated pear genotypes (including 97 genotypes sequenced here) were subsequently aligned to the pangenome graph, revealing numerous structural variant hotspots and selective sweeps during pear diversification. As predicted, the Ma1 allele was found to be absent in varieties with low organic acid content, and this association was functionally validated by Ma1 overexpression in pear fruit and calli. Overall, these results reveal the contributions of ASE to fruit-quality heterosis and provide a robust pangenome reference for high-resolution allele discovery and association mapping.

Identification of long non-coding RNAs in response to microsporidia infection in Silkworm, Bombyx mori.

Microsporidia Nosema bombycis (Nb) is a cellular parasite responsible for pébrine disease in silkworms, significantly impacting the sericulture industry. Long non-coding RNAs (lncRNAs), which are RNA fragments longer than 200 nucleotides, are pivotal in a range of cellular and physiological functions. However, the potential role of silkworm lncRNAs in response to Nb infection remains unknown. This study conducted transcriptome sequencing on both larvae and Nb-infected midguts of silkworms, identifying 1,440 lncRNAs across all examined midgut samples. Within the Nb-infected group, 42 differentially expressed lncRNAs (DElncRNAs) and 305 differentially expressed mRNAs (DEmRNAs) were detected. Functional annotation and pathway analysis showed that these DEmRNAs are mostly involved in metabolism, apoptosis, autophagy, and other key pathways. The co-expression network of DEmRNAs and DElncRNAs illustrates that 1 gene could be regulated by multiple lncRNAs and 1 lncRNA may target multiple genes, indicating that the regulation of lncRNA is intricate and networked. In addition, the DElncRNA-miRNA-mRNA network showed that some DElncRNAs may be involved in the immune response and metabolism through miRNA. Notably, the study observed an increase in lncRNA MSTRG857.1 following Nb infection, which may promote Nb proliferation. These findings offer insights into the complex interplay between insects and microsporidia.

Efficacy and Safety of Enhanced Recovery After Surgery (ERAS) Protocols for Patients Undergoing Minimally Invasive Transforaminal Lumbar Interbody Fusion Surgery: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the efficacy and safety of enhanced recovery after surgery (ERAS) in minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) for lumbar degenerative disease (LDD). METHODS: Electronic databases including PubMed, Embase, the Cochrane Library, Web of Science, Clinical Trials.gov, etc. were searched from inception to October 2023. Randomized controlled trials (RCTs) and cohort studies (CSs) comparing ERAS program with traditional protocol of MIS-TLIF for LDD were included. RESULTS: A total of 11 studies were included for final analysis. The pooled results of RCTs showed that compared with MIS-TLIF, the ERAS program used in MIS-TLIF could reduce the length of hospital stay, operation time, intraoperative blood loss and incidence of postoperative complications, decrease visual analog scale and Oswestry Disability Index (ODI) score, and improve patient satisfaction (P < 0.05). However, the pooled results of CSs revealed no statistical difference in the ODI score, fusion rate, operation time, and incidence of complications between the two groups (P > 0.05). CONCLUSIONS: Compared with MIS-TLIF, the ERAS program used in MIS-TLIF could effectively shorten the length of hospital stay, operation time, decrease intraoperative blood loss, and incidence of postoperative complications, promote postoperative pain relief, functional recovery, and patient satisfaction. This study confirmed the value of ERAS in MIS-TLIF surgery and provided evidence for the standardization of ERAS in the future. Considering that the pooled results of RCTs and CSs are not completely consistent, more high-quality studies are needed to confirm these conclusions.

A Recursive Non-Uniform Sampling Estimator for Asynchronous Nonlinear Systems.

In this paper, we consider the problem of asynchronous estimation in the presence of packet losses for the randomly sampling nonlinear system. Packet losses occur at the control input and at the measurement side. Firstly, the synchronization of the asynchronous sampling system is realized by weighting the state of the adjacent state update points. Secondly, the projection theorem is used to estimate the system state at the sampling time. Due to modeling errors and unmodeled dynamics, obtaining an accurate dynamic model is challenging. Therefore, observation inference based on interpolation techniques is proposed to solve the asynchronous estimation problem. Furthermore, the algorithm is extended to multi-sensor systems to obtain a distributed fusion estimator. Finally, simulation experiments are conducted to validate the effectiveness of the algorithm.

Machine learning methods predict recurrence of pN3b gastric cancer after radical resection.

Background: The incidence of stage pN3b gastric cancer (GC) is low, and the clinical prognosis is poor, with a high rate of postoperative recurrence. Machine learning (ML) methods can predict the recurrence of GC after surgery. However, the prognostic significance for pN3b remains unclear. Therefore, we aimed to predict the recurrence of pN3b through ML models. Methods: This retrospective study included 336 patients with pN3b GC who underwent radical surgery. A 3-fold cross-validation was used to partition the participants into training and test cohorts. Linear combinations of new variable features were constructed using principal component analysis (PCA). Various ML algorithms, including random forest, support vector machine (SVM), logistic regression, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and Gaussian naive Bayes (GNB), were utilized to establish a recurrence prediction model. Model performance was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Python was used for the analysis of ML algorithms. Results: Nine principal components with a cumulative variance interpretation rate of 90.71% were identified. The output results of the test set showed that random forests had the highest AUC (0.927) for predicting overall recurrence with an accuracy rate of 80.5%. Random forests had the highest AUC (0.940) for predicting regional recurrence with an accuracy of 89.7%. For predicting distant recurrence, random forests had the highest AUC (0.896) with an accuracy of 84.3%. For peritoneal recurrence, random forests had the highest AUC (0.923) with an accuracy of 83.3%. Conclusions: ML can personalize the prediction of postoperative recurrence in patients with GC with stage pN3b.

The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning.

Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.

Gene replacement therapy in Bietti crystalline corneoretinal dystrophy: an open-label, single-arm, exploratory trial.

Bietti crystalline corneoretinal dystrophy is an inherited retinal disease caused by mutations in CYP4V2, which results in blindness in the working-age population, and there is currently no available treatment. Here, we report the results of the first-in-human clinical trial (NCT04722107) of gene therapy for Bietti crystalline corneoretinal dystrophy, including 12 participants who were followed up for 180-365 days. This open-label, single-arm exploratory trial aimed to assess the safety and efficacy of a recombinant adeno-associated-virus-serotype-2/8 vector encoding the human CYP4V2 protein (rAAV2/8-hCYP4V2). Participants received a single unilateral subretinal injection of 7.5 × 1010 vector genomes of rAAV2/8-hCYP4V2. Overall, 73 treatment-emergent adverse events were reported, with the majority (98.6%) being of mild or moderate intensity and considered to be procedure- or corticosteroid-related; no treatment-related serious adverse events or local/systemic immune toxicities were observed. Compared with that measured at baseline, 77.8% of the treated eyes showed improvement in best-corrected visual acuity (BCVA) on day 180, with a mean ± standard deviation increase of 9.0 ± 10.8 letters in the 9 eyes analyzed (p = 0.021). By day 365, 80% of the treated eyes showed an increase in BCVA, with a mean increase of 11.0 ± 10.6 letters in the 5 eyes assessed (p = 0.125). Importantly, the patients' improvement observed using multifocal electroretinogram, microperimetry, and Visual Function Questionnaire-25 further supported the beneficial effects of the treatment. We conclude that the favorable safety profile and visual improvements identified in this trial encourage the continued development of rAAV2/8-hCYP4V2 (named ZVS101e).

Inhibition of METTL3 in macrophages provides protection against intestinal inflammation.

Inflammatory bowel disease (IBD) is prevalent, and no satisfactory therapeutic options are available because the mechanisms underlying its development are poorly understood. In this study, we discovered that increased expression of methyltransferase-like 3 (METTL3) in macrophages was correlated with the development of colitis and that depletion of METTL3 in macrophages protected mice against dextran sodium sulfate (DSS)-induced colitis. Mechanistic characterization indicated that METTL3 depletion increased the YTHDF3-mediated expression of phosphoglycolate phosphatase (PGP), which resulted in glucose metabolism reprogramming and the suppression of CD4+ T helper 1 (Th1) cell differentiation. Further analysis revealed that glucose metabolism contributed to the ability of METTL3 depletion to ameliorate colitis symptoms. In addition, we developed two potent small molecule METTL3 inhibitors, namely, F039-0002 and 7460-0250, that strongly ameliorated DSS-induced colitis. Overall, our study suggests that METTL3 plays crucial roles in the progression of colitis and highlights the potential of targeting METTL3 to attenuate intestinal inflammation for the treatment of colitis.

Targeting epigenetic and post-translational modifications regulating pyroptosis for the treatment of inflammatory diseases.

Inflammatory diseases, including infectious diseases, diabetes-related diseases, arthritis-related diseases, neurological diseases, digestive diseases, and tumor, continue to threaten human health and impose a significant financial burden despite advancements in clinical treatment. Pyroptosis, a pro-inflammatory programmed cell death pathway, plays an important role in the regulation of inflammation. Moderate pyroptosis contributes to the activation of native immunity, whereas excessive pyroptosis is associated with the occurrence and progression of inflammation. Pyroptosis is complicated and tightly controlled by various factors. Accumulating evidence has confirmed that epigenetic modifications and post-translational modifications (PTMs) play vital roles in the regulation of pyroptosis. Epigenetic modifications, which include DNA methylation and histone modifications (such as methylation and acetylation), and post-translational modifications (such as ubiquitination, phosphorylation, and acetylation) precisely manipulate gene expression and protein functions at the transcriptional and post-translational levels, respectively. In this review, we summarize the major pathways of pyroptosis and focus on the regulatory roles and mechanisms of epigenetic and post-translational modifications of pyroptotic components. We also illustrate these within pyroptosis-associated inflammatory diseases. In addition, we discuss the effects of novel therapeutic strategies targeting epigenetic and post-translational modifications on pyroptosis, and provide prospective insight into the regulation of pyroptosis for the treatment of inflammatory diseases.

Label-free Raman imaging for screening of anti-inflammatory function food.

Natural bioactive compounds and plant constituents are considered to have a positive anti-inflammatory effect. This study aimed to establish a screening technique for anti-inflammatory function in foods based on label-free Raman imaging. A visible anti-inflammatory analysis method based on coherent anti-Stokes Raman scattering (CARS) was established with an LPS-induced RAW264.7 cell model. Dynamic changes in proteins and lipids were determined at laser pump light wavelengths of 2956 cm-1 and 2856 cm-1, respectively. The method was applied to a plant-based formula (JC) with anti-inflammatory activity. Q-TOF-MS and HPLC analyses revealed the main active constituents of JC as quercetin, kaempferol, l-glutamine, and sodium copper chlorophyllin. In in vitro and in vivo verification experiments, JC showed significant anti-inflammatory activity by regulating the TLR4/NF-κB pathway. In conclusion, this study successfully established a label-free and visible method for screening anti-inflammatory constituents in plant-based food products, which will facilitate the evaluation of functional foods.

Self-Reporting Microsensors Inspired by Noctiluca Scintillans for Small-Defect Positioning and Electrical-Stress Visualization in Polymers.

Small defects induce concentrated electrical stress in dielectric polymers, leading to premature failure of materials. Existing sensing methods fail to effectively visualize these defects owing to the invisible-energy state of the electric field. Thus, it is necessary to establish a nondestructive method for the real-time detection of small defects in dielectric polymers. In this study, a self-reporting microsensor (SRM) inspired by Noctiluca scintillans is designed to endow materials with the ability of self-detection for defects and electrical stress. The SRM leverages the energy of a nearby electric field to emit measurable fluorescence, enabling defect localization and diagnosis as well as electrical-stress visualization. A controllable dielectric microsphere is constructed to achieve an adjustable electroluminescence threshold for the SRM, thereby increasing its detection accuracy while decreasing the electroluminescence threshold. The potential degradation in the polymer performance owing to SRM implantation is addressed by assembling long molecular chains on the SRM surface to spontaneously generate an interpenetrating network. Results of finite element analyses and experiments demonstrate that the SRM can effectively realize nondestructive visualization and positioning of small defects and concentrated electrical stress in polymers, positioning it as a promising sensing method for monitoring the electric field and charge distribution in materials.

The Effect of Respiratory Muscle Training on Swallowing Function in Patients With Stroke: A Systematic Review and Meta-Analysis.

BACKGROUND: The improvement of swallowing function after stroke is a significant challenge faced by patients and health care professionals. However, the current evidence synthesis of the effects of respiratory muscle training (RMT) on swallowing function is limited. OBJECTIVE: To assess the effectiveness of RMT on swallowing recovery in patients undergoing stroke. METHODS: The CKNI, WanFang Data, PubMed, CINAHL, Web of Science, Embase, MEDLINE, and Cochrane Library databases were searched for studies evaluating RMT interventions' effect on swallowing outcomes. Risks of bias were evaluated using the approach recommended by the Cochrane Collaboration tool and a summary of findings table was generated using the GRADE approach. Outcomes were synthesized using a random-effects meta-analysis model. RESULTS: RMT interventions reduced the risk of aspiration (SMD = 1.19; 95% CI, 0.53-1.84), the recovery process of water swallowing function (RR = 1.22; 95% CI, 1.05-1.42), and the activity of the swallowing muscles (SMD = 2.91; 95% CI, 2.22-3.61). However, there was no significant effect of RMT on the functional level of oral intake (SMD = 0.70; 95% CI, -0.03 to 1.42). CONCLUSIONS: RMT can be regarded as an innovative, auxiliary means in the near future to better manage and improve swallowing function, given its improving effect on work outcomes in this review.

Revealing new insights: Two-center evidence of microplastics in human vitreous humor and their implications for ocular health.

Microplastics (MPs), an emerging environmental contaminant, have raised growing health apprehension due to their detection in various human biospecimens. Despite extensive research into their prevalence in the environment and the human body, the ramifications of their existence within the enclosed confines of the human eye remain largely unexplored. Herein, we assembled a cohort of 49 patients with four ocular diseases (macular hole, macular epiretinal membrane, retinopathy and rhegmatogenous retinal detachment) from two medical centers. After processing the samples with an optimized method, we utilized Laser Direct Infrared (LD-IR) spectroscopy and Pyrolysis Gas Chromatography/Mass Spectrometry (Py-GC/MS) to analyze 49 vitreous samples, evaluating the characteristics of MPs within the internal environment of the human eye. Our results showed that LD-IR scanned a total of 8543 particles in the composite sample from 49 individual vitreous humor samples, identifying 1745 as plastic particles, predominantly below 50 µm. Concurrently, Py-GC/MS analysis of the 49 individual samples corroborated these findings, with nylon 66 exhibiting the highest content, followed by polyvinyl chloride, and detection of polystyrene. Notably, correlations were observed between MP levels and key ocular health parameters, particularly intraocular pressure and the presence of aqueous humor opacities. Intriguingly, individuals afflicted with retinopathy demonstrated heightened ocular health risks associated with MPs. In summary, this research provides significant insights into infiltration of MP pollutants within the human eye, shedding light on their potential implications for ocular health and advocating for further exploration of this emerging health risk.

Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study.

Background: Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA. Methods: We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of CRTAM in RA. In addition, comparisons of diagnostic efficacy and biological roles among CRTAM and other classic biomarkers of RA were also performed. Results: Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including CRTAM, PTTG1IP, ITGB2, and MMP13, were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of CRTAM could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including MMP3, S100A8, S100A9, IL6, COMP, LAG3, and ENTPD1. Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes. Conclusion: CRTAM, PTTG1IP, ITGB2, and MMP13 were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, CRTAM might fuel cancer progression through immune signals, especially among RA patients.

Quantitative analysis of the quality constituents of Lonicera japonica Thunberg based on Raman spectroscopy.

Quantitative analysis of the quality constituents of Lonicera japonica (Jinyinhua [JYH]) using a feasible method provides important information on its evaluation and applications. Limitations of sample pretreatment, experimental site, and analysis time should be considered when identifying new methods. In response to these considerations, Raman spectroscopy combined with deep learning was used to establish a quantitative analysis model to determine the quality of JYH. Chlorogenic acid and total flavonoids were identified as analysis targets via network pharmacology. High performance liquid chromatograph and ultraviolet spectroscopy were used to construct standard curves for quantitative analysis. Raman spectra of JYH extracts (1200) were collected. Subsequently, models were built using partial least squares regression, Support Vector Machine, Back Propagation Neural Network, and One-dimensional Convolutional Neural Network (1D-CNN). Among these, the 1D-CNN model showed superior prediction capability and had higher accuracy (R2 = 0.971), and lower root mean square error, indicating its suitability for rapid quantitative analysis.