MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

Prevalencia de Retinopatia en pacientes del programa integral de diabetes del centro de Salud San Genaro de Villa, Chorillos / Prevalence and factors associated with retinopathy in patients of the integral diabetes program of the san genaro de villa Chorillos health center, Lima-Peru

Objetivos: Determinar la prevalencia y los factores asociados a retinopatía en pacientes del Programa Integral de Diabetes del Centro de Salud San Genaro de Villa Chorrillos. Métodos: Estudio descriptivo, observacional, transversal, prospectivo; con una muestra de 119 adultos y adultos mayores. Se utilizo el muestreo no probabilístico por conveniencia. Las variables estudiadas fueron retinopatía diabética, Tipo de retinopatía diabética, grado de retinopatía diabética, edad, sexo, grado de instrucción, tiempo de enfermedad, tiempo de pertenencia la programa, tipo de tratamiento, antecedente personal de hipertensión arterial, antecedente personal de dislipidemia, presión arterial sistólica (PAS) promedio, presión arterial diastólica (PAD) promedio, Índice de masa corporal (IMC), Hemoglobina Glicosilada (HbA1c), Colesterol total, Colesterol LDL, Colesterol HDL, Triglicéridos, depuración de creatinina, microalbuminuria, eficiencia visual de Snell-Sterlling, patología ocular asociada y presión ocular. Se emplearon métodos estadísticos descriptivos. Resultados: La prevalencia de retinopatía diabética (RD) fue de 15,1% de los cuales el 77,8% es RD No proliferativa y el 22,2% RD proliferativa. En relación a los grados en la RD No Proliferativa el 64,3% es leve y el 35,7% moderada; y en la RD Proliferativa el 25% es temprana, el 25% de alto riesgo y el 50% severa. El valor bioquímico que mostro una considerable diferencia fue la microalbuminuria alcanzando un valor de 356,9 mg/dl/24hrs. Conclusiones: La prevalencia de retinopatía es de 15,1% de los cuales el 77,8%% es retinopatía no proliferativa y de 22,2% retinopatía proliferativa y los factores asociados fueron la presión arterial sistólica (p<0,001) y la microalbuminuria (p<0,001).

Objectives: To determine the prevalence and factors associated with retinopathy in patients of the Comprehensive Diabetes Program of the San Genaro Health Center in Villa Chorrillos. Methods: Descriptive, observational, cross-sectional, prospective study; with a sample of 119 adults and older adults. Non-probabilistic convenience sampling was used. The variables studied were diabetic retinopathy, type of diabetic retinopathy, degree of diabetic retinopathy, age, sex, educational level, time of illness, time belonging to the program, type of treatment, personal history of arterial hypertension, personal history of dyslipidemia, mean systolic blood pressure (SBP), mean diastolic blood pressure (DBP), Body Mass Index (BMI), Glycosylated Hemoglobin (HbA1c), Total Cholesterol, LDL Cholesterol, HDL Cholesterol, Triglycerides, creatinine clearance, microalbuminuria, visual efficiency of Snell-Sterling, associated ocular pathology and ocular pressure. Descriptive statistical methods were used. Results: The prevalence of diabetic retinopathy (DR) was 15.1%, of which 77.8% is nonproliferative RD and 22.2% proliferative RD. In relation to the degrees in Non-Proliferative DR, 64.3% is mild and 35.7% moderate; and in Proliferative DR, 25% is early, 25% high risk and 50% severe. The biochemical value that showed a considerable difference was microalbuminuria, reaching a value of 356.9 mg/dl/24hrs. Conclusions: The prevalence of retinopathy is 15.1%, of which 77.8% is non-proliferative retinopathy and 22.2% proliferative retinopathy and the associated factors were systolic blood pressure (p<0.001) and microalbuminuria(p<0.001).

Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples.

OBJECTIVE: Survival probability predictions in critically ill patients are mainly used to measure the efficacy of intensive care unit (ICU) treatment. The available models are functions induced from data on thousands of patients. Eventually, some of the variables used for these purposes are not part of the clinical routine, and may not be registered in some patients. In this paper, we propose a new method to build scoring functions able to make reliable predictions, though functions whose induction only requires records from a small set of patients described by a few variables. METHODS: We present a learning method based on the use of support vector machines (SVM), and a detailed study of its prediction performance, in different contexts, of groups of variables defined according to the source of information: monitoring devices, laboratory findings, and demographic and diagnostic features. RESULTS: We employed a data set collected in general ICUs at 10 units of hospitals in Spain, 6 of which include coronary patients, while the other 4 do not treat coronary diseases. The total number of patients considered in our study was 2501, 19.83% of whom did not survive. Using these data, we report a comparison between the SVM method proposed here with other approaches based on logistic regression (LR), including a second-level recalibration of release III of the acute physiology and chronic health evaluation (APACHE, a scoring system commonly used in ICUs) induced from the available data. The SVM method significantly outperforms them all from a statistical point of view. Comparison with the commercial version of APACHE III shows that the SVM scores are slightly better when working with data sets of more than 500 patients. CONCLUSIONS: From a practical point of view, the implications of the research reported here may be helpful to address the construction of cheap and reliable prediction systems in accordance with the peculiarities of ICUs and kinds of patients.

Prevention of glucocorticoid-induced apoptosis in osteocytes and osteoblasts by calbindin-D28k.

UNLABELLED: This study show for the first time that calbindin-D28k can prevent glucocorticoid-induced bone cell death. The anti-apoptotic effect of calbindin-D28k involves inhibition of glucocorticoid induced caspase 3 activation as well as ERK activation. INTRODUCTION: Recent studies have indicated that deleterious effects of glucocorticoids on bone involve increased apoptosis of osteocytes and osteoblasts. Because the calcium-binding protein calbindin-D28k has been reported to be anti-apoptotic in different cell types and in response to a variety of insults, we investigated whether calbindin-D28k could protect against glucocorticoid-induced cell death in bone cells. MATERIALS AND METHODS: Apoptosis was induced by addition of dexamethasone (dex; 10-6 M) for 6 h to MLO-Y4 osteocytic cells as well as to osteoblastic cells. Apoptosis percentage was determined by examining the nuclear morphology of transfected cells. Caspase 3 activity was evaluated in bone cells and in vitro. SELDI mass spectrometry (MS) was used to examine calbindin-D28k-caspase 3 interaction. Phosphorylation of calbindin-D28k was examined by 32P incorporation as well as by MALDI-TOF MS. ERK activation was determined by Western blot. RESULTS: The pro-apoptotic effect of dex in MLO-Y4 cells was completely inhibited in cells transfected with calbindin-D28k cDNA (5.6% apoptosis in calbindin-D28k transfected cells compared with 16.2% apoptosis in vector-transfected cells, p < 0.05). Similar results were observed in osteoblastic cells. We found that dex-induced apoptosis in bone cells was accompanied by an increase in caspase 3 activity. This increase in caspase 3 activity was inhibited in the presence of calbindin-D28k. In vitro assays indicated a concentration-dependent inhibition of caspase 3 by calbindin-D28k (Ki = 0.22 microM). Calbindin-D28k was found to inhibit caspase 3 specifically because the activity of other caspases was unaffected by calbindin-D28k. The anti-apoptotic effect of calbindin-D28k in response to dex was also reproducibly associated with an increase in the phosphorylation of ERK 1 and 2, suggesting that calbindin-D28k affects more than one signal in the glucocorticoid-induced apoptotic pathway. CONCLUSION: Calbindin-D28k, a natural non-oncogenic protein, could be an important target in the therapeutic intervention of glucocorticoid-induced osteoporosis.