High Glucose Levels Research Articles

Edukasi Pencegahan Stroke pada Masyarakat Desa Cigugur Girang

Stroke patients could get sequelae, and it will decrease the patient’s quality of life. Stroke could be caused by comorbid diseases such as hypertension, diabetes mellitus, and dyslipidemia. Thus, stroke could be prevented by reducing the comorbid diseases. The prevention of stroke is necessary to be done in order to reduce stroke cases, especially in Bandung’s rural area. Villagers have limited access to health information and health services. In this community service, the methods we used were to screen the two risk factors of stroke (blood pressure and blood glucose ad random measurement) in Cigugur Girang Village, West Bandung area. The aim of this community service is to find comorbid factors of stroke and give education to people who have one or more stroke risk factors. The results of the blood pressure measurement screening showed that more than 40% of the residents of Cigugur Girang village had blood pressure above normal, and 5% of the population had blood sugar levels higher than normal. The risk factor for stroke in residents of Cigugur Girang Village mainly is high blood pressure. In this community service, people with high blood pressure were educated on how to maintain normal blood pressure and how to change their lifestyle. On the other hand, some participant has high blood glucose level. High blood glucose levels will develop into diabetes mellitus. In this education program, we gave information about the complications of high blood glucose, including stroke. Thus, this education program could increase community awareness to prevent stroke.

Suppression the glucose-induced ferroptosis in endothelial cells by 4OI-loading exosomes hydrogel for the treatment of diabetic foot ulcer

The prevalence of diabetic foot ulcer in individuals with diabetes is approximately 15%, and it is frequently accompanied by vascular dysfunction and infection. The management challenges make it the primary cause for amputation; however, the underlying mechanisms remain poorly understood, thereby impeding the advancement of novel wound dressings and medication. Herein, single-cell sequencing analysis revealed that ferroptosis induced by high glucose levels in vascular endothelial cells (VECs) represents a significant molecular pathological feature in diabetic foot ulcer (DFU) tissue. Furthermore, 4-Octyl itaconate (4OI) exhibited a pronounced protective effect against VECs injury induced by high glucose (HG), and its underlying molecular mechanisms involve the mitigation of HG-induced reactive oxygen species (ROS) injury and ferroptosis in VECs through the regulation Keap1/Nrf2/GPX4. For enhanced application, incubation at 37℃ was used to load 4OI into exosomes, which were subsequently encapsulated within an Alg-DA/GelMA-based hydrogel matrix. Remarkably, the utilization of 4OI-Exo-Gel not only expedited wound healing but also significantly improved the overall quality of healing in DFU. In conclusion, these findings not only demonstrate, for the first time, the remarkable ability of 4OI to enhance diabetic wound healing by mitigating VECs ferroptosis induced by HG, but also provide evidence for the potential clinical translation of a 4OI-loaded exosomes hydrogel in diabetic wound therapy.

Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis.

Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk. We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation. The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics. Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Gene-Environment Interactions Significantly Alter the Obesity Risk of SH2B1 rs7498665 Carriers.

Src homology 2 B adaptor protein 1 (SH2B1) gene and variants have been found to be associated with common obesity. We aimed to investigate the association between the common missense variant SH2B1 rs7498665 and common obesity risk as well as interactions with lifestyle variables in an Israeli population. An adult cohort (n=3,070; ≥18 years) with the SH2B1 rs7498665 variant and lifestyle, behavior (online questionnaire), and blood glucose data was analyzed. Associations between this variant, obesity risk (body mass index [BMI] ≥25 and ≥30 kg/m2), and interactions with behavioral and lifestyle factors (stress levels, eating habits score [EHS], physical activity [PA], and wine consumption) were investigated. Association and gene-environment interactions were analyzed using binary logistic regressions with interaction. SH2B1 rs7498665 carriers were significantly (P<0.05) more likely to be overweight (BMI ≥25 kg/m2) or obese (BMI ≥30 kg/m2) in recessive (odds ratio [OR], 1.90 and 1.36, respectively), additive (OR, 1.24 and 1.14, respectively), and codominant (OR, 2.00 and 1.41, respectively) genetic models. SH2B1 rs7498665 interacted with lifestyle and behavioral factors as well as glucose levels. PA and moderate wine consumption (1 to 3 drinks/week) reduced obesity risk (OR, 0.35 and 0.71, respectively). Conversely, carriers of two risk alleles who reported high stress levels, had ≥median EHS, and who had a fasting glucose level ≥90 mg/dL had a significantly increased obesity risk (OR, 3.63 and 5.82, respectively). Carrying SH2B1 rs7498665 significantly elevates the risk of obesity. Actionable lifestyle and behavioral factors significantly modulate the rs7498665 genetic predisposition to obesity; PA and moderate wine consumption attenuate the risk, while high stress, EHS, and fasting glucose level increase the obesity risk.

Cerium dioxide nanozyme doped hybrid hydrogel with antioxidant and antibacterial abilities for promoting diabetic wound healing

Diabetic wounds are among the most severe chronic complications in patients with diabetes. Owing to the detrimental effects of high glucose and high levels of reactive oxygen species (ROS) in the wound microenvironment, diabetic wound healing is significantly challenging. To address the issues of elevated ROS levels and infection susceptibility in diabetic wounds, we designed a composite hydrogel system loaded with cerium dioxide nanoparticles (CeO2 NPs) and vancomycin. This hydrogel aimed to reshape the wound microenvironment by continuously consuming ROS and inhibiting infection. Experimental results demonstrated that the CeO2 NPs-loaded hydrogel demonstrated an 85.40% clearance rate of hydroxyl radicals (·OH) and achieved over 90% inhibition against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa), and effectively promoted the healing of diabetic mouse wounds. This cerium dioxide nanozyme hybrid hydrogel system can exert antioxidative and antibacterial effects and serve as a wound dressing to promote the healing of infected diabetic wounds.

Efficacy of atrial fibrillation predictors including MVP ECG risk score compared between single or bilateral internal thoracic artery use

BackgroundAtrial fibrillation (AF) is the most common complication after Coronary Artery Bypass Surgery (CABG). Despite advanced treatment methods, primary prevention is crucial. Many factors have been investigated as markers for AF, but further research is required.CABG is currently superior to Primary Coronary Intervention (PCI) in some cases due to Left Anterior Descending Artery (LAD)- Internal Thoracic Artery (ITA) anastomosis. However, graft choice for non-LAD vessels is still controversial. Our study compared the incidence of arrhythmia between patients with single ITA or bilateral ITA (BITA).MethodsThe study included 84 isolated CABG patients. The patients were divided into two groups: single ITA and BITA. Patients who developed AF were recorded and compared.Results73.8%(n = 62) of the patients were male and 26.2%(n = 22) were female. While single ITA was used in 48.8%(n = 41) of the patients, BITA was used in 51.2%(n = 43). AF was detected in 15.5%(n = 13) of the patients. AF was observed in 5(12.2%) patients in the single ITA group and 8(18.6%) in the BITA group. 76.9%(n = 10) of the patients with AF rhythm had Diabetes Mellitus (DM)(p = 0.011). Biphasic P wave, length of P wave duration, and total Morpholog-Voltage-P Wave ECG (MVP ECG) score height were statistically significantly different.ConclusionsThe development of AF was similar in both groups. The presence of DM, high blood glucose levels, and ECG findings can detect a predisposition to postoperative AF. MVP ECG risk score is effective as an AF marker and can be used in surgical patient groups.

In silico Identification of Fructose-1,6-biphosphatase Inhibitory Potentials of Xanthones Isolated from African Medicinal Plants: An Integrated Computational Approach

Background: Type 2 diabetes mellitus continues to pose a threat to the existence of the human race. The increasing number of diabetic subjects can be effectively controlled by targeting enzymes responsible for high blood glucose levels. Xanthones are a class of phytochemicals that possesses promising pharmacological potentials. Objective: This study identified fructose 1,6-biphosphatase (FBPase) inhibitors by exploring xanthones isolated from African medicinal plants through ensemble docking, molecular dynamics simulation and density functional theory methods. Methods: The study used ensemble docking, molecular dynamics simulation and density functional theory (B3LYP/6-3G (d,p) basis set) and ADMET methods to select lead compound that may be effective as fructose-I,6-biphosphatase inhibitor. Results: The ensemble docking results identified globulixanthone C (-10.0 kcal/mol), 1-Isomangostin (- 9.0 kcal/mol), laurentixanthone A (-9.0 kcal/mol), bangangxanthone A (-8.9 kcal/mol) and staudtiixanthone B (-8.8 kcal/mol) as potential inhibitors of fructose-1,6-biphosphatase. Molecular dynamics studies showed the xanthones established good binding mode and their binding energy ranged from -74.057 to 53.669 kJ/mol. Also, the electronic and ADMET studies of the xanthones elucidated their excellent pharmacological potential. Conclusion: The study identified xanthones as potential fructose-1,6-biphosphatase inhibitors. The ligands' binding energy and MMPBSA calculations supported their possible inhibitory property. Also, the ADMET properties estimated show the ligands as suitable drug candidates as fructose-1,6-biphosphatase inhibitors. Further in vitro and in vivo investigation of the hit molecules is necessary to develop new FBPase inhibitors.

Predictive capacity of fetal pancreatic circumference for gestational diabetes mellitus.

To assess the capacity of fetal pancreatic size, before standard blood glucose testing for screening and diagnosis, to predict maternal gestational diabetes mellitus (GDM). This was a retrospective cohort study of low-risk pregnant women recruited during routine second-trimester fetal anatomical screening at 20-25 weeks' gestation at two ultrasound units in Israel between 2017 and 2020. The predictive performance of fetal pancreatic circumference ≥ 80th and ≥ 90th centiles and glucose challenge test (GCT) was examined for the outcome of GDM. The independent-samples t-test was used to compare mean pancreatic circumference centile between pregnancies with GDM and those without GDM. Diagnostic performance was evaluated with 2 × 2 contingency tables and receiver-operating-characteristics (ROC) curves. Overall, 195 women were selected for statistical analysis. Twenty-four (12.3%) women were diagnosed subsequently with GDM. The mean ± SD fetal pancreatic circumference centile was significantly higher in the GDM group compared with the non-GDM group (82.4 ± 14.6 vs 62.8 ± 27.6; P < 0.001). The pancreatic circumference centile was correlated positively with the estimated fetal weight centile (Pearson's coefficient, 0.243; P = 0.001). The 80th centile cut-off for pancreatic circumference had the highest sensitivity (70.8%) and positive predictive value (23.3%) for future maternal GDM, with the best trade-off between sensitivity and specificity achieved at the 75th centile cut-off (sensitivity, 79%; specificity, 60%). The GCT had better specificity (90.2%) and negative predictive value (97.9%) compared with both cut-offs in pancreatic circumference. The area under the ROC curve was higher for pancreatic circumference compared with GCT (0.71 vs 0.64) and only the former was statistically significant (P = 0.001). Fetal pancreatic circumference has a higher positive predictive capacity compared with GCT. Measuring pancreatic circumference can identify pregnancies at high risk for maternal GDM, thereby promoting earlier diagnosis and treatment, decreasing the time period during which the fetus is exposed to high maternal glucose levels and improving infant outcome. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Gender difference in coronary artery disease

Background: Coronary artery disease (CAD) continues to exert a substantial impact on global health, being the main factor causing morbidity and mortality in North America and Europe. Men and women have different incidences of CAD and risk factors, which has been widely reported across populations. In light of a notable scarcity of studies examining the variation in CAD risk among different subgroups. We investigated how cholesterol, glucose, blood pressure, and age affect CAD risk in different sex and smoking groups. Method: Framingham Heart Study, a longest-running cardiovascular epidemiological investigation used furnishing valuable insights into CAD risk. Investigating the relationship between these characteristics and the occurrence of CHD involved using logistic regression. Result: For blood pressure variable among smoking group, there were significant increase in male group (=0.0255, P&lt;0.001) compared with non-smoking group (=0.0164 in male, =0.0162 among female group, P&lt;0.001) and reduction among female group (=0.0089, P&lt;0.001). Within the female group, the age variable exhibited a notable reduction in smoking group (=0.0460, P=0.0020) compared with the non-smoking group (=0.0829, P&lt;0.001). After adding the interactions between smoking and blood pressure, smoking and age. In the male group, both smoking and the interaction become insignificant, whereas in the female group the interaction is significant. Conclusion: Our study suggested that high level of glucose, total cholesterol and blood pressure increased the risk of CAD. By dividing into subgroup, we discovered that the combined presence of smoking and increased blood pressure, and of smoking and age could potentially exert a more adverse effect on pressure wave reflection in women compared to men.

AN INTELLIGENT COMPUTATIONAL APPROACHES FOR DIABETES RISK PREDICTION: A PROACTIVE HEALTHCARE PARADIGM DIABOLIC

Diabetes mellitus, a long-term metabolic disease marked by high blood glucose levels, is a major global health concern. Diabetes must be identified and treated early to reduce complications and enhance patient outcomes. In this study, we propose a unique deep learning framework, named as, Diabetic prediction utilizing Optimized Learning Classifier (DIABOLIC) for diabetes detection.The original contribution of this paper is to develop a resilient prediction model by leveraging an advanced computational algorithms to reliably predict the probability of getting diabetes. In the proposed framework, a special Tumultuo Dwarf Mongoose Optimization (TuD-MO) technique is used to extract the most important and critical features from the preprocessed dataset. Also, a Fused Deep Convolution Random Network (FDCRN) is developed to precisely identify diabetic patients based on the selected attributes. Moreover, a detailed performance analysis is completed in order to validate and extensively explore the outcomes of the DIABOLIC model.Our test findings show that, when it comes to diabetes detection, DIABOLIC outperforms cutting-edge techniques in terms of predictive performance, with excellent sensitivity, specificity, and accuracy. In addition, we perform thorough interpretability investigations in order to clarify the underlying characteristics and processes that underlie the predictions produced by DIABOLIC. Overall, our research shows how deep learning techniques, like DIABOLIC, can improve diabetes detection and tailored healthcare plans, which will benefit public health campaigns and patient outcomes.

The injection of maternal B complex vitamin during the transition period: The impact on performance, thyroid hormones levels and immunological parameters in the Sannen goats and their kids, as well as the faeces status of newborn kids.

It is proven that B vitamins through promote a wide range of metabolic pathways in animals as cofactors improve animal performance. This study was conducted to investigate the impacts of maternal B complex vitamin injection on performance and plasma parameters in goats and their offspring, as well as the faeces status of newborn kids. In this research, the pregnant goats (3 years old) were randomly divided into two groups: the control group (without B complex vitamin injection) and the B complex vitamin group (5mL B complex vitamin injection per animal). The animals were injected with 5mL B complex vitamin twice during the transition period (5 weeks pre- and 5 weeks post-kidding). The goats during the transition period and kids on days 10, 20 and 30 were weighed. Feed intake by goats and consumption of milk and starter in kids were recorded daily. The dry matter digestibility by kids was tested by collecting samples of faeces and feed for 5 days in the last week. Chemical analysis was determined using the AOAC method. The kids' faeces were prepared daily during the study. The blood samples of goats and newborn kids were taken 7 days after kidding. Then, levels of B group vitamin, as well as concentrations of liver enzymes, thyroid hormones and immunological parameters, were determined in plasma of goat and their offspring. In addition, concentrations of glucose and insulin were measured in goat plasma (Asadi et al., 2024). According to results, the performances of goats and their offspring, as well as kids' faeces status, were improved by maternal B complex vitamin injection (p<0.0001). The levels of cobalamin, pyridoxine, thiamine, folic acid, nicotinic, pantothenic and unconjugated pteridine increased in the plasma of goats and their kids in the B complex vitamin group compared with the control group during the transition period (p<0.0001). Injection of maternal B complex vitamin raised the plasma levels of triiodothyronine and tetraiodothyronine, immunoglobulin G and immunoglobulin M in goats and their offspring (p<0.0001). Higher levels of glucose and lower levels of insulin were determined in the goats injected with B complex vitamin (p<0.0001). These results suggest that maternal B complex vitamin injection is required for the improvement of performance, health status and the blood plasma parameters in pregnant goats and their kids.

Jie-Du-Tong-Luo formula protects C2C12 myotubes against high glucose and palmitic acid injury by activating the PI3K/Akt/PPARγ pathway in vitro

IntroductionIn prior reports, Jie-Du-Tong-Luo (JDTL) was reported to help control insulin secretion and blood glucose in patients with diabetes, while also protecting liver and pancreatic islet cells against injury caused by exposure to high glucose (HG) levels. This study was thus developed to assess the effects of JDTL on HG and palmitic acid (PA)-induced muscle injury and to explore the mechanistic basis for these effects. MethodsA model of muscle injury was established using mouse C2C12 myotubes treated with HG + PA. A proteomics approach was used to assess changes in protein levels following JDTL treatment, after which Western immunoblotting was employed to validate significantly affected pathways. ResultsJDTL was able to protect against HG + PA-induced muscle cell injury in this experimental system, altering lipid metabolism and inflammatory activity in these injured C2C12 myotubes. Western blotting suggested that JDTL is capable of activating PI3K/Akt/PPARγ signaling to control lipid metabolism without any corresponding impact on the inflammatory NF-κB pathway. ConclusionsThese data highlight the ability of JDTL to protect against HG + PA-induced injury to muscle cells, and suggest that the underlying basis for such efficacy is related to the PI3K/Akt/PPARγ pathway-mediated modulation of lipid metabolism.

The angiotensin II/type 1 angiotensin II receptor pathway is implicated in the dysfunction of albumin endocytosis in renal proximal tubule epithelial cells induced by high glucose levels

It is well-established that dysfunction of megalin-mediated albumin endocytosis by proximal tubule epithelial cells (PTECs) and the activation of the Renin-Angiotensin System (RAS) play significant roles in the development of Diabetic Kidney Disease (DKD). However, the precise correlation between these factors still requires further investigation. In this study, we aimed to elucidate the potential role of angiotensin II (Ang II), a known effector of RAS, as the mediator of albumin endocytosis dysfunction induced by high glucose (HG) in PTECs. To achieve this, we utilized LLC-PK1 and HK-2 cells, which are well-established in vitro models of PTECs. Using albumin-FITC or DQ-albumin as tracers, we observed that incubation of LLC-PK1 and HK-2 cells with HG (25 mM for 48 h) significantly reduced canonical receptor-mediated albumin endocytosis, primarily due to the decrease in megalin expression. HG increased the concentration of Ang II in the LLC-PK1 cell supernatant, a phenomenon associated with an increase in angiotensin-converting enzyme (ACE) expression and a decrease in prolyl carboxypeptidase (PRCP) expression. ACE type 2 (ACE2) expression remained unchanged. To investigate the potential impact of Ang II on HG effects, the cells were co-incubated with angiotensin receptor inhibitors. Only co-incubation with 10−7 M losartan (an antagonist for type 1 angiotensin receptor, AT1R) attenuated the inhibitory effect of HG on albumin endocytosis, as well as megalin expression. Our findings contribute to understanding the genesis of tubular albuminuria observed in the early stages of DKD, which involves the activation of the Ang II/AT1R axis by HG.

Comparison of Binary Logistic Regression and SVM to Classify Diabetes Sufferers

Diabetes is a chronic metabolic disorder characterized by high levels of glucose in the blood due to disruption of the insulin hormone which functions as a regulator of the balance of blood sugar levels. This disease continues to increase in prevalence in various countries, making it a global health problem. Diabetes has trigger factors that contribute to the incidence of the disease, such as age, gender, smoking habits, healthy eating patterns, high blood pressure, and others. Diagnosis of diabetes can be done by carrying out a fasting blood sugar test, a 2-hour postprandial (PP) blood sugar test, and a random blood sugar test. However, it is very possible for diagnoses made by health workers to have errors due to subjectivity and different experiences, so a fast and precise classification method is needed to classify patients undergoing diabetes examination based on variables related to diabetes. The classification method used in this research is binary logistic regression and Support Vector Machine (SVM). A similar study carried out classification of diabetes sufferers using the Naive Bayes and KNN methods by comparing the results with SVM, so in this study the binary logistic regression method and SVM will be used to determine the performance of the classification method. The data used is secondary data. Next, the data is divided into training and testing data. The analysis results show that the SVM method is slightly superior in classification accuracy of testing data, namely 97.75%. With this research, it is hoped that decisions on patients undergoing diabetes examination will be faster, more precise and effective, and classification methods with better performance can be applied

Antioxidant and Immunomodulatory Polymer Vesicles for Effective Diabetic Wound Treatment through ROS Scavenging and Immune Modulating.

Chronic diabetic wound patients usually show high glucose levels and systemic immune disorder, resulting in high reactive oxygen species (ROS) levels and immune cell dysfunction, prolonged inflammation, and delayed wound healing. Herein, we prepared an antioxidant and immunomodulatory polymer vesicle for diabetic wound treatment. This vesicle is self-assembled from poly(ε-caprolactone)36-block-poly[lysine4-stat-(lysine-mannose)22-stat-tyrosine)16] ([PCL36-b-P[Lys4-stat-(Lys-Man)22-stat-Tyr16]). Polytyrosine is an antioxidant polypeptide that can scavenge ROS. d-Mannose was introduced to afford immunomodulatory functions by promoting macrophage transformation and Treg cell activation through inhibitory cytokines. The mice treated with polymer vesicles showed 23.7% higher Treg cell levels and a 91.3% higher M2/M1 ratio than those treated with PBS. Animal tests confirmed this vesicle accelerated healing and achieved complete healing of S. aureus-infected diabetic wounds within 8 days. Overall, this is the first antioxidant and immunomodulatory vesicle for diabetic wound healing by scavenging ROS and regulating immune homeostasis, opening new avenues for effective diabetic wound healing.

Factors Related to Diagnosis of Diabetes After Detecting High Blood Glucose Levels Through Screening: One-Year Follow-up Among Publicly Insured Adults in Gunma, Japan.

This study aimed to assess the diagnosis of diabetes after detecting high blood glucose levels through screening among insured individuals in Gunma, Japan. Data for men and women 35 to 74 years of age were provided by Japan Health Insurance Association, and 4133 individuals with high blood glucose levels while not currently being treated for diabetes were included in the study. About 13% received a diagnosis of diabetes at a subsequent physician visit, and individuals who were under treatment for hypertension were less likely to receive the added diagnosis of diabetes compared with those not being treated for hypertension (odds ratio = 0.42 from a logistic regression model). Fasting blood glucose levels were significantly improved in the next year only among individuals with a confirmed diagnosis of diabetes.

Acute radiation skin injury in stage III-IV head and neck cancer: scale correlates and predictive model

PurposeActive radiation skin injury (ARSI) has the highest incidence of acute adverse reactions caused by radiotherapy (RT) in patients with head and neck cancer (HNC). This study aimed to screen risk factors that can facilitate the identification of HNC patients at high risk of ARSI.MethodsData from 255 stage III-IV HNC patients who underwent intensity-modulated radiation therapy (IMRT) were collected. The data from our medical records, including clinical characteristics and hematological indices before RT, were retrospectively collected and arranged. The Common Terminology Criteria for Adverse Events Criteria (CTCAE), Radiation Therapy Oncology Group Criteria (RTOG), World Health Organization Criteria (WHO), Oncology Nursing Society (ONS), Acute Radiation Dermatitis Graduation Scale, Douglas & Fowler and Radiation Dermatitis Severity Scale (RDSS) were used to assess ARSI. Of these, CTCAE was used for further analysis. Binary logistic regression analyses were used to identity risk factors. To establish the correction between each risk factor and the ARSI score, the odds ratio (OR) and 95% confidence interval (CI) were computed.ResultsThe assessment results of the CTCAE with RTOG, WHO, ONS, Graduation Scale, Douglas & Fowler and RDSS have good consistency. After radiotherapy, 18.4% of patients had at least 3 (3 +) grade ARSI. Multivariate logistic regression analysis revealed that the KPS score, blood glucose level, white blood cell count, and plasma free thyroxine (FT4) concentration were independent risk factors for 3 + grade ARSI. A nomogram was constructed on the basis of these risk factors, which demonstrated good predictive power according to the area under the ROC curve (AUC). The satisfactory consistency and clinical efficacy of the nomogram were confirmed by calibration curves and decision curve analysis (DCA).ConclusionA low KPS score, high blood glucose level, high white blood cell count, and high thyroid hormone prior to radiotherapy for stage III-IV HNC are independent risk factors for grade 3 + RSI.

IMPLEMENTATION OF DATA MINING FOR DIABETES PREDICTION USING THE C4.5 ALGORITHM

This study focuses on the implementation of data mining techniques to predict diabetes using the C4.5 algorithm. Diabetes is a syndrome characterized by metabolic disturbances and abnormally high blood glucose levels due to insulin deficiency or decreased tissue sensitivity to insulin. Maintaining blood sugar levels is crucial for health, as it is a vital energy source for cells and tissues. The research employs various classification attributes, including weight, gender (as an auxiliary attribute), blood pressure, blood sugar levels, and diabetes history. These attributes are used to help individuals predict whether their diabetes is hereditary or non-hereditary.

Effect of extracellular matrix stiffness on efficacy of Dapagliflozin for diabetic cardiomyopathy

BackgroundExtracellular matrix (ECM) stiffness is closely related to the progress of diabetic cardiomyopathy (DCM) and the response of treatment of DCM to anti-diabetic drugs. Dapagliflozin (Dapa) has been proven to have cardio-protective efficacy for diabetes and listed as the first-line drug to treat heart failure. But the regulatory relationship between ECM stiffness and treatment efficacy of Dapa remains elusive.Materials and methodsThis work investigated the effect of ECM stiffness on DCM progression and Dapa efficacy using both in vivo DCM rat model and in vitro myocardial cell model with high glucose injury. First, through DCM rat models with various levels of myocardial injury and administration with Dapa treatment for four weeks, the levels of myocardial injury, myocardial oxidative stress, expressions of AT1R (a mechanical signal protein) and the stiffness of myocardial tissues were obtained. Then for mimicking the stiffness of myocardial tissues at early and late stages of DCM, we constructed cell models through culturing H9c2 myocardial cells on the polyacrylamide gels with two stiffness and exposed to a high glucose level and without/with Dapa intervention. The cell viability, reactive oxygen species (ROS) levels and expressions of mechanical signal sensitive proteins were obtained.ResultsThe DCM progression is accompanied by the increased myocardial tissue stiffness, which can synergistically exacerbate myocardial cell injury with high glucose. Dapa can improve the ECM stiffness-induced DCM progression and its efficacy on DCM is more pronounced on the soft ECM, which is related to the regulation pathway of AT1R-FAK-NOX2. Besides, Dapa can inhibit the expression of the ECM-induced integrin β1, but without significant impact on piezo 1.ConclusionsOur study found the regulation and effect of biomechanics in the DCM progression and on the Dapa efficacy on DCM, providing the new insights for the DCM treatment. Additionally, our work showed the better clinical prognosis of DCM under early Dapa intervention.

Predictive Modelling for Diabetes and Insulin Dosage Using Machine Learning

Now a days diabetes has become a chronic disease and managing this requires strict regular diet and workout to avoid various health issues and high blood glucose levels. To keep blood glucose at normal level in human body, diabetic patients have to be suggested with proper insulin dosage. It becomes difficult to predict the right amount of insulin to diabetes patients. To do this, Machine Learning(ML) method is used for identifying weather a person is suffering from diabetic and if he/she is suffering, right amount of insulin should be suggested to that patient. k-nearest neighbors(KNN) technique is employed to predict weather a patient is diabetic or not and Random Forest Regression technique is utilized for suggesting appropriate quantity of insulin dosage for the diabetic patient. Results are generated using the above-mentioned techniques.