Safety and efficacy of luspatercept for the treatment of anemia in patients with myelofibrosis.

The ACE-536-MF-001 trial enrolled patients with myelofibrosis (n = 95) into 4 cohorts: patients in cohorts 1 and 3A were non-transfusion dependent (NTD) and had anemia; patients in cohorts 2 and 3B were transfusion dependent (TD); patients in cohort 3A/3B had stable ruxolitinib treatment prior to and during the study. All patients received luspatercept (1.0-1.75 mg/kg, 21-day cycles). Treatment was extended if clinical benefit was observed at day 169. The primary endpoint was anemia response rate (NTD, ≥1.5 g/dL hemoglobin increase from baseline; TD, transfusion-independence) over any 12-week period during the primary treatment period (weeks 1-24). Overall, 14% of patients in cohorts 1 and 3A, 10% in cohort 2, and 26% in cohort 3B met the primary endpoint. In cohorts 1 and 3A (NTD), 27% and 50% of patients respectively had mean hemoglobin increase ≥1.5 g/dL from baseline. Among TD patients, ~50% had ≥50% reduction in transfusion burden. Reduction in total symptom score was observed in all cohorts, with the greatest response rate seen in cohort 3A. Overall, 94% of patients had ≥1 adverse event (AE); 47% had ≥1 treatment-related AE (TRAE; 11% grade ≥3), most frequently hypertension (18%), managed with medical intervention. One patient had a serious TRAE leading to luspatercept discontinuation. Nine patients died on treatment (unrelated to study drug). In most patients, ruxolitinib dose and spleen size remained stable. In patients with myelofibrosis, luspatercept improved anemia and transfusion burden across cohorts; the safety profile was consistent with previous studies. NCT03194542 clinicaltrials.gov.

Sargassum fusiforme fucoidan modifies the gut microbiota during alleviation of streptozotocin-induced hyperglycemia in mice.

Diabetes is a complicated endocrine and metabolic disorder, which has become an epidemic health issue worldwide. Fucoidan is extensively distributed in the brown algae and several marine invertebrates exhibiting diverse biological activities. In the present study, the physicochemical property of Sargassum fusiforme fucoidan (SFF) and its effects on streptozotocin (STZ)-induced diabetic mice and gut microbiota were investigated. Diabetes mice not only showed abnormal blood glucose, but also accompanied by multiple symptoms, such as gradual emaciation, decreased body weight, increased food and water intake. Compared with diabetic mice after 6-week treatment, administration of SFF significantly decreased the fasting blood glucose, diet and water intake. Furthermore, SFF attenuated the pathological change in the heart and liver, improved the liver function, and suppressed oxidative stress in STZ-induced diabetic mice. Simultaneously, SFF significantly altered the gut microbiota in the faeces of diabetic mice, decreased the relative abundances of the diabetes-related intestinal bacteria, which is a potential mechanism for relieving the symptoms of diabetes. Therefore, SFF might be considered as one of the promising complementary and alternative medicines for the management of diabetes mellitus in future.

Multilevel functional clustering analysis.

In this article, we investigate clustering methods for multilevel functional data, which consist of repeated random functions observed for a large number of units (e.g., genes) at multiple subunits (e.g., bacteria types). To describe the within- and between variability induced by the hierarchical structure in the data, we take a multilevel functional principal component analysis (MFPCA) approach. We develop and compare a hard clustering method applied to the scores derived from the MFPCA and a soft clustering method using an MFPCA decomposition. In a simulation study, we assess the estimation accuracy of the clustering membership and the cluster patterns under a series of settings: small versus moderate number of time points; various noise levels; and varying number of subunits per unit. We demonstrate the applicability of the clustering analysis to a real data set consisting of expression profiles from genes activated by immunity system cells. Prevalent response patterns are identified by clustering the expression profiles using our multilevel clustering analysis.