Integrated plasma proteomics and N-glycoproteomics reveals alterations in the N-glycosylation in Chinese hepatocellular carcinoma patients.

Hepatocellular carcinoma (HCC) is a life-threatening disease that presents diagnostic challenges due to the absence of reliable biomarkers. Recently, plasma proteomics and glycoproteomics have emerged as powerful tools for identifying potential diagnostic biomarkers for various diseases. In this study, we conducted a comprehensive proteomic and glycoproteomic analysis of plasma samples from 11 HCC patients and 11 healthy control (HC) individuals. We identified 20 differentially expressed (DE) proteins and 32 DE intact glycosylated peptides (IGPs) that can effectively differentiate between HCC patients and HC samples. Our findings demonstrate that IGP profiles had better predictive power than protein profiles for screening HCC. Pathways associated with DE proteins and IGPs were identified. It was reported that the protein expression level of galectin 3 binding protein (LGALS3BP) and its N-linked glycosylation at the N398 and N551 sites might serve as valuable candidates for HCC diagnosis. These results highlight the importance of N-glycoproteomics in advancing our understanding of HCC and suggest possible candidates for the future diagnosis of this disease.

Revealing the clinical effect and biological mechanism of acupuncture in COPD: A review.

BACKGROUND: To provide new ideas for the clinical and mechanism research of acupuncture in the treatment of chronic obstructive pulmonary disease (COPD), this study systematically reviews clinical research and the progress of basic research of acupuncture in the treatment of COPD. METHODS: PubMed and Web of Science databases were searched using acupuncture and COPD as keywords in the last 10 years, and the included literature was determined according to exclusion criteria. FINDINGS: Acupuncture can relieve clinical symptoms, improve exercise tolerance, anxiety, and nutritional status, as well as hemorheological changes (blood viscosity), reduce the inflammatory response, and reduce the duration and frequency of COPD in patients with COPD. Mechanistically, acupuncture inhibits M1 macrophage activity, reduces neutrophil infiltration, reduces inflammatory factor production in alveolar type II epithelial cells, inhibits mucus hypersecretion of airway epithelial cells, inhibits the development of chronic inflammation in COPD, and slows tissue structure destruction. Acupuncture may control pulmonary COPD inflammation through the vagal-cholinergic anti-inflammatory, vagal-adrenomedullary-dopamine, vagal-dual-sensory nerve fiber-pulmonary, and CNS-hypothalamus-orexin pathways. Furthermore, acupuncture can increase endogenous cortisol levels by inhibiting the HPA axis, thus improving airway antioxidant capacity and reducing airway inflammation in COPD. In conclusion, the inhibition of the chronic inflammatory response is the key mechanism of acupuncture treatment for COPD.

Proteomics reveals that cell density could affect the efficacy of drug treatment.

In vitro cell biology study plays a fundamental role in biological and drug development research, but the repeatability and accuracy of cell studies remain to be low. Various uncertainties during the cell culture process could introduce bias into drug research. In this study, we evaluate the potential effects and underlying mechanisms induced by cell number differences in the cell seeding process. Normally, drug experiments are initiated 24 h after cell seeding, and the difference in the cell number at the time of inoculation leads to the difference in cell confluence (cell density) when drug research is conducted. While cell confluence is closely related to intercellular communication, surface protein interaction, cell autocrine as well as paracrine protein expression of cells, it might have a potential impact on the effect of biological studies such as drug treatment. This study used proteomics technology to comprehensively explore the different protein expression patterns between cells with different confluences. Due to the high sensitivity and high throughput of liquid chromatography-mass spectrometry (LC-MS/MS) detection, it was hired to evaluate the protein expression differences of Hep3B cells with 3 different confluences (30%, 50%, and 70%). The differential expressed proteins were analyzed by the Reactome pathway and the Gene Ontology (GO) pathway. Significant differences were identified across three confluences in terms of the number of proteins identified, the protein expression pattern, and the expression level of certain KEGG pathways. We found that those proteins involved in the cell cycle pathway were differently expressed: the higher the cell confluence, the higher these proteins expressed. A cell cycle inhibitor palbociclib was selected to further verify this observation. Palbociclib in the same dose was applied to cells with different confluence, the results indicated that the growth inhibition effect of palbociclib increases along with the increasing trend of cell cycle protein expression. The result indicated that cell density did influence the effect of drug treatment. Furthermore, three other drugs, cisplatin, paclitaxel, and imatinib, were used to treat the three liver cancer cell lines Hep3B, SUN387, and MHCC97, and a similar observation was obtained that drug effect would be different when the cell confluences were different. Therefore, selecting an appropriate number of cells for plating is vitally important at the beginning of a drug study.

Revealing the biological mechanism of acupuncture in alleviating excessive inflammatory responses and organ damage in sepsis: a systematic review.

Sepsis is a systemic inflammation caused by a maladjusted host response to infection. In severe cases, it can cause multiple organ dysfunction syndrome (MODS) and even endanger life. Acupuncture is widely accepted and applied in the treatment of sepsis, and breakthroughs have been made regarding its mechanism of action in recent years. In this review, we systematically discuss the current clinical applications of acupuncture in the treatment of sepsis and focus on the mechanisms of acupuncture in animal models of systemic inflammation. In clinical research, acupuncture can not only effectively inhibit excessive inflammatory reactions but also improve the immunosuppressive state of patients with sepsis, thus maintaining immune homeostasis. Mechanistically, a change in the acupoint microenvironment is the initial response link for acupuncture to take effect, whereas PROKR2 neurons, high-threshold thin nerve fibres, cannabinoid CB2 receptor (CB2R) activation, and Ca2+ influx are the key material bases. The cholinergic anti-inflammatory pathway of the vagus nervous system, the adrenal dopamine anti-inflammatory pathway, and the sympathetic nervous system are key to the transmission of acupuncture information and the inhibition of systemic inflammation. In MODS, acupuncture protects against septic organ damage by inhibiting excessive inflammatory reactions, resisting oxidative stress, protecting mitochondrial function, and reducing apoptosis and tissue or organ damage.

Investigation on acquired palbociclib resistance by LC-MS based multi-omics analysis.

Palbociclib is a specific CDK4/6 inhibitor that has been widely applied in multiple types of tumors. Different from cytotoxic drugs, the anticancer mechanism of palbociclib mainly depends on cell cycle inhibition. Therefore, the resistance mechanism is different. For clinical cancer patients, drug resistance is inevitable for almost all cancer therapies including palbociclib. We have trained palbociclib resistant cells in vitro to simulate the clinical situation and applied LC-MS multi-omics analysis methods including proteomic, metabolomic, and glycoproteomic techniques, to deeply understand the underly mechanism behind the resistance. As a result of proteomic analysis, the resistant cells were found to rely on altered metabolic pathways to keep proliferation. Metabolic processes related to carbohydrates, lipids, DNA, cellular proteins, glucose, and amino acids were observed to be upregulated. Most dramatically, the protein expressions of COX-1 and NDUFB8 have been detected to be significantly overexpressed by proteomic analysis. When a COX-1 inhibitor was hired to combine with palbociclib, a synergistic effect could be obtained, suggesting the altered COX-1 involved metabolic pathway is an important reason for the acquired palbociclib resistance. The KEGG pathway of N-glycan biosynthesis was identified through metabolomics analysis. N-glycoproteomic analysis was therefore included and the global glycosylation was found to be elevated in the palbociclib-resistant cells. Moreover, integration analysis of glycoproteomic data allowed us to detect a lot more proteins that have been glycosylated with low abundances, these proteins were considered to be overwhelmed by those highly abundant proteins during regular proteomic LC-MS detection. These low-abundant proteins are mainly involved in the cellular biology processes of cell migration, the regulation of chemotaxis, as well as the glycoprotein metabolic process which offered us great more details on the roles played by N-glycosylation in drug resistance. Our result also verified that N-glycosylation inhibitors could enhance the cell growth inhibition of palbociclib in resistant cells. The high efficiency of the integrated multi-omics analysis workflow in discovering drug resistance mechanisms paves a new way for drug development. With a clear understanding of the resistance mechanism, new drug targets and drug combinations could be designed to resensitize the resistant tumors.

Comparative analyses of bat genomes identify distinct evolution of immunity in Old World fruit bats.

Bats have been identified as natural reservoir hosts of several zoonotic viruses, prompting suggestions that they have unique immunological adaptations. Among bats, Old World fruit bats (Pteropodidae) have been linked to multiple spillovers. To test for lineage-specific molecular adaptations in these bats, we developed a new assembly pipeline to generate a reference-quality genome of the fruit bat Cynopterus sphinx and used this in comparative analyses of 12 bat species, including six pteropodids. Our results reveal that immunity-related genes have higher evolutionary rates in pteropodids than in other bats. Several lineage-specific genetic changes were shared across pteropodids, including the loss of NLRP1, duplications of PGLYRP1 and C5AR2, and amino acid replacements in MyD88. We introduced MyD88 transgenes containing Pteropodidae-specific residues into bat and human cell lines and found evidence of dampened inflammatory responses. By uncovering distinct immune adaptations, our results could help explain why pteropodids are frequently identified as viral hosts.

A Liquid Metal Microdroplets Initialized Hemicellulose Composite for 3D Printing Anode Host in Zn-Ion Battery.

Maintaining a steady affinity between gallium-based liquid metals (LM) and polymer binders, particularly under continuous mechanical deformation, such as extrusion-based 3D printing or plating/stripping of Zinc ion (Zn2+ ), is very challenging. Here, an LM-initialized polyacrylamide-hemicellulose/EGaIn microdroplets hydrogel is used as a multifunctional ink to 3D-print self-standing scaffolds and anode hosts for Zn-ion batteries. The LM microdroplets initiate acrylamide polymerization without additional initiators and cross-linkers, forming a double-covalent hydrogen-bonded network. The hydrogel acts as a framework for stress dissipation, enabling recovery from structural damage due to the cyclic plating/stripping of Zn2+ . The LM-microdroplet-initialized polymerization with hemicelluloses can facilitate the production of 3D printable inks for energy storage devices.

DualMMP-GAN: Dual-scale multi-modality perceptual generative adversarial network for medical image segmentation.

Multi-modality magnetic resonance imaging (MRI) can reveal distinct patterns of tissue in the human body and is crucial to clinical diagnosis. But it still remains a challenge to obtain diverse and plausible multi-modality MR images due to expense, noise, and artifacts. For the same lesion, different modalities of MRI have big differences in context information, coarse location, and fine structure. In order to achieve better generation and segmentation performance, a dual-scale multi-modality perceptual generative adversarial network (DualMMP-GAN) is proposed based on cycle-consistent generative adversarial networks (CycleGAN). Dilated residual blocks are introduced to increase the receptive field, preserving structure and context information of images. A dual-scale discriminator is constructed. The generator is optimized by discriminating patches to represent lesions with different sizes. The perceptual consistency loss is introduced to learn the mapping between the generated and target modality at different semantic levels. Moreover, generative multi-modality segmentation (GMMS) combining given modalities with generated modalities is proposed for brain tumor segmentation. Experimental results show that the DualMMP-GAN outperforms the CycleGAN and some state-of-the-art methods in terms of PSNR, SSMI, and RMSE in most tasks. In addition, dice, sensitivity, specificity, and Hausdorff95 obtained from segmentation by GMMS are all higher than those from a single modality. The objective index obtained by the proposed methods are close to upper bounds obtained from real multiple modalities, indicating that GMMS can achieve similar effects as multi-modality. Overall, the proposed methods can serve as an effective method in clinical brain tumor diagnosis with promising application potential.

Uncovering interpretable potential confounders in electronic medical records.

Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We explore how unstructured clinical text can be used to reduce selection bias and improve medical practice. We develop a framework based on natural language processing to uncover interpretable potential confounders from text. We validate our method by comparing the estimated hazard ratio (HR) with and without the confounders against established RCTs. We apply our method to four cohorts built from localized prostate and lung cancer datasets from the Stanford Cancer Institute and show that our method shifts the HR estimate towards the RCT results. The uncovered terms can also be interpreted by oncologists for clinical insights. We present this proof-of-concept study to enable more credible causal inference using observational data, uncover meaningful insights from clinical text, and inform high-stakes medical decisions.

Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records.

PURPOSE: Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer. METHODS: We used a total number of 4,412 patients with 483,782 clinical notes from the Stanford Cancer Institute Research Database containing patients with nonmetastatic prostate, oropharynx, and esophagus cancer. We trained treatment identification models for each cancer type separately and compared performance of using only structured, only unstructured (bag-of-words, doc2vec, fasttext), and combinations of both (structured + bow, structured + doc2vec, structured + fasttext). We optimized the identification model among five machine learning methods (logistic regression, multilayer perceptrons, random forest, support vector machines, and stochastic gradient boosting). The treatment information recorded in the cancer registry is the gold standard and compares our methods to an identification baseline with billing codes. RESULTS: For prostate cancer, we achieved an f1-score of 0.99 (95% CI, 0.97 to 1.00) for radiation and 1.00 (95% CI, 0.99 to 1.00) for surgery using structured + doc2vec. For oropharynx cancer, we achieved an f1-score of 0.78 (95% CI, 0.58 to 0.93) for chemoradiation and 0.83 (95% CI, 0.69 to 0.95) for surgery using doc2vec. For esophagus cancer, we achieved an f1-score of 1.0 (95% CI, 1.0 to 1.0) for both chemoradiation and surgery using all combinations of structured and unstructured data. We found that employing the free-text clinical notes outperforms using the billing codes or only structured data for all three cancer types. CONCLUSION: Our results show that treatment identification using free-text clinical notes greatly improves upon the performance using billing codes and simple structured data. The approach can be used for treatment cohort identification and adapted for longitudinal cancer treatment identification.

A Probabilistic Model to Support Radiologists' Classification Decisions in Mammography Practice.

We developed a probabilistic model to support the classification decisions made by radiologists in mammography practice. Using the feature observations and Breast Imaging Reporting and Data System (BI-RADS) classifications from radiologists examining diagnostic and screening mammograms, we modeled their decisions to understand their judgments. Our model could help improve the decisions made by radiologists using their own feature observations and classifications while maintaining their observed sensitivities. Based on 112,433 mammographic cases from 36,111 patients and 13 radiologists at 2 separate institutions with a 1.1% prevalence of malignancy, we trained a probabilistic Bayesian network (BN) to estimate the malignancy probabilities of lesions. For each radiologist, we learned an observed probabilistic threshold within the model. We compared the sensitivity and specificity of each radiologist against the BN model using either their observed threshold or the standard 2% threshold recommended by BI-RADS. We found significant variability among the radiologists' observed thresholds. By applying the observed thresholds, the BN model showed a 0.01% (1 case) increase in false negatives and a 28.9% (3612 cases) reduction in false positives. When using the standard 2% BI-RADS-recommended threshold, there was a 26.7% (47 cases) increase in false negatives and a 47.3% (5911 cases) reduction in false positives. Our results show that we can significantly reduce screening mammography false positives with a minimal increase in false negatives. We find that learning radiologists' observed thresholds provides valuable information regarding the conservativeness of clinical practice and allows us to quantify the variability in sensitivity across and within institutions. Our model could provide support to radiologists to improve their performance and consistency within mammography practice.