M1-like macrophages are potent producers of anti-viral interferons and M1-associated marker-positive lung macrophages are decreased during rhinovirus-induced asthma exacerbations

Background Macrophages (Mф) can be M1/M2 polarized by Th1/2 signals, respectively. M2-like Mф are thought to be important in asthma pathogenesis, and M1-like in anti-infective immunity, however their roles in virus-induced asthma exacerbations are unknown. Our objectives were (i) to assess polarised Mф phenotype responses to rhinovirus (RV) infection in vitro and (ii) to assess Mф phenotypes in healthy subjects and people with asthma before and during experimental RV infection in vivo. Methods We investigated characteristics of polarized/unpolarized human monocyte-derived Mф (MDM, from 3–6 independent donors) in vitro and evaluated frequencies of M1/M2-like bronchoalveolar lavage (BAL) Mф in experimental RV-induced asthma exacerbation in 7 healthy controls and 17 (at baseline) and 18 (at day 4 post infection) people with asthma. Findings We observed in vitro: M1-like but not M2-like or unpolarized MDM are potent producers of type I and III interferons in response to RV infection (P<0.0001), and M1-like are more resistant to RV infection (P<0.05); compared to M1-like, M2-like MDM constitutively produced higher levels of CCL22/MDC (P = 0.007) and CCL17/TARC (P<0.0001); RV-infected M1-like MDM were characterized as CD14+CD80+CD197+ (P = 0.002 vs M2-like, P<0.0001 vs unpolarized MDM). In vivo we found reduced percentages of M1-like CD14+CD80+CD197+ BAL Mф in asthma during experimental RV16 infection compared to baseline (P = 0.024). Interpretation Human M1-like BAL Mф are likely important contributors to anti-viral immunity and their numbers are reduced in patients with allergic asthma during RV-induced asthma exacerbations. This mechanism may be one explanation why RV-triggered clinical and pathologic outcomes are more severe in allergic patients than in healthy subjects. Funding ERC FP7 Advanced grant 233015, MRC Centre Grant G1000758, Asthma UK grant 08–048, NIHR Biomedical Research Centre funding scheme, NIHR BRC Centre grant P26095, the Predicta FP7 Collaborative Project grant 260895, RSF grant 19-15-00272, Megagrant No 14.W03.31.0024

Non-nociceptive roles of opioids in the CNS: opioids' effects on neurogenesis, learning, memory and affect.

Mortality due to opioid use has grown to the point where, for the first time in history, opioid-related deaths exceed those caused by car accidents in many states in the United States. Changes in the prescribing of opioids for pain and the illicit use of fentanyl (and derivatives) have contributed to the current epidemic. Less known is the impact of opioids on hippocampal neurogenesis, the functional manipulation of which may improve the deleterious effects of opioid use. We provide new insights into how the dysregulation of neurogenesis by opioids can modify learning and affect, mood and emotions, processes that have been well accepted to motivate addictive behaviours

Impact of Safety-Related Dose Reductions or Discontinuations on Sustained Virologic Response in HCV-Infected Patients: Results from the GUARD-C Cohort.

BACKGROUND: Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice. METHODS: A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively. RESULTS: SVR24 rates were 46.1% (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1, 2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced ≥1 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with ≥1 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not ≥5. CONCLUSIONS: In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginterferon alfa-2a/ribavirin.This study was sponsored by F. Hoffmann-La Roche Ltd, Basel, Switzerland. Support for third-party writing assistance for this manuscript, furnished by Blair Jarvis MSc, ELS, of Health Interactions, was provided by F. Hoffmann-La Roche Ltd, Basel, Switzerland

Impact of safety-related dose reductions or discontinuations on sustained virologic response in HCV-infected patients: Results from the GUARD-C Cohort

Background: Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice. Methods: A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively. Results: SVR24 rates were 46.1 % (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1,2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced 651 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with 651 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not 655. Conclusions: In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginter-feron alfa-2a/ribavirin

Publisher Correction: Fundamental social motives measured across forty-two cultures in two waves.

This is the final version. Available from Nature Research via the DOI in this record.

Fundamental social motives measured across forty-two cultures in two waves.

This is the final version. Available from Nature Research via the DOI in this record. Code availability: All code used to process and visualize the data, including information on software packages used, is freely available in the OSF projectHow does psychology vary across human societies? The fundamental social motives framework adopts an evolutionary approach to capture the broad range of human social goals within a taxonomy of ancestrally recurring threats and opportunities. These motives-self-protection, disease avoidance, affiliation, status, mate acquisition, mate retention, and kin care-are high in fitness relevance and everyday salience, yet understudied cross-culturally. Here, we gathered data on these motives in 42 countries (N = 15,915) in two cross-sectional waves, including 19 countries (N = 10,907) for which data were gathered in both waves. Wave 1 was collected from mid-2016 through late 2019 (32 countries, N = 8,998; 3,302 male, 5,585 female; Mage = 24.43, SD = 7.91). Wave 2 was collected from April through November 2020, during the COVID-19 pandemic (29 countries, N = 6,917; 2,249 male, 4,218 female; Mage = 28.59, SD = 11.31). These data can be used to assess differences and similarities in people's fundamental social motives both across and within cultures, at different time points, and in relation to other commonly studied cultural indicators and outcomes.National Science FoundationFAPESP (São Paulo Research Foundation)Czech Science FoundationCzech Science FoundationInstitute of Psychology, Czech Academy of SciencesUniversidad de la FronteraAberystwyth Universit

Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the exact details of the probe is widely acknowledged as a challenge. Due to various complexity constraints, the probe is often only approximated in a simplified geometry, leading to a source for modeling inconsistencies. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and the sample properties, circumventing the explicit probe modeling process. In this work we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the probe-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general

Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general

Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general