UAV-based RGB and TIR imaging for geothermal monitoring: a case study at Kestanbol geothermal field, Northwestern Turkey.

Kestanbol is one of the most important geothermal fields in NW Turkey. This study conducted the first-ever surveys over a 10 ha reach of the Kestanbol geothermal field using an unmanned aerial vehicle (UAV) equipped with visible (RGB) and thermal infrared (TIR) cameras. Low-altitude flights below 40 m above the ground were operated above the Kestanbol geothermal field. Approximately 3500 RGB and TIR images were captured using the UAV. We recorded high-resolution RGB and TIR data of the Kestanbol geothermal field and applied the structure from motion (SfM) algorithm to identify the distribution of geothermal springs and seeps. The Kestanbol geothermal field was monitored to create a georeferenced RGB orthophoto, RGB 3D surface model, thermal anomaly map, and digital surface model (DSM) of the area with centimeter-level accuracy. In the TIR orthophoto, the surface temperature in the geothermal field was found to be between 15 and 75 °C. All the thermal anomalies revealed by the survey were verified by field observations. The geothermal springs and seeps were parallel to the NE-SW regional tectonic trends. The results of this study demonstrate an effective technique for monitoring and assessing geothermal water using UAV-based RGB and TIR imaging and provide an accurate basis for geothermal development projects. RGB and TIR imaging using UAVs are considered promising methods for improving the assessment of the effects of geothermal water on the environment.

Design, synthesis and biological evaluation of 3,5-diaryl isoxazole derivatives as potential anticancer agents.

The present study was carried out in the attempt to synthesize a new class of potential anticancer agents comprising eleven compounds (24-34) sharing the 3,5-diarylisoxazole as a core. The chemical structure of the new synthesized compounds was established by IR, 1H NMR, 13C NMR and elemental analysis. Their biological potential towards prostate cancer was evaluated by using cancer PC3 cells and non-tumorigenic PNT1a cells. Interestingly, compound 26 distinguished from others with a quite high selectivity value that is comparable to 5-FU. The binding mode of 26 towards Ribosomal protein S6 kinase beta-1 (S6K1) was investigated at a molecular level of detail by employing docking simulations based on GLIDE standard precision as well as MM-GBSA calculations.

From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.

Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 µM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.

Machine learning study: from the toxicity studies to tetrahydrocannabinol effects on Parkinson's disease.

Aim: Investigating molecules having toxicity and chemical similarity to find hit molecules. Methods: The machine learning (ML) model was developed to predict the arylhydrocarbon receptor activity of anti-Parkinson's and US FDA-approved drugs. The ML algorithm was a support vector machine, and the dataset was Tox21. Results: The ML model predicted apomorphine in anti-Parkinson's drugs and 73 molecules in FDA-approved drugs as active. The authors were curious if there is any molecule like apomorphine in these 73 molecules. A fingerprint similarity analysis of these molecules was conducted and found tetrahydrocannabinol (THC). Molecular docking studies of THC for dopamine receptor 1 (affinity = -8.2 kcal/mol) were performed. Conclusion: THC may affect dopamine receptors directly and could be useful for Parkinson's disease.

Arylhydrocarbon receptor has tissue-specific roles in xenobiotic metabolism, the immune system, inflammation and cancer. Studies showed that carbidopa and dopamine are agonists of arylhydrocarbon receptor. Parkinson's disease is a neurodegenerative disease and depends on the dopamine system's dysregulation. There is a strong relationship between the dopamine system and cannabinoids. In this study, the possibility of the agonist effect of tetrahydrocannabinol on dopamine receptors was investigated by a machine learning method.

Evaluating the additional effects of arthrocentesis on the condylar pathways of temporomandibular joint in patients with internal derangement treated with stabilizing splint.

OBJECTIVES: The purpose of this investigation is to find out the differences in the condylar pathways of the temporomandibular joint (TMJ) in patients treated with stabilizing splint and arthrocentesis accompanied by stabilizing splint in 6 months period. STUDY DESIGN: Forty patients who were assigned a clinical diagnosis of internal derangement of the TMJ were included in this study. Full arch stabilizing splints were made for all patients. Patients were divided into two groups; group A (patients who underwent arthrocentesis), group B (patients who did not underwent arthrocentesis). Computerized axiography was performed for each group before therapy (T0), on the 3(rd) month (T1), after arthrocentesis (TA) (for group A) and on 6th month (T2). Curve parameters were recorded in three-dimension; X axis (posteroanterior), Y axis (mediolateral), Z axis (superoinferior). Also the distance from the starting point of the movement on sagittal plane (S) was calculated. RESULTS: There was a significant increase in the value of Z (p = 0.014) and S (p = 0.038) in mediotrusion movement in group A. Also, there was a significant increase in the value of Z (p = 0.007) in protrusion/retrusion movement, value of Z (p = 0.019) and S value (p = 0.002) in mediotrusion movement and value of Y in open/close movement in group B. CONCLUSIONS: No positive effects on condylar pathways of an additional arthrocentesis to splint therapy alone in patients with internal derangement were found in this study.