Upregulation of the voltage-gated sodium channel beta2 subunit in neuropathic pain models: characterization of expression in injured and non-injured primary sensory neurons

The development of abnormal primary sensory neuron excitability and neuropathic pain symptoms after peripheral nerve injury is associated with altered expression of voltage-gated sodium channels (VGSCs) and a modification of sodium currents. To investigate whether the beta2 subunit of VGSCs participates in the generation of neuropathic pain, we used the spared nerve injury (SNI) model in rats to examine beta2 subunit expression in selectively injured (tibial and common peroneal nerves) and uninjured (sural nerve) afferents. Three days after SNI, immunohistochemistry and Western blot analysis reveal an increase in the beta2 subunit in both the cell body and peripheral axons of injured neurons. The increase persists for >4 weeks, although beta2 subunit mRNA measured by real-time reverse transcription-PCR and in situ hybridization remains unchanged. Although injured neurons show the most marked upregulation,beta2 subunit expression is also increased in neighboring non-injured neurons and a similar pattern of changes appears in the spinal nerve ligation model of neuropathic pain. That increased beta2 subunit expression in sensory neurons after nerve injury is functionally significant, as demonstrated by our finding that the development of mechanical allodynia-like behavior in the SNI model is attenuated in beta2 subunit null mutant mice. Through its role in regulating the density of mature VGSC complexes in the plasma membrane and modulating channel gating, the beta2 subunit may play a key role in the development of ectopic activity in injured and non-injured sensory afferents and, thereby, neuropathic pain

Survival analysis of localized prostate cancer with deep learning.

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics [Formula: see text] over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved [Formula: see text] 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained [Formula: see text] 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system

Tyrphostins reduce chemotherapy-induced intestinal injury in mice: assessment by a biochemical assay

Intestinal injury that results from chemotherapy belongs to the major factors of dose-limitation in tumour therapy. The tyrphostins AG1714 and AG1801 reduce cisplatin and 5-FU-induced small intestinal mucosal damage, using a quantitative biochemical assay. The assay is based on the determination of the enzymatic activity of gamma-glutamyl transpeptidase, a marker of the brush border epithelium of the small intestine

Virtual testing of advanced composites, cellular materials and biomaterials: A review

This paper documents the emergence of virtual testing frameworks for prediction of the constitutive responses of engineering materials. A detailed study is presented, of the philosophy underpinning virtual testing schemes: highlighting the structure, challenges and opportunities posed by a virtual testing strategy compared with traditional laboratory experiments. The virtual testing process has been discussed from atomistic to macrostructural length scales of analyses. Several implementations of virtual testing frameworks for diverse categories of materials are also presented, with particular emphasis on composites, cellular materials and biomaterials (collectively described as heterogeneous systems, in this context). The robustness of virtual frameworks for prediction of the constitutive behaviour of these materials is discussed. The paper also considers the current thinking on developing virtual laboratories in relation to availability of computational resources as well as the development of multi-scale material model algorithms. In conclusion, the paper highlights the challenges facing developments of future virtual testing frameworks. This review represents a comprehensive documentation of the state of knowledge on virtual testing from microscale to macroscale length scales for heterogeneous materials across constitutive responses from elastic to damage regimes

Effects of Macromolecular Crowding on Protein Conformational Changes

Many protein functions can be directly linked to conformational changes. Inside cells, the equilibria and transition rates between different conformations may be affected by macromolecular crowding. We have recently developed a new approach for modeling crowding effects, which enables an atomistic representation of “test” proteins. Here this approach is applied to study how crowding affects the equilibria and transition rates between open and closed conformations of seven proteins: yeast protein disulfide isomerase (yPDI), adenylate kinase (AdK), orotidine phosphate decarboxylase (ODCase), Trp repressor (TrpR), hemoglobin, DNA β-glucosyltransferase, and Ap4A hydrolase. For each protein, molecular dynamics simulations of the open and closed states are separately run. Representative open and closed conformations are then used to calculate the crowding-induced changes in chemical potential for the two states. The difference in chemical-potential change between the two states finally predicts the effects of crowding on the population ratio of the two states. Crowding is found to reduce the open population to various extents. In the presence of crowders with a 15 Å radius and occupying 35% of volume, the open-to-closed population ratios of yPDI, AdK, ODCase and TrpR are reduced by 79%, 78%, 62% and 55%, respectively. The reductions for the remaining three proteins are 20–44%. As expected, the four proteins experiencing the stronger crowding effects are those with larger conformational changes between open and closed states (e.g., as measured by the change in radius of gyration). Larger proteins also tend to experience stronger crowding effects than smaller ones [e.g., comparing yPDI (480 residues) and TrpR (98 residues)]. The potentials of mean force along the open-closed reaction coordinate of apo and ligand-bound ODCase are altered by crowding, suggesting that transition rates are also affected. These quantitative results and qualitative trends will serve as valuable guides for expected crowding effects on protein conformation changes inside cells

Tuning Curvature and Stability of Monoolein Bilayers by Designer Lipid-Like Peptide Surfactants

This study reports the effect of loading four different charged designer lipid-like short anionic and cationic peptide surfactants on the fully hydrated monoolein (MO)-based Pn3m phase (Q224). The studied peptide surfactants comprise seven amino acid residues, namely A6D, DA6, A6K, and KA6. D (aspartic acid) bears two negative charges, K (lysine) bears one positive charge, and A (alanine) constitutes the hydrophobic tail. To elucidate the impact of these peptide surfactants, the ternary MO/peptide/water system has been investigated using small-angle X-ray scattering (SAXS), within a certain range of peptide concentrations (R≤0.2) and temperatures (25 to 70°C). We demonstrate that the bilayer curvature and the stability are modulated by: i) the peptide/lipid molar ratio, ii) the peptide molecular structure (the degree of hydrophobicity, the type of the hydrophilic amino acid, and the headgroup location), and iii) the temperature. The anionic peptide surfactants, A6D and DA6, exhibit the strongest surface activity. At low peptide concentrations (R = 0.01), the Pn3m structure is still preserved, but its lattice increases due to the strong electrostatic repulsion between the negatively charged peptide molecules, which are incorporated into the interface. This means that the anionic peptides have the effect of enlarging the water channels and thus they serve to enhance the accommodation of positively charged water-soluble active molecules in the Pn3m phase. At higher peptide concentration (R = 0.10), the lipid bilayers are destabilized and the structural transition from the Pn3m to the inverted hexagonal phase (H2) is induced. For the cationic peptides, our study illustrates how even minor modifications, such as changing the location of the headgroup (A6K vs. KA6), affects significantly the peptide's effectiveness. Only KA6 displays a propensity to promote the formation of H2, which suggests that KA6 molecules have a higher degree of incorporation in the interface than those of A6K

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability