Glioblastoma (GBM) is the most aggressive and lethal brain tumor. Artificial neural networks (ANNs) have the potential to make accurate predictions and improve decision making. The aim of this study was to create an ANN model to predict 15-month survival in GBM patients according to gene expression databases. Genomic data of GBM were downloaded from the CGGA, TCGA, MYO, and CPTAC. Logistic regression (LR) and ANN model were used. Age, gender, IDH wild-type/mutant and the 31 most important genes from our previous study, were determined as input factors for the established ANN model. 15-month survival time was used to evaluate the results. The normalized importance scores of each covariate were calculated using the selected ANN model. The area under a receiver operating characteristic (ROC) curve (AUC), Hosmer-Lemeshow (H-L) statistic and accuracy of prediction were measured to evaluate the two models. SPSS 26 was utilized. A total of 551 patients (61% male, mean age 55.5 ± 13.3 years) patients were divided into training, testing, and validation datasets of 441, 55 and 55 patients, respectively. The main candidate genes found were: FN1, ICAM1, MYD88, IL10, and CCL2 with the ANN model; and MMP9, MYD88, and CDK4 with LR model. The AUCs were 0.71 for the LR and 0.81 for the ANN analysis. Compared to the LR model, the ANN model showed better results: Accuracy rate, 83.3 %; H-L statistic, 6.5 %; and AUC, 0.81 % of patients. The findings show that ANNs can accurately predict the 15-month survival in GBM patients and contribute to precise medical treatment.

In this work, neutron spectra are unfolded using artificial neural networks (ANNs). The neutron response of the NE213 scintillator detector, characterized by the pulse height distribution, is calculated to obtain the necessary data for unfolding the energy spectrum. This is achieved using both analytical response functions and response functions generated by the MCNPX-PHOTRACK code. In this query, the Levenberg-Marquardt method (LMM), which has a high computational speed in the learning method, is used to train the network. The performance of the ANN for unfolding the neutron energy spectrum of the NE213 scintillation detector was evaluated by comparing its results to the established Gravel method. The ANN method consistently produced spectra with a single peak closely matching the incident energy, while the Gravel method showed additional peaks and distortions. Quantitative analysis revealed a lower relative energy peak difference (indicating higher accuracy) for the ANN method compared to Gravel, particularly when noise was introduced into the data. These results suggest that ANNs offer a more robust and accurate approach for neutron spectrum unfolding.

This study presents a novel and efficient approach for pullulan production using artificial neural networks (ANNs) to optimize semi-solid-state fermentation (S-SSF) on faba bean biomass (FBB). This method achieved a record-breaking pullulan yield of 36.81 mg/g within 10.82 days, significantly exceeding previous results. Furthermore, the study goes beyond yield optimization by characterizing the purified pullulan, revealing its unique properties including thermal stability, amorphous structure, and antioxidant activity. Energy-dispersive X-ray spectroscopy and scanning electron microscopy confirmed its chemical composition and distinct morphology. This research introduces a groundbreaking combination of ANNs and comprehensive characterization, paving the way for sustainable and cost-effective pullulan production on FBB under S-SSF conditions. Additionally, the study demonstrates the successful integration of pullulan with Ag@TiO2 nanoparticles during synthesis using Fusarium oxysporum. This novel approach significantly enhances the stability and efficacy of the nanoparticles by modifying their surface properties, leading to remarkably improved antibacterial activity against various human pathogens. These findings showcase the low-cost production medium, and extensive potential of pullulan not only for its intrinsic properties but also for its ability to significantly improve the performance of nanomaterials. This breakthrough opens doors to diverse applications in various fields.

This study examines the impact of injection parameters on the weld line strength of the polyamide 6 and 30% fiberglass (PA6 + 30% FG) composite samples. The effects of filling time, packing time, packing pressure, melt temperature, and mold temperature on the ultimate tensile strength (UTS) and the elongation value of the weld line are investigated. The results reveal that the filling time factor has the lowest influence rate. On the contrary, the packing pressure has the most considerable value of UTS standard deviation, indicating that this factor has a high impact rate. The melt temperature factor has the highest elongation standard deviation, pointing out the strong impact of melt temperature on the elongation value. In reverse, the filling time factor has the lowest elongation standard deviation, showing the low impact of this factor on the elongation value. Increasing the mold temperature enhances the elongation value greatly because a higher temperature generates a better connection in the weld line area. Although the UTS value improves modestly when the mold temperature control system is used, the elongation result from the mold temperature parameter is better than expected. The UTS result from all parameters presents a minor deviation; therefore, it is lower than expected. The optimal strength result from artificial neural networks with genetic algorithm optimization is 85.1 MPa, which is higher than the best experiment result of 76.8 MPa. Scanning electron microscopy (SEM) results show that the interface between the fiberglass and the PA matrix has high adherence. The fracture surface is smooth, indicating that the PA6 + 30% FG composite sample has a high fragility level. The findings could help to increase the injection sample\'s weld line strength by optimizing the injection molding conditions.

A multilayered bidirectional associative memory neural network is proposed to account for learning nonlinear types of association. The model (denoted as the MF-BAM) is composed of two modules, the Multi-Feature extracting bidirectional associative memory (MF), which contains various unsupervised network layers, and a modified Bidirectional Associative Memory (BAM), which consists of a single supervised network layer. The MF generates successive feature patterns from the original inputs. These patterns change the relationship between the inputs and targets in a way that the BAM can learn. The model was tested on different nonlinear tasks, such as the N-bit, Double Moon and its variants, and the 3-class spiral task. Behaviors were reported through learning errors, decision zones, and recall performances. Results showed that it was possible to learn all tasks consistently. By manipulating the number of units per layer and the number of unsupervised network layers in the MF, it was possible to change the level of nonlinearity observed in the decision boundaries. Furthermore, results indicated that different behaviors were achieved from the same set of inputs by using the different generated patterns. These findings are significant as they showed how a BAM-inspired model could solve nonlinear tasks in a more cognitively plausible fashion.

Climate change can increase the spread of infectious diseases and public health concerns. Malaria is one of the endemic infectious diseases of Iran, whose transmission is strongly influenced by climatic conditions. The effect of climate change on malaria in the southeastern Iran from 2021 to 2050 was simulated by using artificial neural networks (ANNs). Gamma test (GT) and general circulation models (GCMs) were used to determine the best delay time and to generate the future climate model under two distinct scenarios (RCP2.6 and RCP8.5). To simulate the various impacts of climate change on malaria infection, ANNs were applied using daily collected data for 12 years (from 2003 to 2014). The future climate of the study area will be hotter by 2050. The simulation of malaria cases elucidated that there is an intense increasing trend in malaria cases under the RCP8.5 scenario until 2050, with the highest number of infections occurring in the warmer months. Rainfall and maximum temperature were identified as the most influential input variables. Optimum temperatures and increased rainfall provide a suitable environment for the transmission of parasites and cause an intense increase in the number of infection cases with a delay of approximately 90 days. ANNs were introduced as a practical tool for simulating the impact of climate change on the prevalence, geographic distribution, and biological activity of malaria and for estimating the future trend of the disease in order to adopt protective measures in endemic areas.

Simultaneous detection of mixed bacteria accurately and sensitively is a major challenge in microbial quality control field. In this study, we proposed a label-free SERS technique coupled with partial least squares regression (PLSR) and artificial neural networks (ANNs) for quantitative analysis of Escherichia coli, Staphylococcus aureus and Salmonella typhimurium simultaneously. SERS-active and reproducible Raman spectra can be acquired directly upon the bacteria and Au@Ag@SiO2 nanoparticle composites on the surface of gold foil substrates. After applying different preprocessing models, SERS-PLSR and SERS-ANNs quantitative analysis models were developed to map SERS spectra of concentrations of the Escherichia coli, Staphylococcus aureus and Salmonella typhimurium, respectively. Both models achieved high prediction accuracy and low prediction error, while the performance of SERS-ANNs model in both quality of fit (R2 > 0.95) and accuracy of predictions (RMSE < 0.06) was superior to SERS-PLSR model. Therefore, it is feasible to develop simultaneous quantitative analysis of mixed pathogenic bacteria by proposed SERS methodology.

In the animal kingdom, continuously erupting incisors provided an attractive model for studying the enamel matrix and mineral composition of teeth during development. Enamel, the hardest mineral tissue in the vertebrates, is a tissue sensitive to external conditions, reflecting various disturbances in its structure. The developing dental enamel was monitored in a series of incisor samples extending the first four weeks of postnatal life in the spiny mouse. The age-dependent changes in enamel surface morphology in the micrometre and nanometre-scale and a qualitative assessment of its mechanical features were examined by applying scanning electron microscopy (SEM) and atomic force microscopy (AFM). At the same time, structural studies using XRD and vibrational spectroscopy made it possible to assess crystallinity and carbonate content in enamel mineral composition. Finally, a model for predicting the maturation based on chemical composition and structural factors was constructed using artificial neural networks (ANNs). The research presented here can extend the existing knowledge by proposing a pattern of enamel development that could be used as a comparative material in environmental, nutritional, and pharmaceutical research.

The present study was investigated the production dental guides by using additive manufacturing stereolithography (SLA) technology, and the dimensional aperture values of the dental guides for dental implant treatment using artificial intelligence technology. The aim of this study is to benefit from artificial neural networks (ANNs) to classify the results obtained from the new production freedom of SLA by changing the existing design concept of dental guides. In the study, the Three Dimensional (3D) anatomical model was designed by using Mimics programme the data obtained from the Cone Beam Computed Tomography (CBCT) images of the patient. Three different dental guide designs were performed using the 3-Matic programme for dental implant treatment on the obtained 3D anatomical model. Dental guide designs and mandible model were produced with a SLA 3D printer, and a data set was created using a 3D scanner. The dimensional aperture values were obtained by performing the 3D registration process between the mandible and dental guides. The data set was analyzed both statistically with Jamovi 2.0.0 software and ANNs. The results showed that the minimum and maximum aperture values obtained from the dental guides were very close to each other, indicating that the guides were compatible with the mandible bone. The statistical results showed that the dimensional aperture values decrease in proportion to the values with minimum arithmetic mean value in the data set, and it was determined that the dental guide-3 was the most suitable model for the mandible. When all test data in the confusion matrix obtained from ten different aritificial neural network models created using ANNs were examined, it was been seen that ANN model-5 was the most successful model with an accuracy rate of 99%.

UNASSIGNED: Spontaneous rupture bleeding is a fatal hepatocellular carcinoma (HCC) complication and a significant determinant of survival outcomes. This study aimed to develop and validate a novel artificial neural network (ANN)-based survival prediction model for patients with spontaneous HCC rupture after transcatheter arterial embolization (TAE).
UNASSIGNED: Patients with spontaneous HCC rupture bleeding who underwent TAE at our hospital between January 2010 and December 2018 were included in our study. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to screen clinical variables related to prognosis. We incorporated the above clinical variables identified by LASSO Cox regression into the ANNs model. Multilayer perceptron ANNs were used to develop the 1-year overall survival (OS) prediction model for patients with spontaneous HCC ruptured bleeding in the training set. The area under the receiver operating characteristic curve and decision curve analysis were used to compare the predictive capability of the ANNs model with that of existing conventional prediction models.
UNASSIGNED: The median survival time for the whole set was 11.8 months, and the 1-year OS rate was 47.5%. LASSO Cox regression revealed that sex, extrahepatic metastasis, macroscopic vascular invasion, tumor number, hepatitis B surface antigen, hepatitis B e antigen, tumor size, alpha-fetoprotein, fibrinogen, direct bilirubin, red blood cell, and γ-glutamyltransferase were risk factors for OS. An ANNs model with 12 input nodes, seven hidden nodes, and two corresponding prognostic outcomes was constructed. In the training set and the validation set, AUCs for the ability of the ANNs model to predict the 1-year OS of patients with spontaneous HCC rupture bleeding were 0.923 (95% CI, 0.890-0.956) and 0.930 (95% CI, 0.875-0.985), respectively, which were higher than that of the existing conventional models (all P < 0.0001).
UNASSIGNED: The ANNs model that we established has better survival prediction performance.