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The newly synthesized imidazole derivative namely, 4,5-bis[(E)-2-phenylethenyl]-1H,1′H-2,2′-biimidazole (KA1), was studied for its molecular geometry, docking studies, spectral analysis and density functional theory (DFT) studies. Experimental vibrational frequencies were compared with scaled ones. The reactivity sites were determined using average localized ionization analysis (ALIE), electron localized function (ELF), localized orbital locator (LOL), reduced density gradient (RDG), Fukui functions and frontier molecular orbital (FMO). Due to the solvent effect, a lower gas phase energy gap was observed. Through utilization of the noncovalent interaction (NCI) method, the hydrogen bond interaction, steric effect and Vander Walls interaction were investigated. Molecular docking simulations were employed to determine the specific atom inside the molecules that exhibits a preference for binding with protein. The parameters for the molecular electrostatic potential (MESP) and global reactivity descriptors were also determined. The thermodynamic characteristics were determined through calculations employing the B3LYP/cc-pVDZ basis set. Antimicrobial activity was carried out using the five different microorganisms like Escherichia coli, Streptococcus pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae and Candida albicans.

Feasibly synthesized novel rhodamine based chemosensor (RS) has been described for the detection of paramagnetic and biologically important Cu2+ and Fe3+ ions. Among all the transition metal ions, this chemosensor was able to precisely spot out the Cu2+/Fe3+ ion couple by its spirolactam cleavage, consequently enhanceing their absorbances at 521 and 529 nm for Cu2+ and Fe3+ ions respectively even at low concentrations [263 pM (Cu2+) and 2 nM (Fe3+)], which is prominently lower than the WHO and the United States Environmental Protection Agency (USEPA) strategies. This metal pair (Cu2+/Fe3+) has more tendencies to cleave the lactam ring even in the presence of higher concentrations of other relevant metal ions. Such remarkable behaviour of RS has been motivated us to apply zebrafish bio-imaging and molecular logic circuits.

The power system network is becoming dense and much more complex. Dynamic and continuous monitoring of the power system network is required to avoid power outages. Phasor Measurement Unit (PMU) is one such device that provides state estimation parameters of dynamic power system networks that are used for designing relays and controlling the system network. Power Quality problems such as voltage sags and swells can damage the sensitive load and cause abnormal voltage, current, and frequency conditions in a power system network. Fast and major developments in power electronics technology have made it possible to mitigate the power quality problems. Dynamic Voltage Restorer (DVR) is a voltage sag compensation device that compensates the difference in voltage avoiding interruptions and restores the voltage to the pre-fault value. This paper discusses a technique used for monitoring, protecting, and controlling the system under fault condition using PMU and compensate voltage sag for a sensitive load using DVR based on the data collected by placing PMU in an optimal location. The outcome of the above analysis is recorded which shows that effective monitoring, controlling, and voltage sag compensation can be done with less number of PMU’s placed optimally in conjunction with DVR.

The study of using deep learning for detection of various thoracic diseases has been an active and challenging research area. Chest X-rays are currently the most common and globally used radiology practices for detecting thoracic diseases. Patients suffering from thoracic diseases need to take Chest X-Rays which are read by radiologists and a report is generated by them. However, today with the increase in the number of thoracic patients, a quick method to classify the disease and generate the report has become necessary. Also, patient history has to be considered for diagnosis. This paper offers a comparative study on the various deep learning techniques that can process chest x-rays and are capable of detecting the different thoracic diseases. Also, a technique has been proposed to classify 14 diseases namely Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Pleural thickening based on the given X-rays using Residual Neural Network.

Chronic irritation to the mucosa manifests as reactive lesions or mucocele in pediatric population. Reactive lesions may range from developmental, inflammatory to neoplastic. Adverse oral habits such as lip biting and cheek biting also contribute to these types of oral lesions. We present a case of a 9-year-old pediatric patient who reported with the complaint of swelling in the right side of the lower lip and history of chronic lip biting. After intraoral examination, clinical diagnosis of mucocele was made. Following excision, the histopathological features were suggestive of fibroepithelial polyp. To avoid misdiagnosis, proper history, clinical and histopathological correlation is advisable to evade misdiagnosis and disparity between clinical and histopathological diagnosis.

Background: Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee arthroplasty prosthesis using automated deep learning models., Methods: Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior-posterior (AP) and lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with saliency maps for visualization., Results: After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%, the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing model correctly and uniquely identified the implants which could be visualized using saliency maps., Conclusion: Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps give us a better understanding of which regions the model is focusing on while predicting the results., Competing Interests: Conflict of interestThe authors declare no conflict of interest., (© Indian Orthopaedics Association 2021.)

The prevalence of polycystic ovary syndrome (PCOS) has been gradually increasing among adult females worldwide. Laparoscopy drilling on ovary is the only available temporary solution with a high incidence of reoccurrence. S100A8 with S100A9 complex is believed to facilitate the cyst migration in PCOS condition. The high evident protein interaction network studies between PCOS biomarkers, cancer invasion markers, and the interactors of S100A8 confirm that this protein has strong interaction with other selective PCOS biomarkers, which may be associative in the immature cyst invasion process. Through the network studies, intensive structural and pathway analysis, S100A8 is identified as a targetable protein. In this research, the non-SELEX in silico method is adapted to construct RNA Library based on the consensus DNA sequence of Glucocorticoid Response Element (GRE) and screened the best nucleotide fragments which are bound within the active sites of the target protein. Selected sequences are joined as a single strand and screened the one which competitively binds with minimal energy. In vitro follow-up of this computational research, the designed RNA aptamer was used to infect the MCF7 cell line through Lipofectamine 2000 mediated delivery to study the anti-cell migration effect. Wound Scratch assay confirms that the synthesized 18-mer oligo has significant inhibition activity toward tumor cell migration at the cellular level., (Copyright © 2020 Manibalan, Shobana, Kiruthika, Achary, Swathi, Venkatalakshmi, Thirukumaran, Suhasini and Roopathy.)

Mixed ligand complexes of ruthenium(II), [Ru(itpy)(bpy)Cl]ClO(4)1, [Ru(itpy)(phen)Cl]ClO(4)2, [Ru(bitpy)(bpy)Cl]ClO(4)3 and [Ru(bitpy)(phen)Cl]ClO(4)4 have been synthesized and characterized. Complex 3 has also been characterized crystallographically. These complexes exhibit photolability of the Ru-Cl bond. Upon irradiation at 440 nm in the presence of nucleosides and nucleotides the complexes exchange chloride for the nucleoside or nucleotide. The photolability of the Ru-Cl bond depends on the nature of the substituent in the tridentate tpy ligand. Photolysis of the complexes in the presence of a nucleoside or nucleotide also produces 8-oxoguanine due to the oxidation of guanine by the excited states of the complexes. These four complexes exhibit photonuclease properties and bring about the cleavage of plasmid DNA when irradiated at 440 nm. These complexes have been found to be toxic towards NIH 3T3 cells under photolytic conditions.