Influence of the 6^1S_0-6^3P_1 Resonance on Continuous Lyman-alpha Generation in Mercury

Continuous coherent radiation in the vacuum-ultraviolet at 122 nm (Lyman-alpha) can be generated using sum-frequency mixing of three fundamental laser beams in mercury vapour. One of the fundamental beams is at 254 nm wavelength, which is close to the 6^1S_0-6^3P_1 resonance in mercury. Experiments have been performed to investigate the effect of this one-photon resonance on phasematching, absorption and the nonlinear yield. The efficiency of continuous Lyman-alpha generation has been improved by a factor of 4.5.Comment: 8 pages, 7 figure

A Halomethane thermochemical network from iPEPICO experiments and quantum chemical calculations

Internal energy selected halomethane cations CH3Cl+, CH2Cl2+, CHCl3+, CH3F+, CH2F2+, CHClF2+ and CBrClF2+ were prepared by vacuum ultraviolet photoionization, and their lowest energy dissociation channel studied using imaging photoelectron photoion coincidence spectroscopy (iPEPICO). This channel involves hydrogen atom loss for CH3F+, CH2F2+ and CH3Cl+, chlorine atom loss for CH2Cl2+, CHCl3+ and CHClF2+, and bromine atom loss for CBrClF2+. Accurate 0 K appearance energies, in conjunction with ab initio isodesmic and halogen exchange reaction energies, establish a thermochemical network, which is optimized to update and confirm the enthalpies of formation of the sample molecules and their dissociative photoionization products. The ground electronic states of CHCl3+, CHClF2+ and CBrClF2+ do not confirm to the deep well assumption, and the experimental breakdown curve deviates from the deep well model at low energies. Breakdown curve analysis of such shallow well systems supplies a satisfactorily succinct route to the adiabatic ionization energy of the parent molecule, particularly if the threshold photoelectron spectrum is not resolved and a purely computational route is unfeasible. The ionization energies have been found to be 11.47 ± 0.01 eV, 12.30 ± 0.02 eV and 11.23 ± 0.03 eV for CHCl3, CHClF2 and CBrClF2, respectively. The updated 0 K enthalpies of formation, ∆fHo0K(g) for the ions CH2F+, CHF2+, CHCl2+, CCl3+, CCl2F+ and CClF2+ have been derived to be 844.4 ± 2.1, 601.6 ± 2.7, 890.3 ± 2.2, 849.8 ± 3.2, 701.2 ± 3.3 and 552.2 ± 3.4 kJ mol–1, respectively. The ∆fHo0K(g) values for the neutrals CCl4, CBrClF2, CClF3, CCl2F2 and CCl3F and have been determined to be –94.0 ± 3.2, –446.6 ± 2.7, –702.1 ± 3.5, –487.8 ± 3.4 and –285.2 ± 3.2 kJ mol–1, respectively

Transition between Variscan and Alpine cycles in the Pyrenean-Cantabrian Mountains (N Spain): Geodynamic evolution of near-equator European Permian basins

In the northern Iberian Peninsula, the Pyrenean-Cantabrian orogenic belt extends E-W for ca. 1000 km between the Atlantic Ocean and Mediterranean Sea. This orogen developed from the collision between Iberia and Eurasia, mainly in Cenozoic times. Lower-middle Permian sediments crop out in small, elongated basins traditionally considered independent from each other due to misinterpretations on incomplete lithostratigraphic data and scarce radiometric ages. Here, we integrate detailed stratigraphic, sedimentary, tectonic, paleosol and magmatic data from well-dated lithostratigraphic units. Our data reveal a similar geodynamic evolution across the Pyrenean-Cantabrian Ranges at the end of the Variscan cycle. Lower-middle Permian basins started their development under an extensional regime related to the end of the Variscan Belt collapse, which stars in late Carboniferous times in the Variscan hinterland. This orogenic collapse transitioned to Pangea breakup at the middle Permian times in the study region. Sedimentation occurred as three main tectono-sedimentary extensional phases. A first phase (Asselian-Sakmarian), which may have even started at the end of the Carboniferous (Gzhelian) in some sections, is mainly represented by alluvial sedimentation associated with calc-alkaline magmatism. A second stage (late Artinskian-early Kungurian), represented by al-luvial, lacustrine and palustrine sediments with intercalations of calc-alkaline volcanic beds, shows a clear up-ward aridification trend probably related to the late Paleozoic icehouse-greenhouse transition. The third and final stage (Wordian-Capitanian) comprised of alluvial deposits with intercalations of alkaline and mafic beds, rarely deposited in the Cantabrian Mountains, and underwent significant pre-and Early Mesozoic erosion in some segments of the Pyrenees. This third stage can be related to a transition towards the Pangea Supercontinent breakup, not generalized until the Early/Middle Triassic at this latitude because the extensional process stopped about 10 Myr (Pyrenees) to 30 Myr (Cantabrian Mountains). When compared to other well-dated basins near the paleoequator, the tectono-sedimentary and climate evolution of lower-middle Permian basins in Western and Central Europe shows common features. Specifically, we identify coeval periods with magmatic activity, extensional tectonics, high subsidence rates and thick sedi-mentary record, as well as prolonged periods without sedimentation. This comparison also identifies some evolutionary differences between Permian basins that could be related to distinct locations in the hinterland or foreland of the Variscan orogen. Our data provide a better understanding of the major crustal re-equilibration and reorganization that took place near the equator in Western-Central Europe during the post-Variscan period

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

Varespladib and cardiovascular events in patients with an acute coronary syndrome: the VISTA-16 randomized clinical trial

IMPORTANCE: Secretory phospholipase A2(sPLA2) generates bioactive phospholipid products implicated in atherosclerosis. The sPLA2inhibitor varespladib has favorable effects on lipid and inflammatory markers; however, its effect on cardiovascular outcomes is unknown. OBJECTIVE: To determine the effects of sPLA2inhibition with varespladib on cardiovascular outcomes. DESIGN, SETTING, AND PARTICIPANTS: A double-blind, randomized, multicenter trial at 362 academic and community hospitals in Europe, Australia, New Zealand, India, and North America of 5145 patients randomized within 96 hours of presentation of an acute coronary syndrome (ACS) to either varespladib (n = 2572) or placebo (n = 2573) with enrollment between June 1, 2010, and March 7, 2012 (study termination on March 9, 2012). INTERVENTIONS: Participants were randomized to receive varespladib (500 mg) or placebo daily for 16 weeks, in addition to atorvastatin and other established therapies. MAIN OUTCOMES AND MEASURES: The primary efficacy measurewas a composite of cardiovascular mortality, nonfatal myocardial infarction (MI), nonfatal stroke, or unstable angina with evidence of ischemia requiring hospitalization at 16 weeks. Six-month survival status was also evaluated. RESULTS: At a prespecified interim analysis, including 212 primary end point events, the independent data and safety monitoring board recommended termination of the trial for futility and possible harm. The primary end point occurred in 136 patients (6.1%) treated with varespladib compared with 109 patients (5.1%) treated with placebo (hazard ratio [HR], 1.25; 95%CI, 0.97-1.61; log-rank P = .08). Varespladib was associated with a greater risk of MI (78 [3.4%] vs 47 [2.2%]; HR, 1.66; 95%CI, 1.16-2.39; log-rank P = .005). The composite secondary end point of cardiovascular mortality, MI, and stroke was observed in 107 patients (4.6%) in the varespladib group and 79 patients (3.8%) in the placebo group (HR, 1.36; 95% CI, 1.02-1.82; P = .04). CONCLUSIONS AND RELEVANCE: In patients with recent ACS, varespladib did not reduce the risk of recurrent cardiovascular events and significantly increased the risk of MI. The sPLA2inhibition with varespladib may be harmful and is not a useful strategy to reduce adverse cardiovascular outcomes after ACS. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01130246. Copyright 2014 American Medical Association. All rights reserved

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score >= 5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

Neutrophils in cancer: neutral no more

Neutrophils are indispensable antagonists of microbial infection and facilitators of wound healing. In the cancer setting, a newfound appreciation for neutrophils has come into view. The traditionally held belief that neutrophils are inert bystanders is being challenged by the recent literature. Emerging evidence indicates that tumours manipulate neutrophils, sometimes early in their differentiation process, to create diverse phenotypic and functional polarization states able to alter tumour behaviour. In this Review, we discuss the involvement of neutrophils in cancer initiation and progression, and their potential as clinical biomarkers and therapeutic targets

Parvimonas micra can translocate from the subgingival sulcus of the human oral cavity to colorectal adenocarcinoma

[Abstract] Oral and intestinal samples from a cohort of 93 colorectal cancer (CRC) patients and 30 healthy controls (non-CRC) were collected for microbiome analysis. Saliva (28 non-CRC and 94 CRC), feces (30 non-CRC and 97 CRC), subgingival fluid (20 CRC), and tumor tissue samples (20 CRC) were used for 16S metabarcoding and/or RNA sequencing (RNAseq) approaches. A differential analysis of the abundance, performed with the ANCOM-BC package, adjusting the P-values by the Holm-Bonferroni method, revealed that Parvimonas was significantly over-represented in feces from CRC patients (P-value < 0.001) compared to healthy controls. A total of 11 Parvimonas micra isolates were obtained from the oral cavity and adenocarcinoma of CRC patients. Genome analysis identified a pair of isolates from the same patient that shared 99.2% identity, demonstrating that P. micra can translocate from the subgingival cavity to the gut. The data suggest that P. micra could migrate in a synergistic consortium with other periodontal bacteria. Metatranscriptomics confirmed that oral bacteria were more active in tumor than in non-neoplastic tissues. We suggest that P. micra could be considered as a CRC biomarker detected in non-invasive samples such as feces.The present authors want to thank all the cancer patients of the University Hospital of A Coruña for participating in this study, collaborating with us despite their health problems. We warmly want to thank Gema Carro Díaz and Montserrat Ingelmo Sánchez (surgical service nurses of HUAC) for their support during patient's recruitment time and also with sample collection. Moreover, we want to appreciate the valuable assistance received in anaerobic culturing by David Velasco Fernández (microbiologist) and Ana María Fernández Liñares (technician), and in FFPE samples processing by the pathology service technicians: Diego Barco Díaz, Cristina Vázquez Costa and Ana María Mejuto Rial. This work would not be possible without all the professionals of the Microbiology, Surgery, Pathology and Oncology services and Biobank from CHUAC who support this microbiome and cancer research project. This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project PI20/00413 and co-funded by the European Union to MP. The work has been also supported by the project RTI2018-102032-B-I00 from the Spanish Ministry of Science and Innovation to AM and by the CIBER INF ISCIII (CB21/13/00055) to GB and MP. Biobank of University Hospital Complex of A Coruña was supported by Instituto de Salud Carlos III – Fondos FEDER (EU) by grant PT20/00128. K. Conde-Pérez was financially supported with a predoctoral fellowship by the Asociación Española Contra el Cáncer (AECC). E. Buetas is supported by a grant from the Spanish Ministry of Science and Innovation with the reference PRE2019-088126. S. Ladra, E. Martin-De Arribas and Iago Iglesias-Corrás are supported by grants from GAIN (Xunta de Galicia, Spain) with references ED431C 2021/53 and ED431GXunta de Galicia; ED431C 2021/53Xunta de Galicia; ED431

The multispecies microbial cluster of Fusobacterium, Parvimonas, Bacteroides and Faecalibacterium as a precision biomarker for colorectal cancer diagnosis

[Abstract] The incidence of colorectal cancer (CRC) has increased worldwide, and early diagnosis is crucial to reduce mortality rates. Therefore, new noninvasive biomarkers for CRC are required. Recent studies have revealed an imbalance in the oral and gut microbiomes of patients with CRC, as well as impaired gut vascular barrier function. In the present study, the microbiomes of saliva, crevicular fluid, feces, and non-neoplastic and tumor intestinal tissue samples of 93 CRC patients and 30 healthy individuals without digestive disorders (non-CRC) were analyzed by 16S rRNA metabarcoding procedures. The data revealed that Parvimonas, Fusobacterium, and Bacteroides fragilis were significantly over-represented in stool samples of CRC patients, whereas Faecalibacterium and Blautia were significantly over-abundant in the non-CRC group. Moreover, the tumor samples were enriched in well-known periodontal anaerobes, including Fusobacterium, Parvimonas, Peptostreptococcus, Porphyromonas, and Prevotella. Co-occurrence patterns of these oral microorganisms were observed in the subgingival pocket and in the tumor tissues of CRC patients, where they also correlated with other gut microbes, such as Hungatella. This study provides new evidence that oral pathobionts, normally located in subgingival pockets, can migrate to the colon and probably aggregate with aerobic bacteria, forming synergistic consortia. Furthermore, we suggest that the group composed of Fusobacterium, Parvimonas, Bacteroides, and Faecalibacterium could be used to design an excellent noninvasive fecal test for the early diagnosis of CRC. The combination of these four genera would significantly improve the reliability of a discriminatory test with respect to others that use a single species as a unique CRC biomarker.The authors want to appreciate the good disposition of all the CRC patients for collaborating with us. We would like to thank Gema Carro Díaz and Montserrat Ingelmo Sánchez for their assistance during patients' recruitment time and also during tissue sample collection. This work would not be possible without the support of professionals from the Microbiology, Surgery, Pathology and Oncology Services and Biobank of CHUAC (Spain). The present study received funding from the Instituto de Salud Carlos III (ISCIII), Spain, through the project PI20/00413, cofunded by the European Union (EU) to MP. The whole work has been also supported by the project RTI2018-102032-B-I00 from the Spanish Ministry of Science and Innovation to AM and by CIBER INFEC ISCIII (CB21/13/00055) to GB and MP Biobank of A Coruña was supported by ISCIII-Fondos FEDER (EU) by grant PT20/00128. KC-P was financially supported by the Spanish Association against Cancer (AECC). EB was supported by a grant from the Spanish Ministry of Science and Innovation PRE2019-088126. SL and EM-DA were supported by grants from GAIN (Xunta de Galicia, Spain) with references ED431C 2021/53 and ED431G 2019/01.Xunta de Galicia; ED431C 2021/53Xunta de Galicia; ED431G 2019/0