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Introduction: This study aimed to evaluate the relationship between pathological and clinical risk classifications in newly diagnosed prostate cancer patients, and 68Ga-PSMA PET/CT data and serum Prostate Specific Antigen (PSA) values. Method: A total of 203 patients who were diagnosed with prostate cancer between 2019 and 2023, who had not yet received treatment and who underwent 68Ga-PSMA PET/CT for staging purposes were included in this study. Results: There was a substantial correlation between D’Amico risk classification, Gleason score, ISUP classification, and the presence or absence of metastasis (p < 0.0001). The median SUVmax value of the prostate gland and the D’Amico risk classification were statistically significantly correlated. (p < 0.0001). There was a statistically significant correlation between the ISUP classification and the PSA value and prostate gland SUVmax value (p < 0.0001). There was a significant correlation between the median SUVmax values of the prostate gland at the time of diagnosis and the patients with and without metastases (p < 0.0001). According to the data obtained from ROC analysis, patients with prostate gland SUVmax values of 8.75 and above were found to have a high probability of metastasis with a sensitivity of 78.9% and a specificity of 59.05%. Conclusion: Our study showed that 68Ga-PSMA PET/CT is a highly effective method for staging newly diagnosed high-risk prostate cancer. The probability of metastasis was found to be dramatically increased in Gleason 8 and above. According to D’Amico risk classification, metastasis was detected in at least half of high-risk patients. Since the sensitivity of metastasis was 78.9% in patients with prostate gland SUVmax value above 8.75, we think that these patients should be carefully reported in terms of metastasis.

The increasing global reliance on renewable energy sources, particularly solar energy, underscores the critical importance of accurate solar irradiance forecasting. As solar capacity continues to grow, precise predictions of solar irradiance become essential for optimizing the performance and reliability of photovoltaic (PV) systems. This study introduces a novel hybrid forecasting model that integrates Nonlinear Autoregressive with Exogenous Inputs (NARX) with Long Short-Term Memory (LSTM) networks. The purpose is to enhance the precision of predicting daily solar irradiance in fluctuating meteorological scenarios, particularly in southwestern France. The hybrid model employs the NARX model’s capacity to handle complex non-linear relationships and the LSTM’s aptitude to manage long-term dependencies in time-series data. The performance metrics of the hybrid NARX-LSTM model were thoroughly assessed, revealing a mean absolute error (MAE) of 9.58 W/m2, a root mean square error (RMSE) of 16.30 W/m2, and a Coefficient of Determination (R2) of 0.997. Consequently, the proposed hybrid model outperforms the benchmark model in all metrics, showing a significant improvement in prediction accuracy and better alignment with the observed data. These results highlight the model’s effectiveness in enhancing forecasting accuracy under unpredictable conditions, improving solar energy integration into power systems, and ensuring more reliable energy predictions.

Nanoporous gold (np-Au) has found its use in applications ranging from catalysis to biosensing, where pore morphology plays a critical role in performance. While the morphology evolution of bulk np-Au has been widely studied, knowledge about its thin-film form is limited. This work hypothesizes that the mechanical compliance of the thin film substrate can play a critical role in the morphology evolution. Via experimental and finite-element-analysis approaches, we investigate the morphological variation in np-Au thin films deposited on compliant silicone (PDMS) substrates of a range of thicknesses anchored on rigid glass supports and compare those to the morphology of np-Au deposited on glass. More macroscopic (10 s to 100 s of microns) cracks and discrete islands form in the np-Au films on PDMS compared to on glass. Conversely, uniformly distributed microscopic (100 s of nanometers) cracks form in greater numbers in the np-Au films on glass than those on PDMS, with the cracks located within the discrete islands. The np-Au films on glass also show larger ligament and pore sizes, possibly due to higher residual stresses compared to the np-Au/PDMS films. The effective elastic modulus of the substrate layers decreases with increasing PDMS thickness, resulting in secondary np-Au morphology effects, including a reduction in macroscopic crack-to-crack distance, an increase in microscopic crack coverage, and a widening of the microscopic cracks. However, changes in the ligament/pore widths with PDMS thickness are negligible, allowing for independent optimization for cracking. We expect these results to inform the integration of functional np-Au films on compliant substrates into emerging applications, including flexible electronics.

In this study, we designed a high-performance, compact E-shaped microstrip antenna optimized for intelligent transportation systems, operating at 5.8 GHz. Utilizing simulation tools such as CST Studio Suite 2022 Learning Edition, Ansys HFSS 2022 R1, and MATLAB 2022b PCB Antenna Designer, we ensured consistent physical parameters. Fabricated with a 1.6 mm thick FR-4 substrate and a 50 Ω microstrip line-feeding technique, the antenna measures 35 × 50 × 1.6 mm³, smaller than already existing designs. At 5.75 GHz, it exhibits a return loss of −23.68 dB and a VSWR of 1.140 dB, ensuring stable performance within the desired frequency band. Our findings recommend its integration into vehicle-to-infrastructure wireless communication systems. Comparison across simulation environments and laboratory measurements highlights the close alignment of results with those from Ansys HFSS 2022 R1, affirming its reliability.

Compared to natural images in artificial datasets, it is more challenging to improve the spatial resolution of remote sensing optical image data using super-resolution techniques. Historical aerial images are primarily grayscale due to single-band acquisition, which further limits their recoverability. To avoid data limitations, it is advised to employ a data collection consisting of images with homogeneously distributed intensity values of land use/cover objects at various resolution values. Thus, two different datasets were created. In line with the proposed approach, images of bare land, farmland, residential areas, and forested regions were extracted from orthophotos of different years with different spatial resolutions. In addition, images with intensity values in a more limited range for the same categories were obtained from a single year’s orthophoto to highlight the contribution of the suggested approach. Training of two different datasets was performed independently using a deep learning-based super-resolution model, and the same test images were enhanced individually with the weights of both models. The results were assessed using a variety of quality metrics in addition to visual interpretation. The findings indicate that the suggested dataset structure and content can enable the recovery of more details and effectively remove the smoothing effect. In addition, the trend of the metric values matches the visual perception results.

The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

Ornithine transcarbamylase deficiency (OTCD) is the most common inherited disorder of the urea cycle and, in general, is transmitted as an X-linked recessive trait. Defects in the OTC gene cause an impairment in ureagenesis, resulting in hyperammonemia, which is a direct cause of brain damage and death. Patients with late-onset OTCD can develop symptoms from infancy to later childhood, adolescence or adulthood. Clinical manifestations of adults with OTCD vary in acuity. Clinical symptoms can be aggravated by metabolic stressors or the presence of a catabolic state, or due to increased demands upon the urea. A prompt diagnosis and relevant biochemical and genetic investigations allow the rapid introduction of the right treatment and prevent long-term complications and mortality. This narrative review outlines challenges in diagnosing and managing patients with late-onset OTCD.

Ornithine transcarbamylase deficiency (OTCD) is the most common urea cycle disorder with high unmet needs, as current dietary and medical treatments may not be sufficient to prevent hyperammonemic episodes, which can cause death or neurological sequelae. To date, liver transplantation is the only curative choice but is not widely available due to donor shortage, the need for life-long immunosuppression and technical challenges. A field of research that has shown a great deal of promise recently is gene therapy, and OTCD has been an essential candidate for different gene therapy modalities, including AAV gene addition, mRNA therapy and genome editing. This review will first summarise the main steps towards clinical translation, highlighting the benefits and challenges of each gene therapy approach, then focus on current clinical trials and finally outline future directions for the development of gene therapy for OTCD.

The gut–brain axis embodies the bi-directional communication between the gastrointestinal tract and the central nervous system (CNS), where vagal afferent neurons (VANs) serve as sensors for a variety of gut-derived signals. The gut is colonized by a large and diverse population of microorganisms that communicate via small (effector) molecules, which also act on the VAN terminals situated in the gut viscera and consequently influence many CNS processes. However, the convoluted in vivo environment makes it difficult to study the causative impact of the effector molecules on VAN activation or desensitization. Here, we report on a VAN culture and its proof-of-principle demonstration as a cell-based sensor to monitor the influence of gastrointestinal effector molecules on neuronal behavior. We initially compared the effect of surface coatings (poly-L-lysine vs. Matrigel) and culture media composition (serum vs. growth factor supplement) on neurite growth as a surrogate of VAN regeneration following tissue harvesting, where the Matrigel coating, but not the media composition, played a significant role in the increased neurite growth. We then used both live-cell calcium imaging and extracellular electrophysiological recordings to show that the VANs responded to classical effector molecules of endogenous and exogenous origin (cholecystokinin serotonin and capsaicin) in a complex fashion. We expect this study to enable platforms for screening various effector molecules and their influence on VAN activity, assessed by their information-rich electrophysiological fingerprints.

Numerous deep learning techniques have been explored in pursuit of achieving precise road segmentation; nonetheless, this task continues to present a significant challenge. Exposing shadows and the obstruction of objects are the most important difficulties associated with road segmentation using optical image data alone. By incorporating additional data sources, such as LiDAR data, the accuracy of road segmentation can be improved in areas where optical images are insufficient to segment roads properly. The missing information in spectral data due to the object blockage and shadow effect can be compensated by the integration of 2D and 3D information. This study proposes a feature-wise fusion strategy of optical images and point clouds to enhance the road segmentation performance of a deep learning model. For this purpose, high-resolution satellite images and airborne LiDAR point cloud collected over Florida, USA, were used. Eigenvalue-based and geometric 3D property-based features were calculated based on the LiDAR data. These optical images and LiDAR-based features were used together to train, end-to-end, a deep residual U-Net architecture. In this strategy, the high-level features generated from optical images were concatenated with the LiDAR-based features before the final convolution layer. The consistency of the proposed strategy was evaluated using ResNet backbones with a different number of layers. According to the obtained results, the proposed fusion strategy improved the prediction capacity of the U-Net models with different ResNet backbones. Regardless of the backbone, all models showed enhancement in prediction statistics by 1% to 5%. The combination of optical images and LiDAR point cloud in the deep learning model has increased the prediction performance and provided the integrity of road geometry in woodland and shadowed areas.

Neuroinflammation plays a central role in many neurological disorders, ranging from traumatic brain injuries to neurodegeneration. Electrophysiological activity is an essential measure of neuronal function, which is influenced by neuroinflammation. In order to study neuroinflammation and its electrophysiological fingerprints, there is a need for in vitro models that accurately capture the in vivo phenomena. In this study, we employed a new tri-culture of primary rat neurons, astrocytes, and microglia in combination with extracellular electrophysiological recording techniques using multiple electrode arrays (MEAs) to determine the effect of microglia on neural function and the response to neuroinflammatory stimuli. Specifically, we established the tri-culture and its corresponding neuron-astrocyte co-culture (lacking microglia) counterpart on custom MEAs and monitored their electrophysiological activity for 21 days to assess culture maturation and network formation. As a complementary assessment, we quantified synaptic puncta and averaged spike waveforms to determine the difference in excitatory to inhibitory neuron ratio (E/I ratio) of the neurons. The results demonstrate that the microglia in the tri-culture do not disrupt neural network formation and stability and may be a better representation of the in vivo rat cortex due to its more similar E/I ratio as compared to more traditional isolated neuron and neuron-astrocyte co-cultures. In addition, only the tri-culture displayed a significant decrease in both the number of active channels and spike frequency following pro-inflammatory lipopolysaccharide exposure, highlighting the critical role of microglia in capturing electrophysiological manifestations of a representative neuroinflammatory insult. We expect the demonstrated technology to assist in studying various brain disease mechanisms.

Civilizational populism is readily observable in Turkish politics. This study analyzes how the ruling political parties in Turkey (Islamist JDP and ultra-nationalist NAP) reframe extant civilizational ideas into a populist format to reproduce civilizational populism for ideological and pragmatic reasons. The research implements a two-level analysis of civilizational populism. On the first level, the article reveals the intellectual origins of the ideas on civilizations. On the second level, it analyses how these ideas are used by political actors in constructing populist civilizational discourse in daily politics. This methodological approach to civilizational populism shows that politicians use old ideas that are well-known by their supporters while framing them as their populist narrative. Thus, civilizational populism occurs in Turkey as a continuity. This study finds out that (i) Civilizational populism is a salient phenomenon proving how Islam and nationalism are socially and politically coded as friendly categories in Turkey; (ii) Political actors are the agents of populism but not the inventors of the many ideas they instrumentalize; (iii) Civilizational populism in Turkey is constructed on an anti-Western narrative which is essentialist and culturalist. Consequently, the article reveals that the culturist and essentialist critique levelled by other civilizations at Western political actors can be levelled similarly at the Muslim political elite.

Adsorption on a functionalized surface can be an effective way of purifying polyphenols from complex plant extracts. Polymeric resins that rely on hydrophobic interactions suffer from low selectivity, weak affinity towards polyphenols, and lack tunability therefore making the purification of polyphenols less efficient. In this study, a purification process for the recovery of polyphenols from grape pomace extract was successfully developed using hydrogen bonding affinity ligands grafted on silica particles and PEG-assisted elution solvents. Bare silica (SiO2) and polyethylene glycol (mPEG)-grafted silica microparticles with molecular weights of 2000 and 5000 were tested to determine their polyphenol binding and release characteristics. Functionalizing the surface of bare silica with mPEG ligands increased the adsorption capacity by 7.1- and 11.4-fold for mPEG-2000 and mPEG-5000 compared to bare silica particles, respectively. This was likely due to the introduction of more polyphenol binding sites with mPEG functionalization. Altering the molecular weight (MW) of mPEG grafted on silica surfaces provided tunability in the adsorption capacity. A complete recovery of polyphenols (~99.9%) from mPEG-grafted silica particles was achieved by utilizing PEG−ethanol or PEG−water cosolvent systems. Recovered polyphenols showed up to ~12-fold antioxidant activity compared to grape pomace extract. This study demonstrates that mPEG-grafted silica particles and elution of polyphenols with PEG cosolvents can potentially be used for large-scale purification of polyphenols from complex plant extracts and simplify the use of polyphenols, as PEG facilitates remarkable solvation and is an ideal medium for the final formulation of polyphenols.

The collection of solid waste is a challenging issue, especially in highly urbanized areas. In developing countries, landfilling is currently the preferred method for disposing of solid waste, but each landfill has a limited lifecycle. Therefore, changes in the amount of stored waste should be monitored for the sustainable management of such areas. In this study, volumetric changes in a landfill were examined using a low-cost unmanned aerial vehicle (UAV). Aerial photographs obtained from five different flights, covering approximately two years, were used in the volume calculations. Values representing the amount of remaining space between the solid waste and a reference plane were determined using digital elevation models, which were produced based on the structure from motion (SfM) approach. The obtained results and potential of UAVs in the photogrammetric survey of a landfill were further evaluated and interpreted by considering other possible techniques, ongoing progress, and the information existing in an environmental impact assessment report. As a result of the study, it was proved that SfM carried out using a low-cost UAV has a high potential for use in the reconstruction of a landfill. Outcomes were obtained over a short period, without the need for direct contact with the solid waste, making the UAV preferable for use in planning and decision-making studies.

Remarkable progress has been made to date in the discovery of material binding peptides and their utilization in nanotechnology, which has brought new challenges and opportunities. Nowadays phage display is a versatile tool, important for the selection of ligands for proteins and peptides. This combinatorial approach has also been adapted over the past decade to select material-specific peptides. Screening and selection of such phage displayed material binding peptides has attracted great interest, in particular because of their use in nanotechnology. Phage display selected peptides are either synthesized independently or expressed on phage coat protein. Selected phage particles are subsequently utilized in the synthesis of nanoparticles, in the assembly of nanostructures on inorganic surfaces, and oriented protein immobilization as fusion partners of proteins. In this paper, we present an overview on the research conducted on this area. In this review we not only focus on the selection process, but also on molecular binding characterization and utilization of peptides as molecular linkers, molecular assemblers and material synthesizers.

Nanoporous gold (np-Au) electrode coatings have shown improved neural electrophysiological recording fidelity in vitro, in part due to reduced surface coverage by astrocytes. This reduction in astrocytic spreading has been attributed to the influence of electrode nanostructure on focal adhesion (FA) formation. This study describes the development and use of a microfluidic flow cell for imposing controllable hydrodynamic shear on astrocytes cultured on gold surfaces of different morphologies, in order to study the influence of nanostructure on astrocyte adhesion strength as a function of np-Au electrode morphology. Astrocyte detachment (a surrogate for adhesion strength) monotonically increased as feature size was reduced from planar surfaces to np-Au, demonstrating that adhesion strength is dependent on nanostructure. Putative mechanisms responsible for this nanostructure-driven detachment phenomenon are also discussed.

Molecular diagnostics have significantly advanced the early detection of diseases, where the electrochemical sensing of biomarkers (e.g., DNA, RNA, proteins) using multiple electrode arrays (MEAs) has shown considerable promise. Nanostructuring the electrode surface results in higher surface coverage of capture probes and more favorable orientation, as well as transport phenomena unique to nanoscale, ultimately leading to enhanced sensor performance. The central goal of this study is to investigate the influence of electrode nanostructure on electrically-guided immobilization of DNA probes for nucleic acid detection in a multiplexed format. To that end, we used nanoporous gold (np-Au) electrodes that reduced the limit of detection (LOD) for DNA targets by two orders of magnitude compared to their planar counterparts, where the LOD was further improved by an additional order of magnitude after reducing the electrode diameter. The reduced electrode diameter also made it possible to create a np-Au MEA encapsulated in a microfluidic channel. The electro-grafting reduced the necessary incubation time to immobilize DNA probes into the porous electrodes down to 10 min (25-fold reduction compared to passive immobilization) and allowed for grafting a different DNA probe sequence onto each electrode in the array. The resulting platform was successfully used for the multiplexed detection of three different biomarker genes relevant to breast cancer diagnosis.

The Equality-Generalized Travelling Salesman Problem (E-GTSP), which is an extension of the Travelling Salesman Problem (TSP), is stated as follows: given groups of points within a city, like banks, supermarkets, etc., find a minimum cost Hamiltonian cycle that visits each group exactly once. It can model many real-life combinatorial optimization scenarios more efficiently than TSP. This study presents five spatially driven search-algorithms for possible transformation of E-GTSP to TSP by considering the spatial spread of points in a given urban city. Presented algorithms are tested over 15 different cities, classified by their street-network’s fractal-dimension. Obtained results denote that the R-Search algorithm, which selects the points from each group based on their radial separation with respect to the start–end point, is the best search criterion for any E-GTSP to TSP conversion modelled for a city street network. An 8.8% length error has been reported for this algorithm.

Cancer cells are refractory to most forms of chemotherapy. Conventional and alternative drugs, such as herbal extracts, have been developed to target cancer cells. This study aims to test the cytotoxic effects of Pelargonium endlicherianum root extracts on prostate cancer cells. In vitro cytotoxic activities of the methanolic and ethanolic root extracts (0–150 µg/mL) were screened against androgen dependent independent (PC-3) prostate cancer cell lines by MTT assay. Ethanolic root extract induced a significant decrease in cell viability characterized by 100 < IC50 < 150 µg/mL. The results of this study support the efficacy of P. endlicherianum as an anticancer agent for prostate cancer and a potential adjuvant treatment to current chemotherapeutic agents used in the treatment of prostate tumors. Further studies of the effects of individual flavonoids alone and in combination with each other and with currently used therapies are needed.