Aberrant TIMP-1 production in tumor-associated fibroblasts drives the selective benefits of nintedanib in lung adenocarcinoma

The fibrotic tumor microenvironment is a pivotal therapeutic target. Nintedanib, a clinically approved multikinase antifibrotic inhibitor, is effective against lung adenocarcinoma (ADC) but not squamous cell carcinoma (SCC). Previous studies have implicated the secretome of tumor-associated fibroblasts (TAFs) in the selective effects of nintedanib in ADC, but the driving factor(s) remained unidentified. Here we examined the role of tissue inhibitor of metalloproteinase-1 (TIMP-1), a tumor-promoting cytokine overproduced in ADC-TAFs. To this aim, we combined genetic approaches with in vitro and in vivo preclinical models based on patient-derived TAFs. Nintedanib reduced TIMP-1 production more efficiently in ADC-TAFs than SCC-TAFs through a SMAD3-dependent mechanism. Cell culture experiments indicated that silencing TIMP1 in ADC-TAFs abolished the therapeutic effects of nintedanib on cancer cell growth and invasion, which were otherwise enhanced by the TAF secretome. Consistently, co-injecting ADC cells with TIMP1-knockdown ADC-TAFs into immunocompromised mice elicited a less effective reduction of tumor growth and invasion under nintedanib treatment compared to tumors bearing unmodified fibroblasts. Our results unveil a key mechanism underlying the selective mode of action of nintedanib in ADC based on the excessive production of TIMP-1 in ADC-TAFs. We further pinpoint reduced SMAD3 expression and consequent limited TIMP-1 production in SCC-TAFs as key for the resistance of SCC to nintedanib. These observations strongly support the emerging role of TIMP-1 as a critical regulator of therapy response in solid tumors.     

Beyond Cases and Deaths: The Benefits of Auxiliary Data Streams In Tracking the COVID-19 Pandemic: Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators—derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity—from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in “flat” or “down” directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during “up” trends.

Tracking and forecasting indicators from public health reporting streams—such as confirmed cases and deaths in the COVID-19 pandemic—are crucial for understanding disease spread, correctly formulating public policy responses, and rationally planning future public health resource needs. A companion paper (1) describes our research group’s efforts, beginning in April 2020, in curating and maintaining a database of real-time indicators that track COVID-19 activity and other relevant phenomena. The signals (a term we use synonymously with “indicators”) in this database are accessible through the COVIDcast Application Programming Interface (API) (2), as well as associated R (3) and Python (4) packages, for convenient data fetching and processing. In the current paper, we quantify the utility provided by a core set of these indicators for two fundamental prediction tasks: probabilistic forecasting of COVID-19 case rates and prediction of future COVID-19 case hotspots (defined by the event that a relative increase in COVID-19 cases exceeds a certain threshold).At the outset, we should be clear that our intent in this paper is not to provide an authoritative take on cutting-edge COVID-19 forecasting methods. Similarly, some authors, e.g., ref. 5, have pointed out numerous mishaps of forecasting during the pandemic, and it is not our general intent to fix them here. Instead, we start with a basic and yet reasonably effective predictive model for future trends in COVID-19 cases and present a rigorous, quantitative assessment of the added value provided by auxiliary indicators that are derived from data sources that operate outside of traditional public health streams. In particular, we consider five indicators derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google searches.To assess this value in as direct terms as possible, we base our study around a very simple basic model: an autoregressive model, in which COVID cases in the near future are predicted using a linear combination of COVID cases in the near past. Forecasting carries a rich literature, offering a wide range of sophisticated techniques (see, e.g., ref. 6 for a review); however, we purposely avoid enhancements such as order selection, correction of outliers/anomalies in the data, and inclusion of regularization or nonlinearities. Similarly, we do not account for other factors that may well aid in forecasting, such as age-specific effects, holiday adjustments, and the effects of public health mandates. All that said, despite its simplicity, the basic autoregressive model that we consider in this paper exhibits competitive performance (see SI Appendix for details) with many of the top COVID-19 case forecasters submitted to the US COVID-19 Forecast Hub (7), which is the official source of forecasts used in public communications by the US Centers for Disease Control and Prevention (CDC). The strong performance of the autoregressive model here is in line with the fact that simple, robust models have also consistently been among the best-performing ones for COVID-19 death forecasting (8).In the companion paper (1), we analyze correlations between various indicators and COVID case rates. These correlations are natural summaries of the contemporaneous association between an indicator and COVID cases, but they fall short of delivering a satisfactory answer to the question that motivates the current article: Is the information contained in an indicator demonstrably useful for the prediction tasks we care about? Note that even lagged correlations cannot deliver a complete answer. Demonstrating utility for prediction is a much higher standard than simply asking about correlations; to be useful in forecast or hotspot models, an indicator must provide relevant information that is not otherwise contained in past values of the case rate series itself [cf. the pioneering work on Granger causality (9, 10), as well as the further references given below]. We assess this directly by inspecting the difference in predictive performance of simple autoregressive models trained with and without access to past values of a particular indicator.We find that each of the five indicators we consider—three based on COVID-related outpatient visits from medical insurance claims, one on self-reported symptoms from online surveys, and one on Google searches for anosmia or ageusia—provide an overall improvement in accuracy when incorporated into the autorgressive model. This is true both for COVID-19 case forecasting and for hotspot prediction. Further analysis reveals that the gains in accuracy depend on the pandemic’s dynamics at prediction time: The biggest gains in accuracy appear during times in which cases are “flat” or trending “down”; but the indicator based on Google searches offers a notable improvement when cases are trending “up.”Careful handling of data revisions plays a key role in our analysis. Signals computed from surveillance streams are often subject to latency and/or revision. For example, a signal based on aggregated medical insurance claims may be available after just a few days, but it can then be substantially revised over the next several weeks as additional claims are submitted and/or processed late. Correlations between such a signal and case rates calculated “after the fact” (i.e., computed retrospectively, using the finalized values of this signal) will not deliver an honest answer to the question of whether this signal would have been useful in real time. Instead, we build predictive models using only the data that would have been available as of the prediction date and compare the ensuing predictions in terms of accuracy. The necessity of real-time data for honest forecast evaluations has been recognized in econometrics for a long time (11–21), but it is often overlooked in epidemic forecasting despite its critical importance (22).Finally, it is worth noting that examining the importance of additional features for prediction is a core question in inferential statistics and econometrics, with work dating back to at least ref. 9. Still today, drawing rigorous inference based on predictions, without (or with lean) assumptions, is an active field of research from both applied and theoretical angles (23–32). Our take in the current work is in line with much of this literature; however, to avoid making any explicit assumptions, we do not attempt to make formal significance statements and, instead, broadly examine the stability of our conclusions with respect to numerous modes of analysis.     

Dielectric relaxation and relationships of local molecular mobility in linear and comb-like thermotropic polymers

The dielectric behavior of several thermotropic linear and comb-like polymers with flexible spacers and mesogenic fragments with different structure has been studied in a frequency range from 60 Hz to 1 MHz in the bulk state (below the glass transition temperature). Two regions of dipole relaxation caused by local mobility were detected. It was shown that the relaxation parameters of the first process virtually do not change with increasing spacer length and have similar values in polymers with different structure. In the case of the second process, in increase in spacer length considerably increases the mobility of kinetic elements.     

Reorientation of prostanoid production accompanies “activation” of adult microglial cells in culture

Using morphological, immunocytochemical, and functional parameters we have previously shown that highly purified adult rat microglial cells undergo a process of “activation” when cultured in a serum-containing medium in the absence of added proinflammatory substances or other factors (Slepko and Levi: Glia 16:241–246, 1996). Here we studied the lipopolysaccharide (LPS)-evoked production of two prostanoids, thromboxane A₂ (measured as thromboxane B₂) (TXB₂) and prostaglandin E₂ (PGE₂), as a function of microglial “activation.” LPS induced a greater time- and dose-dependent release of TXB₂, compared to PGE₂, in the less “activated” cells. Further “activation” led to amplified synthesis of PGE₂ and not of TXB₂, so that the TXB₂/PGE₂ ratio changed from 2.2 to 0.25 between the 2nd and 4th day in culture. Western blot experiments showed that the LPS-evoked expression of the inducible form of cyclooxygenase (COX) was markedly higher in cells exhibiting a more “activated” phenotype. The expression of the constitutive isoform of COX was low in all conditions, was slightly greater in more “activated” cells, and was not affected by LPS. Neither progression in microglial “activation” nor LPS treatment enhanced thromboxane synthase activity. We hypothesize that reorientation of prostanoid synthesis toward a major production of PGE₂ in the more “activated” cells can be largely attributed to an increased inducibility of cellular COX expression, combined with the inability of thromboxane synthase to cope with the increased availability of the COX product prostaglandin H₂ (PGH₂), the common precursor of TXA₂ and PGE₂. In view of the different, and at times opposite, functional activity of TXB₂ and PGE₂, the described change in prostanoid production pattern may contribute to the role of “activated” microglia in inflammation and host defense. J. Neurosci. Res. 49:292–300, 1997. © 1997 Wiley-Liss, Inc.     

Análise de Custo-Efetividade da Terapia com Evolocumabe em Pacientes com Alto Risco de Eventos Cardiovasculares no Contexto do SUS – Brasil

Background:Hypertrophic cardiomyopathy (HCM) and left ventricular hypertrophy (LVH) secondary to systemic hypertension (HTN) may be associated with left atrial (LA) functional abnormalities.Objectives:We aimed to characterize LA mechanics in HCM and HTN and determine any correlation with the extent of left ventricular (LV) fibrosis measured by cardiac magnetic resonance (CMR) in HCM patients.Methods:Two-dimensional speckle tracking-derived longitudinal LA function was acquired from apical views in 60 HCM patients, 60 HTN patients, and 34 age-matched controls. HCM patients also underwent CMR, with measurement of late gadolinium enhancement (LGE) extension. Association with LA strain parameters was analyzed. Statistical significance was set at p<0.05.Results:Mean LV ejection fraction was not different between the groups. The E/e’ ratio was impaired in the HCM group and preserved in the control group. LA mechanics was significantly reduced in HCM, compared to the HTN group. LA strain rate in reservoir (LASRr) and in contractile (LASRct) phases were the best discriminators of HCM, with an area under the curve (AUC) of 0.8, followed by LA strain in reservoir phase (LASr) (AUC 0.76). LASRr and LASR-ct had high specificity (89% and 91%, respectively) and LASr had sensitivity of 80%. A decrease in 2.79% of LA strain rate in conduit phase (LASRcd) predicted an increase of 1cm in LGE extension (r²=0.42, β 2.79, p=0.027).Conclusions:LASRr and LASRct were the best discriminators for LVH secondary to HCM. LASRcd predicted the degree of LV fibrosis assessed by CMR. These findings suggest that LA mechanics is a potential predictor of disease severity in HCM.     

The Consumption of Food-Based Iodine in the Immediate Pre-Pregnancy Period in Madrid Is Insufficient. San Carlos and Pregnancy Cohort Study

A pre-gestational thyroid reserve of iodine is crucial to guarantee the increased demand for thyroid hormone production of early pregnancy. An iodine intake ≥150 µg/day is currently recommended. The objective of this study was to assess average pre-gestational food-based iodine consumption in pregnant women at their first prenatal visit (<12 gestational weeks), and its association with adverse materno-fetal events (history of miscarriages, early fetal losses, Gestational Diabetes, prematurity, caesarean sections, and new-borns large/small for gestational age). Between 2015–2017, 2523 normoglycemic women out of 3026 eligible had data in the modified Diabetes Nutrition and Complication Trial (DNCT) questionnaire permitting assessment of pre-gestational food-based iodine consumption, and were included in this study. Daily food-based iodine intake was 123 ± 48 µg, with 1922 (76.1%) not reaching 150 µg/day. Attaining this amount was associated with consuming 8 weekly servings of vegetables (3.84; 3.16–4.65), 1 of shellfish (8.72; 6.96–10.93) and/or 2 daily dairy products (6.43; 5.27–7.86). Women who reached a pre-gestational intake ≥150 µg had lower rates of hypothyroxinemia (104 (17.3%)/384 (21.4%); p = 0.026), a lower miscarriage rate, and a decrease in the composite of materno-fetal adverse events (0.81; 0.67–0.98). Reaching the recommended iodine pre-pregnancy intake with foods could benefit the progression of pregnancy.     

In Vitro Characterization of In Situ Alloyed Ti6Al4V(ELI)-3 at.% Cu Obtained by Laser Powder Bed Fusion

The intensive cytotoxicity of pure copper is effectively kills bacteria, but it can compromise cellular behavior, so a rational balance must be found for Cu-loaded implants. In the present study, the individual and combined effect of surface composition and roughness on osteoblast cell behavior of in situ alloyed Ti6Al4V(ELI)-3 at.% Cu obtained by laser powder bed fusion was studied. Surface composition was studied using scanning electron microscopy, energy dispersive spectroscopy, and X-ray diffraction. Surface roughness measurements were carried out using confocal microscopy. In vitro osteoblast performance was evaluated by means of cell morphology observation of cell viability, proliferation, and mineralization. In vitro studies were performed at 1, 7, and 14 days of cell culture, except for cell mineralization at 28 days, on grounded and as-built (rough) samples with and without 3 at.% Cu. The addition of 3 at.% Cu did not show cell cytotoxicity but inhibited cell proliferation. Cell mineralization tends to be higher for samples with 3 at.% Cu content. Surface roughness inhibited cell proliferation too, but showed enhanced cell mineralization capacity and therefore, higher osteoblast performance, especially when as-built samples contained 3 at.% Cu. Cell proliferation was only observed on ground samples without Cu but showed the lowest cell mineralization.