Analytical Validation of Multiplex Biomarker Assay to Stratify Colorectal Cancer into Molecular Subtypes.

Previously, we classified colorectal cancers (CRCs) into five CRCAssigner (CRCA) subtypes with different prognoses and potential treatment responses, later consolidated into four consensus molecular subtypes (CMS). Here we demonstrate the analytical development and validation of a custom NanoString nCounter platform-based biomarker assay (NanoCRCA) to stratify CRCs into subtypes. To reduce costs, we switched from the standard nCounter protocol to a custom modified protocol. The assay included a reduced 38-gene panel that was selected using an in-house machine-learning pipeline. We applied NanoCRCA to 413 samples from 355 CRC patients. From the fresh frozen samples (n = 237), a subset had matched microarray/RNAseq profiles (n = 47) or formalin-fixed paraffin-embedded (FFPE) samples (n = 58). We also analyzed a further 118 FFPE samples. We compared the assay results with the CMS classifier, different platforms (microarrays/RNAseq) and gene-set classifiers (38 and the original 786 genes). The standard and modified protocols showed high correlation (> 0.88) for gene expression. Technical replicates were highly correlated (> 0.96). NanoCRCA classified fresh frozen and FFPE samples into all five CRCA subtypes with consistent classification of selected matched fresh frozen/FFPE samples. We demonstrate high and significant subtype concordance across protocols (100%), gene sets (95%), platforms (87%) and with CMS subtypes (75%) when evaluated across multiple datasets. Overall, our NanoCRCA assay with further validation may facilitate prospective validation of CRC subtypes in clinical trials and beyond

Upcycling spent brewery grains through the production of carbon adsorbents: application to the removal of carbamazepine from water

Spent brewery grains, a by-product of the brewing process, were used as precursor of biochars and activated carbons to be applied to the removal of pharmaceuticals from water. Biochars were obtained by pyrolysis of the raw materials, while activated carbons were produced by adding a previous chemical activation step. The influence of using different precursors (from distinct fermentation processes), activating agents (potassium hydroxide, sodium hydroxide, and phosphoric acid), pyrolysis temperatures, and residence times was assessed. The adsorbents were physicochemically characterized and applied to the removal of the antiepileptic carbamazepine from water. Potassium hydroxide activation produced the materials with the most promising properties and adsorptive removals, with specific surface areas up to 1120 m2 g-1 and maximum adsorption capacities up to 190 ± 27 mg g-1 in ultrapure water. The adsorption capacity suffered a reduction of < 70% in wastewater, allowing to evaluate the impact of realistic matrices on the efficiency of the materials.publishe

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

A seven-Gene Signature assay improves prognostic risk stratification of perioperative chemotherapy treated gastroesophageal cancer patients from the MAGIC trial

BACKGROUND: Following neoadjuvant chemotherapy for operable gastroesophageal cancer, lymph node metastasis is the only validated prognostic variable; however, within lymph node groups there is still heterogeneity with risk of relapse. We hypothesized that gene profiles from neoadjuvant chemotherapy treated resection specimens from gastroesophageal cancer patients can be used to define prognostic risk groups to identify patients at risk for relapse. PATIENTS AND METHODS: The Medical Research Council Adjuvant Gastric Infusional Chemotherapy (MAGIC) trial (n = 202 with high quality RNA) samples treated with perioperative chemotherapy were profiled for a custom gastric cancer gene panel using the NanoString platform. Genes associated with overall survival (OS) were identified using penalized and standard Cox regression, followed by generation of risk scores and development of a NanoString biomarker assay to stratify patients into risk groups associated with OS. An independent dataset served as a validation cohort. RESULTS: Regression and clustering analysis of MAGIC patients defined a seven-Gene Signature and two risk groups with different OS [hazard ratio (HR) 5.1; P < 0.0001]. The median OS of high- and low-risk groups were 10.2 [95% confidence interval (CI) of 6.5 and 13.2 months] and 80.9 months (CI: 43.0 months and not assessable), respectively. Risk groups were independently prognostic of lymph node metastasis by multivariate analysis (HR 3.6 in node positive group, P = 0.02; HR 3.6 in high-risk group, P = 0.0002), and not prognostic in surgery only patients (n = 118; log rank P = 0.2). A validation cohort independently confirmed these findings. CONCLUSIONS: These results suggest that gene-based risk groups can independently predict prognosis in gastroesophageal cancer patients treated with neoadjuvant chemotherapy. This signature and associated assay may help risk stratify these patients for post-surgery chemotherapy in future perioperative chemotherapy-based clinical trials

Proteomic Biomarkers for Acute Interstitial Lung Disease in Gefitinib-Treated Japanese Lung Cancer Patients

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10−25), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control

Precision measurement of CP\it{CP} violation in the penguin-mediated decay Bs0→ϕϕB_s^{0}\rightarrow\phi\phi

A flavor-tagged time-dependent angular analysis of the decay $B_s^{0}\rightarrow\phi\phi$ is performed using $pp$ collision data collected by the LHCb experiment at % at $\sqrt{s}=13$ TeV, the center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb^{-1}. The $\it{CP}$-violating phase and direct $\it{CP}$-violation parameter are measured to be $\phi_{s\bar{s}s} = -0.042 \pm 0.075 \pm 0.009$ rad and $|\lambda|=1.004\pm 0.030 \pm 0.009$, respectively, assuming the same values for all polarization states of the $\phi\phi$ system. In these results, the first uncertainties are statistical and the second systematic. These parameters are also determined separately for each polarization state, showing no evidence for polarization dependence. The results are combined with previous LHCb measurements using $pp$ collisions at center-of-mass energies of 7 and 8 TeV, yielding $\phi_{s\bar{s}s} = -0.074 \pm 0.069$ rad and $|lambda|=1.009 \pm 0.030$. This is the most precise study of time-dependent $\it{CP}$ violation in a penguin-dominated $B$ meson decay. The results are consistent with $\it{CP}$ symmetry and with the Standard Model predictions.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-001.html (LHCb public pages

Gender differences in the use of cardiovascular interventions in HIV-positive persons; the D:A:D Study

Peer reviewe

Measurement of the Λb0→Λ(1520)μ+μ−\Lambda_{b}^{0}\to \Lambda(1520) \mu^{+}\mu^{-} differential branching fraction

The branching fraction of the rare decay $\Lambda_{b}^{0}\to \Lambda(1520) \mu^{+}\mu^{-}$ is measured for the first time, in the squared dimuon mass intervals, $q^2$, excluding the $J/\psi$ and $\psi(2S)$ regions. The data sample analyzed was collected by the LHCb experiment at center-of-mass energies of 7, 8, and 13 TeV, corresponding to a total integrated luminosity of $9\ \mathrm{fb}^{-1}$. The result in the highest$q^{2}$interval,$q^{2} >15.0\ \mathrm{GeV}^2/c^4$, where theoretical predictions have the smallest model dependence, agrees with the predictions.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-050.html (LHCb public pages