DEVELOPMENT AND VALIDATION OF STABILITY INDICATING RP-HPLC METHOD FOR DETERMINATION OF OLANZAPINE IN PHARMACEUTICAL DOSAGE FORMS

Background: Spectrophotometric analysis fulfills requirements where the simultaneous estimation of the drugcombination can be done with similar effectiveness as that of chromatographic methods. Simultaneousestimation of drug combination is generally done by separation using chromatographic methods like HPLC, GC, and HPTLC, etc. These methods are accurate and precise with good reproducibility, but the cost of analysis isquite high owing to expensive instrumentation, reagent, and expertise. Hence it is advisable to develop a simpler and cost-effective method for the simultaneous estimation of drugs for routine analysis of formulation.Methods: A descriptive study design was used and information was obtained through various literature reviews. RP-HPLC method was used and data were analyzed.Conclusion: The developed stability-indicating HPLC method for quantitative estimation of olanzapine in bulkand pharmaceutical dosage forms is fast, simple, accurate, and more precise. Validation of this method wasaccomplished, getting results meeting all requirements. Thus, the developed HPLC method can be used forroutine quality control tests. &nbsp

Oral Health Knowledge, Attitude and Practices among Adults toward Tooth Loss and Utilization of Dental Services in Moradabad District

Background and objectives: For centuries, people have accepted tooth loss as an inevitable part of the human condition. Tooth loss impairs the quality of life, often substantially, andaffects the well being of the person. The objectives of study were to evaluate behavioral characteristics of the adults like beliefs about tooth loss and utilization of available health services which might be associated with tooth loss. Methodology: A cross-sectional survey was conducted on 1,200 adults of Moradabad district, aged 35 to 74 years, 565 from urban area and 635 from rural area who were selected by multistage systematic random sampling technique. Data was collected by an interview followed by examination for the numberof missing teeth. Results: Mean number of missing teeth in the study population was 4.2. Around half of the study population, i.e. 51.1% of the adults claimed that they had no dental treatment facilities nearby. Among the 602 adults (50.2%) who had utilized dental services earlier, greatest response for reason of dental visit was for extraction of teeth  (48.7%). Conclusion: The findings from this study are useful in identifying the sociodemographic and behavioral characteristics associated with tooth loss among the study population. The insights gained from this study illustrate the need for tailoring Oral Health Promotion Programs and Services for the community, as the modification of these nondisease independent factors can reduce the tooth loss and improve the oral health of the adults of Moradabad district

Association of metabolic syndrome and lower urinary tract symptoms amongst South Indian postmenopausal women

Background: Women spend one third of their life in menopause. The age related anatomical and physiological changes predispose them to MetS and lower urinary tract symptoms (LUTS). The aim was to study the prevalence of metabolic syndrome (MetS) and lower urinary tract symptoms in postmenopausal women attending menopause clinic, to study the correlation of LUTS and body composition among women with MetS.Methods: 154 post-menopausal women who attended menopause clinic at the Christian Medical College Hospital Vellore, were recruited. MetS was diagnosed using IDF criteria. LUTS were assessed BFLUTS questionnaires. Blood was taken to assess serum fasting glucose and lipid profile. DEXA was performed to assess the whole-body composition.Results: Of 154 postmenopausal women, 64% had MetS and 43% of women had a total LUTS score > 5. 90% of women had filling symptoms,57% had incontinence,17% had voiding symptoms,14 % had quality of life issues and 6 % had sexual symptoms. However, there was no statistical significant difference between two groups in correlating the variables of MetS with LUTS (P >0.05). The percentage of total body fat by DEXA scan was significantly greater (P=0.006) in women with MetS (37.32±5.04) when compared to the women without MetS (34.629±3.65).Conclusions: Prevalence of MetS among the study population was 64 %. LUTS were observed in 43% of the patients. There was no significant difference in LUTS in women with MetS and without Mets. However, there was a significant difference in body composition among women with and without MetS

Drowsy Driver Detection System (DDDS)

Driver weariness is one of the key causes of road mishaps in the world. Detecting the drowsiness of the driver can be one of the surest ways of quantifying driver fatigue. In this project we have developed an archetype drowsiness detection system. This mechanism works by monitoring the eyes of the driver and sounding an alarm when he/she feels heavy eyed. The system constructed is a non-intrusive real-time perceiving system. The priority is on improving the safety of the driver. In this mechanism the eye blink of the driver is detected. If the driver?s eyes remain closed for greater than a certain period of time, the driver is deemed to be tired and an alarm is sounded. The programming for this is carried out in OpenCV using the Haar cascade library for the detection of facial features

Prevalence of metabolic syndrome among postmenopausal women in South India

Background: As the average life expectancy of women extends 20-30 years after menopause, the medical impacts of changes leading to metabolic syndrome on postmenopausal women are significant. The menopausal state has been noted to be an independent risk factor for the occurrence of metabolic syndrome. This study was conducted to look at the prevalence of metabolic syndrome in postmenopausal women.Methods: A prospective cross sectional study was done and postmenopausal women were assessed for metabolic syndrome using the International Diabetes Federation Criteria.Results: The prevalence of metabolic syndrome was found to be 64%. Women with metabolic syndrome had a higher systolic blood pressure and larger waist circumference, however did not differ in terms of diabetes and dyslipidemia. There was no significant difference with regards to frequency and severity of menopausal symptoms between women with and without metabolic syndrome.Conclusions: The increased prevalence of metabolic syndrome in postmenopausal women may be directly due to ovarian insufficiency and indirectly due to metabolic consequences of central fat redistribution with estrogen deficiency

Drowsy Driver Detection System

Driver weariness is one of the key causes of road mishaps in the world. Detecting the drowsiness of the driver can be one of the surest ways of quantifying driver fatigue. In this project we aim to develop an archetype drowsiness detection system. This mechanism works by monitoring the eyes of the driver and sounding an alarm when he/she feels heavy eyed. The system so constructed is a non-intrusive real-time observing system. The primacy is on improving the safety of the driver. In this mechanism the eye blink of the driver is detected. If the driver’s eyes remain closed for more than a certain span of time, the driver is believed to be tired and an alarm is sounded. The programming for this is carried out in OpenCV using the Haar cascade library for the detection of facial features

Vibrational Force on Accelerating Orthodontic Tooth Movement: A Systematic Review and Meta-Analysis

This study aimed to systematically gather and analyze the current level of evidence for the effectiveness of the vibrational force in accelerating orthodontic tooth movement (OTM). This systematic review was conducted using three electronic databases: Scopus, PubMed, and Google Scholar until March 2022. The search was done through the following journals: European Journal of Orthodontics, American Journal of Orthodontics and Dentofacial Orthopedics, The Angle Orthodontist, Progress in Orthodontics, and Seminars in Orthodontics. Human or animal studies that have evaluated the effect of vibrational force on the rate of OTM were selected. A meta-analysis was performed for the rate of canine movement per month. Database research, elimination of duplicate studies, data extraction, and risk of bias assessment were performed by authors independently and in duplication. A fixed and random-effect meta-analysis was performed to evaluate the effect of vibrational forces. A total of 19 studies (6 animal and 13 human studies) that met the inclusion criteria were included. Meta-analysis was performed based on four human clinical trials. Three out of four studies showed no significant difference in the rate of canine movement between vibrational force and control groups. The limitation of this study was the small sample size and significant heterogeneity among the studies. Although vibrational forces have been shown to accelerate OTM in experimental studies, the results are inconsistent in clinical studies. The inability to apply desired peak load to the targeted teeth may be the main factor in inconsistent clinical outcomes

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems