Thermal-to-Electrical Conversion Based on Salinity Gradient Driven by Evaporation.

Constructing concentration differences between anions and cations at the ends of an ionic conductor is an effective strategy in electricity generation for powering wearable devices. Temperature gradient or salinity gradient is the driving force behind such devices. But their corresponding power generation devices are greatly limited in actual application due to their complex structure and harsh application conditions. In this study, a novel ionic concentration gradient electric generator based on the evaporation difference of the electrolyte is proposed. The device can be constructed without the need for semipermeable membranes, and operation does not need to build a temperature difference. As a demonstration, a PVA-Na ionic hydrogel is prepared as an electrolyte for the device and achieved a thermovoltage of more than 200 mV and an energy density of 77.94 J m-2 at 323 K. Besides, the device exhibits the capability to sustain a continuous voltage output for a duration exceeding 1500 min, as well as enabling charging and discharging cycles for 100 iterations. For practical applications, a module comprising 16 sub-cells is constructed and successfully utilized to directly power a light-emitting diode. Wearable devices and their corresponding cell modules are also developed to recycle body heat.

Quantification of GPC1(+) Exosomes Based on MALDI-TOF MS In Situ Signal Amplification for Pancreatic Cancer Discrimination and Evaluation.

Pancreatic cancer (PC) has a high mortality, with a fairly low five-year survival rate, because of its delayed diagnosis. Recently, liquid biopsy, especially based on exosomes, has attracted vast attention, thanks to its low invasiveness. Herein, we constructed a protocol for pancreatic cancer related Glypican 1 (GPC1) exosome quantification, based on in situ mass spectrometry signal amplification, by utilizing mass tag molecules on gold nanoparticles (AuNPs). Exosomes were extracted and purified by size-exclusion chromatography (SEC), captured by TiO2 modified magnetic nanoparticles, and then targeted specifically by anti-GPC1 antibody modified on AuNPs. With matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), the signal of PC biomarker, GPC1, was converted to a mass tag signal and amplified. With addition of a certain amount of internal standard molecules modified on AuNPs, the relative intensity ratio of mass tag to internal standard was proportional to the concentration of GPC1(+) exosomes derived from pancreatic cancer cell lines, PANC-1, with good linearity (R2 = 0.9945) in a wide dynamic range from 7.1 × 10 to 7.1 × 106 particles/µL. This method was further applied to plasma samples from healthy control (HC) and pancreatic cancer patients with different tumor load, and exhibited a great potential in discriminating diagnosed PC patients from HC, and has the monitoring potential in PC progression.

Interventions to Facilitate Shared Decision-Making Using Decision Aids with Coronary Heart Disease Patients: Systematic Review and Meta-Analysis.

Background: Coronary heart disease (CHD) is the leading cause of death in the world. There are some decision-making conflicts in the management of chest pain, treatment methods, stent selection, and other aspects due to the unstable condition of CHD in the treatment stage. Although using decision aids to facilitate shared decision-making (SDM) contributes to high-quality decision-making, it has not been evaluated in the field of CHD. This review systematically assessed the effects of SDM in patients with CHD. Methods: We conducted a systematic review and meta-analysis of randomized controlled trials of SDM interventions in patients with CHD from database inception to 1 June 2022 (PROSPERO [Unique identifier: CRD42022338938]). We searched for relevant studies in the PubMed, Embase, Cochrane Library, Web of Science, CNKI, and Wan Fang databases. The primary outcomes were knowledge and decision conflict. The secondary outcomes were satisfaction, patient participation, trust, acceptance, quality of life, and psychological condition. Results: A total of 8244 studies were retrieved. After screening, ten studies were included in the analysis. Compared with the control group, SDM intervention with patient decision aids obviously improved patients' knowledge, decision satisfaction, participation, and medical outcomes and reduced decision-making conflict. There was no significant effect of SDM on trust. Conclusions: This study showed that SDM intervention in the form of decision aids was beneficial to decision-making quality and treatment outcomes among patients with CHD. The results of SDM interventions need to be evaluated in different environments.

The Impact of Shared Decision-Making on the Quality of Decision Making in Aortic Dissection: A before-and-after Comparison Study.

Background: Complex surgical plans and consideration of risks and benefits often cause decisional conflicts for decision-makers in aortic dissection (AD) surgery, resulting in decision delay. Shared decision-making (SDM) improves decision readiness and reduces decisional conflicts. The purpose of this study was to investigate the impact of SDM on decision quality in AD. Methods: One hundred and sixty AD decision-makers were divided into two groups: control (n = 80) and intervention (n = 80). The surgical plan for the intervention group was determined using patient decision aids. The primary outcome was decisional conflict. Secondary outcomes included decision preparation, decision satisfaction, surgical method, postoperative complications, actual participation role, and duration of consultation. The data were analyzed with SPSS 26.0 (IBM Corp., Chicago, IL, USA). p < 0.05 was considered statistically significant. Results: The decisional conflict score was significantly lower in the intervention group than in the control group (p < 0.001). The decision preparation and decision satisfaction scores in the intervention group were significantly higher than those in the control group (p < 0.001). There were more SDM decision-makers in the intervention group (16 [20%] vs. 42 [52.50%]). There was no statistical significance in the choice of surgical, postoperative complications, duration of consultation, and hospital and post-operative intensive care unit stay time (p = 0.267, p = 0.130, p = 0.070, p = 0.397, p = 0.421, respectively). Income, education level, and residence were the influencing factors of decision-making conflict. Conclusions: SDM can reduce decisional conflict, improve decision preparation and satisfaction, and help decision-makers actively participate in the medical management of patients with AD without affecting the medical outcome.

Recent Advances on Small-Molecule Antagonists Targeting TLR7.

Toll-like receptor 7 (TLR7) is a class of pattern recognition receptors (PRRs) recognizing the pathogen-associated elements and damage and as such is a major player in the innate immune system. TLR7 triggers the release of pro-inflammatory cytokines or type-I interferons (IFN), which is essential for immunoregulation. Increasing reports also highlight that the abnormal activation of endosomal TLR7 is implicated in various immune-related diseases, carcinogenesis as well as the proliferation of human immunodeficiency virus (HIV). Hence, the design and development of potent and selective TLR7 antagonists based on small molecules or oligonucleotides may offer new tools for the prevention and management of such diseases. In this review, we offer an updated overview of the main structural features and therapeutic potential of small-molecule antagonists of TLR7. Various heterocyclic scaffolds targeting TLR7 binding sites are presented: pyrazoloquinoxaline, quinazoline, purine, imidazopyridine, pyridone, benzanilide, pyrazolopyrimidine/pyridine, benzoxazole, indazole, indole, and quinoline. Additionally, their structure-activity relationships (SAR) studies associated with biological activities and protein binding modes are introduced.

Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints.

Plasma exosomes have shown great potential for liquid biopsy in clinical cancer diagnosis. Herein, we present an integrated strategy for isolating and analyzing exosomes from human plasma rapidly and then discriminating different cancers excellently based on deep learning fingerprints of plasma exosomes. Sequential size-exclusion chromatography (SSEC) was developed efficiently for separating exosomes from human plasma. SSEC isolated plasma exosomes, taking as less as 2 h for a single sample with high purity such that the discard rates of high-density lipoproteins and low/very low-density lipoproteins were 93 and 85%, respectively. Benefitting from the rapid and high-purity isolation, the contents encapsulated in exosomes, covered by plasma proteins, were well profiled by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS). We further analyzed 220 clinical samples, including 79 breast cancer patients, 57 pancreatic cancer patients, and 84 healthy controls. After MS data pre-processing and feature selection, the extracted MS feature peaks were utilized as inputs for constructing a multi-classifier artificial neural network (denoted as Exo-ANN) model. The optimized model avoided overfitting and performed well in both training cohorts and test cohorts. For the samples in the independent test cohort, it realized a diagnosed accuracy of 80.0% with an area under the curve of 0.91 for the whole group. These results suggest that our integrated pipeline may become a generic tool for liquid biopsy based on the analysis of plasma exosomes in clinics.

Quantitative proteomic analysis of the effects of dietary deprivation of methionine and cystine on A549 xenograft and A549 xenograft-bearing mouse.

Methionine (Met) and cystine (CySS) are key sulfur donors in cell metabolism and are important nutrients for sustaining tumor growth; however, the molecular effects associated with their deprivation remain to be characterized. Here, we applied a xenograft mouse model to assess the impact of their deprivation on A549 xenografts and the xenograft-bearing animal. Results show that Met and CySS deprivation inhibits A549 growth in vitro, not in vivo. Deprivation was detrimental to the xenograft-bearing mouse, as demonstrated by weight loss and renal dysfunction. Differentially expressed proteins in A549 xenograft and mouse kidneys were characterized using quantitative proteomics. Functional annotation and protein-protein interaction network analysis revealed the enriched signaling pathways, including focal adhesion (Fn1) in the A549 xenograft, and xenobiotic metabolism (Cyp2e1) and glutathione metabolism (Ggt1) in the mouse kidney. Met and CySS deprivation inhibits the migratory and invasive properties of cancer cells, as evidenced by reduced expression of the epithelial to mesenchymal transition marker N-cadherin in A549 cells in vitro. Moreover, IGFBP1 protein expression was inhibited in both A549 xenograft and mouse kidneys. This study provides the first insights into changes within the proteome profile and biological processes upon Met and CySS deprivation in a A549 xenograft mouse model.

Deconstruction of Heterogeneity of Size-Dependent Exosome Subpopulations from Human Urine by Profiling N-Glycoproteomics and Phosphoproteomics Simultaneously.

The heterogeneous populations of exosomes with distinct nanosize have impeded our understanding of their corresponding function as intercellular communication agents. Profiling signaling proteins packaged in each size-dependent subtype can disclose this heterogeneity of exosomes. Herein, new strategy was developed for deconstructing heterogeneity of distinct-size urine exosome subpopulations by profiling N-glycoproteomics and phosphoproteomics simultaneously. Two-dimension size exclusion liquid chromatography (SEC) was utilized to isolate large exosomes (L-Exo), medium exosomes (M-Exo), and small exosomes (S-Exo) from human urine samples. Then, hydrophilic carbonyl-functionalized magnetic zirconium-organic framework (CFMZOF) was developed as probe for capturing the two kinds of post-translational modification (PTM) peptides simultaneously. Finally, liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with database search was used to characterize PTM protein contents. We identified 144 glycoproteins and 44 phosphoproteins from L-Exo, 156 glycoproteins, and 46 phosphoproteins from M-Exo and 134 glycoproteins and 10 phosphoproteins from S-Exo. The ratio of the proteins with simultaneous glycosylation and phosphorylation is 11%, 9%, and 3% in L-Exo, M-Exo, and S-Exo, respectively. Based on label-free quantification intensity results, both principal component analysis and Pearson's correlation coefficients indicate that distinct-size exosome subpopulations exist significant differences in PTM protein contents. Analysis of high abundance PTM proteins in each exosome subset reveals that the preferentially packaged PTM proteins in L-Exo, M-Exo, and S-Exo are associated with immune response, biological metabolism, and molecule transport processes, respectively. Our PTM proteomics study based on size-dependent exosome subtypes opens a new avenue for deconstructing the heterogeneity of exosomes.

Titanium(IV)-functionalized zirconium-organic frameworks as dual-metal affinity probe for recognition of endogenous phosphopeptides prior to mass spectrometric quantification.

A zirconium-organic framework was modified with titanium(IV) ions to obtain a modified framework that is shown to be a viable sorbent for selective capture of phosphopeptides. This dual-metal affinity probe exhibits 0.1 fM limits of detection and excellent size-exclusion effect (the mass ratio of ß-casein digests/BSA/intact ß-casein is 1:1000:1000). This is attributed to abundant Ti(IV) and Zr(IV) coordination sites and high porosity. The performance of the sorbent for extracting endogenous phosphopeptides from human serum and saliva was investigated. Especially, 105 endogenous phosphopeptides from saliva were captured specifically. In addition, the amino acid frequency of the enriched phosphopeptides was analyzed. Conservation of sequence around the identified phosphorylated sites from saliva confirmed that phosphorylation took place in the proline-directed motifs. Graphical abstractSchematic representation of a method for the specific enrichment of phosphopeptides by a modified metal-organic framework. Following size-exclusion elution, the phosphopeptides are quantified by mass spectrometry.

Joint segmentation of tumors in 3D PET-CT images with a network fusing multi-view and multi-modal information.

Objective. Joint segmentation of tumors in positron emission tomography-computed tomography (PET-CT) images is crucial for precise treatment planning. However, current segmentation methods often use addition or concatenation to fuse PET and CT images, which potentially overlooks the nuanced interplay between these modalities. Additionally, these methods often neglect multi-view information that is helpful for more accurately locating and segmenting the target structure. This study aims to address these disadvantages and develop a deep learning-based algorithm for joint segmentation of tumors in PET-CT images.Approach. To address these limitations, we propose the Multi-view Information Enhancement and Multi-modal Feature Fusion Network (MIEMFF-Net) for joint tumor segmentation in three-dimensional PET-CT images. Our model incorporates a dynamic multi-modal fusion strategy to effectively exploit the metabolic and anatomical information from PET and CT images and a multi-view information enhancement strategy to effectively recover the lost information during upsamping. A Multi-scale Spatial Perception Block is proposed to effectively extract information from different views and reduce redundancy interference in the multi-view feature extraction process.Main results. The proposed MIEMFF-Net achieved a Dice score of 83.93%, a Precision of 81.49%, a Sensitivity of 87.89% and an IOU of 69.27% on the Soft Tissue Sarcomas dataset and a Dice score of 76.83%, a Precision of 86.21%, a Sensitivity of 80.73% and an IOU of 65.15% on the AutoPET dataset.Significance. Experimental results demonstrate that MIEMFF-Net outperforms existing state-of-the-art models which implies potential applications of the proposed method in clinical practice.

Identification of potential biomarkers for cerebral palsy and the development of prediction models.

Cerebral palsy (CP) is a prevalent motor disorder originating from early brain injury or malformation, with significant variability in its clinical presentation and etiology. Early diagnosis and personalized therapeutic interventions are hindered by the lack of reliable biomarkers. This study aims to identify potential biomarkers for cerebral palsy and develop predictive models to enhance early diagnosis and prognosis. We conducted a comprehensive bioinformatics analysis of gene expression profiles in muscle samples from CP patients to identify candidate biomarkers. Six key genes (CKMT2, TNNT2, MYH4, MYH1, GOT1, and LPL) were validated in an independent cohort, and potential biological pathways and molecular networks involved in CP pathogenesis were analyzed. The importance of processes such as functional regulation, energy metabolism, and cell signaling pathways in the muscles of CP patients was emphasized. Predictive models of muscle sample biomarkers related to CP were developed and visualized. Calibration curves and receiver operating characteristic analysis demonstrated that the predictive models exhibit high sensitivity and specificity in distinguishing individuals at risk of CP. The identified biomarkers and developed prediction models offer significant potential for early diagnosis and personalized management of CP. Future research should focus on validating these biomarkers in larger cohorts and integrating them into clinical practice to improve outcomes for individuals with CP.

HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions.

Due to the complex behavior arising from non-uniqueness, symmetry, and bifurcations in the solution space, solving inverse problems of nonlinear differential equations (DEs) with multiple solutions is a challenging task. To address this, we propose homotopy physics-informed neural networks (HomPINNs), a novel framework that leverages homotopy continuation and neural networks (NNs) to solve inverse problems. The proposed framework begins with the use of NNs to simultaneously approximate unlabeled observations across diverse solutions while adhering to DE constraints. Through homotopy continuation, the proposed method solves the inverse problem by tracing the observations and identifying multiple solutions. The experiments involve testing the performance of the proposed method on one-dimensional DEs and applying it to solve a two-dimensional Gray-Scott simulation. Our findings demonstrate that the proposed method is scalable and adaptable, providing an effective solution for solving DEs with multiple solutions and unknown parameters. Moreover, it has significant potential for various applications in scientific computing, such as modeling complex systems and solving inverse problems in physics, chemistry, biology, etc.

Specific isolation and quantification of PD-L1 positive tumor derived exosomes for accurate breast cancer discrimination via aptamer-functionalized magnetic composites and SERS immunoassay.

PD-L1 positive tumor derived exosomes (TEXsPD-L1) play a significant role in disease progression, tumor metastasis and cancer immunotherapy. However, the overlap of PD-L1 between TEXs and non-tumor derived exosomes (non-TEXs) restricts the specific isolation and quantification of TEXPD-L1 from clinical samples. Herein, a new aptamer-functionalized and hydrophilic immunomagnetic substrate was designed by decorating generation 5 polyamidoamine dendrimers (G5 PAMAM), zwitterionic trimethylamine N-oxide (TMAO) and EpCAM (Epithelial cell adhesion molecule) aptamers on magnetic cores sequentially (Fe3O4@PAMAM@TMAO@Aptamer, named as FPTA) for rapid target and efficient capture of TEXs. The FPTA substrate gathered excellent characters of strong magnetic responsiveness of Fe3O4, abundant affinity sites of PAMAM, strong hydrophilicity of TMAO and enhanced affinity properties of EpCAM aptamers. Because of these advantages, FPTA can isolate TEXs quickly within 30min with high capture efficiency of 90.5 % ± 3.0 % and low nonspecific absorption of 8.2 % ± 2.0 % for non-TEXs. Furthermore, PD-L1 (Programmed cell death-ligand 1) positive TEXs (TEXsPD-L1) from the captured TEXs were recognized and quantitatively analyzed by utilizing SERS (surface-enhanced Raman spectroscopy) reporter molecules 4-NTP (4-Nitrothiophenol) on PD-L1 aptamers-functionalized gold immunoaffinity probe. The signal of TEXsPD-L1 was converted to SERS signal of 4-NTP at 1344 cm-1 which exhibited a linear correlation to concentration of TEXsPD-L1(R2 = 0.9905). With these merits, this strategy was further applied to clinical plasma samples from breast cancer (BC) patients and healthy controls (HC), exhibited an excellent diagnosis accuracy with area under curve (AUC) of receiver operating characteristic (ROC) curve reaching 0.988. All these results demonstrate that the FPTA immunomagnetic substrate combined with SERS immunoaffinity probe may become a generic tool for specific isolation and quantitative analysis of PD-L1 positive tumor-derived exosomes in clinics.

Immunomagnetic capture and traceless release of native tumor-derived exosomes from human plasma for exploring interaction with recipient cells by aptamer-functionalized nanoflowers.

BACKGROUND: Tumor-derived exosomes (TEXs) play an important role in the development process of cancer, which can transport a large number of carcinogenic molecules to normal cells, and subsequently promote tumor metastasis. However, TEXs that were utilized in most of previous researches were obtained from the cell medium of tumor cell lines, which cannot reflect the physiological state of primary cells in vivo. Isolation of native TEXs from human plasma with intact function is contributed to exploring the interaction between TEXs and recipient cells for understanding their true biological functions. RESULTS: We developed a strategy that involves both capture and release processes to obtain native TEXs from plasma of cancer patients. An MoS2-based immunomagnetic probe (Fe3O4@MoS2-Au-Aptamer, named as FMAA) with the advantages of high surface area, magnetic response and abundant affinity sites was designed and synthesized to capture TEXs through recognizing high-expression tumor-associated antigens of EpCAM. With the assistance of complementary sequences of EpCAM, TEXs were released with non-destruction and no residual labels. According to NTA analysis, 107-108 TEXs were recovered from per mL plasma of breast cancer patients. The interaction between native TEXs and normal epithelial cells confirms TEXs could induce significant activation of autophagy of recipient cells with co-culture for 12 h. Proteomics analysis demonstrated a total of 637 proteins inside epithelial cells had dynamic expression with the stimulation of TEXs and 5 proteins in the pathway of autophagy had elevated expression level. SIGNIFICANCE: This work not only obtains native TEXs from human plasma with non-destruction and no residual labels, but also explores the interaction between TEXs and recipient cells for understanding their true biological functions, which will accelerate the application of TEXs in the field of biomarkers and therapeutic drugs.

Structural and functional properties of Maillard-reacted casein phosphopeptides with different carbohydrates.

This study used glucose, fructose, maltose and dextran to explore the effects of different carbohydrates on the Maillard reaction of casein phosphopeptides (CPP). The color parameter results showed that heating time from 1 to 5 h led to brown color, which was consistent with the observed increased in browning intensity. Fourier transform infrared spectroscopy results verified that four carbohydrates reacted with CPP to produce Maillard conjugates. Fluorescence spectroscopy showed that the Maillard reaction changed the tertiary structure of CPP by decreasing the intrinsic fluorescence intensity and surface hydrophobicity compared with the CPP-carbohydrate mixture. At the same time, the Maillard reaction effectively improved the emulsifying properties, reducing power and DPPH radical scavenging activity of CPP. Furthermore, this study also found that glucose and fructose improved CPP more than maltose and dextran. Therefore, monosaccharides have good potential in modifying CPP via the Maillard reaction.

Single-cell transcriptome analysis reveals dynamic changes of the preclinical A549 cancer models, and the mechanism of dacomitinib.

The in vitro A549 cells, and A549 xenografts in nude mouse, were two commonly used models for anti-cancer drug discovery. However, the biological and molecular characteristics of these two classic models, and also the dynamic transcriptome changes after dacomitinib exposure remains elusive. We performed single-cell RNA sequencing to define the transcriptome profile at single-cell resolution, and processed tumor samples for bulk RNA and protein analysis to validate the differently expressed genes. Transcriptome profiling revealed that the in vitro A549 cells are heterogeneous. The minimal subpopulation of the in vitro A549 cells, which were characterized by the signature of response to unfolded protein, became the overriding subpopulation of the xenografts. The EGFR non-activating A549 cells were resistant to dacomitinib in vitro, while A549 xenografts were comparatively sensitive as EGFR-activating HCC827 xenografts. Dacomitinib inhibited MAPK signaling pathway, and increased the immune response in the A549 xenografts. A phagocytosis checkpoint stanniocalcin-1 (STC1) was significantly inhibited in dacomitinib-treated xenografts. So here our study gives the first insight of the heterogeneity of the two classic models, and the translational potential of dacomitinib being used into a broader patient population rather than EGFR common activating mutation.

Simultaneous analysis of cellular glycoproteome and phosphoproteome in cervical carcinoma by one-pot specific enrichment.

Cervical carcinoma is a kind of common gynecological cancer, the physiopathology of which is consanguineously connected with protein glycosylation and phosphorylation. Here, the magnetic binary metal oxide composites with the functionalization of hydrophilic tripeptide is designed and synthesized for simultaneous enrichment of glycopeptide and phosphopeptide in HeLa cells. Multiple kinds of metal sites realize more comprehensive enrichment of phosphopeptides compared with single metal, and ultra-hydrophilic property of glutathione further endows the composites with excellent affinity towards glycopeptides. The composites exhibited low detection limit (0.5 fmol/µL) and high selectivity (digests of ß-casein and BSA, 1:500, m/m) for phosphopeptides, also showed good enrichment efficiency for glycopeptide including detection limit (0.1 fmol/µL) and selectivity (digests of HRP and BSA, 1:50, m/m). Meanwhile, the composites still possess outstanding enrichment capability towards phosphopeptide and glycopeptide after stored for two months or six consecutive times reuse. Eventually, 1177 phosphopeptides and 438 glycopeptides are identified simultaneously from 100 µg HeLa cell digests. As anticipated, potential biomarkers, such as heat shock protein beta-1, DNA topoisomerase 2-alpha, proliferation marker protein Ki-67 and 60 kDa heat shock protein are detected, suggesting its promising application in discovery and screening of cervical carcinoma.

Data-driven causal model discovery and personalized prediction in Alzheimer's disease.

With the explosive growth of biomarker data in Alzheimer's disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model's parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer's Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Strategy for high-throughput identification of protein complexes by array-based multi-dimensional liquid chromatography-mass spectrometry.

Comprehensive elucidation of the composition of multiprotein complexes in model organisms is essential to understand conserved biological systems, but large-scale mapping physical association networks is still challenging due to limited throughput of present methods. In this work, a strategy coupling array-based online two-dimensional liquid chromatography (array-based 2D-LC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was demonstrated for high throughput and in-depth identification of protein complexes from cultured human HeLa cell extracts. Mixed-bed ion-exchange column was employed as the first dimensional (1stD) separating mode and an array consisting of eight reversed phase columns was developed as the second dimensional (2ndD) mode. Taking advantage of array parallel strategy, this online system showed an 8-fold increase in throughput. After array-based online 2D-LC separation, altogether 256 × 2ndD fractions were collected for further LC-MS/MS analysis. Public databases of protein-protein interaction (PPI) and co-elution curves identified by LC-MS were applied to reconstruct the protein complexes. A rigorous inspection was operated by cataloging the protein complexes into chromatographic fractions to minimize the number of false positives. As result, a total number of 4,436 proteins were identified and 26,092 elution curves were graphed. A network consisting of 47,745 PPIs was established among 2,201 proteins and presented 1,530 putative protein complexes with high confidence. Most of the identified PPIs were linked to diverse biological processes and may reveal further disease mechanism and therapeutic strategy.

A novel SAD process: Match of anammox and denitrification.

Anammox biotechnology has been widely applied for its attractive advantages, but its application has been seriously limited due to the instinctive drawback of nitrate production. In this work, a novel Sequential Anammox and Denitrification (SAD) system was developed for the advanced nitrogen removal by using solid carbon source (SCS) and coupling anammox with denitrification. The long-term operation results demonstrated that the SAD system could remove the total nitrogen (TN) efficiently, with the effluent TN concentration of 1.4 ± 0.5 mg N/L, the TN removal efficiency (NRE) of 99.3 ± 0.2%, and the TN removal rate (NRR) of 1.7 ± 0.1 kg/(m3·d). The determination results showed that SCS had a good property for sustained release of COD, with a dissolved organic yield (by COD) of 1.1 g-COD/g-rice. When the addition rate was set at 6 g-rice/7-days, the COD release rate of 0.9 kg-COD/(m3·d) from SCS matched the nitrate production rate of 1.2 × 10-1 kg-N/(m3·d) from anammox with consumption ratio of 7.5. The analysis on the microbial community revealed that Candidatus_Brocadia and Denitratisoma were the dominant functional bacteria for anammox and denitrification, which contributed to about 92.7% and 6.6% of the total nitrogen removal, respectively. This work is helpful for the innovation and application of anammox-based technology.