Background: Drug resistance is a common problem in cancer chemotherapy. Results: Transcriptomic and metabolomic data show that resistant leukemia cells exhibit reduced glutamine dependence, enhanced glucose dependence, and altered fatty acid metabolism. Conclusion:The metabolism of resistant leukemia cells is fundamentally rewired. Significance: Understanding the metabolic cost of resistance can lead to novel therapeutic strategies.

Background Understanding the false negative rates of SARS-CoV-2 RT-PCR testing is pivotal for the management of the COVID-19 pandemic and it has implications for patient management. Our aim was to determine the real-life clinical sensitivity of SARS-CoV-2 RT-PCR. Methods This population-based retrospective study was conducted in March–April 2020 in the Helsinki Capital Region, Finland. Adults who were clinically suspected of SARS-CoV-2 infection and underwent SARS-CoV-2 RT-PCR testing, with sufficient data in their medical records for grading of clinical suspicion were eligible. In addition to examining the first RT-PCR test of repeat-tested individuals, we also used high clinical suspicion for COVID-19 as the reference standard for calculating the sensitivity of SARS-CoV-2 RT-PCR. Results All 1,194 inpatients (mean [SD] age, 63.2 [18.3] years; 45.2% women) admitted to COVID-19 cohort wards during the study period were included. The outpatient cohort of 1,814 individuals (mean [SD] age, 45.4 [17.2] years; 69.1% women) was sampled from epidemiological line lists by systematic quasi-random sampling. The sensitivity (95% CI) for laboratory confirmed cases (repeat-tested patients) was 85.7% (81.5–89.1%) inpatients; 95.5% (92.2–97.5%) outpatients, 89.9% (88.2–92.1%) all. When also patients that were graded as high suspicion but never tested positive were included in the denominator, the sensitivity (95% CI) was: 67.5% (62.9–71.9%) inpatients; 34.9% (31.4–38.5%) outpatients; 47.3% (44.4–50.3%) all. Conclusions The clinical sensitivity of SARS-CoV-2 RT-PCR testing was only moderate at best. The relatively high false negative rates of SARS-CoV-2 RT-PCR testing need to be accounted for in clinical decision making, epidemiological interpretations, and when using RT-PCR as a reference for other tests.

Figure 1 Multimodal MRI findings during the course of an episode of hemiplegic migraine. (A) Day 1, 6 hours post-onset. Dense hemiparesis, aphasia, and severe headache associated with normal diffusion weighted imaging (DWI), magnetic resonance angiography (MRA), and gross hyperperfusion of the left hemisphere on factor analysis of dynamic studies (FADS). (B) Day 4. Weakness improving, dysphasic, persistent headache. Hyperperfusion of inferior frontal lobe on FADS. (C) Three months. Asymptomatic. Full resolution of perfusion changes. (DWI not repeated at three months).

IntroductionPancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available.ObjectivesThe aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS).MethodsA discovery cohort comprising PDAC (n = 44) and CP (n = 23) samples was analyzed by LC-MS followed by univariate (Student’s t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n = 20) and CP (n = 31) samples.ResultsGlycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.ConclusionUsing LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-017-1199-6) contains supplementary material, which is available to authorized users.

Background: The long-term sequelae after are not yet fully known. Our aim was to evaluate subjective symptoms and quality of life in Finnish hospitalized COVID-19 patients at six months follow-up. Methods: Hospitalised adult patients with laboratory-confirmed SARS-CoV-2 infection from March to June 2020 were recruited. We conducted a survey on demographics and comorbidities, ten specific symptoms, and a RAND-36 quality of life questionnaire six months after hospital discharge. We collected clinical data manually from medical records. Results: 101 patients (54 male) out of 246 invited completed the survey. Their median age was 60 years, and the mean hospital length of stay was 15 d. Most patients (90%) experienced symptoms, the most common of which were tiredness (88%), fatigue (79%), sleeping problems (76%), and dyspnoea (70%). In regard to gender, women showed a shorter time of hospitalization (p ¼ .048) and lower peak flow of supplementary oxygen (p ¼ .043). Women reported more frequently dyspnoea, fatigue, tiredness, sleeping problems, and mood problems (p ¼ .008-.033), and a lower quality of life in seven of eight dimensions (p < .001-.015). Five explanatory variables for the reduced quality of life were identified in multivariate analysis: age, female sex, BMI, sleep apnoea, and duration of mechanical ventilation. Of the patients who worked full-time before COVID-19, 11% had not returned to work. Conclusions: Most patients experienced symptoms six months after hospital discharge. Women reported more symptoms and a lower quality of life than men. These findings highlight the differences in recovery between men and women and call for active rehabilitation of COVID-19 patients. KEYWORDS COVID-19 coronavirus disease symptoms quality of life follow-up hospitalised patients ARTICLE HISTORY

Untargeted metabolic profiling has generated large activity in the field of clinical biomarker discovery. Yet, no clinically approved metabolite biomarkers have emerged with failure in validation phases often being a reason. To investigate why, we have applied untargeted metabolic profiling in a retrospective cohort of serum samples representing non-related diseases. Age and gender matched samples from patients diagnosed with pneumonia, congestive heart failure, lymphoma and healthy controls were subject to comprehensive metabolic profiling using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). The metabolic profile of each diagnosis was compared to the healthy control group and significant metabolites were filtered out using t-test with FDR correction. Metabolites found to be significant between each disease and healthy controls were compared and analyzed for overlap. Results show that despite differences in etiology and clinical disease presentation, the fraction of metabolites with an overlap between two or more diseases was 61%. A majority of these metabolites can be associated with immune responses thus representing non-disease specific events. We show that metabolic serum profiles from patients representing non-related diseases display very similar metabolic differences when compared to healthy controls. Many of the metabolites discovered as disease specific in this study have further been associated with other diseases in the literature. Based on our findings we suggest non-related disease controls in metabolomics biomarker discovery studies to increase the chances of a successful validation and future clinical applications.

Considering the physicochemical diversity of the metabolome, untargeted metabolomics will inevitably discriminate against certain compound classes. Efforts are nevertheless made to maximize the metabolome coverage. Contrary to the main steps of a typical liquid chromatography-mass spectrometry (LC-MS) metabolomics workflow, such as metabolite extraction, the sample reconstitution step has not been optimized for maximal metabolome coverage. This sample concentration step typically occurs after metabolite extraction, when dried samples are reconstituted in a solvent for injection on column. The aim of this study was to evaluate the impact of the sample reconstitution solvent composition on metabolome coverage in untargeted LC-MS metabolomics. Lysogeny Broth medium samples reconstituted in MeOH/HO ratios ranging from 0 to 100% MeOH and analyzed with untargeted reversed phase LC-MS showed that the highest number of metabolite features (n = 1500) was detected in samples reconstituted in 100% HO. As compared to a commonly used reconstitution solvent mixture of 50/50 MeOH/HO, our results indicate that the small fraction of compounds increasing in peak area response by the addition of MeOH to HO, 5%, is outweighed by the fraction of compounds with decreased response, 57%. We evaluated our results on human serum samples from lymphoma patients and healthy control subjects. Reconstitution in 100% HO resulted in a higher number of significant metabolites discriminating between these two groups than both 50% and 100% MeOH. These findings show that the sample reconstitution step has a clear impact on the metabolome coverage of MeOH extracted biological samples, highlighting the importance of the reconstitution solvent composition for untargeted discovery metabolomics.

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures–Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p = 0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products.Electronic supplementary materialThe online version of this article (doi:10.1007/s00216-014-7704-0) contains supplementary material, which is available to authorized users.