The gut microbiota (GM) is related to obesity and other metabolic diseases. To detect GM markers for obesity in patients with different metabolic abnormalities and investigate their relationships with clinical indicators, 1,914 Chinese adults were enrolled for 16S rRNA gene sequencing in this retrospective study. Based on GM composition, Random forest classifiers were constructed to screen the obesity patients with (Group OA) or without metabolic diseases (Group O) from healthy individuals (Group H), and high accuracies were observed for the discrimination of Group O and Group OA (areas under the receiver operating curve (AUC) equal to 0.68 and 0.76, respectively). Furthermore, six GM markers were shared by obesity patients with various metabolic disorders (Bacteroides, Parabacteroides, Blautia, Alistipes, Romboutsia and Roseburia). As for the discrimination with Group O, Group OA exhibited low accuracy (AUC = 0.57). Nonetheless, GM classifications to distinguish between Group O and the obese patients with specific metabolic abnormalities were not accurate (AUC values from 0.59 to 0.66). Common biomarkers were identified for the obesity patients with high uric acid, high serum lipids and high blood pressure, such as Clostridium XIVa, Bacteroides and Roseburia. A total of 20 genera were associated with multiple significant clinical indicators. For example, Blautia, Romboutsia, Ruminococcus2, Clostridium sensu stricto and Dorea were positively correlated with indicators of bodyweight (including waistline and body mass index) and serum lipids (including low density lipoprotein, triglyceride and total cholesterol). In contrast, the aforementioned clinical indicators were negatively associated with Bacteroides, Roseburia, Butyricicoccus, Alistipes, Parasutterella, Parabacteroides and Clostridium IV. Generally, these biomarkers hold the potential to predict obesity-related metabolic abnormalities, and interventions based on these biomarkers might be beneficial to weight loss and metabolic risk improvement.

Missing values and dropouts are common issues in longitudinal studies in all areas of medicine and public health. Intent-to-treat (ITT) analysis has become a widely accepted method for the analysis of controlled clinical trials. In most controlled clinical trials, some patients do not complete their intended followup according to the protocol for a variety of reasons; this problem generates missing values. Missing values lead to concern and confusion in identifying the ITT population, which makes the data analysis more complex and challenging. No adequate strategy exists for ITT analyses of longitudinal controlled clinical trial data with missing values. Several ad hoc strategies for dealing with missing values for an ITT analysis are common in the practice of controlled clinical trials. We performed a detailed investigation based on simulation studies to develop recommendations for this situation. We compared sizes (type I errors) and power between some popular ad hoc approaches and the linear mixed model approach under different missing value scenarios. Our results suggest that, for studies with a high percentage of missing values, the mixed model approach without any ad hoc imputation is more powerful than other options.

We consider Fisher-KPP equation with advection: u t = u xx − βu x + f (u) for x ∈ (g(t), h(t)), where g(t) and h(t) are two free boundaries satisfying Stefan conditions. This equation is used to describe the population dynamics in advective environments. We study the influence of the advection coefficient −β on the long time behavior of the solutions. We find two parameters c 0 and β * with β * > c 0 > 0 which play key roles in the dynamics, here c 0 is the minimal speed of the traveling waves of Fisher-KPP equation. More precisely, by studying a family of the initial data {σφ} σ>0 (where φ is some compactly supported positive function), we show that: (1) in case β ∈ (0, c 0 ), there exists σ * 0 such that spreading happens when σ > σ * (i.e., u(t, · ; σφ) → 1 locally uniformly in R) and vanishing happens when σ ∈ (0, σ * ] (i.e., [g(t), h(t)] remains bounded and u(t, · ; σφ) → 0 uniformly in [g(t), h(t)]); (2) in case β ∈ (c 0 , β * ), there exists σ * > 0 such that virtual spreading happens when σ > σ * (i.e., u(t, · ; σφ) → 0 locally uniformly in [g(t), ∞) and u(t, · + ct; σφ) → 1 locally uniformly in R for some c > β − c 0 ), vanishing happens when σ ∈ (0, σ * ), and in the transition case σ = σ * , u(t, · + o(t); σφ) → V * (· − (β − c 0 )t) uniformly, the latter is a traveling wave with a "big head" near the free ✩ 1715 boundary x = (β − c 0 )t and with an infinite long "tail" on the left; (3) in case β = c 0 , there exists σ * > 0 such that virtual spreading happens when σ > σ * and u(t, · ; σφ) → 0 uniformly in [g(t), h(t)] when σ ∈ (0, σ * ]; (4) in case β β * , vanishing happens for any solution.

Oct4 may be a potential biomarker for the initiation, progression, and differentiation of human GC.

Pancreatic cancer is arguably the deadliest cancer type. The efficacy of current therapies is often hindered by the inability to predict patient outcome. As such, the development of tools for early detection and risk prediction is key for improving outcome and quality of life. Here, we introduce the plasminogen receptor S100A10 as a novel predictive biomarker and a driver of pancreatic tumor growth and invasion. We demonstrated that S100A10 mRNA and protein are overexpressed in human pancreatic tumors compared to normal ducts and nonductal stroma. S100A10 mRNA and methylation status were predictive of overall survival and recurrence‐free survival across multiple patient cohorts. S100A10 expression was driven by promoter methylation and the oncogene KRAS. S100A10 knockdown reduced surface plasminogen activation, invasiveness, and in vivo growth of pancreatic cancer cell lines. These findings delineate the clinical and functional contribution of S100A10 as a biomarker in pancreatic cancer.