A study on fractional tumor-immune interaction model related to lung cancer via generalized Laguerre polynomials.

BACKGROUND: Cancer, a complex and deadly health concern today, is characterized by forming potentially malignant tumors or cancer cells. The dynamic interaction between these cells and their environment is crucial to the disease. Mathematical models can enhance our understanding of these interactions, helping us predict disease progression and treatment strategies. METHODS: In this study, we develop a fractional tumor-immune interaction model specifically for lung cancer (FTIIM-LC). We present some definitions and significant results related to the Caputo operator. We employ the generalized Laguerre polynomials (GLPs) method to find the optimal solution for the FTIIM-LC model. We then conduct a numerical simulation and compare the results of our method with other techniques and real-world data. RESULTS: We propose a FTIIM-LC model in this paper. The approximate solution for the proposed model is derived using a series of expansions in a new set of polynomials, the GLPs. To streamline the process, we integrate Lagrange multipliers, GLPs, and operational matrices of fractional and ordinary derivatives. We conduct a numerical simulation to study the effects of varying fractional orders and achieve the expected theoretical results. CONCLUSION: The findings of this study demonstrate that the optimization methods used can effectively predict and analyze complex phenomena. This innovative approach can also be applied to other nonlinear differential equations, such as the fractional Klein-Gordon equation, fractional diffusion-wave equation, breast cancer model, and fractional optimal control problems.

Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio.

The novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use fast and inexpensive images from computed tomography (CT) scans. In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), convolutional auto-encoder neural network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19. The dataset consists of 1252 CT scans that are positive and 1230 CT scans that are negative for COVID-19 virus. The proposed models have priority over the other models that there is no need of pre-trained networks and data augmentation for them. The classification accuracies of ResNet-50, VGG-16, CNN, and CAENN were obtained 92.24%, 94.07%, 93.84%, and 93.04% respectively. Among ML classifiers, the nearest neighbor (NN) had the highest performance with an accuracy of 94%.

Accurate prediction of nutritional value of sorghum grain using image analysis.

1. This study evaluated the application of L (lightness)*a (redness) and *b (blueness) colour analysis and chemical compositions to predict the nutritional value of sorghum grain. 2. A total of 12 varieties of sorghum grain were analysed for L*a*b colours, chemical composition, energy and total and digestible amino acid content. Regression models based on the linear, non-linear and the interaction effects of inputs were applied to predict the nutritional value of sorghum grains either using L*a*b colour or chemical composition, as the model inputs. 3. The results illustrated a significant relationship between a*b and/or chemical compositions with energy content in the samples of sorghum grain. The provided estimation equations presented high goodness of fit in terms of R2adj ranging from 0.744 to 0.999. 4. Total and digestible amino acids of sorghum grain were estimated based on a*b and chemical compositions data with the goodness of fit ranging from 0.641 to 0.999 (R2adj). 5. In conclusion, the L*a*b colour analysis may be used for developing equations to predict nutritional value of sorghum grain as an alternative approach to the conventional time-consuming and costly chemical and bioassay methods.

Comparative effects of metformin and pioglitazone on YKL-40 in type 2 diabetes: a randomized clinical trial.

PURPOSE: Metformin and pioglitazone are believed to exert their long-term benefits by means of amelioration of chronic low-grade inflammation, a key event in development of diabetes and its long-term complications. The present trial was designed to investigate the comparative efficacy of the two anti-diabetes medications on serum concentrations of YKL-40, a novel marker of inflammation. METHODS: In a parallel-group, open-label, randomized trial setting (ClinicalTrials.gov Identifier No. NCT01521624), 84 newly diagnosed, medication-naïve type 2 diabetes patients were assigned to metformin 1,000 mg daily (n = 42) or pioglitazone 30 mg daily (n = 42). Serum concentrations of YKL-40, along with highly sensitive C-reactive protein, indices of glycemic control and lipid profile were measured at baseline and after 3 months. RESULTS: In the analyzed sample (metformin = 40, pioglitazone = 42), both medications were equally effective with regard to control of hyperglycemia, and hsCRP reduction (p > 0.05). However, metformin caused a significant decline in weight (p = 0.005), BMI (p = 0.004), and total cholesterol levels (p = 0.028) of the patients. Metformin also significantly reduced YKL-40 concentrations after 3 months (1.90 ± 17 vs. 1.66 ± 0.15 µg/L, p = 0.019). The amount of change in the pioglitazone arm did not reach statistical significance (2.18 ± 0.14 vs. 2.25 ± 0.16 µg/L, p = 0.687). When compared, metformin was significantly more effective than pioglitazone with respect to YKL-40 reduction in both univariate (p = 0.020, effect size = 6.7%) and multivariate models (p = 0.047, effect size = 5.7%). CONCLUSIONS: Metformin is more effective in reduction of YKL-40 concentration in short term and the effect seems to be independent of degree of glycemic control, or hsCRP reduction.

An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model.

Over the past few years, the widespread outbreak of COVID-19 has caused the death of millions of people worldwide. Early diagnosis of the virus is essential to control its spread and provide timely treatment. Artificial intelligence methods are often used as powerful tools to reach a COVID-19 diagnosis via computed tomography (CT) samples. In this paper, artificial intelligence-based methods are introduced to diagnose COVID-19. At first, a network called CT6-CNN is designed, and then two ensemble deep transfer learning models are developed based on Xception, ResNet-101, DenseNet-169, and CT6-CNN to reach a COVID-19 diagnosis by CT samples. The publicly available SARS-CoV-2 CT dataset is utilized for our implementation, including 2481 CT scans. The dataset is separated into 2108, 248, and 125 images for training, validation, and testing, respectively. Based on experimental results, the CT6-CNN model achieved 94.66% accuracy, 94.67% precision, 94.67% sensitivity, and 94.65% F1-score rate. Moreover, the ensemble learning models reached 99.2% accuracy. Experimental results affirm the effectiveness of designed models, especially the ensemble deep learning models, to reach a diagnosis of COVID-19.

Early and High-Accuracy Diagnosis of Parkinson's Disease: Outcomes of a New Model.

Parkinson's disease (PD) is one of the significant common neurological disorders of the current age that causes uncontrollable movements like shaking, stiffness, and difficulty. The early clinical diagnosis of this disease is essential for preventing the progression of PD. Hence, an innovative method is proposed here based on combining the crow search algorithm and decision tree (CSADT) for the early PD diagnosis. This approach is used on four crucial Parkinson's datasets, including meander, spiral, voice, and speech-Sakar. Using the presented method, PD is effectively diagnosed by evaluating each dataset's critical features and extracting the primary practical outcomes. The used algorithm was compared with other machine learning algorithms of k-nearest neighbor (KNN), support vector machine (SVM), naive Baye (NB), multilayer perceptron (MLP), decision tree (DT), random tree, logistic regression, support vector machine of radial base functions (SVM of RBFs), and combined classifier in terms of accuracy, recall, and combination measure F1. The analytical results emphasize the used algorithm's superiority over the other selected ones. The proposed model yields nearly 100% accuracy through various trials on the datasets. Notably, a high detection speed achieved the lowest detection time of 2.6 seconds. The main novelty of this paper is attributed to the accuracy of the presented PD diagnosis method, which is much higher than its counterparts.

An optimization method for studying fractional-order tuberculosis disease model via generalized Laguerre polynomials.

Tuberculosis (TB) is a deadly contagious disease that affects vital organs of the body, especially the lungs. Although the disease is preventable, there are still concerns about its continued spread. Without effective prevention or appropriate treatment, TB infection can be fatal to humans. This paper presents a fractional-order TB disease (FTBD) model to analyze TB dynamics and a new optimization method to solve it. The method is based on the basis functions of generalized Laguerre polynomials (GLPs) and some new operational matrices of derivatives in the Caputo sense. Finding the optimal solution to the FTBD model is reduced to solving a system of nonlinear algebraic equations with the aid of GLPs using the Lagrange multipliers method. A numerical simulation is also carried out to determine the impact of the presented method on the susceptible, exposed, infected without treatment, infected with treatment, and recovered cases in the population.

Prediction of the true digestible amino acid contents from the chemical composition of sorghum grain for poultry.

Accurate knowledge of true digestible amino acid (TDAA) contents of feedstuffs is necessary to accurately formulate poultry diets for profitable production. Several experimental approaches that are highly expensive and time consuming have been used to determine available amino acids. Prediction of the nutritive value of a feed ingredient from its chemical composition via regression methodology has been attempted for many years. The artificial neural network (ANN) model is a powerful method that may describe the relationship between digestible amino acid contents and chemical composition. Therefore, multiple linear regressions (MLR) and ANN models were developed for predicting the TDAA contents of sorghum grain based on chemical composition. A precision-fed assay trial using cecectomized roosters was performed to determine the TDAA contents in 48 sorghum samples from 12 sorghum varieties differing in chemical composition. The input variables for both MLR and ANN models were CP, ash, crude fiber, ether extract, and total phenols whereas the output variable was each individual TDAA for every sample. The results of this study revealed that it is possible to satisfactorily estimate the TDAA of sorghum grain through its chemical composition. The chemical composition of sorghum grain seems to highly influence the TDAA contents when considering components such as CP, crude fiber, ether extract, ash and total phenols. It is also possible to estimate the TDAA contents through multiple regression equations with reasonable accuracy depending on composition. However, a more satisfactory prediction may be achieved via ANN for all amino acids. The R(2) values for the ANN model corresponding to testing and training parameters showed a higher accuracy of prediction than equations established by the MLR method. In addition, the current data confirmed that chemical composition, often considered in total amino acid prediction, could be also a useful predictor of true digestible values of selected amino acids for poultry.

Estimation and modeling true metabolizable energy of sorghum grain for poultry.

Sorghum grain is an important ingredient in poultry diets. The TMEn content of sorghum grain is a measure of its quality. As for the other feed ingredients, the biological procedure used to determine the TMEn value of sorghum grain is costly and time consuming. Therefore, it is necessary to find an alternative method to accurately estimate the TMEn content. In this study, 2 methods of regression and artificial neural network (ANN) were developed to describe the TMEn value of sorghum grain based on chemical composition of ash, crude fiber, CP, ether extract, and total phenols. A total of 144 sorghum samples were used to determine chemical composition and TMEn content using chemical analyses and bioassay technique, respectively. The values were consequently subjected to regression and ANN analysis. The fitness of the models was tested using R(2) values, MS error, and bias. The developed regression and ANN models could accurately predict the TMEn of sorghum samples from their chemical composition. The goodness of fit in terms of R(2) values corresponding to testing and training of the ANN model showed a higher accuracy of prediction than the equation established by regression method. In terms of MS error, the ANN model showed lower residuals distribution than the regression model. The results suggest that the ANN model may be used to accurately estimate the TMEn value of sorghum grain from its corresponding chemical composition.

Erratum to "A Novel Method for Extraction of Galegine by Molecularly Imprinted Polymer (MIP) Technique Reinforced with Graphene Oxide and Its Evaluation Using Polarography".

[This corrects the article DOI: 10.1155/2020/3646712.].

Adenosine 3'-5'-monophosphate in rat pineal gland: increase induced by light.

Rats were maintained in alternating periods of 12 hours of light and 12 hours of darkness. The concentration of adenosine 3',5'-monophosphate in pineal gland was six times higher at the end of the light period than at the end of the period of darkness. This effect of light was abolished in blinded animals.

Oxidative stress and antioxidant therapy in Parkinson's disease.

Parkinson's disease, known also as striatal dopamine deficiency syndrome, is a degenerative disorder of the central nervous system characterized by akinesia, muscular rigidity, tremor at rest, and postural abnormalities. In early stages of parkinsonism, there appears to be a compensatory increase in the number of dopamine receptors to accommodate the initial loss of dopamine neurons. As the disease progresses, the number of dopamine receptors decreases, apparently due to the concomitant degeneration of dopamine target sites on striatal neurons. The loss of dopaminergic neurons in Parkinson's disease results in enhanced metabolism of dopamine, augmenting the formation of H2O2, thus leading to generation of highly neurotoxic hydroxyl radicals (OH.). The generation of free radicals can also be produced by 6-hydroxydopamine or MPTP which destroys striatal dopaminergic neurons causing parkinsonism in experimental animals as well as human beings. Studies of the substantia nigra after death in Parkinson's disease have suggested the presence of oxidative stress and depletion of reduced glutathione; a high level of total iron with reduced level of ferritin; and deficiency of mitochondrial complex I. New approaches designed to attenuate the effects of oxidative stress and to provide neuroprotection of striatal dopaminergic neurons in Parkinson's disease include blocking dopamine transporter by mazindol, blocking NMDA receptors by dizocilpine maleate, enhancing the survival of neurons by giving brain-derived neurotrophic factors, providing antioxidants such as vitamin E, or inhibiting monoamine oxidase B (MAO-B) by selegiline. Among all of these experimental therapeutic refinements, the use of selegiline has been most successful in that it has been shown that selegiline may have a neurotrophic factor-like action rescuing striatal neurons and prolonging the survival of patients with Parkinson's disease.

Allogeneic hematopoietic stem cell transplantation in Hodgkin lymphoma: a systematic review and meta-analysis.

Allogeneic stem cell transplantation (allo-SCT) outcomes in patients with Hodgkin lymphoma (HL) remain poorly defined. We performed a meta-analysis of allo-SCT studies in HL patients. The primary endpoints were 6-month, 1-year, 2-year and 3-year relapse-free survival (RFS) and overall survival (OS). A total of 42 reports (1850 patients) was included. The pooled estimates (95% confidence interval) for 6-month, 1-year, 2-year and 3-year RFS were 77 (59-91)%, 50 (42-57)%, 37 (31-43)% and 31 (25-37)%, respectively. The corresponding numbers for OS were 83 (75-91)%, 68 (62-74)%, 58 (52-64)% and 50 (41-58)%, respectively. There was statistical heterogeneity among studies in all outcomes. In meta-regression, accrual initiation year in 2000 or later was associated with higher 6-month (P=0.012) and 1-year OS (P=0.046), and pre-SCT remission with higher 2-year OS (P=0.047) and 1-year RFS (P=0.016). In conclusion, outcomes of allo-SCT in HL have improved over time, with 5-10% lower non-relapse mortality and relapse rates, and 15-20% higher RFS and OS in studies that initiated accrual in 2000 or later compared with earlier studies. However, there is no apparent survival plateau, demonstrating the need to improve on current allo-SCT strategies in relapsed/refractory HL.

Fasting hyperinsulinaemia and 2-h glycaemia predict coronary heart disease in patients with type 2 diabetes.

AIM: Patients with diabetes are at greater risk of cardiovascular events. Insulin resistance (IR) and hyperinsulinaemia are both related to an increased cardiovascular risk, but whether IR predicts coronary heart disease (CHD) independently of other risk factors in patients with type 2 diabetes (T2D) is a topic of considerable controversy. The aim of the present study was to evaluate the prospective relationship of fasting insulin, HOMA-IR, fasting plasma glucose (FPG) and 2-h post-load glucose (2hPG) load with CHD incidence among such patients. METHODS: A total of 2607 patients with T2D were enrolled in a community-dwelling cohort and followed for an average of 7.2 years. Conventional CHD risk factors, FPG, 2hPG, fasting insulin levels and HOMA-IR index were measured at baseline. Cox regression hazard ratios (HRs) were used to assess CHD risk. RESULTS: A total of 299 'hard' CHD events were registered (in 114 women and 185 men). Increasing levels of fasting insulinaemia were positively associated with CHD incidence. This correlation persisted after controlling for gender, body mass index, blood pressure, lipid profile, medication use and HbA1c [HR for each increase in quartile (fully adjusted model): 1.18 (95% CI: 1.06-1.32); P<0.01]. 2hPG showed a non-linear association with incident CHD [HR of highest vs lowest quartile: 1.64 (95% CI: 1.03-2.61)]. Fasting glycaemia was not associated with CHD risk, whereas HOMA-IR had a direct and independent correlation with CHD risk [HR for each one-quartile increase: 1.19 (95% CI: 1.07-1.34); P<0.01]. CONCLUSION: Fasting insulin levels are positively associated with incidence of CHD in T2D. Furthermore, 2hPG appears to be a significant predictor of incident CHD independently of other risk factors, including HbA1c. These findings suggest that strategies targeting the reduction of insulinaemia and post-load glycaemia may be useful for preventing cardiovascular complications.

The comparative effect of pioglitazone and metformin on serum osteoprotegerin, adiponectin and intercellular adhesion molecule concentrations in patients with newly diagnosed type 2 diabetes: a randomized clinical trial.

AIM: The etiologic role of inflammatory pathways in the development of diabetic complications, especially cardiovascular events, has been established. The anti-inflammatory role of metformin and pioglitazone has been described; however, no study to date has compared the efficacy of these common oral agents in this regard. In this study, the authors aimed to compare the anti-inflammatory properties of pioglitazone and metformin, with respect to their effect on serum concentrations of highly sensitive C-reactive protein (hsCRP), osteoprotegerin (OPG), intercellular adhesion molecule-1 (ICAM-1) and adiponectin. METHODS: In an open-label randomized clinical trial, 117 patients with newly diagnosed type 2 diabetes mellitus were visited; 84 fulfilled the inclusion criteria, and were randomly allocated to 2 arms receiving either 1,000 mg/d metformin or 30 mg/d pioglitazone, respectively. Biochemical assessments were made at baseline and the end of the 3 months trial. RESULTS: Significant reduction in FPG, insulin and HbA1c in women and men of both arms were observed. Log-hsCRP values significantly decreased in both arms. A decreasing, but non-significant trend in log-OPG levels was observed in women of the metformin arm (p=0.063). A greater reduction in log-ICAM levels was identifiable in men receiving pioglitazone compared to the other arm (p=0.008); in addition, the same trend was observed in log-OPG values (p=0.029). Nonetheless, reduction in log-ICAM and log-OPG levels was comparable between the 2 arms. A significant increase in adiponectin was observed in both men and women in the pioglitazone arm (p<0.001), whereas changes were non-significant in the metformin arm. CONCLUSION: Remarkably, patients receiving pioglitazone revealed more significant reduction in inflammatory markers.

Reduction in asymmetric dimethylarginine plasma levels by coenzyme Q10 supplementation in patients with type 2 diabetes mellitus.

AIM: According to many studies, supplementation with Coenzyme Q10 (CoQ10) yields beneficial results in terms of endothelial function in type 2 diabetes mellitus. Despite these promising results, data elucidating the effect of CoQ10 on plasma levels of asymmetric dimethylarginine (ADMA), as a recently discussed cardiovascular risk factor, is lacking. This study was designed to investigate the effect of CoQ10 supplementation on endothelial function, specifically by evaluating plasma ADMA levels. METHODS: Sixty-four type 2 diabetic patients were randomly assigned to two groups; either receiving 200mg/d oral dose of CoQ10 (N.=31) or receiving placebo (N.=33) for 12 weeks. Clinical and biochemical assessments were performed before and after the trial for evaluating ADMA, serum nitrite and nitrate (NOx), hemoglobin A1c and lipid profile. RESULTS: The intervention resulted in a significant improvement in ADMA, NOx , low-density lipoprotein and hemoglobin A1c levels in CoQ10 compared to placebo group. Interestingly, difference in changes of these parameters were also significant (P=0.01, 0.03, 0.04 and 0.03, respectively). CONCLUSION: Supplementation with CoQ10 yields beneficial effects on ADMA levels, leading to decreased diabetic cardiovascular events.

Dopamine, GABA, cholecystokinin and opioids in neuroleptic-induced tardive dyskinesia.

The long-term administration of neuroleptics causes tardive dyskinesia, which closely resembles levodopa-induced dyskinesias, and is brought about through complex mechanisms which are ill-defined. It is generally believed that the pathogenesis of tardive dyskinesia relates closely to the chronic blockade of dopamine receptor sites and that its pathophysiology results from a hypersensitivity of dopamine receptor sites. In the therapeutic management of neuroleptic-induced tardive dyskinesia, in addition to reserpine and lithium, diazepam, baclofen, or gamma-vinyl-gamma-aminobutyric acid have also been advocated. However, the reported beneficial effects of diazepam and GABA-mimetic agents in ameliorating the symptoms of tardive dyskinesia may occur through a mechanism which does not necessarily link transmission involving both dopamine and GABA. The presence of high concentrations of both cholecystokinin and opioids in the striatum also suggests that these peptides not only may influence dopaminergic transmission, but that they may also be relevant to the psychopathology of schizophrenia and to the therapeutic effects of neuroleptics. Indeed, the acute and chronic administration of neuroleptics alters the levels of cholecystokinin and opioids and their receptors in several brain regions including the striatum. However, neuroleptics also alter the biochemical integrity of neurotensin, neuropeptide Y, substance P and somatostatin, which may also play a role in the overall expression of the neuroleptic-induced extrapyramidal reactions.

The nullification by diazepam of haloperidol-induced increases in the level of striatal dopamine but not in the activity of glutamic acid decarboxylase.

In the therapeutic management of neuroleptic-induced tardive dyskinesia, diazepam, baclofen or gamma-vinyl-gamma-aminobutyric acid have been advocated. It has been postulated, but not proven, that the beneficial effects of these agents in tardive dyskinesia may be mediated by enhancing GABAergic transmission. In this study, it is reported that, during a 3-day withdrawal period following daily administration of 3 mg/kg of haloperidol (i.p.) for 3 weeks, the activity of glutamic acid decarboxylase in the striatum increased from 72.6 +/- 7.8 to 92.5 +/- 10.2 nmol 14CO2/mg protein/hr, and the concentration of dopamine in the striatum increased from 7.87 +/- 0.23 to 8.86 +/- 0.38 micrograms/g wet tissue. Diazepam (5 mg/kg, i.p.), given during the withdrawal period from haloperidol was able to nullify the enhancement in the concentration of dopamine but not in the activity of glutamic acid decarboxylase in the striatum. The results of these studies are interpreted to indicate that the reported beneficial effects of diazepam and GABA-mimetic agents in ameliorating the symptoms of tardive dyskinesia may occur through a mechanism which does not necessarily link transmission involving both dopamine and GABA.

Convulsant activity of pyridoxal sulphate and phosphonoethyl pyridoxal: antagonism by GABA and its synthetic analogues.

Pyridoxal phosphate and its synthetic analogues--pyridoxal 5'-sulphate and the 5-phosphonoethyl analogue of pyridoxal (phosphonoethyl pyridoxal) in doses of 0.125-0.250 (mumol/10 microliters/i.c.v./rat), caused epileptic seizures characterized by running fits, vocalization, muscular fasciculation and tonic-clonic convulsions. These effects were specific and could not be demonstrated with 5'-deoxypyridoxal, N-methylpyridoxal phosphate or the 5-trans-carboxyethenyl analogue of pyridoxal phosphate (carboxyethenyl pyridoxal). Structure-activity relationships of these analogues indicated that the presence of a CHO in position 4 of the pyridine ring was essential, since its conversion to CH2NH2 or CH2OH abolished activity. The presence of an unsubstituted N was essential, since convulsions did not occur with N-methylpyridoxal phosphate. The presence of the hydroxyl group in position 5' was essential since 5'-deoxypyridoxal was inactive. The convulsive activity was potentiated in the presence of both CHO and PO4, CHO and CH2--CH2PO2-4 but especially CHO and --OSO23-- groups. This seizure activity was prevented, attenuated or reversed by intracerebroventricular administration of 20 microliter of GABA (1 mumol), muscimol (0.025 mumol), trans-4-aminocrotonic acid (0.25 mumol), isoguvacine (0.25 mumol) or THIP (0.25 mumol), but not by biogenic amines. An understanding of the mechanism of pyridoxal phosphate-related seizures may provide additional insights not only about GABA receptor sites but also about the biochemical manifestation and expression of epilepsy.