Search

Objective: The present work is aim to design, development and fabrication of mouth dissolving tablets containing extract of Tribulus terrestris for the treatment of hypertension. Methods: The extract of fruits of Tribulus terrestris was formulated as mouth dissolving tablets (MDTs) by full factorial design at 32 levels and prepared by direct compression method using super integrants like sodium starch glycolate and cross povidone. Furthermore, the tablet was evaluated for thickness, hardness, weight variation, wetting time, disintegration time, and in vitro drug release study. Results: The tablets were analyzed for a variety of characteristics, such as hardness (2.4-2.9 kg/cm2), friability (0.33-1.7%), disintegration time (20-34 s), drug content (95.32-99.09%), water uptake ratio (26-48%), wetting time (29-69 s), and in vitro drug release illustrated in 5 min (99.04-68.21%). There was no interaction between both the drug and the polymer, according to FTIR and DSC studies. Conclusion: The research revealed that Tribulus terrestris fruits extract can be designed, developed and fabricated into mouth dissolving tablet for the treatment of hypertension with improved bioavailability and expected patient compliance.

This article address approaches to the development of methods for assessing the psychological state of social network members during the coronavirus pandemic through sentiment analysis of messages. The purpose of the work is to determine the psychological tension index by using a previously developed thematically ranked dictionary. Researchers have investigated methods to evaluate psychological tension among social network users and to forecast the psychological distress. The approach is novel in the sense that it ranks emojis by mood, considering both the emotional tone of tweets and the emoji’s dictionary meanings. A novel method is proposed to assess the dynamics of the psychological state of social network users as an indicator of their subjective well-being, and develop targeted interventions for help. Based on the ranking of the Emotional Vocabulary Index (EVI) and Subjective Well-being Index (SWI), a scheme is developed to predict the development of psychological tension. The significance lies in the efficient assessment of the fluctuations in the mental wellness of network users as an indication of their emotions and a prerequisite for further predictive analysis. The findings gave a computed value of EVI of 306.15 for April 2022. The prediction accuracy of 88.75% was achieved.

Spondylolisthesis refers to the slippage of one vertebral body over the adjacent one. It is a chronic condition that requires early detection to prevent unpleasant surgery. The paper presents an optimized deep learning model for detecting spondylolisthesis in X-ray radiographs. The dataset contains a total of 299 X-ray radiographs from which 156 images are showing the spine with spondylolisthesis and 143 images are of the normal spine. Image augmentation technique is used to increase the data samples. In this study, VGG16 and InceptionV3 models were used for the image classification task. The developed model is optimized by utilizing the TFLite model optimization technique. The experimental result shows that the VGG16 model has achieved a 98% accuracy rate, which is higher than InceptionV3’s 96% accuracy rate. The size of the implemented model is reduced up to four times so it can be used on small devices. The compressed VGG16 and InceptionV3 models have achieved 100% and 96% accuracy rate, respectively. Our finding shows that the implemented models were outperformed in the diagnosis of lumbar spondylolisthesis as compared to the model suggested by Varcin et al. (which had a maximum of 93% accuracy rate). Also, the developed quantized model has achieved higher accuracy rate than Zebin and Rezvy’s (VGG16 + TFLite) model with 90% accuracy. Furthermore, by evaluating the model’s performance on other publicly available datasets, we have generalised our approach on the public platform.

Recognizing and authenticating wheat varieties is critical for quality evaluation in the grain supply chain, particularly for methods for seed inspection. Recognition and verification of grains are carried out manually through direct visual examination. Automatic categorization techniques based on machine learning and computer vision offered fast and high-throughput solutions. Even yet, categorization remains a complicated process at the varietal level. The paper utilized machine learning approaches for classifying wheat seeds. The seed classification is performed based on 7 physical features: area of wheat, perimeter of wheat, compactness, length of the kernel, width of the kernel, asymmetry coefficient, and kernel groove length. The dataset is collected from the UCI library and has 210 occurrences of wheat kernels. The dataset contains kernels from three wheat varieties Kama, Rosa, and Canadian, with 70 components chosen at random for the experiment. In the first phase, K-nearest neighbor, classification and regression tree, and Gaussian Naïve Bayes algorithms are implemented for classification. The results of these algorithms are compared with the ensemble approach of machine learning. The results reveal that accuracies calculated for KNN, decision, and Naïve Bayes classifiers are 92%, 94%, and 92%, respectively. The highest accuracy of 95% is achieved through the ensemble classifier in which decision is made based on hard voting.

In response to the first COVID-19 surge in 2020, secondary care outpatient services were rapidly reconfigured to provide specialist review for disease sequelae. At our institution, comprising hospitals across three sites in London, we initially implemented a COVID-19 follow-up pathway that was in line with expert opinion at the time but more intensive than initial clinical guidelines suggested. We retrospectively evaluated the resource requirements for this service, which supported 526 patients from April 2020 to October 2020. At the 6-week review, 193/403 (47.9%) patients reported persistent breathlessness, 46/336 (13.7%) desaturated on exercise testing, 167/403 (41.4%) were discharged from COVID-19-related secondary care services and 190/403 (47.1%) needed 12-week follow-up. At the 12-week review, 113/309 (36.6%) patients reported persistent breathlessness, 30/266 (11.3%) desaturated on exercise testing and 150/309 (48.5%) were discharged from COVID-19-related secondary care services. Referrals were generated to multiple medical specialties, particularly respiratory subspecialties. Our analysis allowed us to justify rationalising and streamlining provisions for subsequent COVID-19 waves while reassured that opportunities for early intervention were not being missed.

Context: Corona virus disease 2019 (COVID-19) was declared as pandemic by WHO on March 10, 2020. Several countries around the globe have seen a two-wave pattern of reported cases. India is witnessing unprecedented spike in COVID-19 cases again since March 2021 especially in Maharashtra. Newer insights in pathogenesis of diseases, diagnosis and treatment modalities continue to evolve in case of novel infection. Aim: To study and compare laboratory parameters in COVID cases in first wave in 2020. Design: Retrospective cross-sectional observational study. Materials and Methods: Total 400 cases; 354 RTPCR and 46 RAT confirmed cases of COVID-19 done at dedicated COVID Hospital. Statistical Analysis: Comparison of laboratory parameters was done between 72 Severe and 328 Non-Severe cases by unpaired t-test. Results: Statistically significant differences were seen in severe cases as compared to non severe cases in Lymphocyte count, Eosinophil count, Neutrophil Lymphocyte Ratio, CRP, D-dimer, Ferritin levels. WBC count, Platelet count and ALT did not show significant difference between severe and non severe cases. Conclusion: Lymphopenia, raised N/L ratio, Eosinopenia, increased D-dimer, Ferritin, CRP are associated with severe COVID disease. The routine laboratory tests can diagnose the disease, predict prognosis and complications and monitor treatment response. Keywords: COVID- 19, Laboratory parameters, RTPCR, Prognosis, Severe disease.

Background: Corona virus disease 2019 (COVID-19) is a new emerging infectious disease, first identified in China in December 2019, caused by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). Objective: To study and analyze the prevalence, demographic characteristics of healthcare workers (HCWs) who tested positive for COVID-19 in a dedicated Covid hospital in Mumbai. Materials & Methods: This was a retrospective observational study of HCWs with COVID-19 in a dedicated Covid Hospital, Mumbai from April to October 2020. HCW tested positive for COVID-19 was 55 from April to October 2020.The percentage of infected hospital staff was 9.8%. Most of the infected HCWs were in the age group of 51 to 60. Mean age was 42 years. Mortality was 0.1%. Majority HCWs (70.9%) acquired infection from community; 20% acquired from the hospital and 9.1% acquired infection from infected coworkers. Majority of the affected HCWs were nurses followed by doctors. Comorbidities were reported in 23.6% of infected HCWs. 5.4% of infected HCWs received infection because of aerosol generated procedures. 58.1% of HCWs got infection despite receiving prophylaxis like HCQ, Arsenicumalbicum. Conclusion: HCWs are at an increased risk of COVID-19 in the workplace. Preparedness of HICC (Hospital Infection Control Committee), frequent training sessions and mock drills are of paramount importance in preventing spread of infectious disease. Coordination between Hospital Administrators, Public Health Officials of national and international organizations, Engineering Department, Occupational Health and Safety staff is essential in combating pandemic. Keywords: Infection Control, Health Care workers, Lessons learnt, Hospital Preparedness

This study highlights the development of a lab-scale, indigenously designed; Packed-Bed Biofilm Reactors (PBBR) packed with brick pieces. The developed biofilm in the reactor was used for the decolourisation and biodegradation of the textile industry effluent. The PBBR was continuously operated for 264 days, during which 301 cycles of batch and continuous treatment were operated. In batch mode under optimised conditions, more than 99% dye decolourisation and ≥ 92% COD reduction were achieved in 6 h of contact time upon supplementation of effluent with 0.25 g L− 1 glucose, 0.25 g L− 1 urea, and 0.1 g L− 1 phosphates. A decolourisation rate of 133.94 ADMI units h− 1 was achieved in the process. PBBR, when operated in continuous mode, showed ≥ 95% and ≥ 92% reduction in ADMI and COD values. Subsequent aeration and passage through charcoal reactor assisted in achieving ≥ 96% reduction in COD and ADMI values. An overall increase of 81% in dye-laden effluent decolourisation rate, from 62 mg L− 1 h− 1 to 262 mg L− 1 h− 1, was observed upon increasing the flow rate from 18 mL h− 1 to 210 mL h− 1. Dye biodegradation was determined by UV-Vis and FTIR spectroscopy and toxicity study. SEM analysis showed the morphology of the attached-growth biofilm.

Nanoparticles (NPs) are particles less than 100 nm in size. Silver nanoparticles have many important chemical and biological applications. These applications drag the attention of researchers for the production of silver nanoparticles. The objective of the study is to synthesize silver nanoparticles from the green method without using any hazardous chemicals. In the present study, Ocimum tenuiflorum (Tulsi) leaf extract was used to synthesize silver nanoparticles (Ag-NPs) from silver nitrate. Moreover, Tulsi leaf extract was also exploited, both traditionally and commercially, for their antibacterial potential which is essential oil components. The synthesis of Ag-NPs was visually deduced by a change in colour from light yellow to reddish-brown. UV-Visible Spectrometry, X-ray diffraction (XRD), and Transmission Electron Microscopic (TEM) analysis were then used to characterize synthesized nanoparticles. This study also determined the effect of temperature and effect of silver nitrate concentration on nanoparticle synthesis. Synthesized silver nanoparticles showed absorbance ‘peaks’ at 420 nm with UV-visible spectrometry. X-ray diffraction studies showed that the synthesized silver nanoparticles were crystalline. The mean crystallite size was estimated using the Debye-Scherrer equation. However, TEM showed that silver nanoparticles have spherical (40 nm) shape with a narrow size distribution. These synthesized nanoparticles were used to evaluate antimicrobial activity against the most common pathogens of nosocomial infections, MRSA, E. coli, Salmonella typhi, and Klebsiella pneumoniae (ESBL). O. tenuiflorum extract showed strong potential for the synthesis of silver nanoparticles from silver nitrate and the synthesized silver nanoparticles had efficient antimicrobial properties against different pathogens including drug-resistant pathogens in vitro. These synthesized nanoparticles showed maximum ‘zones’ of inhibition against MRSA among these four organisms.

Background: Periodontitis is inflammatory disorder resulting in progressive destruction of the periodontal ligament and alveolar bone with pocket formation, recession, or both. Vitamin D has a benefit in bone metabolism and anti-inflammatory activity involving T-cell homeostasis. The objective of this study was to compare the vitamin D level in patient with chronic periodontitis and healthy periodontium and evaluate its correlation.Methods: A comparative cross-sectional study was done in 80 patients (40 with healthy periodontium and 40 with chronic periodontitis) visiting to Department of Dental Surgery (Periodontology and Oral Implantology Section) of Bir Hospital. Parameters measured were plaque index, gingival index, clinical attachment level and pocket depth for the evaluation of healthy periodontium and chronic periodontitis. Results: The result showed 16.58±6.65ng/mL and 19.06±11.52ng/mL level of vitamin D in female and male respectively (p-value 0.230) and 16.85±13.30 ng/mL and 19.78±5.87 ng/mL level of vitamin D in healthy and chronic periodontitis groups respectively (p-value 0.209).Conclusions: There are no differences in the level of serum vitamin D between healthy and chronic periodontitis groups. No association was seen between vitamin D level and chronic periodontitis.Keywords: Bone metabolism; chronic periodontitis; clinical attachment level; healthy periodontium; vitamin D; periodontal pocket

Introduction: Periodontal diseases comprise a group of inflammatory diseases caused by bacteria that colonise the tooth surface and infect surrounding soft tissues, ultimately leading to tooth loss. Vitamin D, calcium, alkaline phosphatase, and parathyroid hormone have role in bone metabolism both in health or in disease. Objective: To compare serum Vitamin D, calcium, alkaline phosphatase, and parathyroid hormone levels in patients with generalised chronic periodontitis and healthy periodontium. Methods: A cross-sectional analytical study was done in 80 patients visiting Department of Dental Surgery from July to December 2019. Clinical parameters measured were plaque index, gingival index, clinical attachment level, and pocket depth in the healthy periodontium and generalised chronic periodontitis. Blood investigation for serum Vitamin D, calcium, alkaline phosphatase (ALP), and parathyroid hormone (PTH) level were done. Data was analysed in SPSS v.16. Results: Out of total 80 patients enrolled (mean age of 36.20±7.37 years), 40 (50%) patients had healthy periodontium and 40 (50%) had generalised chronic periodontitis. Mean levels of serum Vitamin D (P=0.006) and calcium (P=0.020) were significantly higher in chronic periodontitis patient compared to healthy controls while no significant intergroup differences were seen with respect to ALP (P=0.267) and PTH (P=0.351). Conclusion: No significant differences were found in the levels of ALP and PTH between generalised chronic periodontitis and healthy patients and higher levels of Vitamin D and calcium were seen in generalised chronic periodontitis patients as compared to healthy patients.

Introduction: Treatment of periodontal diseases done by surgical therapy depends upon extent and severity of disease. The ultimate goal of periodontal reconstructive surgery is to regenerate tissues destroyed during periodontal disease. Objective: To evaluate the effectiveness of bovine-derived xenograft with collagen membrane in treatment of intrabony defects by comparing it with open flap debridement alone. Methods: This non-randomised controlled trial was conducted after ethical clearance, at Bir hospital from 2018 March to 2019 April. The study recruited 38 patients by convenience sampling, age from 25-44 years, with chronic periodontitis, and willing to sign informed consent. Intrabony defects were treated by open flap debridement with bovine-derived xenograft and bioresorbable collagen membrane (Test group) and open flap debridement alone (Control group). Probing pocket depth, clinical attachment level, gingival recession, oral hygiene status, and gingival status were assessed at baseline and six months. Results: Six months after therapy, in Test group probing pocket depth reduction was 5.2 mm and gain in mean clinical attachment level was 4.3 mm. In Control group, mean probing pocket depth reduction was 3.8 mm and mean gain in clinical attachment level was 2.7 mm. The test treatment resulted in statistically higher probing pocket depth reduction and clinical attachment level gain than Control group. Conclusion: Both therapies resulted in significant probing pocket depth reductions and clinical attachment gains, and treatment with open flap debridement with bovine-derived xenografts and collagen membrane resulted in significantly higher probing pocket depth reduction and clinical attachment gain than treatment with open flap debridement alone.

TNF‐blockade has shown clear therapeutic value in rheumatoid arthritis and other immune‐mediated inflammatory diseases, however its mechanism of action is not fully elucidated. We investigated the effects of TNF‐blockade on CD4+ T cell activation, maturation, and proliferation, and assessed whether TNF‐inhibitors confer regulatory potential to CD4+ T cells. CyTOF and flow cytometry analysis revealed that in vitro treatment of human CD4+ T cells with the anti‐TNF monoclonal antibody adalimumab promoted IL‐10 expression in CD4+ T cells, whilst decreasing cellular activation. In line with this, analysis of gene expression profiling datasets of anti‐TNF‐treated IL‐17 or IFN‐γ‐producing CD4+ T cells revealed changes in multiple pathways associated with cell cycle and proliferation. Kinetics experiments showed that anti‐TNF treatment led to delayed, rather than impaired T‐cell activation and maturation. Whilst anti‐TNF‐treated CD4+ T cells displayed some hyporesponsiveness upon restimulation, they did not acquire enhanced capacity to suppress T‐cell responses or modulate monocyte phenotype. These cells however displayed a reduced ability to induce IL‐6 and IL‐8 production by synovial fibroblasts. Together, these data indicate that anti‐TNF treatment delays human CD4+ T‐cell activation, maturation, and proliferation, and this reduced activation state may impair their ability to activate stromal cells., Through deep phenotyping using CyTOF and functional assays we have shown that TNF inhibition using adalimumab altered the phenotype and function of multiple populations of human CD4+ T‐cells, leading to an overall decreased pro‐inflammatory potential. These results provide insight into the anti‐TNF mechanism of action in human CD4+ T‐cells.

Estrogen receptor alpha (ERα), a nuclear receptor protein encoded by the estrogen receptor1 (ESR1) gene, is an important biomarker in breast cancer diagnosis. Any dysregulation in its expression can actively implicate the development and progression of the disease. ERα is abnormally expressed in around 60% of the active cases, making it an important therapeutic target. In this study, we report the application of computational approaches to identify suitable drug-like molecules, which share similar ligand binding dynamics with ERα. Structure-based virtual screening(SBVS), docking, and inhibitor dynamics are used to study the ligand binding and interaction profiling of the anticipated ligand molecule, at the active site of the 4-hydroxytamoxifen (OHT) protein (PDB Id: 3ERT). SBVS analysis follows HTVS, SP, and XP protocol in comparison to the ZINC and NCI, to retrieve 20bestligandhits as effective inhibitors; All the compounds have shown significant interaction with active site residues (Leu346, Thr347, Asp351, Glu353, Trp383, Leu387, and Arg394) of the 4-hydroxytamoxifen. Moreover, the docking study was used to screen the top 5 compounds: ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569. We also, employed molecular dynamics simulations to explore the binding dynamics present at the atomic level. Our MDS results have revealed the compounds (ZINC13377936 and NCI35753) with outstanding binding stability and lesser fluctuations. Both above hits possess a high potential as future therapeutic agents, acting by the mechanism of competitive inhibition against the ERα protein in breast cancer.

Over the last few years, different contaminants have posed a danger to the quality of the water. Hence modelling and forecasting water quality are very important in the management of water contamination. The paper proposes an ensemble machine learning-based model for assessing water quality. The results of the proposed model are compared with several machine learning models, including k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree. The considered dataset contains seven statistically important parameters: pH, conductivity, dissolved oxygen, Biochemical Oxygen Demand, nitrate, total coliform, and fecal coliform. The water quality index is calculated for assessing water quality. To utilize an ensemble approach, a voting classifier has been designed with hard voting. The highest prediction accuracy of 99.5% of the water quality index is presented by the voting classifier as compared to the prediction accuracy of 99.2%, 90%, 79%, and 99% presented through k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree, respectively. This was further enhanced to 99.74% using particle swarm based optimization.

Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%)

Multilocular Cystic Renal Neoplasm of Low Malignant Potential (MCRNLMP) represents a rare variant of clear cell (conventional) renal carcinoma. They constitute between 3-6% of clear cell Renal Cell Carcinoma (RCC). The RCC constitutes less than 1% of all renal tumours. The MCRNLMP has an excellent prognosis with no reports of recurrence or metastasis. Authors reported a case of 67-year-old male patient on account of its rarity, co-existing pyelonephritis and incidental detection of MCRNLMP. Differentiation between MCRNLMP, RCC variants and other cystic lesions with clear cells is important as prognosis, treatment differ markedly. Radiological Bosnaik classification of complex cysts in category IIF and III is challenging and requires microscopic examination for correct diagnosis. Histopathology shows cysts separated and fibrous septae having groups of clear cells with low grade nuclei. Immunohistochemistry with Carbonic Anhydrase-IX (CA-IX), Epithelial Membrane Antigen (EMA), Cytokeratin (CK7) and CD10 is confirmatory. Diagnosis requires detailed imaging studies, meticulous grossing of nephrectomy specimens, extensive sampling of cystic tissue to find clear cells and immunohistochemistry (IHC).

BackgroundThe symptoms, radiography, biochemistry and healthcare utilisation of patients with COVID-19 following discharge from hospital have not been well described.MethodsRetrospective analysis of 401 adult patients attending a clinic following an index hospital admission or emergency department attendance with COVID-19. Regression models were used to assess the association between characteristics and persistent abnormal chest radiographs or breathlessness.Results75.1% of patients were symptomatic at a median of 53 days post discharge and 72 days after symptom onset and chest radiographs were abnormal in 47.4%. Symptoms and radiographic abnormalities were similar in PCR-positive and PCR-negative patients. Severity of COVID-19 was significantly associated with persistent radiographic abnormalities and breathlessness. 18.5% of patients had unscheduled healthcare visits in the 30 days post discharge.ConclusionsPatients with COVID-19 experience persistent symptoms and abnormal blood biomarkers with a gradual resolution of radiological abnormalities over time. These findings can inform patients and clinicians about expected recovery times and plan services for follow-up of patients with COVID-19.

International audience; Inner product functional encryption (IPFE) [1] is a popular primitive which enables inner product computations on encrypted data. In IPFE, the ciphertext is associated with a vector x, the secret key is associated with a vector y and decryption reveals the inner product x, y. Previously, it was known how to achieve adaptive indistinguishability (IND) based security for IPFE from the DDH, DCR and LWE assumptions [8]. However, in the stronger simulation (SIM) based security game, it was only known how to support a restricted adversary that makes all its key requests either before or after seeing the challenge ciphertext, but not both. In more detail, Wee [46] showed that the DDH-based scheme of Agrawal et al. (Crypto 2016) achieves semi-adaptive simulation-based security, where the adversary must make all its key requests after seeing the challenge ciphertext. On the other hand, O'Neill showed that all IND-secure IPFE schemes (which may be based on DDH, DCR and LWE) satisfy SIM based security in the restricted model where the adversary makes all its key requests before seeing the challenge ciphertext. In this work, we resolve the question of SIM-based security for IPFE by showing that variants of the IPFE constructions by Agrawal et al., based on DDH, Paillier and LWE, satisfy the strongest possible adaptive SIM-based security where the adversary can make an unbounded number of key requests both before and after seeing the (single) challenge ciphertext. This establishes optimal security of the IPFE schemes, under all hardness assumptions on which it can (presently) be based.

The nuclear dynamics of the low-lying first four electronic states of the prototypical indenyl radical is investigated based on first principles calculations to rationalize the experimental vibronic structure of the radical. The study is performed following both time-dependent and time-independent quantum-chemistry approaches using a model diabatic Hamiltonian. The construction of model Hamiltonians is based on the fits of the adiabatic energies calculated from the electronic structure method. The analyses of the static and dynamics results of the present study corroborate the experimental findings regarding the shape of the spectrum, vibrational progressions and the lifetime of the excited state. Finally, the present theoretical investigations suggest that the electronic non-adiabatic effect is extremely important for a detailed study of the vibronic structure and the electronic relaxation mechanism of the low-lying electronic states of the indenyl radical.

Free gingival graft is one of the technique used for root coverage so as to increase the inadequate keratinized tissue. The donor tissue is most frequently taken from the palate and generally the donor site is not covered or protected. Platelet rich fibrin is one of the good option for the wound coverage of donor site after free gingival graft so as to reduce the pain perception to patient and faster wound healing. The case report includes two cases with gingival recession, treated on the same day with free gingival graft with the use of platelet rich fibrin in the donor site in case 1 and without platelet rich fibrin in case 2 respectively which were compared for pain perception and wound healing postoperatively at 24 hours, 3 days, 5 days, 7 days and 14 days. There was less pain perception and discomfort in the donor site with the use of platelet rich fibrin and enhanced healing compared to donor site without platelet rich fibrin. Keywords: Donor site; free gingival graft; platelet rich fibrin.

Introduction: Glaucoma is the second leading cause of irreversible blindness worldwide and third leading cause in India. The disease progresses even intraocular pressure (IOP) is well under control; hence, now modern medicine is looking for strategies that are neuroprotective in glaucomatous optic neuropathy (GON) management. Aim: This study aimed to propound the concept of Chakshushya Rasayana and diuretic therapies and also evaluate the neuroprotective and IOP-lowering effects of Ayurvedic line of management in primary open-angle glaucoma (POAG). Materials and Methods: In this randomized parallel-group trial, patients having POAG were randomized with equal probability to one of the two treatment groups. Participants were assessed on the basis of subjective parameters such as blurred vision, frequent changes of presbyopic glasses (FCPG), delayed dark adaptation (DDA), visual field defect (VFD) and headache; objective parameters such as best-corrected visual acuity (BCVA), IOP and optic nerve head changes and perimetry findings such as mean deviation (MD) and Glaucoma Hemifield Test. In Group A, after Koshtha Shodhana and Nasya, Tarpana and Ashchyotana with Shigru Pallava Arka were done locally and Punarnavashtaka Kwatha and Gokshuradi Guggulu were given internally for 52 days along with modern antiglaucoma eye drop and in Group B, patients already taking antiglaucoma eye drop were kept under observation for 2 months. Results: Patients in Group A showed better results in blurred vision, FCPG, DDA, VFD, headache, BCVA, IOP and MD. Patients in Group B showed better results in blurred vision and FCPG. A comparison of both groups showed significant results in blurred vision, DDA, VFD, BCVA, IOP and MD. Conclusion: The clinical study concludes that Ayurvedic treatment protocol along with antiglaucoma eye drop in Group A patients was found to be more effective in reducing the IOP and controlling the progression of GON along with modern anti-glaucoma eye drop. Early diagnosis and proper management can prevent, arrest, or delay progression of POAG.

A low cost, non-explosive process for the synthesis of graphene oxide (GO) is demonstrated. Using suitable choice of reaction parameters including temperature and time, this recipe does not require expensive membranes for filtration of carbonaceous and metallic residues. A pre-cooling protocol is introduced to control the explosive nature of the highly exothermic reactions during the oxidation process. This alleviates the requirement for expensive membranes and completely eliminates the explosive nature of intermediate reaction steps when compared to existing methods. High quality of the synthesized GO is corroborated using a host of characterization techniques including X-ray diffraction, optical spectroscopy, X-ray photoemission spectroscopy and current-voltage characteristics. Simple reduction protocol using ultra-violet light is demonstrated for potential application in the area of photovoltaics. Using different reduction protocols together with the proposed inexpensive method, reduced GO samples with tunable conductance over a wide range of values is demonstrated. Density functional theory is employed to understand the structure of GO. We anticipate that this scalable approach will catalyze large scale applications of GO.

As technology scales in nanometer regime leakage current are becoming important metric of comparable importance to leakage current for the analysis and design of complex logic circuits. In this paper, we did the comparative analysis of leakage current for carry look ahead logic circuits. The simulation results depicts that the proposed design leads to efficient in terms of standby leakage power. We have performed simulations using Cadence Spectra 90nm standard CMOS technology at room temperature with supply voltage of 1V.

Orchestrator is a middleware which coordinates the sequence of execution of distributed tasks. Numerous Orchestrators are available for different applications. A composite Orchestrator for the orchestration of matrix operations, robotic tasks and control systems is does not exist. The paper proposes the design and implementation of a composite Orchestrator. The proposed Orchestrator is orchestrating matrix operations, robotic tasks and tasks in control systems. It is implemented as a super class of the different Orchestrators. Multithreading and RMI in Java is used to implement the Orchestrator.

We provide efficient constructions for trace-and-revoke systems with public traceability in the black-box confirmation model. Our constructions achieve adaptive security, are based on standard assumptions and achieve significant efficiency gains compared to previous constructions. Our constructions rely on a generic transformation from inner product functional encryption (IPFE) schemes to trace-and-revoke systems. Our transformation requires the underlying IPFE scheme to only satisfy a very weak notion of security -- the attacker may only request a bounded number of random keys -- in contrast to the standard notion of security where she may request an unbounded number of arbitrarily chosen keys. We exploit the much weaker security model to provide a new construction for bounded collusion and random key IPFE from the learning with errors assumption (LWE), which enjoys improved efficiency compared to the scheme of Agrawal et al. [CRYPTO'16]. Together with IPFE schemes from Agrawal et al., we obtain trace and revoke from LWE, Decision Diffie Hellman and Decision Composite Residuosity.

We provide efficient constructions for trace-and-revoke systems with public traceability in the black-box confirmation model. Our constructions achieve adaptive security, are based on standard assumptions and achieve significant efficiency gains compared to previous constructions.\ud \ud Our constructions rely on a generic transformation from inner product functional encryption (IPFE) schemes to trace-and-revoke systems. Our transformation requires the underlying IPFE scheme to only satisfy a very weak notion of security -- the attacker may only request a bounded number of random keys -- in contrast to the standard notion of security where she may request an unbounded number of arbitrarily chosen keys. We exploit the much weaker security model to provide a new construction for bounded collusion and random key IPFE from the learning with errors assumption (LWE), which enjoys improved efficiency compared to the scheme of Agrawal et al. [CRYPTO'16].\ud \ud Together with IPFE schemes from Agrawal et al., we obtain trace and revoke from LWE, Decision Diffie Hellman and Decision Composite Residuosity.

Corporate Social Responsibility (CSR) is very known concept in India. Ever since their inception, corporates like the Tata Group, the Aditya Birla Group, and Indian Oil Corporation, to name a few, have been involved in serving the community. Through donations and charity events, many other organizations have been doing their part for the society. The basic objective of CSR in these days is to maximize the company's overall impact on the society and stakeholders. CSR policies, practices and programs are being comprehensively integrated by an increasing number of companies throughout their business operations and processes. Many corporates feel that CSR is not just another form of indirect expense but is important for protecting the goodwill and reputation, defending attacks and increasing business competitiveness. The present study contributes to the CSR research by exploring how consumer perception on CSR influences consumers' attitude and purchase behaviour. Considering that the past CSR research tends to focus mostly on Indian perspectives, this study extends the scope of CSR research on Eastern culture. This research produced mixed results, suggesting that CSR is still a concept waiting to be applied in the developing country. Consumers are often unaware and unsupportive towards corporate social responsibility. This is the opposite finding of consumer perception in developed countries, where most consumers are willing to support CSR launched by corporations. Nevertheless, there is an interesting finding; when consumers have to buy similar products with the same price and quality, CSR could be the determining factor. The main aim of this research paper is to analyze and understand the role of corporate social responsibility in the overall consumer's perceptions and reactions towards CSR activities.

Water is the most precious gift of nature and is a valued natural resource for the existence of living beings. Management of this natural resource is thus of utmost importance. The present study was carried out to determine the presence of a few heavy metals viz, lead, chromium, copper and mercury in the various samples of Upper Lake, Bhopal. The samples were analyzed during both the pre-monsoon and the post-monsoon season. From the observations it was concluded that a general increase in the concentration was observed in the post monsoon season due to surface runoff coming into the lake water in the rainy season.