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PurposeMyocardial dysfunction is common in sepsis but optimal treatment strategies are unclear. The inodilator, levosimendan was suggested as a possible therapy; however, the levosimendan to prevent acute organ dysfunction in Sepsis (LeoPARDS) trial found it to have no benefit in reducing organ dysfunction in septic shock. In this study we evaluated the effects of levosimendan in patients with and without biochemical cardiac dysfunction and examined its non-inotropic effects.MethodsTwo cardiac biomarkers, troponin I (cTnI) and N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and five inflammatory mediators were measured in plasma from patients recruited to the LeoPARDS trial at baseline and over the first 6 days. Mean total Sequential Organ Failure Assessment (SOFA) score and 28-day mortality were compared between patients with normal and raised cTnI and NT-proBNP values, and between patients above and below median values.ResultsLevosimendan produced no benefit in SOFA score or 28-day mortality in patients with cardiac dysfunction. There was a statistically significant treatment by subgroup interaction (p = 0.04) in patients with NT-proBNP above or below the median value. Those with NT-proBNP values above the median receiving levosimendan had higher SOFA scores than those receiving placebo (mean daily total SOFA score 7.64 (4.41) vs 6.09 (3.88), mean difference 1.55, 95% CI 0.43–2.68). Levosimendan had no effect on the rate of decline of inflammatory biomarkers.ConclusionAdding levosimendan to standard care in septic shock was not associated with less severe organ dysfunction nor lower mortality in patients with biochemical evidence of cardiac dysfunction.

Background This study assessed the ability of mid-regional proadrenomedullin (MR-proADM) in comparison to conventional biomarkers (procalcitonin (PCT), lactate, C-reactive protein) and clinical scores to identify disease severity in patients with sepsis. Methods This is a secondary analysis of a randomised controlled trial in patients with severe sepsis or septic shock across 33 German intensive care units. The association between biomarkers and clinical scores with mortality was assessed by Cox regression analysis, area under the receiver operating characteristic and Kaplan-Meier curves. Patients were stratified into three severity groups (low, intermediate, high) for all biomarkers and scores based on cutoffs with either a 90% sensitivity or specificity. Results 1089 patients with a 28-day mortality rate of 26.9% were analysed. According to the Sepsis-3 definition, 41.2% and 58.8% fulfilled the criteria for sepsis and septic shock, with respective mortality rates of 20.0% and 32.1%. MR-proADM had the strongest association with mortality across all Sepsis-1 and Sepsis-3 subgroups and could facilitate a more accurate classification of low (e.g. MR-proADM vs. SOFA: N = 265 vs. 232; 9.8% vs. 13.8% mortality) and high (e.g. MR-proADM vs. SOFA: N = 161 vs. 155; 55.9% vs. 41.3% mortality) disease severity. Patients with decreasing PCT concentrations of either ≥ 20% (baseline to day 1) or ≥ 50% (baseline to day 4) but continuously high MR-proADM concentrations had a significantly increased mortality risk (HR (95% CI): 19.1 (8.0–45.9) and 43.1 (10.1–184.0)). Conclusions MR-proADM identifies disease severity and treatment response more accurately than established biomarkers and scores, adding additional information to facilitate rapid clinical decision-making and improve personalised sepsis treatment.

Objective Knee osteoarthritis (OA) is a heterogeneous, complex joint pathology of unknown aetiology. Biomarkers have been widely used to investigate OA but currently available biomarkers lack specificity and sensitivity. Therefore, novel biomarkers are needed to better understand the pathophysiological processes of OA initiation and progression. Methods Surface enhanced laser desorption/ionisation-time of flight-mass spectrometry proteomic technique was used to analyse protein expression levels in 284 serum samples from patients with knee OA classified according to Kellgren and Lawrence (K&L) score (0–4). OA serum samples were also compared to serum samples provided by healthy individuals (negative control subjects; NC; n=36) and rheumatoid arthritis (RA) patients (n=25). Proteins that gave similar signal in all K&L groups of OA patients were ignored, whereas proteins with increased or decreased levels of expression were selected for further studies. Results Two proteins were found to be expressed at higher levels in sera of OA patients at all four K&L scores compared to NC and RA, and were identified as V65 vitronectin fragment and C3fpeptide. Of the two remaining proteins, one showed increased expression (unknown protein at m/z of 3762) and the other (identified as connective tissue-activating peptide III protein) was decreased in K&L scores >2 subsets compared to NC, RA and K&L scores 0 or 1 subsets. Conclusion The authors detected four unexpected biomarkers (V65 vitronectin fragment, C3f peptide, CTAP-III and m/z 3762 protein) that could be relevant in the pathophysiological process of OA as having significant correlation with parameters reflecting local inflammation and bone remodelling, as well as decrease in cartilage turnover.

Developing a mechanistic understanding of the impact of food structure and composition on human health has increasingly involved simulating digestion in the upper gastrointestinal tract. These simulations have used a wide range of different conditions that often have very little physiological relevance, and this impedes the meaningful comparison of results. The standardized protocol presented here is based on an international consensus developed by the COST INFOGEST network. The method is designed to be used with standard laboratory equipment and requires limited experience to encourage a wide range of researchers to adopt it. It is a static digestion method that uses constant ratios of meal to digestive fluids and a constant pH for each step of digestion. This makes the method simple to use but not suitable for simulating digestion kinetics. Using this method, food samples are subjected to sequential oral, gastric and intestinal digestion while parameters such as electrolytes, enzymes, bile, dilution, pH and time of digestion are based on available physiological data. This amended and improved digestion method (INFOGEST 2.0) avoids challenges associated with the original method, such as the inclusion of the oral phase and the use of gastric lipase. The method can be used to assess the endpoints resulting from digestion of foods by analyzing the digestion products (e.g., peptides/amino acids, fatty acids, simple sugars) and evaluating the release of micronutrients from the food matrix. The whole protocol can be completed in ~7 d, including ~5 d required for the determination of enzyme activities.Brodkorb et al. provide a standardized static in vitro protocol for the study of gastrointestinal food digestion and the analysis of digestion products.

In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21 million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, resulting in chromatographic retention time and fragment ion intensity predictions that exceed the quality of the experimental data. Integrating Prosit into database search pipelines led to more identifications at >10× lower false discovery rates. We show the general applicability of Prosit by predicting spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition and improving the analysis of metaproteomes. Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of peptide sequence alone.A deep learning–based tool, Prosit, predicts high-quality peptide tandem mass spectra, improving peptide-identification performance compared with that of traditional proteomics analysis methods.

Objective: To evaluate the efficacy and tolerability of chondroitin sulphate (chondroitin sulphate) in knee osteoarthritis. Patients and methods: A 24-week, randomised placebo-controlled trial of chondroitin sulphate (1 g/day) in patients with symptomatic knee osteoarthritis as measured on a visual analgue scale. Pain on daily activities and Lequesne’s Index were the primary efficacy criteria. Secondary outcomes included the rate of responders according to the outcome measures in rheumatoid arthritis clinical trials of the Osteoarthritis Research Society International (OMERACT-OARSI) criteria, quality of life, patient’s/physician’s global assessments and carry-over effect after treatment. Biochemical markers of bone (CTX-I), cartilage (CTX-II) and synovium (hyaluronic acid) metabolism were also measured. Safety was assessed by recording adverse events (AEs). Statistical analysis was performed on the inter-group differences in the intention-to-treat population. Results: 307 patients were included in the study. 28 (9%) patients discontinued the study because of lack of efficacy or AEs. At the end of treatment, the decrease in pain was −26.2 (24.9) and −19.9 (23.5) mm and improved function was −2.4 (3.4) (−25%) and −1.7 (3.3) (−17%) in the chondroitin sulphate and placebo groups, respectively (p = 0.029 and 0.109). The OMERACT-OARSI responder rate was 68% in the chondroitin sulphate and 56% in the placebo group (p = 0.03). The investigator’s assessments and short form 12 (SF-12) physical component reported improvement more frequently in the chondroitin sulphate than in the placebo group (p = 0.044 and 0.021, respectively). No significant difference was observed between treatment groups for changes in biomarkers over 24 weeks. However, there was a significant difference between non-responders and responders according to the OARSI criteria for 24-week changes of CTX-I (p = 0.018) and CTX-II (p = 0.014). Tolerance was considered to be satisfactory. Conclusion: This study failed to show an efficacy of chondroitin sulphate on the two primary criteria considered together, although chondroitin sulphate was slightly more effective than placebo on pain, OMERACT-OARSI response rate, investigator’s assessment and quality of life.

The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography–mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC’s ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.DeepLC, a deep learning-based peptide retention time predictor, can predict retention times for unmodified peptides as well as peptides with previously unseen modifications.

Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.Machine learning and deep learning models are used to predict high-quality tandem mass spectra, providing benefits over traditional analysis methods for interpreting proteomics data.

Background The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China has been declared a public health emergency of international concern. The cardiac injury is a common condition among the hospitalized patients with COVID-19. However, whether N terminal pro B type natriuretic peptide (NT-proBNP) predicted outcome of severe COVID-19 patients was unknown. Methods The study initially enrolled 102 patients with severe COVID-19 from a continuous sample. After screening out the ineligible cases, 54 patients were analyzed in this study. The primary outcome was in-hospital death defined as the case fatality rate. Research information and following-up data were obtained from their medical records. Results The best cut-off value of NT-proBNP for predicting in-hospital death was 88.64 pg/mL with the sensitivity for 100% and the specificity for 66.67%. Patients with high NT-proBNP values (> 88.64 pg/mL) had a significantly increased risk of death during the days of following-up compared with those with low values (≤88.64 pg/mL). After adjustment for potential risk factors, NT-proBNP was independently correlated with in-hospital death. Conclusion NT-proBNP might be an independent risk factor for in-hospital death in patients with severe COVID-19. Trial registration ClinicalTrials, NCT04292964. Registered 03 March 2020,

In this Review, the authors provide a guide through to the different steps involved in selected reaction monitoring as well as discuss its applications. Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that is emerging in the field of proteomics as a complement to untargeted shotgun methods. SRM is particularly useful when predetermined sets of proteins, such as those constituting cellular networks or sets of candidate biomarkers, need to be measured across multiple samples in a consistent, reproducible and quantitatively precise manner. Here we describe how SRM is applied in proteomics, review recent advances, present selected applications and provide a perspective on the future of this powerful technology.