Adverse drug reaction profile at psychiatry outpatient department of a tertiary care centre

Background: Monitoring adverse drug reactions (ADRs) helps in alerting physicians and developing strategies to prevent and minimize the risk of developing ADRs. Data regarding pattern of ADRs due to psychotropic medications is scanty. Hence, the study was planned to assess ADRs among psychiatry outpatients of a tertiary care hospital in Maharashtra.Methods: A prospective, observational study was conducted in psychiatry outpatient department of a tertiary care centre for 3 months. Cases were enrolled by active and passive surveillance after obtaining informed consent. Demographic details, adverse event details, history of medications were recorded. Pattern of ADRs was studied according to demographic parameters, drug class, organ system affected, causality (WHO - Uppsala Monitoring Centre Scale) and severity (modified Hartwig and Siegel Scale).Results: Out of total 1200 patients screened, 77 qualified the inclusion and exclusion criteria and 92 ADRs were reported; overall incidence rate of 6.41%. Maximum ADRs were reported in the age group of 31- 40 years. 63.63% subjects received more than 2 psychotropic drugs. Among 24 types of ADRs observed, tremor (13.04%) was the commonest, closely followed by somnolence. Antipsychotics (45.65%) were most frequently incriminated and central nervous system (46.73%) the most often affected. Trifluoperazine (11.96%) was the commonest drug, followed by olanzapine and haloperidol (10.53% each). Causality analysis yielded 66 ADRs as “probable” and on severity analysis 80.43% were mild.Conclusions: The study provides an insight into pattern of ADRs in psychiatry outpatients. It is prudent to communicate this to treating physicians as well as counsel patients (and caregivers). Initiatives and concerted efforts involving all stakeholders in healthcare can go a long way in decreasing drug-related morbidity and health costs

Photonic crystal spatial filtering in broad aperture diode laser

Broad aperture semiconductor lasers usually suffer from low spatial quality of the emitted beams. Due to the highly compact character of such lasers, the use of conventional intracavity spatial filters is problematic. We demonstrate that extremely compact photonic crystal spatial filters, incorporated into a laser resonator, can improve the beam spatial quality and, correspondingly, increase the brightness of the emitted radiation. We report the decrease in the M2 value from 47 down to 28 due to photonic crystal spatial intracavity filtering and the increase in the brightness by a factor of 1.5, giving a proof of principle of intracavity photonic crystal spatial filtering in broad area semiconductor lasers.Peer ReviewedPostprint (published version

Random Forest Algorithm for Real-Time Health Monitoring Throught Iot Data

The last decade made significant progress in the empire of orientation to the health monitoring systems after the invention of wearable devices, simplifying health monitoring on a daily base. Devices combining “Internet-of-Things” and “Machine learning” technologies provide a solution that is persistent, objective, and feasible for remote monitoring, thereby facilitating ambient assisted living. This study aims to utilize a Random Forest machine learning algorithm to address clinical issues after achieving results on ML computations implemented on a dataset. In the subsequent tests, certain data will be collected, e.g., vital signs and body temperature heart rate, blood pressure, etc, utilizing IoT implemented devices. Health tracker devices combined with a series of body sensors revolutionize the system of living and health care regarding patient activity. Smartwatches bring the sensation of being one of the principal devices that often provide information regarding the step counter, heart rate, and sleep pattern, which is also crucial. The combination of the intelligent system of SPO2, heart rate, and body temperature sensors is often integrated with smartwatches find application, collecting the data and transferring it to the cloud for further analysis achieved by ML algorithm and Random Forest Machine Learning algorithm utilization. The testing phase pursues the notion, aiming to identify the level of accuracy in clinical issue detection, which confirms the system demonstrated in the work is efficient for remote monitoring

Constrained global optimization of multivariate polynomials using polynomial B-spline form and B-spline consistency prune approach

In this paper, we propose basic and improved algorithms based on polynomial B-spline form for constrained global optimization of multivariate polynomial functions. The proposed algorithms are based on a branch-and-bound framework. In improved algorithm we introduce several new ingredients, such as B-spline box consistency and B-spline hull consistency algorithm to prune the search regions and make the search more efficient. The performance of the basic and improved algorithm is tested and compared on set of test problems. The results of the tests show the superiority of the improved algorithm over the basic algorithm in terms of the chosen performance metrics. We compare optimal value of global minimum obtained using the proposed algorithms with CENSO, GloptiPoly and several state-of-the-art NLP solvers, on set of $11$ test problems. The results of the tests show the superiority of the proposed algorithm and CENSO solver (open source solver for global optimization of B-spline constrained problem) in that it always captures the global minimum to the user-specified accuracy

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Asymmetric supercapacitor based on nanostructured Ce-doped NiO (Ce:NiO) as positive and reduced graphene Oxide (rGO) as negative electrode

Asymmetric supercapacitor was fabricated successfully using cerium (Ce) doped NiO as positive electrode and reduced graphene oxide (rGO) as negative electrode in aqueous KOH electrolyte. Initially, Ce doped nickel oxide nanostructures were synthesized by simple Sol‐Gel method. To investigate the effect of Ce doping in nickel oxide, different molar percentages of cerium with respect to nickel were incorporated. Among the different compositions, 1%Ce:NiO exhibits high specific capacitance and excellent electrochemical activities. Further, asymmetric supercapacitor based on 1%Ce:NiO and rGO was fabricated which could be cycled reversibly in the high‐voltage range of 0 to 1.4 V and displays intriguing performances with a specific capacitance of 110 F/g at a scan rate of 5 mV/s with a maximum energy density of 26.27 Wh/kg (based on the total mass of active materials, 0.78 mg). Importantly, this asymmetric device exhibits an excellent electrochemical stability of 91.6 % over 1000 cycles.Swati R. Gawali, Deepak P. Dubal, Virendrakumar G. Deonikar, Santosh S. Patil, Seema D. Patil, Pedro Gomez‐Romero, Deepak R. Patil, Jayashree Pan

Amphiphilic polypyridyl ruthenium complexes: Synthesis, characterization and aggregation studies

Synthesis and characterization of five amphiphilic ruthenium(11) complexes of the type [Ru(Cn)(3)]center dot(PF6)(2) (Cn = 4,4'-dialkyl-2,2'-bipyridine, n = 5(4,4'-dipentyl), 6(4,4'-dihexyl), 7(4,4'-diheptyl), 8(4,4'-dioctyl), 9 (4,4'-dinonyl)) have been investigated. Single crystal X-ray structures of 4,4'-dipentyl-2,2'-bipyridine (C5), 4,4'-dioctyl-2,2'-bipyridine (C8) ligands and [Ru(C5)(3)](PF6)(2) complex are elucidated. Structural inferences are corroborated through the density functional theory. Molecular aggregations in these systems in aqueous and non-aqueous media have further been analyzed from FESEM experiments. (C) 2019 Elsevier Ltd. All rights reserved