Delayed diagnosis of ocular graft-versus-host disease after allogeneic hematopoietic stem cell transplantation.

PURPOSE: To investigate the delayed diagnosis of chronic ocular graft-versus-host disease (coGVHD) after allogeneic hematopoietic stem cell transplantation (alloHCT), and further analyze potential confounding factors. METHODS: This cross-sectional study included 118 patients newly diagnosed as coGVHD after alloHCT at Zhongshan Ophthalmic Center, Sun Yat-sen University. All participants finished the flow path of medical history taking, detailed ophthalmological examination and questionnaire-based survey. coGVHD was diagnosed and graded by International Chronic Ocular GVHD Consensus Group (ICOGCG) criteria. Lag time of diagnosis was defined as interval between noting of ocular symptoms and confirmed diagnosis of coGVHD (TN-D). We further compared the clinical parameters between groups categorized by the median TN-D as medium and long delay groups. RESULTS: The median TN-D was 6.3 [IQR 2.8-14.5] months. Most coGVHD patients underwent delayed diagnosis of coGVHD longer than 3 months (70 %, 83 of 118), with 90 of 118 diagnosed as severe coGVHD (76 %). The long delay group exhibited higher ICOGCG scores (10 [IQR 9-10.5] vs. 9 [IQR 8-10], P = 0.039) and more pronounced ocular signs, including conjunctival injection, meibomian gland loss, fibrotic tarsal conjunctiva, symblepharon, and corneal complications (all P < 0.05). Delayed diagnosis was strikingly correlated with seeking ophthalmic medical care twice or more prior to diagnosis (adjusted OR = 5.42, 95%CI: 1.40-21.06, P = 0.015) and accurate knowledge of ocular discomfort symptoms in coGVHD (adjusted OR = 0.29, 95%CI: 0.08-1.00, P = 0.050). CONCLUSIONS: Delayed diagnosis of coGVHD, associated with disease severity, was common among alloHCT recipients in southern China. Improving patient education and the awareness of ophthalmologists may facilitate early diagnosis of coGVHD.

Production of D -tagatose, bioethanol, and microbial protein from the dairy industry by-product whey powder using an integrated bioprocess.

We designed and constructed a green and sustainable bioprocess to efficiently coproduce D -tagatose, bioethanol, and microbial protein from whey powder. First, a one-pot biosynthesis process involving lactose hydrolysis and D -galactose redox reactions for D -tagatose production was established in vitro via a three-enzyme cascade. Second, a nicotinamide adenine dinucleotide phosphate-dependent galactitol dehydrogenase mutant, D36A/I37R, based on the nicotinamide adenine dinucleotide-dependent polyol dehydrogenase from Paracoccus denitrificans was created through rational design and screening. Moreover, an NADPH recycling module was created in the oxidoreductive pathway, and the tagatose yield increased by 3.35-fold compared with that achieved through the pathway without the cofactor cycle. The reaction process was accelerated using an enzyme assembly with a glycine-serine linker, and the tagatose production rate was 9.28-fold higher than the initial yield. Finally, Saccharomyces cerevisiae was introduced into the reaction solution, and 266.5 g of D -tagatose, 162.6 g of bioethanol, and 215.4 g of dry yeast (including 38% protein) were obtained from 1 kg of whey powder (including 810 g lactose). This study provides a promising sustainable process for functional food (D -tagatose) production. Moreover, this process fully utilized whey powder, demonstrating good atom economy.

Role of Pmk1, Mpk1, or Hog1 in the mitogen-activated protein kinase pathway of Aspergillus cristatus.

Aspergillus cristatus is a probiotic fungus known for its safety and abundant secondary metabolites, making it a promising candidate for various applications. However, limited progress has been made in researching A. cristatus due to challenges in genetic manipulation. The mitogen-activated protein kinase (MAPK) signaling pathway is involved in numerous physiological processes, but its specific role in A. cristatus remains unclear. In this study, we successfully developed an efficient polyethylene glycol (PEG)-mediated protoplast transformation method for A. cristatus, enabling us to investigate the function of Pmk1, Mpk1, and Hog1 in the MAPK signaling pathway. Our findings revealed that Pmk1, Mpk1, and Hog1 are crucial for sexual reproduction, melanin synthesis, and response to external stress in A. cristatus. Notably, the deletion of Pmk1, Mpk1, or Hog1 resulted in the loss of sexual reproduction capability in A. cristatus. Overall, this research on MAPK will contribute to the continued understanding of the reproductive strategy and melanin synthesis mechanism of A. cristatus.

Macular Choroidal Thickness and the Risk of Referable Diabetic Retinopathy in Type 2 Diabetes: A 2-Year Longitudinal Study.

Purpose: The purpose of this study was to evaluate the associations between choroidal thickness (CT) and the 2-year incidence of referable diabetic retinopathy (RDR). Methods: This was a prospective cohort study. Patients with type 2 diabetes in Guangzhou, China, aged 30 to 80 years underwent comprehensive examinations, including standard 7-field fundus photography. Macular CT was measured using a commercial swept-source optical coherence tomography (SS-OCT) device (DRI OCT Triton; Topcon, Tokyo, Japan). The relative risk (RR) with 95% confidence intervals (CIs) was used to quantify the association between CT and new-onset RDR. The prognostic value of CT was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: A total of 1345 patients with diabetes were included in the study, and 120 (8.92%) of them had newly developed RDR at the 2-year follow-up. After adjusting for other factors, the increased RDR risk was associated with greater HbA1c (RR = 1.35, 95% CI = 1.17-1.55, P < 0.001), higher systolic blood pressure (SBP; RR = 1.02, 95% CI = 1.01-1.03, P = 0.005), lower triglyceride (TG) level (RR = 0.81, 95% CI = 0.69-0.96, P = 0.015), presence of diabetic retinopathy (DR; RR = 8.16, 95% CI = 4.47-14.89, P < 0.001), and thinner average CT (RR = 0.903, 95% CI = 0.871-0.935, P < 0.001). The addition of average CT improved NRI (0.464 ± 0.096, P < 0.001) and IDI (0.0321 ± 0.0068, P < 0.001) for risk of RDR, and it also improved the AUC from 0.708 (95% CI = 0.659-0.757) to 0.761 (95% CI = 0.719-0.804). Conclusions: CT thinning measured by SS-OCT is an early imaging biomarker for the development of RDR, suggesting that alterations in CT play an essential role in DR occurrence.

Visual Impairment and Risk of Dementia: The UK Biobank Study.

PURPOSE: To investigate the relationship between visual impairment (VI) and dementia in the UK Biobank Study. DESIGN: Prospective cohort study. METHODS: A total of 117,187 volunteers (aged 40-69 years) deemed free of dementia at baseline were included. Habitual distance visual acuity worse than 0.3 logMAR units in the better-seeing eye was used to define VI. The incident dementia was based on electronically linked hospital inpatient and death records. RESULTS: During a median follow-up of 5.96 years, the presence of VI was significantly associated with incident dementia (hazard ratio: 1.78; 95% confidence interval: 1.18-2.68; P = .006). There was a clear trend between the severity of VI and risk of dementia (P for trend = .002). CONCLUSIONS: We found VI was associated with increased risk of dementia, with a progressively greater risk among those with worse visual acuity. Our findings suggested that VI might be a modifiable risk factor for dementia and highlighted the potential value of VI elimination to delay the manifestation of dementia.

Improving deep convolutional neural networks with mixed maxout units.

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.