Real-time bioprocess and automated feed control with in-line Raman sensor

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The association of dimethylarginine dimethylaminohydrolase 1 gene polymorphism with type 2 diabetes: a cohort study

<p>Abstract</p> <p>Background</p> <p>Elevated plasma levels of asymmetric dimethylarginine (ADMA) has been reported to be associated with insulin resistance and micro/macrovascular diabetic complications, and may predict cardiovascular events in type 2 diabetic patients. Dimethylarginine dimethylaminohydrolase 1 (DDAH1) is the major enzyme eliminating ADMA in humans, but the effect of genetic variations in <it>DDAH1 </it>on type 2 diabetes and its long-term outcome are unknown.</p> <p>Methods</p> <p>From July 2006 to June 2009, we assessed the association between polymorphisms in <it>DDAH1 </it>and type 2 diabetes in 814 consecutive unrelated subjects, including 309 type 2 diabetic patients and 505 non-diabetic individuals. Six single nucleotide polymorphisms (SNPs) in <it>DDAH1</it>, rs233112, rs1498373, rs1498374, rs587843, rs1403956, and rs1241321 were analyzed. Plasma ADMA levels were determined by high performance liquid chromatography. Insulin sensitivity was assessed by the homeostasis model assessment of insulin resistance (HOMA-IR).</p> <p>Results</p> <p>Among the 6 SNPs, only rs1241321 was significantly associated with a decreased risk of type 2 diabetes (AA <it>vs </it>GG+AG, OR = 0.64, 95% CI 0.47-0.86, p = 0.004). The association remained unchanged after adjustment for plasma ADMA level. The fasting plasma glucose and log HOMA-IR tended to be lower in subjects carrying the homozygous AA genotype of rs1241321 compared with the GG+AG genotypes. Over a median follow-up period of 28.2 months, there were 44 all-cause mortality and 50 major adverse cardiovascular events (MACE, including cardiovascular death, non-fatal myocardial infarction and stroke). Compared with the GG and AG genotypes, the AA genotype of rs1241321 was associated with reduced risk of MACE (HR = 0.31, 95% CI: 0.11-0.90, p = 0.03) and all-cause mortality (HR = 0.18, 95% CI: 0.04-0.80, p = 0.02) only in subgroup with type 2 diabetes. One common haplotype (GGCAGC) was found to be significantly associated with a decreased risk of type 2 diabetes (OR = 0.67, 95% CI = 0.46-0.98, p = 0.04).</p> <p>Conclusions</p> <p>Our results provide the first evidence that SNP rs1241321 in <it>DDAH1 </it>is associated with type 2 diabetes and its long-term outcome.</p

Serum ferritin levels and polycystic ovary syndrome in obese and nonobese women

AbstractObjectiveThe aim of this study is to evaluate serum ferritin levels and polycystic ovary syndrome (PCOS)-related complications in obese and nonobese women.Materials and methodsThis retrospective study included 539 (286 with PCOS and 253 without PCOS).ResultsSerum ferritin correlated with menstrual cycle length, sex hormone-binding globulin, total testosterone, androstenedione, triglyceride, and total cholesterol in both obese and nonobese women. Obese women with high ferritin levels exhibited higher insulin resistance, impaired glucose tolerance, and liver enzymes (glutamic oxaloacetic transaminase, glutamic pyruvic transaminase) than obese women with low ferritin levels. However, among nonobese women, insulin resistance and risk of diabetes were not significantly different between the high and low ferritin groups. Independent of obesity, hypertriglyceridemia was the major metabolic disturbance observed in women with elevated serum ferritin levels.ConclusionElevated serum ferritin levels are associated with increased insulin resistance and risk of diabetes in obese women but not in nonobese women. However, higher serum ferritin levels were correlated with a greater risk of hyperglyceridemia in both obese and nonobese women. Therefore, hypertriglyceridemia in women with PCOS might be associated with iron metabolism

An Exploration of In-Context Learning for Speech Language Model

Ever since the development of GPT-3 in the natural language processing (NLP) field, in-context learning (ICL) has played an important role in utilizing large language models (LLMs). By presenting the LM utterance-label demonstrations at the input, the LM can accomplish few-shot learning without relying on gradient descent or requiring explicit modification of its parameters. This enables the LM to learn and adapt in a black-box manner. Despite the success of ICL in NLP, little work is exploring the possibility of ICL in speech processing. This study proposes the first exploration of ICL with a speech LM without text supervision. We first show that the current speech LM does not have the ICL capability. With the proposed warmup training, the speech LM can, therefore, perform ICL on unseen tasks. In this work, we verify the feasibility of ICL for speech LM on speech classification tasks.Comment: The first two authors contributed equall