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Background: Patient care in ulcerative colitis (UC) remains challenging despite an array of established treatment options and emerging new therapies. The management of UC therapy should be guided by the endoscopic extent of inflammation, disease severity, and prognostic factors of poor outcome. Complete remission, defined as durable symptomatic and endoscopic remission without corticosteroid therapy, is the desired treatment goal. Summary: This review focuses on treatment recommendations for different clinical scenarios in moderate-to-severe UC: Active UC of any extent not responding to aminosalicylates, steroid-dependent UC, steroid-refractory UC, immunomodulator-refractory UC, and acute severe UC. Comprehensive treatment algorithms for daily clinical practice were developed based on published guidelines and current literature. Key Messages: While current treatment options including a number of biologicals and small molecules have evolved UC treatment to achieve sustained remission in a majority of patients, upcoming treatment options with different molecular pathways and different modes of actions will further increase the need for personalized medicine.

We develop results for the use of Lasso and post-Lasso methods to form first-stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p. Our results apply even when p is much larger than the sample size, n. We show that the IV estimator based on using Lasso or post-Lasso in the first stage is root-n consistent and asymptotically normal when the first stage is approximately sparse, that is, when the conditional expectation of the endogenous variables given the instruments can be well-approximated by a relatively small set of variables whose identities may be unknown. We also show that the estimator is semiparametrically efficient when the structural error is homoscedastic. Notably, our results allow for imperfect model selection, and do not rely upon the unrealistic \"beta-min\" conditions that are widely used to establish validity of inference following model selection (see also Belloni, Chernozhukov, and Hansen (2011b)). In simulation experiments, the Lasso-based IV estimator with a data-driven penalty performs well compared to recently advocated many-instrument robust procedures. In an empirical example dealing with the effect of judicial eminent domain decisions on economic outcomes, the Lasso-based IV estimator outperforms an intuitive benchmark. Optimal instruments are conditional expectations. In developing the IV results, we establish a series of new results for Lasso and post-Lasso estimators of nonparametric conditional expectation functions which are of independent theoretical and practical interest. We construct a modification of Lasso designed to deal with non-Gaussian, heteroscedastic disturbances that uses a data-weighted 𝓁₁-penalty function. By innovatively using moderate deviation theory for self-normalized sums, we provide convergence rates for the resulting Lasso and post-Lasso estimators that are as sharp as the corresponding rates in the homoscedastic Gaussian case under the condition that log p = o(n 1/3 ). We also provide a data-driven method for choosing the penalty level that must be specified in obtaining Lasso and post-Lasso estimates and establish its asymptotic validity under non-Gaussian, heteroscedastic disturbances.

Background: Two novel systemic inflammation indices, SII and SIRI, are associated with increased risk of cardiovascular diseases (CVD). However, SII and SIRI are prone to change over time and the association between changeable status and long-term outcome risk remains to be uncovered. This study aims to examine the association between the dynamic status of SII and SIRI and risk of CVD. Methods: This prospective study included a total of 45,809 subjects without MI, stroke and cancer prior to or in 2010 (baseline of this study). The dynamic status of SII and SIRI during 2006, 2008, and 2010 was assessed by dynamic trajectories (primary exposure), annual increase, and average value. The outcome was CVD incidence during 8.6 years' follow-up. Multiple Cox regression models were used to calculate the adjusted hazard ratios (HRs) and confidence intervals (95% CIs). Results: Four dynamic trajectories of SII and SIRI were identified as follows: low stable pattern, moderate stable pattern, increase pattern, and decrease pattern. For SII, compared with \"low stable pattern\", after controlling confounders and level of SII in 2006, adjusted HRs were 1.24 (95% CI = 1.02-1.51) for \"increase pattern\" and 1.11 (95% CI = 1.00-1.23) for \"moderate-stable pattern\" while the association was not significant for \"decrease pattern\". Additionally, the highest group of annual SII increase and average SII had respective HR of 1.20 (95% CI = 1.05-1.37) and 1.32 (95% CI = 1.13-1.55). The results were consistent for SIRI. \"Increase pattern\" and \"moderate stable pattern\" increased the risk of CVD by 38% (HR = 1.38, 95% CI = 1.17-1.63) and 12% (HR = 1.12, 95% CI = 1.01-1.25), while no significant association was found for \"decrease pattern\". The highest group of annual SIRI increase and average SIRI had respective HR of 1.25 (95% CI = 1.09-1.44) and 1.39 (95% CI = 1.19-1.63). Conclusion: Dynamic status of SII and SIRI was significantly associated with risk of CVD, which highlighted that we should focus on the dynamic change of SII and SIRI. Keywords: systemic inflammation, dynamic status, prospective study, cardiovascular diseases

Physical activity (PA) has been shown to be related to physical and mental health. Yet there are few studies on how the frequency of PA relates to health and a healthy lifestyle. We aimed to investigate how the frequency of different PAs is associated with the following health indicators: body mass index (BMI), substance consumption, physical health, and mental health. We focused on three types of PA: (1) medium- to high-intensity aerobic exercise; (2) low- to medium-intensity relaxing exercise; and (3) outdoor leisure PA. A total of 9617 volunteers, aged 19 to 81, participated in the study. The relationships between the frequencies of the three types of PA and health-related and sociodemographic factors were analyzed using multinomial logistic regression. We found that women more frequently engaged in PA type 2, and men in types 1 and 3. A higher frequency of PA was associated with lower BMI and less or no smoking behavior; higher education (PAs 1 and 3); higher age (PAs 2 and 3); better physical health (PAs 1 and 3); and better mental health (PA 3). In conclusion, higher frequency of different PAs was significantly associated with better physical and mental health, less smoking, higher age, and a higher level of education.

Abstract Objectives Pathologic complete response (pCR) rate after neoadjuvant chemotherapy was compared between 141 estrogen receptor (ER)–negative (43%), 41 low ER+ (13%), 47 moderate ER+ (14%), and 98 high ER+ (30%) tumors. Methods Human epidermal growth factor receptor 2–positive cases, cases without semiquantitative ER score, and patients treated with neoadjuvant endocrine therapy alone were excluded. Results The pCR rate of low ER+ tumors was similar to the pCR rate of ER– tumors (37% and 26% for low ER and ER– respectively, P = .1722) but significantly different from the pCR rate of moderately ER+ (11%, P = .0049) and high ER+ tumors (4%, P < .0001). Patients with pCR had an excellent prognosis regardless of the ER status. In patients with residual disease (no pCR), the recurrence and death rate were higher in ER– and low ER+ cases compared with moderate and high ER+ cases. Conclusions Low ER+ breast cancers are biologically similar to ER– tumors. Semiquantitative ER H-score is an important determinant of response to neoadjuvant chemotherapy.

Consider a network where the nodes split into K different communities. The community labels for the nodes are unknown and it is of major interest to estimate them (i.e., community detection). Degree Corrected Block Model (DCBM) is a popular network model. How to detect communities with the DCBM is an interesting problem, where the main challenge lies in the degree heterogeneity. We propose a new approach to community detection which we call the Spectral Clustering On Ratios-of-Eigenvectors (SCORE). Compared to classical spectral methods, the main innovation is to use the entry-wise ratios between the first leading eigenvector and each of the other leading eigenvectors for clustering. Let A be the adjacency matrix of the network. We first obtain the K leading eigenvectors of A, say, We then use R for clustering by applying the &-means method. η̂₁,...,η̂K, and let R̂ be the n × (K-1) matrix such that R̂(i,k)=η̂k+1(i)/η̂₁(i), 1≤i≤n, 1≤k≤K-1. We then use R̂ for clustering by applying the k-means method. The central surprise is, the effect of degree heterogeneity is largely ancillary, and can be effectively removed by taking entry-wise ratios between η̂k+1 and η̂₁, 1≤k≤k-1. The method is successfully applied to the web blogs data and the karate club data, with error rates of 58/1222 and 1/34, respectively. These results are more satisfactory than those by the classical spectral methods. Additionally, compared to modularity methods, SCORE is easier to implement, computationally faster, and also has smaller error rates. We develop a theoretic framework where we show that under mild conditions, the SCORE stably yields consistent community detection. In the core of the analysis is the recent development on Random Matrix Theory (RMT), where the matrix-form Bernstein inequality is especially helpful.

The optimal timing for management of pediatric patients with moderate aortic valve disease [moderate aortic stenosis (modAS) or moderate aortic regurgitation (modAR)] remains unknown and largely unexplored. Although usually asymptomatic, the risk of increased left ventricular (LV) wall stress, irreversible myocardial fibrosis and sudden death in untreated moderate conditions warrants clearer risk stratification for appropriate timely intervention. In this study, we explore the use of a patient-specific mathematical model to introduce a new evaluative parameter of LV performance in patients with moderate aortic valve disease. Synthetic patient data ( N  = 520) representing healthy patients, and patients with modAS or modAR were first generated. Then, data from twenty-five pediatric patients were included in this study (healthy = 9; moderate AS = 8; modAR = 8). The effect of modAS or modAR on LV performance was evaluated by LV stroke work (LVSW) efficiency, a new non-invasive parameter. The results demonstrate that healthy patients possess a very high LVSW efficiency (synthetic data: 91 ± 2%, in vivo data: 92 ± 3%). However, modAS patients have a significant reduction in LVSW efficiency (synthetic data: 78 ± 2%, in vivo data: 76 ± 5%, p  < 0.05), whereas modAR patients had the lowest LVSW efficiency (synthetic data: 58 ± 3%, in vivo data: 66 ± 7%; p  < 0.05). This highlights that patients with moderate aortic valve disease require careful myocardial assessment, regardless of onset of clinical symptoms as their LV performance is significantly reduced. The evaluation of LVSW efficiency offers a promising avenue for future stratification of mixed aortic valve disease for optimal timing of management and intervention.

Introduction There is a high unmet need for safe and effective non-opioid medicines to treat moderate to severe pain without risk of addiction. Voltage-gated sodium channel 1.8 (Na V 1.8) is a genetically and pharmacologically validated pain target that is selectively expressed in peripheral pain-sensing neurons and not in the central nervous system (CNS). Suzetrigine (VX-548) is a potent and selective inhibitor of Na V 1.8, which has demonstrated clinical efficacy and safety in multiple acute pain studies. Our study was designed to characterize the mechanism of action of suzetrigine and assess both nonclinical and clinical data to test the hypothesis that selective Na V 1.8 inhibition translates into clinical efficacy and safety, including lack of addictive potential. Methods Preclinical pharmacology and mechanism of action studies were performed in vitro using electrophysiology and radiolabeled binding methods in cells recombinantly expressing human Na V channels, human proteins, and primary human dorsal root ganglion (DRG) sensory neurons. Safety and addictive potential assessments included in vitro secondary pharmacology studies, nonclinical repeat-dose toxicity and dependence studies in rats and/or monkeys, and a systematic analysis of adverse event data generated from 2447 participants from phase 3 acute pain studies of suzetrigine. Results Suzetrigine is selective against all other Na V subtypes (≥ 31,000-fold) and 180 other molecular targets. Suzetrigine inhibits Na V 1.8 by binding to the protein’s second voltage sensing domain (VSD2) to stabilize the closed state of the channel. This novel allosteric mechanism results in tonic inhibition of Na V 1.8 and reduces pain signals in primary human DRG sensory neurons. Nonclinical and clinical safety assessments with suzetrigine demonstrate no adverse CNS, cardiovascular or behavioral effects and no evidence of addictive potential or dependence. Conclusions The comprehensive pharmacology assessment presented here indicates that suzetrigine represents the first in a new class of non-opioid analgesics that are selective Na V 1.8 pain signal inhibitors acting in the peripheral nervous system to safely treat pain without addictive potential.

Solar-induced chlorophyll fluorescence (SIF) brings major advancements in measuring terrestrial photosynthesis. Several recent studies have evaluated the potential of SIF retrievals from the Orbiting Carbon Observatory-2 (OCO-2) in estimating gross primary productivity (GPP) based on GPP data from eddy covariance (EC) flux towers. However, the spatially and temporally sparse nature of OCO-2 data makes it challenging to use these data for many applications from the ecosystem to the global scale. Here, we developed a new global ‘OCO-2’ SIF data set (GOSIF) with high spatial and temporal resolutions (i.e., 0.05°, 8-day) over the period 2000–2017 based on a data-driven approach. The predictive SIF model was developed based on discrete OCO-2 SIF soundings, remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological reanalysis data. Our model performed well in estimating SIF (R2 = 0.79, root mean squared error (RMSE) = 0.07 W m−2 μm−1 sr−1). The model was then used to estimate SIF for each 0.05° × 0.05° grid cell and each 8-day interval for the study period. The resulting GOSIF product has reasonable seasonal cycles, and captures the similar seasonality as both the coarse-resolution OCO-2 SIF (1°), directly aggregated from the discrete OCO-2 soundings, and tower-based GPP. Our SIF estimates are highly correlated with GPP from 91 EC flux sites (R2 = 0.73, p < 0.001). They capture the expected spatial and temporal patterns and also have remarkable ability to highlight the crop areas with the highest daily productivity across the globe. Our product also allows us to examine the long-term trends in SIF globally. Compared with the coarse-resolution SIF that was directly aggregated from OCO-2 soundings, GOSIF has finer spatial resolution, globally continuous coverage, and a much longer record. Our GOSIF product is valuable for assessing terrestrial photosynthesis and ecosystem function, and benchmarking terrestrial biosphere and Earth system models.

Coronavirus disease is reported to affect the cardiovascular system. We showed that relative bradycardia was a common characteristic for 54 patients with PCR-confirmed mild-to-moderate coronavirus disease in Japan. This clinical sign could help clinicians to diagnose this disease.