High order sliding mode control for restoration of a population of predators in a Lotka-Volterra system.

Human-induced extinction and rapid ecological changes require the development of techniques that can help avoid extinction of endangered species. The most used strategy to avoid extinction is reintroduction of the endangered species, but only 31% of these attempts are successful and they require up to 15 years for their results to be evaluated. In this research, we propose a novel strategy that improves the chances of survival of endangered predators, like lynx, by controlling only the availability of prey. To simulate the prey-predator relationship we used a Lotka-Volterra model to analyze the effects of varying prey availability on the size of the predator population. We calculate the number of prey necessary to support the predator population using a high-order sliding mode control (HOSMC) that maintains the predator population at the desired level. In the wild, nature introduces significant and complex uncertainties that affect species' survival. This complexity suggests that HOSMC is a good choice of controller because it is robust to variability and does not require prior knowledge of system parameters. These parameters can also be time varying. The output measurement required by the HOSMC is the number of predators. It can be obtained using continuous monitoring of environmental DNA that measures the number of lynxes and prey in a specific geographic area. The controller efficiency in the presence of these parametric uncertainties was demonstrated with a numerical simulation, where random perturbations were forced in all four model parameters at each simulation step, and the controller provides the specific prey input that will maintain the predator population. The simulation demonstrates how HOSMC can increase and maintain an endangered population (lynx) in just 21-26 months by regulating the food supply (hares), with an acceptable maximal steady-state error of 3%.

Significant and safe reduction of propofol sedation dose for geriatric population undergoing pacemaker implantation: randomized clinical trial.

OBJECTIVE: A previous multidisciplinary pilot study based on computer simulations for the geriatric population showed that a dose of 0.5 mg/kg/h of propofol could sedate patients older than 65 for pacemaker implantation. The present study validates that the pacemaker implantation can be done in the elderly using 0.5-1 mg/kg/h of propofol with hemodynamic stability. METHODS: 66 patients from 65 to 88 years old scheduled for pacemaker implantation were randomly assigned one of three doses of propofol. The first group received 2 mg/kg/h of propofol (P2) that is within normal range of the sedation dose. The second group received 1 mg/kg/h (P1) dose and the third group received the dose of 0.5 mg/kg/h (P0.5) according to the simulation-predicted dose for geriatric populations. RESULTS: All patients kept MAP between 76 and 85 mmHg, with no hypotension episodes in any of the groups; therefore, they were all hemodynamically stable during the procedure. BIS was between 80 and 65 during the pacemaker implantation for the three groups, BIS of group P2 was significantly lower than the other groups. BIS in groups P1 and P0.5 was within the appropriated range for moderate sedation. Brice was positive for auditory recalls only when there was arousing noise in the operating room. CONCLUSIONS: Moderate sedation, adequate for pacemaker implantation, can be achieved infusing 0.5-1 mg/kg/h of propofol in elderly patients when the patient has proper analgesia management at the device implantation site. The second important condition is to avoid unnecessary and alerting auditory and mechanical stimuli in the operating room, so that the patient will remain calm.

Diabetes Drug Discovery: hIAPP1-37 Polymorphic Amyloid Structures as Novel Therapeutic Targets.

Human islet amyloid peptide (hIAPP1-37) aggregation is an early step in Diabetes Mellitus. We aimed to evaluate a family of pharmaco-chaperones to act as modulators that provide dynamic interventions and the multi-target capacity (native state, cytotoxic oligomers, protofilaments and fibrils of hIAPP1-37) required to meet the treatment challenges of diabetes. We used a cross-functional approach that combines in silico and in vitro biochemical and biophysical methods to study the hIAPP1-37 aggregation-oligomerization process as to reveal novel potential anti-diabetic drugs. The family of pharmaco-chaperones are modulators of the oligomerization and fibre formation of hIAPP1-37. When they interact with the amino acid in the amyloid-like steric zipper zone, they inhibit and/or delay the aggregation-oligomerization pathway by binding and stabilizing several amyloid structures of hIAPP1-37. Moreover, they can protect cerebellar granule cells (CGC) from the cytotoxicity produced by the hIAPP1-37 oligomers. The modulation of proteostasis by the family of pharmaco-chaperones A-F is a promising potential approach to limit the onset and progression of diabetes and its comorbidities.

Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population.

IMPORTANCE: Latino populations have one of the highest prevalences of type 2 diabetes worldwide. OBJECTIVES: To investigate the association between rare protein-coding genetic variants and prevalence of type 2 diabetes in a large Latino population and to explore potential molecular and physiological mechanisms for the observed relationships. DESIGN, SETTING, AND PARTICIPANTS: Whole-exome sequencing was performed on DNA samples from 3756 Mexican and US Latino individuals (1794 with type 2 diabetes and 1962 without diabetes) recruited from 1993 to 2013. One variant was further tested for allele frequency and association with type 2 diabetes in large multiethnic data sets of 14,276 participants and characterized in experimental assays. MAIN OUTCOME AND MEASURES: Prevalence of type 2 diabetes. Secondary outcomes included age of onset, body mass index, and effect on protein function. RESULTS: A single rare missense variant (c.1522G>A [p.E508K]) was associated with type 2 diabetes prevalence (odds ratio [OR], 5.48; 95% CI, 2.83-10.61; P = 4.4 × 10(-7)) in hepatocyte nuclear factor 1-α (HNF1A), the gene responsible for maturity onset diabetes of the young type 3 (MODY3). This variant was observed in 0.36% of participants without type 2 diabetes and 2.1% of participants with it. In multiethnic replication data sets, the p.E508K variant was seen only in Latino patients (n = 1443 with type 2 diabetes and 1673 without it) and was associated with type 2 diabetes (OR, 4.16; 95% CI, 1.75-9.92; P = .0013). In experimental assays, HNF-1A protein encoding the p.E508K mutant demonstrated reduced transactivation activity of its target promoter compared with a wild-type protein. In our data, carriers and noncarriers of the p.E508K mutation with type 2 diabetes had no significant differences in compared clinical characteristics, including age at onset. The mean (SD) age for carriers was 45.3 years (11.2) vs 47.5 years (11.5) for noncarriers (P = .49) and the mean (SD) BMI for carriers was 28.2 (5.5) vs 29.3 (5.3) for noncarriers (P = .19). CONCLUSIONS AND RELEVANCE: Using whole-exome sequencing, we identified a single low-frequency variant in the MODY3-causing gene HNF1A that is associated with type 2 diabetes in Latino populations and may affect protein function. This finding may have implications for screening and therapeutic modification in this population, but additional studies are required.

Risk factors for diabetes, but not for cardiovascular disease, are associated with family history of Type 2 diabetes in subjects from central Mexico.

BACKGROUND: Independent of obesity, family history of type 2 diabetes mellitus (FHT2DM) is another important risk factor for developing diabetes. AIM: To establish the association among FHT2DM, risk factors for diabetes and cardiovascular disease in subjects from central Mexico. SUBJECTS AND METHODS: Clinical and biochemical studies were performed in 383 first-degree relatives of patients with type 2 diabetes and 270 subjects unrelated to patients with type 2 diabetes-all subjects were from the city of Puebla in central Mexico. Logistic regressions were used to assess the association between FHT2DM and metabolic parameters. Cardiovascular risk was classified by dyslipidemia and the Framingham Risk Score (FRS). RESULTS: FHT2DM was associated with risk factors for diabetes, such as increased fasting insulin levels (OR = 1.731, 95% CI = 1.041-2.877), decreased insulin sensitivity (OR = 1.951, 95% CI = 1.236-3.080) and pre-diabetes (OR = 1.63, 95% CI = 1.14-2.33). FHT2DH was not associated with risk factors for cardiovascular disease, such as dyslipidemia (OR = 1.12, 95% CI = 0.70-1.79) and FRS (OR = 0.74, 95% CI = 0.40-1.36) when adjusted for gender, age, smoking and obesity. CONCLUSION: Diabetic risk factors, but not cardiovascular disease risk factors, are associated with a positive family history of diabetes in subjects from central Mexico, independent of the presence of obesity.

Minimally-Invasive and Efficient Method to Accurately Fit the Bergman Minimal Model to Diabetes Type 2.

Introduction: Diabetes mellitus is a global burden that is expected to grow 25 % by 2030. This will increase the need for prevention, diagnosis and treatment of diabetes. Animal and individualized in silico models will allow understanding and compensation for inter and intra-individual differences in treatment and management strategies for diabetic patients. The method presented here can advance the concept of personalized medicine. Methods: Twenty experiments were performed with Sprague-Dawley rats with streptozotocin induced experimental diabetes in which the insulin-glucose response curve was recorded over 60-100 min using only an insulin pump and a percutaneous glucose sensor. The information was used to fit the five-parameter Bergman Minimal Model to the experimental results using a genetic algorithm with a root-mean-squared optimization rule. Results: The Bergman Minimal Model parameters were estimated with high accuracy, low prediction bias, and low average root-mean-squared error of 15.27 mg/dl glucose. Conclusions: This study demonstrates a simple method to accurately parameterize the Bergman Minimal Model. We used Sprague-Dawley rats since their physiology is close to that of humans. The parameters can be used to objectively characterize the physiological severity of diabetes. In this way, planned treatments can compensate for natural variations of conditions both inter and intra patients. Changes in parameters indicate the patient's diabetic condition using values of glucose effectiveness ( S G = p 1 ) and insulin sensitivity ( S I = p 3 / p 2 ). Quantifying the diabetic patient's condition is consistent with the trend toward personalized medicine. Parameter values can also be used to explain atypical research results of other studies and increase understanding of diabetes.

Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.

Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence. The implementation of machine learning/AI in protein science gives rise to a world of knowledge adventures in the workhorse of the cell and proteome homeostasis, which are essential for making life possible. This opens up epistemic horizons thanks to a coupling of human tacit-explicit knowledge with machine learning power, the benefits of which are already tangible, such as important advances in protein structure prediction. Moreover, the driving force behind the protein processes of self-organization, adjustment, and fitness requires a space corresponding to gigabytes of life data in its order of magnitude. There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and proteostasis networks that are currently unexplored or unrevealed. In this systematic review and biochemical meta-analysis, we aim to contribute to bridging the gap between what we call binomial artificial intelligence (AI) and protein science (PS), a growing research enterprise with exciting and promising biotechnological and biomedical applications. We undertake our task by exploring "the state of the art" in AI and machine learning (ML) applications to protein science in the scientific literature to address some critical research questions in this domain, including What kind of tasks are already explored by ML approaches to protein sciences? What are the most common ML algorithms and databases used? What is the situational diagnostic of the AI-PS inter-field? What do ML processing steps have in common? We also formulate novel questions such as Is it possible to discover what the rules of protein evolution are with the binomial AI-PS? How do protein folding pathways evolve? What are the rules that dictate the folds? What are the minimal nuclear protein structures? How do protein aggregates form and why do they exhibit different toxicities? What are the structural properties of amyloid proteins? How can we design an effective proteostasis network to deal with misfolded proteins? We are a cross-functional group of scientists from several academic disciplines, and we have conducted the systematic review using a variant of the PICO and PRISMA approaches. The search was carried out in four databases (PubMed, Bireme, OVID, and EBSCO Web of Science), resulting in 144 research articles. After three rounds of quality screening, 93 articles were finally selected for further analysis. A summary of our findings is as follows: regarding AI applications, there are mainly four types: 1) genomics, 2) protein structure and function, 3) protein design and evolution, and 4) drug design. In terms of the ML algorithms and databases used, supervised learning was the most common approach (85%). As for the databases used for the ML models, PDB and UniprotKB/Swissprot were the most common ones (21 and 8%, respectively). Moreover, we identified that approximately 63% of the articles organized their results into three steps, which we labeled pre-process, process, and post-process. A few studies combined data from several databases or created their own databases after the pre-process. Our main finding is that, as of today, there are no research road maps serving as guides to address gaps in our knowledge of the AI-PS binomial. All research efforts to collect, integrate multidimensional data features, and then analyze and validate them are, so far, uncoordinated and scattered throughout the scientific literature without a clear epistemic goal or connection between the studies. Therefore, our main contribution to the scientific literature is to offer a road map to help solve problems in drug design, protein structures, design, and function prediction while also presenting the "state of the art" on research in the AI-PS binomial until February 2021. Thus, we pave the way toward future advances in the synthetic redesign of novel proteins and protein networks and artificial metabolic pathways, learning lessons from nature for the welfare of humankind. Many of the novel proteins and metabolic pathways are currently non-existent in nature, nor are they used in the chemical industry or biomedical field.

What can we learn from ß-cell failure biomarker application in diabetes in childhood? A systematic review.

BACKGROUND: The prevalence of diabetes as a catastrophic disease in childhood is growing in the world. The search for novel biomarkers of ß-cell failure has been an elusive task because it requires several clinical and biochemical measurements in order to integrate the risk of metabolic syndrome. AIM: To determine which biomarkers are currently used to identify ß-cell failure among children and adolescents with high risk factors for diabetes mellitus. METHODS: This systematic review was carried out using a modified version of the PICO protocol (Participants/Intervention/Comparison/Outcome). Once our research question was established, terms were individually researched on three different databases (PubMed, BIREME and Web of Science). The total articles obtained underwent a selection process from which the 78 most relevant articles were retrieved to undergo further analysis. They were assessed individually according to quality criteria. RESULTS: First, we made the classification of the ß-cell-failure biomarkers by the target tissue and the evolution of the disease, separating the biomarkers in relation to the types of diabetes. Second, we demonstrated that most biomarkers currently used as early signs of ß-cell failure are those that concern local or systemic inflammation processes and oxidative stress as well as those related to endothelial dysfunction processes. Third, we explored the novelties of diabetes as a protein conformational disease and the novel biomarker called real human islet amyloid polypeptide amyloid oligomers. Finally, we ended with a discussion about the best practice of validation and individual control of using different types of biomarkers in type 1 and type 2 diabetes in order to assess the role they play in the progress of diabetes in childhood. CONCLUSION: This review makes widely evident that most biomarkers currently used as early signs of ß-cell failure are those that concern local or systemic inflammation processes and oxidative stress as well as those related to endothelial dysfunction processes. Landing in the clinical practice we propose that real human islet amyloid polypeptide amyloid oligomers is good for identifying patients with ß-cell damage and potentially could substitute many biomarkers.

Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables.

BACKGROUND AND OBJECTIVES: Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. Accurate detection is key to successful interventions, but diagnosis of sepsis is complicated because the initial signs and symptoms are not specific. Biomarkers that have been proposed have low specificity and sensitivity, are expensive, and not available in every hospital. In this study, we propose the use of artificial intelligence in the form of a neural network to diagnose sepsis using only common laboratory tests and vital signs that are routine and widely available. METHODS: A retrospective, cross sectional cohort of 113 patients from an intensive care unit, each with 48 routinely evaluated vital signs and biochemical parameters was used to train, validate and test a neural network with 48 inputs, 10 neurons in a single hidden layer and one output. The sensitivity and specificity of the neural network as a point sampled diagnostic test was calculated. RESULTS: All but one case were correctly diagnosed by the neural network, with 91% sensitivity and 100% specificity in the validation data set, and 100% sensitivity and specificity in the test data set. CONCLUSIONS: The designed neural network system can identify patients with sepsis, with minimal resources using standard laboratory tests widely available in most health care facilities. This should reduce the burden on the medical staff of a difficult diagnosis and should improve outcomes for patients with sepsis.

The L125F MATE1 variant enriched in populations of Amerindian origin is associated with increased plasma levels of metformin and lactate.

Genetic factors that affect variability in metformin response have been poorly studied in the Latin American population, despite its being the initial drug therapy for type 2 diabetes, one of the most prevalent diseases in that region. Metformin pharmacokinetics is carried out by members of the membrane transporters superfamily (SLCs), being the multidrug and toxin extrusion protein 1 (MATE1), one of the most studied. Some genetic variants in MATE1 have been associated with reduced in vitro metformin transport. They include rs77474263 p.[L125F], a variant present at a frequency of 13.8% in Latin Americans, but rare worldwide (less than 1%). Using exome sequence data and TaqMan genotyping, we revealed that the Mexican population has the highest frequency of this variant: 16% in Mestizos and 27% in Amerindians, suggesting a possible Amerindian origin. To elucidate the metformin pharmacogenetics, a children cohort was genotyped, allowing us to describe, for the first time, a MATE1 rs77474263 TT homozygous individual. An additive effect of the L125F variant was observed on blood metformin accumulation, revealing the highest metformin and lactate serum levels in the TT homozygote, and intermediate metformin values in the heterozygotes. Moreover, a molecular dynamics analysis suggested that the genetic variant effect on metformin efflux could be due to a decreased protein permeability. We conclude that pharmacogenetics could be useful in enhancing metformin pharmacovigilance in populations having a high frequency of the risk genotype, especially considering that these populations also have a higher susceptibility to the diseases for which metformin is the first-choice drug.

Protein-conformational diseases in childhood: Naturally-occurring hIAPP amyloid-oligomers and early ß-cell damage in obesity and diabetes.

BACKGROUND AND AIMS: This is the first time that obesity and diabetes mellitus (DM) as protein conformational diseases (PCD) are reported in children and they are typically diagnosed too late, when ß-cell damage is evident. Here we wanted to investigate the level of naturally-ocurring or real (not synthetic) oligomeric aggregates of the human islet amyloid polypeptide (hIAPP) that we called RIAO in sera of pediatric patients with obesity and diabetes. We aimed to reduce the gap between basic biomedical research, clinical practice-health decision making and to explore whether RIAO work as a potential biomarker of early ß-cell damage. MATERIALS AND METHODS: We performed a multicentric collaborative, cross-sectional, analytical, ambispective and blinded study; the RIAO from pretreated samples (PTS) of sera of 146 pediatric patients with obesity or DM and 16 healthy children, were isolated, measured by sound indirect ELISA with novel anti-hIAPP cytotoxic oligomers polyclonal antibody (MEX1). We carried out morphological and functional studied and cluster-clinical data driven analysis. RESULTS: We demonstrated by western blot, Transmission Electron Microscopy and cell viability experiments that RIAO circulate in the blood and can be measured by ELISA; are elevated in serum of childhood obesity and diabetes; are neurotoxics and works as biomarkers of early ß-cell failure. We explored the range of evidence-based medicine clusters that included the RIAO level, which allowed us to classify and stratify the obesity patients with high cardiometabolic risk. CONCLUSIONS: RIAO level increases as the number of complications rises; RIAOs > 3.35 µg/ml is a predictor of changes in the current indicators of ß-cell damage. We proposed a novel physio-pathological pathway and shows that PCD affect not only elderly patients but also children. Here we reduced the gap between basic biomedical research, clinical practice and health decision making.

Rapid automatic identification of parameters of the Bergman Minimal Model in Sprague-Dawley rats with experimental diabetes for adaptive insulin delivery.

Glucose-Insulin regulation models can be used to individualize insulin therapy. However, the experimental techniques currently used to identify the appropriate parameter sets of an individual are expensive, time consuming, and very unpleasant for the patient. Since there is a wide range of intrapersonal parameter variability, the identified parameters in a laboratory setting (at rest) are not optimal for dynamic conditions of daily activities. In this study we propose a methodology to identify three parameters of Bergman's Minimal Model in streptozotocin-induced diabetic rats from the experimental data of the glucose response to exogenous insulin doses, based on a genetic algorithm (GA). The algorithm requires glucose measurements from a continuous subcutaneous sensor once every 5 min and the amount of injected insulin. The model parameters of 20 in vivo experiments (from 19 rats) were identified with high accuracy and the average root-mean squared (RMS) error between predicted and measured glucose concentration was 17.6 mg/dl. Since the algorithm requires a relatively short (60-120 min) observation time it can be used for real-time parameter identification to optimize insulin infusion systems. Model parameter changes due to experimental settings like drug testing or in natural lifestyle changes should be calculable, on-the-fly, using data from only the glucose sensor and the amount of insulin delivered.

Unpacking the aggregation-oligomerization-fibrillization process of naturally-occurring hIAPP amyloid oligomers isolated directly from sera of children with obesity or diabetes mellitus.

The formation of amyloid oligomers and fibrils of the human islet amyloid polypeptide (hIAPP) has been linked with ß- cell failure and death which causes the onset, progression, and comorbidities of diabetes. We begin to unpack the aggregation-oligomerization-fibrillization process of these oligomers taken from sera of pediatric patients. The naturally occurring or real hIAPP (not synthetic) amyloid oligomers (RIAO) were successfully isolated, we demonstrated the presence of homo (dodecamers, hexamers, and trimers) and hetero-RIAO, as well as several biophysical characterizations which allow us to learn from the real phenomenon taking place. We found that the aggregation/oligomerization process is active in the sera and showed that it happens very fast. The RIAO can form fibers and react with anti-hIAPP and anti-amyloid oligomers antibodies. Our results opens the epistemic horizon and reveal real differences between the four groups (Controls vs obesity, T1DM or T2DM) accelerating the process of understanding and discovering novel and more efficient prevention, diagnostic, transmission and therapeutic pathways.

Cardiovascular risk factors and acculturation in Yaquis and Tepehuanos Indians from Mexico.

BACKGROUND: Cardiovascular (CV) risk factors are influenced by behavioral, cultural, and social factors, suggesting that acculturation plays a significant role in the emergency and growth of chronic disease. The objective of this study was to determine the relation between CV risk factors and the main components of acculturation, in Yaquis and Tepehuanos Indians from Mexico. METHODS: This was a cross-sectional population-based study in Yaquis and Tepehuanos communities from the Yaqui Valley in Sonora and the Sierra Madre Occidental Mountains in Durango, in northwest Mexico. Acculturation status is different in both ethnic groups, with Tepehuanos living in small and remote communities retaining their traditional lifestyle and Yaquis living in well-communicated communities that have assumed Westernized lifestyles. RESULTS: A total of 278 indigenous (120 Tepehuanos and 158 Yaquis) were randomly enrolled. Prevalence of obesity (48.1 and 6.7%, p <0.001), diabetes (18.3 and 0.83%, p <0.001), hypertriglyceridemia (43.0 and 15.0%, p <0.001), alcohol consumption (46.8 and 26.6%, p >0.001), and smoking (29.7 and 15.0%, p = 0.006) were significantly higher in Yaquis Indians. High blood pressure (6.3 and 3.3%, p = 0.40) and low HDL-cholesterol (42.4 and 34.2%, p = 0.22) were similar between Yaquis and Tepehuanos. Multivariate regression analysis adjusted by sex and age showed a significant association between calorie intake from saturated fat, but not other nutrients of customary diet, with hyperglycemia (OR 7.4, 95% CI 2.6-20.1), hypertriglyceridemia (OR 3.1, 95% CI 1.5-6.3), and obesity (OR 3.4, 95% CI 1.6-10.1). CONCLUSIONS: Among the components of acculturation, intake of saturated fat is the most strongly associated with the development of CV risk factors.

Chronic hypoglycemic effect of Malmea depressa root on n5-streptozotocin diabetic rats.

The root of the "Elemuy" tree (Malmea depressa) is highly used in south east Mexico by the Mayan communities to treat type 2 diabetes. The long-term hypoglycemic effect (HbA1c) and the stimulation of insulin secretion resulting from the treatment with butanolic extract was studied using (n5-stz)-induced diabetic rats. We show that after 30 days of daily administration of 10 mg/kg of the butanolic extract, the glucose levels as well as the (HbA1c) levels were lower compared to the control group. This effect was also observed after 45 days of treatment, leading to the conclusion that the effects of chronic butanolic extract treatment are comparable to treatment with the standard drug Bieuglucon. In an acute experiment, we found that a single administration of the extract at a dose of 50 mg/kg stimulates insulin release, which is similar to the result seen with Tolbutamide administration. The new compound, 3-(3-hydroxy-2,4,5-trimethoxyphenyl) propane-1,2 diol, was isolated from the active fraction of the butanolic extract. The results presented here support the utility of the traditional use of the root and suggest that the active fraction of the plant extract could be developed as a phytomedicine.

[Cardiovascular risk factors associated to abdominal obesity in apparently healthy subjects]. / Factores de riesgo cardiovascular asociados a obesidad abdominal en adultos aparentemente sanos.

OBJECTIVE: to determine the relationship between the abdominal obesity and cardiovascular risk factors in apparently healthy subjects from Mexico City. METHODS: a total of 186 apparently healthy men and nonpregnant women from Mexico City, were enrolled in a cross-sectional study. A detailed medical history and physical examination were performed. Abdominal obesity was defined by waist circumference > or = 80 cm for women and > or = 90 cm for men. RESULTS: a total of 125 women (67.2 %) and 61 men (32.8 %) were enrolled. Of them, 151 (81.2 %) had insulin resistance and 130 (69.9 %) abdominal obesity. Among obese subjects 96 (46.2 %) showed metabolic syndrome. There were a high prevalence of hypertriglyceridemia (31 %) and low serum levels of HDL-cholesterol (58 %). CONCLUSIONS: the used cut point for abdominal obesity, despite identifying a high proportion of subjects with cardiovascular risk, did not recognize a high proportion of subjects with disorders in their lipid profile.

Gene variants in AKT1, GCKR and SOCS3 are differentially associated with metabolic traits in Mexican Amerindians and Mestizos.

Amerindian ancestry appears to be a risk factor for metabolic diseases (MetD), making Mexicans an ideal population to better understand the genetic architecture of metabolic health. In this study, we determine the association of genetic variants previously reported with metabolic entities, in two Mexican populations, including the largest sample of Amerindians reported to date. We investigated the association of eigth single-nucleotide polymorphisms (SNPs) in AKT1, GCKR, and SOCS3 genes with different metabolic traits in 1923 Mexican Amerindians (MAs) belonging to 57 ethnic groups, and 855 Mestizos (MEZs). The allele frequency of 7/8 SNPs showed significant differences between MAs and MEZs. Interestingly, some alleles were monomorphic in particular ethnic groups, and highly frequent in other ones. With the exception of GCKR rs1260326T, as expected, all SNP frequencies in the MEZ population had intermediate values between its two main ancestral populations (MAs and Iberian populations in Spain [IBS]). We detected ethnic differences in linkage disequilibrium patterns and haplotype structure between MAs and MEZs, possibly due to the high genetic heterogeneity in these populations. Remarkably, AKT1 was associated with hypertension in MEZs, but not in MAs. GCKR was associated with protection against type 2 diabetes (T2D) in MAs, and with hypertriglyceridemia and protection against low HDL Cholesterol (HDL-C) levels in MEZs. The CAT haplotype in SOCS3 was associated with metabolic syndrome (MetS) in MEZs, and correlated with protection against high blood pressure (HBP) and risk for high waist circumference and T2D in MAs. Our results show differential genetic associations with metabolic traits between MAs and MEZs, possibly due to the differences in genetic structure between these Mexican populations.

Hypoglycemic effect of Tournefortia hirsutissima L., on n-streptozotocin diabetic rats.

The hypoglycemic effect of aqueous and butanolic extracts from Tournefortia hirsutissima (Boraginaceae) was determined on neonatal induced streptozotocin diabetic rats (n-STZ). Oral administration of water extracts at doses of 20 and 80mg/kg, and butanolic extracts (8 and 80mg/kg) significantly lowered the plasma glucose levels in diabetic rats within 3h. Glibenclamide was used as reference and showed similar hypoglycemic effect. Our results support the traditional use of the plant as a hypoglycemic agent; we observe a dose-dependent action of the extracts. HPLC analysis confirmed that the aqueous and butanolic extracts had the same chemical composition.

Tromboelastografía para cirugía general / Thromboelastography for general surgery

Resumen: Las pruebas convencionales de coagulación evalúan el tiempo en que los factores de coagulación se activan en el plasma sanguíneo, el cual carece de componentes celulares, como enzimas y plaquetas que intervienen en el desarrollo de la hemostasia. La tromboelastografía (TEG) realiza un análisis in vitro de la relación entre plaquetas, enzimas, fibrinógeno y otros elementos de la coagulación de manera integral; por lo que se utiliza con mayor frecuencia en cirugías cardíacas, trasplantes y cirugías de malformaciones arteriovenosas, donde la pérdida sanguínea esperada es mayor a 40% del volumen sanguíneo circulante. La inclusión de la TEG en la valoración preanestésica permite evaluar la actividad plaquetaria en los pacientes que utilizan antiagregantes, como clopidogrel o ácido acetilsalicílico, a través del mapeo plaquetario (Platelet Mapping®). Cuando se presenta hemorragia en el período transanestésico, la TEG permite identificar de manera específica el tipo de tratamiento necesario para mejorar la coagulación o la transfusión de elementos formes de la sangre. La TEG permite realizar protocolos más sofisticados de terapia transfusional, lo que implica una disminución de las complicaciones asociadas con la politransfusión y, por ende, la disminución de costos, por lo que estos beneficios justifican el uso rutinario de la TEG para cualquier cirugía general.

Abstract: Conventional coagulation tests evaluate the time in which the coagulation factors are activated in the blood plasma, which lacks cellular components such as enzymes and platelets that are involved in the development of hemostasis. Thromboelastography (TEG) performs an in vitro analysis of the relationship between platelets, enzymes, fibrinogen, and other coagulation elements in an integral way; reason why it is used more frequently in cardiac surgeries, transplants, and surgeries of arteriovenous malformations, where the expected blood loss is greater than 40% of the circulating blood volume. The inclusion of TEG in the pre-anesthetic evaluation allows evaluating platelet activity in patients who use antiplatelets therapy, such as clopidogrel or acetylsalicylic acid, through platelet mapping (Platelet Mapping®). When bleeding occurs in the trans-anesthetic period, TEG specifically identifies the type of treatment necessary to improve coagulation or transfusion of formed blood elements. The TEG allows more sophisticated transfusion therapy protocols to be carried out, which implies a decrease in complications associated with polytransfusion and a reduction in costs, so these benefits justify the routine use of TEG for any general surgery.

Novel insight into streptozotocin-induced diabetic rats from the protein misfolding perspective.

Protein folding is a process of self-assembly defined by the sequence of the amino acids of the protein involved. Additionally, proteins tend to unfold, misfold and aggregate due to both intrinsic and extrinsic causes. Human islet amyloid polypeptide (hIAPP) aggregation is an early step in diabetes mellitus. However, the aggregation of rat IAPP (rIAPP) remains an open question. Adult female Sprague-Dawley rats weighing 150-250 g were divided into two groups. The experimental group (streptozotocin [STZ]) (n = 21) received an intraperitoneal injection of a single dose of 40 mg/kg STZ. We used the mouse anti-IAPP antibody and the anti-amyloid oligomer antibody to study the temporal course of rIAPP oligomerization during STZ-induced diabetes using a wide array of methods, strategies and ideas derived from biochemistry, cell biology, and proteomic medicine. Here, we demonstrated the tendency of rIAPP to aggregate and trigger cooperative processes of self-association or hetero-assembly that lead to the formation of amyloid oligomers (trimers and hexamers). Our results are the first to demonstrate the role of rIAPP amyloid oligomers in the development of STZ-induced diabetes in rats. The IAPP amyloid oligomers are biomarkers of the onset and progression of diabetes and could play a role as therapeutic targets.