Molecular mechanism of Xiaohuoluo wan for rheumatoid arthritis by integrating in vitro and in vivo chemomics and network pharmacology.

The cause of rheumatoid arthritis (RA) is unclear. Xiaohuoluo wan (XHLW) is a classical Chinese medicine that is particularly effective in the treatment of RA. Given the chemical composition of XHLW at the overall level has been little studied and the molecular mechanism for the treatment of RA is not clear, we searched for the potential active compounds of XHLW and explored their anti-inflammatory mechanism in the treatment of RA by flexibly integrating the high-resolution ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based in vitro and in vivo chemomics, network pharmacology, and other means. The results of the study identified that the active compounds of XHLW, such as alkaloids, nucleosides, and fatty acids, may play an anti-inflammatory role by regulating key targets such as IL-2, STAT1, JAK3, and MAPK8, inducing immune response through IL-17 signaling pathway, T-cell receptor, FoxO, tumor necrosis factor (TNF), and so forth, inhibiting the release of inflammatory factors and resisting oxidative stress and other pathways to treat RA. The results of this study provide referable data for the screening of active compounds and the exploration of molecular mechanisms of XHLW in the treatment of RA.

The causal relationship of depression, anxiety, and neuroticism with main indicators of sarcopenia: A Mendelian randomization study.

BACKGROUND: Observational studies have shown the relationship between sarcopenia and psychiatric disorders. However, due to the limitations of traditional research methods, the causal relationship between them has not been accurately concluded. At the same time, considering that sarcopenia is mainly manifested by low muscle strength and low muscle mass, we used Mendelian randomization (MR) analysis in this study to explore the causal relationship of anxiety, depression, and neuroticism with muscle strength and muscle mass, respectively. METHODS: Genetic variants associated with depression were obtained from FinnGen Biobank (Ncase = 33,812, Ncontrol = 271,380), those associated with anxiety were from FinnGen Biobank (Ncase = 21,519, Ncontrol = 307,558), and those associated with neuroticism, including 12 items, were from a large-scale genome-wide association study (N range: 366,301-375,913). Muscle strength was represented by the hand grip strength (HGS), and muscle mass was represented by the appendicular lean mass (ALM) and the body fat percentage. The inverse-variance weighted (IVW) method was used as the primary analysis method, and the Mendelian Randomization Egger (MR-Egger) and the weighted median were used as supplementary methods to test whether the three psychological factors were causally related to these two main indicators of sarcopenia severity. RESULTS: Depression and neuroticism had different degrees of causal influence on muscle mass and strength, which was statistically significant. Specifically, the depression predicted by genes was significantly associated with ALM (beta = -0.043, p = 0.027), low hand grip strength (LHGS, measured for people of 60 years and older) (odds ratio (OR) = 1.129 (1.019-1.251), p = 0.019), right HGS (beta = -0.050, p = 0.001), left HGS (beta = -0.06, p = 0.001), and body fat percentage (beta = 0.035, p = 0.0138). The neuroticism predicted by genes was significantly associated with ALM (beta = -0.073, p = 0.034), LHGS (OR = 1.222 (1.085-1.377), p = 0.001), right HGS (beta = -0.058, p = 0.000), left HGS (beta = -0.080, p < 0.000), and body fat percentage (beta = 0.063, p = 0.008). However, anxiety was only significantly associated with LHGS (OR = 1.215 (1.008-1.465), p = 0.041) but not significantly associated with ALM (beta = 0.033, p = 0.313), right HGS (beta = -0.008, p = 0.678), left HGS (beta = 0.007, p = 0.712), or body fat percentage (beta = 0.022, p = 0.559). CONCLUSION: This study supported the causal association of depression and neuroticism with muscle strength and mass, which are the two main indicators of sarcopenia. At the same time, there was no sufficient evidence for the causal relationship between anxiety and muscle strength or mass. The results of this study pointed to the need to intervene in the mental health of the elderly to prevent sarcopenia or reduce its severity.

An AI Dietitian for Type 2 Diabetes Mellitus Management Based on Large Language and Image Recognition Models: Preclinical Concept Validation Study.

BACKGROUND: Nutritional management for patients with diabetes in China is a significant challenge due to the low supply of registered clinical dietitians. To address this, an artificial intelligence (AI)-based nutritionist program that uses advanced language and image recognition models was created. This program can identify ingredients from images of a patient's meal and offer nutritional guidance and dietary recommendations. OBJECTIVE: The primary objective of this study is to evaluate the competence of the models that support this program. METHODS: The potential of an AI nutritionist program for patients with type 2 diabetes mellitus (T2DM) was evaluated through a multistep process. First, a survey was conducted among patients with T2DM and endocrinologists to identify knowledge gaps in dietary practices. ChatGPT and GPT 4.0 were then tested through the Chinese Registered Dietitian Examination to assess their proficiency in providing evidence-based dietary advice. ChatGPT's responses to common questions about medical nutrition therapy were compared with expert responses by professional dietitians to evaluate its proficiency. The model's food recommendations were scrutinized for consistency with expert advice. A deep learning-based image recognition model was developed for food identification at the ingredient level, and its performance was compared with existing models. Finally, a user-friendly app was developed, integrating the capabilities of language and image recognition models to potentially improve care for patients with T2DM. RESULTS: Most patients (182/206, 88.4%) demanded more immediate and comprehensive nutritional management and education. Both ChatGPT and GPT 4.0 passed the Chinese Registered Dietitian examination. ChatGPT's food recommendations were mainly in line with best practices, except for certain foods like root vegetables and dry beans. Professional dietitians' reviews of ChatGPT's responses to common questions were largely positive, with 162 out of 168 providing favorable reviews. The multilabel image recognition model evaluation showed that the Dino V2 model achieved an average F1 score of 0.825, indicating high accuracy in recognizing ingredients. CONCLUSIONS: The model evaluations were promising. The AI-based nutritionist program is now ready for a supervised pilot study.

Immunometabolism at the service of traditional Chinese medicine.

To enhance therapeutic efficacy and reduce adverse effects, ancient practitioners of traditional Chinese medicine (TCM) prescribe combinations of plant species/animal species and minerals designated "TCM formulae" developed based on TCM theory and clinical experience. TCM formulae have been shown to exert curative effects on complex diseases via immune regulation but the underlying mechanisms remain unknown at present. Considerable progress in the field of immunometabolism, referring to alterations in the intracellular metabolism of immune cells that regulate their function, has been made over the past decade. The core context of immunometabolism is regulation of the allocation of metabolic resources supporting host defense and survival, which provides a critical additional dimension and emerging insights into how the immune system and metabolism influence each other during disease progression. This review summarizes research findings on the significant association between the immune function and metabolic remodeling in health and disease as well as the therapeutic modulatory effects of TCM formulae on immunometabolism. Progressive elucidation of the immunometabolic mechanisms involved during the course of TCM treatment continues to aid in the identification of novel potential targets against pathogenicity. In this report, we have provided a comprehensive overview of the benefits of TCM based on regulation of immunometabolism that are potentially applicable for the treatment of modern diseases.

Construction and Structure Determination of a Three-Dimensional DNA Crystal.

Structural DNA nanotechnology combines branched DNA junctions with sticky-ended cohesion to create self-assembling macromolecular architectures. One of the key goals of structural DNA nanotechnology is to construct three-dimensional (3D) crystalline lattices. Here we present a new DNA motif and a strategy that has led to the assembly of a 3D lattice. We have determined the X-ray crystal structures of two related constructs to 3.1 Å resolution using bromine-derivatized crystals. The motif we used employs a five-nucleotide repeating sequence that weaves through a series of two-turn DNA duplexes. The duplexes are tied into a layered structure that is organized and dictated by a concert of four-arm junctions; these in turn assemble into continuous arrays facilitated by sequence-specific sticky-ended cohesion. The 3D X-ray structure of these DNA crystals holds promise for the design of new structural motifs to create programmable 3D DNA lattices with atomic spatial resolution. The two arrays differ by the use of four or six repeats of the five-nucleotide units in the repeating but statistically disordered central strand. In addition, we report a 2D rhombuslike array formed from similar components.

Optimization of High-Activity earthworm fibrinolytic enzyme extraction methods and protein component analysis.

OBJECTIVE: Optimize the extraction process of earthworm fibrinolytic enzyme. METHODS: Chinese common earthworms underwent a series of purification processes, including grinding, salting out, hydrophobic medium chromatography, ammonium sulfate precipitation, and ion exchange chromatography, to obtain purified earthworm fibrinolytic enzyme. RESULTS: Utilizing Pheretima aspergillum as the starting material, we discovered that the specific activity of lumbrokinase extracted via ammonium sulfate precipitation was 58 U/mg, noticeably surpassing that achieved through heat precipitation and ethanol precipitation methods. After undergoing two rounds of chromatographic separations employing hydrophobic affinity chromatography and anion exchange chromatography, the specific activity of the lumbrokinase protein soared to 9267 U/mg, significantly exceeding the 3,178 U/mg specific activity attained through industrial extraction methods. DISCUSSION: The development of a novel crude extraction method for lumbrokinase protein can significantly boost its activity and purity. The discovery of a high-efficiency purification method and the identification of protein components within highly active lumbrokinase pave the way for further investigations into these proteins.

Automated information extraction model enhancing traditional Chinese medicine RCT evidence extraction (Evi-BERT): algorithm development and validation.

Background: In the field of evidence-based medicine, randomized controlled trials (RCTs) are of critical importance for writing clinical guidelines and providing guidance to practicing physicians. Currently, RCTs rely heavily on manual extraction, but this method has data breadth limitations and is less efficient. Objectives: To expand the breadth of data and improve the efficiency of obtaining clinical evidence, here, we introduce an automated information extraction model for traditional Chinese medicine (TCM) RCT evidence extraction. Methods: We adopt the Evidence-Bidirectional Encoder Representation from Transformers (Evi-BERT) for automated information extraction, which is combined with rule extraction. Eleven disease types and 48,523 research articles from the China National Knowledge Infrastructure (CNKI), WanFang Data, and VIP databases were selected as the data source for extraction. We then constructed a manually annotated dataset of TCM clinical literature to train the model, including ten evidence elements and 24,244 datapoints. We chose two models, BERT-CRF and BiLSTM-CRF, as the baseline, and compared the training effects with Evi-BERT and Evi-BERT combined with rule expression (RE). Results: We found that Evi-BERT combined with RE achieved the best performance (precision score = 0.926, Recall = 0.952, F1 score = 0.938) and had the best robustness. We totally summarized 113 pieces of rule datasets in the regulation extraction procedure. Our model dramatically expands the amount of data that can be searched and greatly improves efficiency without losing accuracy. Conclusion: Our work provided an intelligent approach to extracting clinical evidence for TCM RCT data. Our model can help physicians reduce the time spent reading journals and rapidly speed up the screening of clinical trial evidence to help generate accurate clinical reference guidelines. Additionally, we hope the structured clinical evidence and structured knowledge extracted from this study will help other researchers build large language models in TCM.

Machine learning aided non-invasive diagnosis of coronary heart disease based on tongue features fusion.

BACKGROUND: Coronary heart disease (CHD) is the first cause of death globally. Hypertension is considered to be the most important independent risk factor for CHD. Early and accurate diagnosis of CHD in patients with hypertension can plays a significant role in reducing the risk and harm of hypertension combined with CHD. OBJECTIVE: To propose a non-invasive method for early diagnosis of coronary heart disease according to tongue image features with the help of machine learning techniques. METHODS: We collected standard tongue images and extract features by Diagnosis Analysis System (TDAS) and ResNet-50. On the basis of these tongue features, a common machine learning method is used to customize the non-invasive CHD diagnosis algorithm based on tongue image. RESULTS: Based on feature fusion, our algorithm has good performance. The results showed that the XGBoost model with fused features had the best performance with accuracy of 0.869, the AUC of 0.957, the AUPR of 0.961, the precision of 0.926, the recall of 0.806, and the F1-score of 0.862. CONCLUSION: We provide a feasible, convenient, and non-invasive method for the diagnosis and large-scale screening of CHD. Tongue image information is a possible effective marker for the diagnosis of CHD.

Negative Emotions Are Associated With Older Self-perceived Age: A Cross-section Study From the UK Biobank.

BACKGROUND: Prior research has indicated a potential connection between psychological stress and how individuals perceive their own age. Building on this foundation, the current study explores the relationship between negative emotions and self-perceived age. METHODS: We conducted a cross-sectional analysis using data from the UK Biobank, a comprehensive cohort study representing the UK population. The analysis included 347 892 participants, aged between 39 and 73 years, of which 184 765 were women, accounting for 53.1% of the sample. Participants were categorized into three groups based on their self-perceived age: feeling younger than their chronological age (group Younger), feeling older than their chronological age (group Older), and feeling as old as their actual age (group Same). To investigate the relationship between negative emotions and self-perceived age, we utilized a multinomial logistic regression model with the Younger group serving as the reference category. RESULTS: Of 347 892 participants, after adjusted for covariates, the results showed that participants with irritability, nervous feelings, worrier/anxious feelings or fed-up feelings, worry too long and loneliness/isolation are more likely to be rated as "about your age" or "older than you are," with "younger than you are" as the reference group, indicating that negative emotions may influence one's self-perceived age. Among those negative emotions, irritability has the most significant impact self-perceived age, with the odds ratios (ORs) being 1.44 (95% CI: 1.35-1.54) and 1.11 (95% CI: 1.09-1.14). CONCLUSION: Negative emotions are associated with older self-perceived age, and irritability has the greatest impact. Further studies analyzing self-perceived age are needed to take psychological factors into consideration.

Machine learning-assisted rapid determination for traditional Chinese Medicine Constitution.

The aim of this study was to develop a machine learning-assisted rapid determination methodology for traditional Chinese Medicine Constitution. Based on the Constitution in Chinese Medicine Questionnaire (CCMQ), the most applied diagnostic instrument for assessing individuals' constitutions, we employed automated supervised machine learning algorithms (i.e., Tree-based Pipeline Optimization Tool; TPOT) on all the possible item combinations for each subscale and an unsupervised machine learning algorithm (i.e., variable clustering; varclus) on the whole scale to select items that can best predict body constitution (BC) classifications or BC scores. By utilizing subsets of items selected based on TPOT and corresponding machine learning algorithms, the accuracies of BC classifications prediction ranged from 0.819 to 0.936, with the root mean square errors of BC scores prediction stabilizing between 6.241 and 9.877. Overall, the results suggested that the automated machine learning algorithms performed better than the varclus algorithm for item selection. Additionally, based on an automated machine learning item selection procedure, we provided the top three ranked item combinations with each possible subscale length, along with their corresponding algorithms for predicting BC classification and severity. This approach could accommodate the needs of different practitioners in traditional Chinese medicine for rapid constitution determination.

The association between lower extremity function and cardiovascular diseases risk in older Chinese adults: Longitudinal evidence from a nationwide cohort.

BACKGROUND: Older adults in China are at a high risk of cardiovascular diseases (CVD), and impaired lower extremity function (LEF) is commonly observed in this demographic. This study aimed at assessing the association between LEF and CVD, thus providing valuable insights for clinical practice and public health policies. METHODS: A sample of 4,636 individuals was included from the China Health and Retirement Longitudinal Study (CHARLS) dataset. Logistic regression and cox proportional hazard regression model was utilized to study the association between LEF and CVD incidence. Cross-lagged panel models were utilized to investigate the potential causal association between LEF and CVD over time. RESULTS: Poor LEF was significantly associated with a higher risk of CVD in the total population [OR (95 % CI): 1.62 (1.27-2.05), P < 0.001]. Individuals with poor LEF demonstrated an increased risk of developing CVD [HR (95 % CI): 1.11 (1.02-1.23), P < 0.05], particularly stroke, compared to those with good LEF. And those with poor LEF had higher risks for heart disease [1.21 (1.00-1.45), P < 0.05] and stroke [1.98 (1.47-2.67), P < 0.001]. CONCLUSION: The results suggest the potential usefulness of the Short Physical Performance Battery (SPPB) for classifying stroke risk in older Chinese adults, which also suggested that preventing and/or improving LEF may be beneficial for reducing stroke incidence and promoting healthy aging for older adults.

A high-quality dataset featuring classified and annotated cervical spine X-ray atlas.

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm's development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.

PnMYB4 negatively modulates saponin biosynthesis in Panax notoginseng through interplay with PnMYB1.

Saponins are the main triterpenoid ingredients from Panax notoginseng, a well-known Chinese medicine, and are important sources for producing drugs to prevent and treat cerebrovascular and cardiovascular diseases. However, the transcriptional regulatory network of saponin biosynthesis in P. notoginseng is largely unknown. In the present study we demonstrated that one R2R3-MYB transcription factor, designated PnMYB4, acts as a repressor of saponin accumulation. Suppression of PnMYB4 in P. notoginseng calli significantly increased the saponin content and the expression level of saponin biosynthetic genes. PnMYB4 directly bound to the promoters of key saponin biosynthetic genes, including PnSS, PnSE, and PnDS, to repress saponin accumulation. PnMYB4 and the activator PnMYB1 could interacted with PnbHLH, which is a positive regulator of saponin biosynthesis, to modulate the biosynthesis of saponin. PnMYB4 competed with PnMYB1 for binding to PnbHLH, repressing activation of the promoters of saponin structural genes induced by the PnMYB1-PnbHLH complex. Our study reveals that a complex regulatory module of saponin biosynthesis is associated with positive and negative MYB transcriptional regulators and provides a theoretical basis for improving the content of saponins and efficacy of P. notoginseng.

Efficacy of acupuncture for hypertension in the elderly: a systematic review and meta-analysis.

Background: Hypertension has now developed into a major public health problem worldwide. Under the existing antihypertensive drug treatment paradigm, problems such as decreasing drug resistance and increasing drug side effects can occur for elderly patients. Acupuncture, a core technique in the non-pharmacological treatment of Chinese medicine, plays an important role in the treatment of elevated blood pressure. Objective: This study aimed to systematically evaluate the effect of acupuncture alone or in combination with antihypertensive drugs on the efficiency of reducing blood pressure and controlling blood pressure in elderly patients with hypertension. Methods: Articles of randomized controlled trials of acupuncture for hypertension in the elderly published before November 2022 were searched in 7 databases. The methodological quality of the literature was evaluated using the Cochrane Risk of Bias Assessment Tool. The primary outcome was the efficiency rate of blood pressure reduction, and the secondary outcome was the change in blood pressure after treatment. Results: This study conducted a systematic review and meta-analysis of 12 randomized controlled trials with a total of 1,466 subjects. Among the primary outcome-efficiency rate, acupuncture-only treatment (RR = 1.11, 95% CI: 1.03-1.20, P < 0.01) and acupuncture combined with antihypertensive drug treatment (RR = 1.18, 95% CI: 1.06-1.31, P < 0.01) were significantly different compared with drugs-only treatment. Among the secondary outcomes, SBP (MD: -4.85, 95% CI: -10.39 to -0.69, P = 0.09) and DBP (MD: -1.45, 95% CI: -5.35 to 2.45, P = 0.47) show no significant difference between acupuncture-only treatment and drug-only treatment. Compared to drugs-only treatment, acupuncture plus drugs has more significant efficiency in lowering SBP (MD: -9.81, 95% CI: -13.56 to -6.06, P < 0.01) and DBP (MD: -7.04, 95% CI: -10.83 to -3.24, P < 0.01). Conclusion: For elderly patients with hypertension, acupuncture-only treatment has the same efficiency and antihypertensive effect compared to drug therapy and acupuncture plus drugs outperforms drugs-only treatment. If the patients receive therapy with less frequency per week and longer duration, there will be a more obvious antihypertensive effect. Due to the methodological defects in the included study and the limited sample size of this paper, more well-designed randomized controlled trials are needed for verification. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022376407, PROSPERO (CRD42022376407).

Digitalization of prevention and treatment and the combination of western and Chinese medicine in management of acute heart failure.

Digitalization has emerged as a new trend in healthcare, with great potential and creating many unique opportunities, as well as many challenges. Cardiovascular disease is one of the major causes of disease-related morbidity and mortality worldwide, and the threat to life posed by acute heart failure is evident. In addition to traditional collegiate therapies, this article reviews the current status and subdisciplinary impact of digital healthcare at the level of combined Chinese and Western medical therapies. It also further discusses the prospects for the development of this approach, with the objective of developing an active role for digitalization in the combination of Western and Chinese medicine for the management of acute heart failure in order to support maintenance of cardiovascular health in the population.

Effect of mindfulness on anxiety and depression in insomnia patients: A systematic review and meta-analysis.

Background: As a common clinical symptom, insomnia has a high incidence of combined mental illness and it is also a risk factor for the development of depression, anxiety and suicide. As a new concept in the field of health in recent years, mindfulness therapy can improve insomnia, anxiety and depression, which is a new way to solve such diseases. Objective: This study aims to systematically evaluate the effects of mindfulness compared with conventional treatment on scores of the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) in people with insomnia and anxiety-depressive symptoms. Methods: Articles published before October 2022 were searched from seven databases and included in randomized controlled trials (RCTs) to evaluate mindfulness therapy. The assessment tool of Cochrane bias risk was used to evaluate the methodological quality of the literature. The main outcome indicators were HAMD and HAMA scores, and the secondary outcome indicators were SDS and SAS scores. Results: Ten randomized controlled trials including 1,058 subjects were systematically evaluated and meta-analyzed in this study. In the main outcome indicators, there was a significant difference between mindfulness therapy and conventional treatment in reducing HAMD score (MD: -3.67, 95% CI: -5.22-2.11, p < 0.01) and HAMA score (MD: -3.23, 95% CI: -3.90-2.57, p < 0.01). In the secondary outcome indicators, mindfulness therapy also showed a significant difference in reducing SDS scores (MD: -6.49, 95% CI: -6.86-6.11, p < 0.01) and SAS scores (MD: -7.97, 95% CI: -9.68-6.27, p < 0.01) compared with conventional treatment. Conclusion: For the people with insomnia, anxiety and depression, the use of conventional treatment with the addition of 4-12 weeks of mindfulness treatment can significantly improve anxiety and depression symptoms of patients. This is a new diagnosis and treatment idea recommended for insomniacs with or without anxiety and depression symptoms. Due to the methodological defects in the included study and the limited sample size of this paper, more well-designed randomized controlled trials are needed for verification.

Spatial metabolomics reveal mechanisms of dexamethasone against pediatric pneumonia.

Currently, drugs are limited to treating pediatric pneumonia in clinical practice. It is urgent to find one new precise prevention and control therapy. The dynamically changing biomarkers during the development of pediatric pneumonia could help diagnose this disease, determine its severity, assess the risk of future events, and guide its treatment. Dexamethasone has been recognized as an effective agent with anti-inflammatory activity. However, its mechanisms against pediatric pneumonia remain unclear. In this study, spatial metabolomics was used to reveal the potential and characteristics of dexamethasone. Specifically, bioinformatics was first applied to find the critical biomarkers of differential expression in pediatric pneumonia. Subsequently, Desorption Electrospray Ionization mass spectrometry imaging-based metabolomics screened the differential metabolites affected by dexamethasone. Then, a gene-metabolite interaction network was built to mark functional correlation pathways for exploring integrated information and core biomarkers related to the pathogenesis and etiology of pediatric pneumonia. Further, these were validated by molecular biology and targeted metabolomics. As a result, genes of Cluster of Differentiation19, Fc fragment of IgG receptor IIb, Cluster of Differentiation 22, B-cell linker, Cluster of Differentiation 79B and metabolites of Triethanolamine, Lysophosphatidylcholine(18:1(9Z)), Phosphatidylcholine(16:0/16:0), phosphatidylethanolamine(O-18:1(1Z)/20:4(5Z,8Z,11Z,14Z)) were identified as the critical biomarkers in pediatric pneumonia. B cell receptor signaling pathway and glycerophospholipid metabolism were integrally analyzed as the main pathways of these biomarkers. The above data were illustrated using a Lipopolysaccharides-induced lung injury juvenile rat model. This work will provide evidence for the precise treatment of pediatric pneumonia.

Identification of Potential Biomarkers and Small Molecule Drugs for Cutaneous Melanoma Using Integrated Bioinformatic Analysis.

Background: Cutaneous melanoma (CM) is a type of skin cancer with a high fatality rate, and its pathogenesis has not yet been fully elucidated. Methods: We obtained the gene expression datasets of CM through the Gene Expression Omnibus (GEO) database. Subsequently, robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between CM cases and normal skin controls. Gene functional annotation was performed to explore the potential function of the DEGs. We built the protein-protein interaction (PPI) network by the Interactive Gene database retrieval tool (STRING) and selected hub modules by Molecular Complexity Detection (MCODE). We furthered and validated our results using the TCGA-GTEX dataset. Finally, potential small molecule drugs were predicted by CMap database and verified by molecular docking method. Results: A total of 135 DEGs were obtained by RRA synthesis analysis. GMPR, EMP3, SLC45A2, PDZD2, NPY1R, DLG5 and ADH1B were screened as potential targets for CM. Furazolidone was screened as a potential small molecule drug for the treatment of CM, and its mechanism may be related to the inhibition of CM cell proliferation by acting on GMPR. Conclusion: We identified seven prognostic therapeutic targets associated with CM and furazolidone could be used as a potential drug for CM treatment, providing new prognostic markers, potential therapeutic targets and small molecule drugs for the treatment and prevention of CM.

Association between environmental chemicals co-exposure and peripheral blood immune-inflammatory indicators.

Chronic inflammation is closely related to chronic inflammatory diseases, autoimmune diseases and cancer. Few studies have evaluated the effects of exposure to multiple chemical combinations on immunoinflammatory related indicators and their possible molecular mechanisms. This study explored the effect of exposure to various chemicals on immune-inflammatory biomarkers and its molecular mechanism. Using data from 1,723 participants in the National Health and Nutrition Examination Survey (NHANES, 2011-2012), the aim was to determine the association between chemical mixtures and immunoinflammatory biomarkers [including White blood cell (Wbc), neutrophil (Neu), lymphocytes (Lym), and Neutrophil-to-lymphocyte ratio (NLR)] using linear regression model, weighted quantile sum regression (WQSR) model, and bayesian nuclear machine regression (BKMR) model. Meanwhile, functional enrichment analysis and protein-protein interaction network establishment were performed to explore the molecular mechanism of inflammation induced by high-weight chemicals. In the linear regression model established for each single chemical, the four immunoinflammatory biomarkers were positively correlated with polycyclic aromatic hydrocarbons (PAHs), negatively correlated with perfluoroalkyl substances (PFASs), and positively or negatively correlated with metallic and non-metallic elements. WQSR model showed that cadmium (Cd), perfluorooctane sulfonic acid (PFOS) and perfluorodecanoic acid (PFDE) had the highest weights. In BKMR analysis, the overall effect of chemical mixtures was significantly associated with Lym and showed an increasing trend. The hub genes in high-weight chemicals inflammation-related genes were interleukin-6 (IL6), tumor necrosis factor (TNF), and interleukin-1B (IL1B), etc. They were mainly enriched in inflammatory response, Cytokine-cytokine receptor interaction, Th17 cell differentiation and IL-17 signaling pathway. The above results show that exposure to environmental chemical cocktails primarily promotes an increase in Lym across the immune-inflammatory spectrum. The mechanism leading to the inflammatory response may be related to the activation of IL-6 amplifier by the co-exposure of environmental chemicals.