An innovative approach for selective and robust screening of NBOHs, NBOMes, and LSD in forensic samples using a 3D-Printed electrochemical double cell.

Lysergic acid diethylamide (LSD) and two phenethylamine classes (NBOHs and NBOMes) are the main illicit drugs found in seized blotter papers. The preliminary identification of these substances is of great interest for forensic analysis. In this context, this work constitutes the inaugural demonstration of an efficient methodology for the selective detection of LSD, NBOHs, and NBOMes, utilizing a fully 3D-printed electrochemical double cell (3D-EDC). This novel 3D-EDC enables the use of two working electrodes and/or two supporting electrolytes (at different pHs) in the same detection system, with the possibility of shared or individual auxiliary and pseudo-reference electrodes. Thus, the selective voltammetric detection of these substances is proposed using two elegant strategies: (i) utilizing the same 3D-EDC platform with two working electrodes (boron-doped diamond (BDD) and 3D-printed graphite), and (ii) employing two pH levels (4.0 and 12.0) with 3D-printed graphite electrode. This comprehensive framework facilitates a fast, robust, and uncomplicated electrochemical analysis. Moreover, this configuration enables a rapid and sensitive detection of LSD, NBOHs, and NBOMes in seized samples, and can also provide quantitative analysis. The proposed method showed good stability of the electrochemical response with RSD <9 % for Ip and <5 % for Ep, evaluating all oxidation processes observed for studied analytes (n = 7) at two pH levels, using the same and different (n = 3) working electrodes. It demonstrates a broad linear range (20-100 and 20-70 µmol L-1) and a low LOD (1.0 µmol L-1) for quantification of a model molecule (LSD) at the two pHs studied. Hence, the 3D-EDC combined with voltammetric techniques using BDD and 3D-printed graphite electrodes on the same platform, or only with this last sensor at two pH values, provide a practical and robust avenue for preliminary identification of NBOHs, NBOMes, and LSD. This method embodies ease, swiftness, cost-efficiency, robustness, and selectivity as an on-site screening tool for forensic analysis.

Metabolic responses of the isopod Porcellionides pruinosus to nickel exposure assessed by (1)H NMR metabolomics.

UNLABELLED: This work aimed at characterizing the metabolome of the isopod Porcellionides pruinosus and at assessing its variations over 14 days under laboratory culture conditions and upon exposure to the contaminant metal Nickel (Ni). The spectral profiles obtained by (1)H NMR spectroscopy were thoroughly assigned and subjected to multivariate analysis in order to highlight consistent changes. Over 50 metabolites could be identified, providing considerable new knowledge on the metabolome of these model organisms. Several metabolites changed non-linearly with Ni dose and exposure time, showing distinct variation patterns for initial (4 days) and later time points (7 and 14 days). In particular, at day 4, several amino acids were increased and sugars were decreased (compared to controls), whereas these variations were inverted for longer exposure, possibly reflecting earlier and more intensive moulting. Other variations, namely in betaines and choline-containing compounds, were suggested to relate with osmoregulation and detoxification mechanisms. Ni also had a marked effect on several nucleotides (increased upon exposure) and a moderate impact on lipids (decreased upon exposure). Overall, this study has provided new information on the Ni-induced metabolic adaptations of the P. pruinosus isopod, paving the way for improved mechanistic understanding of how these model organisms handle soil contamination. SIGNIFICANCE: This study provided, for the first time to our knowledge, a detailed picture of the NMR-detectable metabolome of terrestrial isopods and of its fluctuations in time and upon exposure to the contaminant metal Nickel. Several time- and dose-dependent changes were highlighted, providing mechanistic insight into how these important model organisms handle Ni contamination.

NMR metabolomics of human lung tumours reveals distinct metabolic signatures for adenocarcinoma and squamous cell carcinoma.

Lung tumour subtyping, particularly the distinction between adenocarcinoma (AdC) and squamous cell carcinoma (SqCC), is a critical diagnostic requirement. In this work, the metabolic signatures of lung carcinomas were investigated through (1)H NMR metabolomics, with a view to provide additional criteria for improved diagnosis and treatment planning. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (NMR) spectroscopy was used to analyse matched tumour and adjacent control tissues from 56 patients undergoing surgical excision of primary lung carcinomas. Multivariate modeling allowed tumour and control tissues to be discriminated with high accuracy (97% classification rate), mainly due to significant differences in the levels of 13 metabolites. Notably, the magnitude of those differences were clearly distinct for AdC and SqCC: major alterations in AdC were related to phospholipid metabolism (increased phosphocholine, glycerophosphocholine and phosphoethanolamine, together with decreased acetate) and protein catabolism (increased peptide moieties), whereas SqCC had stronger glycolytic and glutaminolytic profiles (negatively correlated variations in glucose and lactate and positively correlated increases in glutamate and alanine). Other tumour metabolic features were increased creatine, glutathione, taurine and uridine nucleotides, the first two being especially prominent in SqCC and the latter in AdC. Furthermore, multivariate analysis of AdC and SqCC profiles allowed their discrimination with a 94% classification rate, thus showing great potential for aiding lung tumours subtyping. Overall, this study has provided new, clear evidence of distinct metabolic signatures for lung AdC and SqCC, which can potentially impact on diagnosis and provide important leads for future research on novel therapeutic targets or imaging tracers.

Low- versus high-dose rituximab for rheumatoid arthritis: a systematic review and meta-analysis.

OBJECTIVE: The approved dose of rituximab (RTX) for rheumatoid arthritis (RA) is 2 × 1,000 mg infusions given 2 weeks apart. There is contradictory evidence regarding the effectiveness of a lower-dose regimen (2 × 500 mg) of RTX. Our aim was to compare the efficacy and safety of low- and high-dose RTX and to test the noninferiority of the low-dose regimen. METHODS: A systematic literature review searching for randomized controlled trials (RCTs) and cohort studies comparing low- and high-dose RTX for RA was conducted using the Embase, PubMed, Cochrane Library, and Web of Science databases. The primary end points were the American College of Rheumatology criteria for 20% improvement (ACR20), ACR50, and ACR70 responses and the Disease Activity Score in 28 joints (DAS28) at 24 and 48 weeks. The secondary end points were patient-reported outcomes (PROs; Health Assessment Questionnaire, Short Form 36, and Functional Assessment of Chronic Illness Therapy-Fatigue scores) and adverse events. Noninferiority of low-dose RTX was tested using different approaches, one of which was based on the fixed margin method. RESULTS: In total, 6 RCTs and 2 cohort studies were identified. Four RCTs were included in the meta-analysis of efficacy outcomes, which showed no significant differences in the primary outcomes between low- and high-dose RTX. Noninferiority criteria of low-dose RTX were met for the ACR20, ACR50, DAS28, and PROs (at 24 and 48 weeks). Serious adverse events did not differ significantly. The results of 2 additional RCTs and a meta-analysis of 2 cohort studies corroborated the results of the meta-analysis of RCTs. CONCLUSION: Low-dose RTX has similar effectiveness and met noninferiority criteria for most primary outcomes. Considering the lower cost, it should be the standard RTX regimen for RA.

Metabolic profiling of biofluids: potential in lung cancer screening and diagnosis.

The knowledge that the organism's metabolome is a potentially informative mirror of the impact of disease and its dynamics has led to promising developments in cancer research, strongly geared toward the discovery of new biomarkers of disease onset and progression. The present text reviews the advances made in the last 10 years in lung cancer research making use of the metabolomics strategies, particularly concerning metabolite profiling of human biofluids (blood serum and plasma, urine and others), expected to reflect the deviant metabolic behavior of lung tumors. The main goal of this article is to provide the reader with an up-to-date summary of the main metabolic variations taking place in biofluids, in relation to lung cancer, as well as of the analytical strategies employed to unveil them. Furthermore, particular needs and challenges are identified and possible developments envisaged.

One-year clinical and radiological evolution of a patient with refractory Takayasu's arteritis under treatment with tocilizumab.

A 28-year-old patient with Takayasu's arteritis (TA) failed to respond to high doses of prednisone in combination with methotrexate, pulses of cyclophosphamide and methylprednisolone, azathioprine, mycophenolate mofetil, adalimumab and monthly infusions of infliximab 5 mg/kg. After the beginning of tocilizumab therapy (4-8 mg/kg at monthly infusions), an impressive improvement in clinical and laboratory parameters of disease activity occurred, allowing the reduction of prednisone dose from 30 to 5 mg/day. However, after the 8th dose the patient developed symptoms of vertebrobasilar insufficiency, despite maintaining a good clinical condition and normal values of inflammatory markers. Angio-computed tomography repeated at one year of therapy showed reduction in aortic wall thickness, but also narrowing of the luminal diameters of the right subclavian, renal arteries, and left vertebral artery. Therefore, despite a significant clinical and laboratory improvement, vascular disease may progress in aortic branches in TA patients under tocilizumab therapy.

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma.

In this work, the variations in the metabolic profile of blood plasma from lung cancer patients and healthy controls were investigated through NMR-based metabonomics, to assess the potential of this approach for lung cancer screening and diagnosis. PLS-DA modeling of CPMG spectra from plasma, subjected to Monte Carlo Cross Validation, allowed cancer patients to be discriminated from controls with sensitivity and specificity levels of about 90%. Relatively lower HDL and higher VLDL + LDL in the patients' plasma, together with increased lactate and pyruvate and decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), and methanol, could be detected. These changes were found to be present at initial disease stages and could be related to known cancer biochemical hallmarks, such as enhanced glycolysis, glutaminolysis, and gluconeogenesis, together with suppressed Krebs cycle and reduced lipid catabolism, thus supporting the hypothesis of a systemic metabolic signature for lung cancer. Despite the possible confounding influence of age, smoking habits, and other uncontrolled factors, these results indicate that NMR-based metabonomics of blood plasma can be useful as a screening tool to identify suspicious cases for subsequent, more specific radiological tests, thus contributing to improved disease management.

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine.

In this study, ¹H NMR-based metabonomics has been applied, for the first time to our knowledge, to investigate lung cancer metabolic signatures in urine, aiming at assessing the diagnostic potential of this approach and gaining novel insights into lung cancer metabolism and systemic effects. Urine samples from lung cancer patients (n = 71) and a control healthy group (n = 54) were analyzed by high resolution ¹H NMR (500 MHz), and their spectral profiles subjected to multivariate statistics, namely, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Projections to Latent Structures (OPLS)-DA. Very good discrimination between cancer and control groups was achieved by multivariate modeling of urinary profiles. By Monte Carlo Cross Validation, the classification model showed 93% sensitivity, 94% specificity and an overall classification rate of 93.5%. The possible confounding influence of other factors, namely, gender and age, have also been modeled and found to have much lower predictive power than the presence of the disease. Moreover, smoking habits were found not to have a dominating influence over class discrimination. The main metabolites contributing to this discrimination, as highlighted by multivariate analysis and confirmed by spectral integration, were hippurate and trigonelline (reduced in patients), and ß-hydroxyisovalerate, α-hydroxyisobutyrate, N-acetylglutamine, and creatinine (elevated in patients relatively to controls). These results show the valuable potential of NMR-based metabonomics for finding putative biomarkers of lung cancer in urine, collected in a minimally invasive way, which may have important diagnostic impact, provided that these metabolites are found to be specifically disease-related.

Can nuclear magnetic resonance (NMR) spectroscopy reveal different metabolic signatures for lung tumours?

This study aims to evaluate the potential of (1)H NMR spectroscopy, combined with multivariate statistics, for discriminating between tumour and non-involved (control) pulmonary parenchyma and for providing biochemical information on different histological types. Paired tissue samples from 24 primary lung tumours were directly analysed by high-resolution magic angle spinning (HRMAS) (1)H NMR spectroscopy (500 MHz), and their spectral profiles subjected to principal component analysis (PCA) and partial least squares regression discriminant analysis (PLS-DA). Tumour and adjacent control parenchyma were clearly discriminated in the PLS-DA model with a high level of sensitivity (95% of tumour samples correctly classified) and 100% specificity (no false positives). The metabolites giving rise to this separation were mainly lactate, glycerophosphocholine, phosphocholine, taurine, reduced glutathione and uridine di-phosphate (elevated in tumours) and glucose, phosphoethanolamine, acetate, lysine, methionine, glycine, myo- and scyllo-inositol (reduced in tumours compared to control tissues). Furthermore, PLS-DA of a sub-set of tumour samples allowed adenocarcinomas to be discriminated from carcinoid tumours and epidermoid carcinomas, highlighting differences in metabolite levels between these histological types, and therefore revealing valuable knowledge on the biochemistry of different types of bronchial-pulmonary carcinomas.

Metabolic profiling of human lung cancer tissue by 1H high resolution magic angle spinning (HRMAS) NMR spectroscopy.

This work aims at characterizing the metabolic profile of human lung cancer, to gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic value in the future. Paired samples of tumor and noninvolved adjacent tissues from 12 lung tumors have been directly analyzed by (1)H HRMAS NMR (500/600 MHz) enabling, for the first time to our knowledge, the identification of over 50 compounds. The effect of temperature on tissue stability during acquisition time has also been investigated, demonstrating that analysis should be performed within less than two hours at low temperature (277 K), to minimize glycerophosphocholine (GPC) and phosphocholine (PC) conversion to choline and reduce variations in some amino acids. The application of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to the standard 1D (1)H spectra resulted in good separation between tumor and control samples, showing that inherently different metabolic signatures characterize the two tissue types. On the basis of spectral integration measurements, lactate, PC, and GPC were found to be elevated in tumors, while glucose, myo-inositol, inosine/adenosine, and acetate were reduced. These results show the valuable potential of HRMAS NMR-metabonomics for investigating the metabolic phenotype of lung cancer.