Search

Background: The use of medicinal plants in herbalism has been documented for thousands of years, with evidence dating back to ancient civilizations such as the, Chinese, Greeks and Egyptians1. Extensive clinical studies have demonstrated that Althaeaofficinalis has properties that promote healing of ulcers2. Aim: To evaluate effectiveness of Althaeaofficinalis seed extract compared to Daflon (diosmin plus hesperidin) in treating hemorrhoids. Methods: A double-blind, randomized clinical trial was carried out in Lahore's urban community. Patients with acute hemorrhoids were randomly divided into two parallel groups, each with 60 participants, for a one-week period. The selection of family physicians for the study was done randomly.6,7,8 Interventions: In the beginning, the participants were given either two capsules of 500mg Althaea seed extract or two tablets of Daflonin morning and evening for four days, followed by two capsules/tablets daily for the next three days. The primary measure of effectiveness was the alleviation of symptoms and signs.9,10 Results: Both groups demonstrated similar improvement from first day tolast day. The intensity of symptoms such as itching, tenesmus, and bleeding ,swelling, painetc reduced markedly in both groups, but the reduction was fast in the Althaeaofficinalis seed extract group observed initial days of the trial. Practical Implications: Estimated prevalence of haemorrhoids ranges from 2.9% to 27.9% worldwide.11,12.13 However, due to cultural practices, it is often ignored and left untreated, It is therefore essential to design preventive measures and control strategies for hemorrhoids. Conclusion: Both Althaea and Daflon were found relieving haemorrhoidal signs and symptoms of hemorrhoids. However, the use of Althaea seed extract resulted in quicker relief and it may be used as good alternative to Daflon. Keywords: Haemorrhoids ,Altthaea seed extract, Althaeaofficinalis, double blind, tenesmus, bleeding

Dentists are particularly vulnerable to ergonomically borne musculoskeletal diseases (MSDs), which decrease efficiency, productivity, and career longevity because to the exact and minute nature of their work. Objective: To determine different consequence of work related disorders among dentist of Lahore, Pakistan. Methods: In this cross-sectional survey 450 dentist of Lahore were approached and a structured proforma based survey was conducted using convenience sampling technique. In the study, dentists between the ages of 24 and 65 were included. Musculoskeletal Disorder has been presented in the form of frequency and percentage whereas age has been shown as mean and standard deviation. Chi square test has been applied to determine the association between musculoskeletal disorder and different study variables. P-value of 0.05 has been deemed significant. Results: In this study out of 450 dentists 236 (52.4%) were male and 214 (47.6%) were females. Mean age of the males were 37.08 ± 9.27 years and females were 33.43 ± 10.42 years. Among the subject’s musculoskeletal disorders were reported by 393(87.3%) dentist. Sick leave was availed by 278 (70.7%) whereas 373 (94.9%) seek medical treatment and 325 (82.7%) prevent daily activity. According to Dentist 388 (98.7%) believes that the disorder is caused by Dental Clinic I.e. Ergonomic issue. Conclusions: With two thirds of dentists taking sick time owing to musculoskeletal disorders, combined with the mental stress, it would appear that dentistry is not an easy profession. This would significantly limit the career of dentists.

Prediction of the performance of students is a matter of concern to the education institutions. The main purpose of placement prediction to know the chances of students getting placed in various companies. The role of placement management system computerizes the manual system and making the job easy for everyone. Students doesn’t know their chances of getting placed and the areas that they have to improve to get placed. There are several classification algorithm and mathematics-based techniques which can be used to classify the student’s information. Naive Bayes, SVM, KNN, Decision Tree, Logistic Regression algorithm is applied to predict student performance which can facilitate to identify the performance of student. Based on the data received from the student, Student’s performance is analyzed with respect to various aspects to check the performance of the student and suggest fields wherein student can improve for better placements., {"references":["1.\tJahnvi Shah , Shivangi Kochrekar , Neha Kale , Sakshi Patil , and Anand Godbole. Campus Placement Prediction. 2017 IEEE Winter Conference onApplications of Computer Vision (WACV). doi:10.1109/wacv.2017.115","2.\tJadhav, A., Jain, R., Fernandes, S., & Shaikh, S. (2019). Placement Prediction Using Multiple Logistic Regression Method. 2019 International Conference on Advances in Computing, Communication and Control(ICAC3).","3.\tVarsha K. Harihar , D. G. Bhalke (2020). Student Placement Prediction System using Machine Learning. 2020 4th International Conference on Computer, Communication and Signal Processing(ICCCSP).","4.\tD. Satish Kumar, Zailan Bin Siri, D.S. Rao, S. Anusha, \"Predicting Student's Campus Placement Probability using Binary Logistic Regression\" in 2019 British MachineVision","5.\tAgyeman, R., Muhammad, R., &Choi,G. S.(2019,March). Campus Placement Prediction Using Supervised Machine Learning Techniques. In 2019 IEEE Conference ,IEEE.","6.\tDr. Rajiv Suresh Kumar , Fathima Dilsha Shilpa A N , Sumayya A (2021). Student Placement Prediction Using Support Vector machine Algorithm. 2021 IEEE 11thAnnual ComputingandCommunicationWorkshop and Conference(CCWC).","7.\tAwasthi, I., Gupta, K., Bhogal, P. S., Anand, S. S., & Soni, P. K. (2021). Model Construction Using ML for Prediction of Student Placement. 2021 6th International Conference on Inventive Computation Technologies(ICICT).","8.\tL'Huillier, G., Hevia, A., Weber, R., & Rios, S. (2010). Students Placement Prediction using Machine Learning. 2010 IEEE International Conference on Intelligence and SecurityInformatics.","9.\tSah,S.,Kulhare,S.,Gray,A.,Venugopalan, S., Prud'Hommeaux, E., &Ptucha, R. (2017). Smart system for student placement prediction. 2017 IEEE Winter Conference onApplications of Computer Vision (WACV).","10.\tJiang, Y., Cui, K., Peng, B., & Xu, C. (2019). PREDICTION OF STUDENT PLACEMENT USING MACHINE LEARNING ALGORITHM. 2019 IEEE/CVF International Conference on Computer Vision Workshop(ICCVW)."]}

Gingivitis is an infection caused by plaque and calculus accumulation. Gingivitis if not treated progress to periodontitis, a more advanced form of gum disease. Periodontal disease was the world's 11th most frequent disease in 2016 according to the World Health Organization. Surgical as well non- surgical periodontal therapies are carried out for periodontal disease treatment. Debridement of supra-gingival and sub-gingival calculus, along with instructions about oral health care methods are the basis of traditional non-surgical periodontal therapy (NTPS). Honey is beneficial to oral health, particularly in the cure of plaque formation, gingiva inflammation, periodontitis and odor control. Honey has antibacterial activity and is effective against both gram-positive as well as gram-negative bacteria. Honey's antibacterial and anti-inflammatory characteristics can promote the granulation tissue formation and enabling damaged cells to be regeneration. Materials And Method: This review followed the guided framework from the approach of Arksey and O'Malley for the scoping review (2005). The methodology was chosen because it has a five-stage framework. Exclusion & inclusion criteria were made and 60 articles were selected. Articles were searched from the PubMed, Google Scholar and other search engines. Results: Based on the data retrieved we concluded that honey can be used as a medium for non-surgical periodontal therapy. Honey have antibacterial and anti-inflammatory properties due high osmolarity, acidic nature and hydrogen peroxide as well non-peroxide components of honey and is used by many researcher for cure of periodontal disease but still there is not very strong evidence that locally given honey affects periodontal tissue healing. Despite this, all of the pockets had a good periodontal repair. Honey's efficacy in the treatment periodontal disease is still not well established and still questionable. To substantiate these findings from the literature, more research will be required.

Acne is a common human skin disease, characterized by areas of skin with seborrhea (scaly red skin), comedones (blackheads and whiteheads), papules (pinheads), pustules (pimples), nodules (large papules) and possibly scarring. Acne affects mostly skin with the densest population of sebaceous follicles. These areas include the face, the upper part of the chest, and the back. Objectives: To determine the association between various psycho-social factors and acne. Design:Population based Case-Control Study. Place & Duration:Galvera Village, Lahore from May 2019 to July 2019. Subjects & Methods. A population based case-control study with 1:1 case to control ratio was conducetd. A total of 100 persons (50 cases and 50 controls) were recruited in the study. Selection was made on laid down criteria from adult population living in rural community of Lahore after taking due consent. Interviews were conducted through a pretested questionnaire by a 10 membered team of group 01, 4th year MBBS students of Rashid Latif Medical College, Lahore under direct supervision of Department of Community Medicine, Rashid Latif Medical College (RLMC). Data was collected, compiled and analyzed through SPSS version 25.

Plant pathogens cause serious diseases to agricultural crops which lead to food insecurity in the world. To combat plant pathogens, various strategies have been developed including the use of agrochemicals. The overuse of these chemicals is now leading to the pesticide-resistant capability of pathogens. To overcome this problem, modern nanobiotechnology offers the production of alternative nano drugs. In this study, we used Mentha spicata for the synthesis of iron oxide nanoparticles using the green synthesis method. The synthesis of Fe2O3 NPs was confirmed through various characterizations. UV–Vis analysis detected a characteristic absorbance at the spectral range of 272 nm. The SEM micrographic analysis at various magnifications displayed circular or rod-shaped nanoparticles with a size ranging from 21 to 82 nm. The elemental EDX characterization showed intense peaks with a weight percent of 57, 34.93, and 8.07 for Fe, O, and, Cl respectively. TGA analysis showed that weight loss at 44–182, 500, and 660°C with no further modification indicates the thermal stability of iron oxide nanoparticles. FTIR spectrum of uncalined detects various bands at 3331, 1625, and 1,437 cm−1 for the hydroxyl group. After calcination two bands at 527 and 434 cm−1 were observed for Fe-O. The antimicrobial in vitro study showed maximum growth inhibition of Phytophthora infestans by the concentration of 100 μg ml−1 of Fe2O3-PE and Fe2O3 NPs. Therefore, this study resulted that bio-stable iron oxide nanoparticles can be used as alternative antimicrobial agents.

A most vital aspect that needs to be ensured is that children, who are definitely one of the most profound and irreplaceable components of any society, receive defense and protection from the adverse conditions and problems that might seem as a ramification of the poor decisions taken by their parents in regards to their children’s socialization. These less than apt or appropriate decisions can undoubtedly lead to the desiccation and dilapidation of that specific potency within children that is vital for facing the challenges of society. To put it concisely, such decisions affect the social prowess that a child entails. This study aimed to bring about a better understanding regarding the effectualness of Parents' decision-making in pertinence to their children from both upper and lower class. An endeavor has been made here to understand which class holds more promise in making decisions that reinforce their children adequately against the challenges of society.

Acute encephalitis syndrome (AES) is a serious public health problem in India specially Muzaffarpur district in Bihar. Bihar has recorded 188 cases of acute encephalitis syndrome, with 45 deaths since January. All casualties are children, the maximum in Muzaffarpur. Every year there is a repeat of the same, still no specific measures have been taken. The death of over 125 children in Muzaffarpur due to Acute Encephalitis Syndrome is very shocking. These children come from low-income group families and are poorly nourished. It is time to remember Elizabeth Barrett Browning for her poem ‘The Cry of the Children’ dedicated to the condition of children in England who were made to clean chimneys and work in hazardous industries. As a result, many would catch serious diseases and eventually die an early death. The poem examines children's manual labor forced upon them by their exploiters. It was published in August 1843 in Blackwood's Magazine. However, since then England has moved far ahead. All the children go to school, get proper nutrition and health care required of them.

Stock Trading has been one of the most important parts of the financial world for decades. People investing in the share market analyze the financial history of a corporation, the news related to it and study huge amounts of data so as to predict its stock price trend. The right investment i.e. buying and selling a company stock at the right time leads to monetary benefits and can make one a millionaire overnight. The stock market is an extremely fluctuating platform wherein data is produced in humongous quantities and is influenced by numerous disparate factors such as socio-political issues, financial activities like splits and dividends, news as well as rumors. This work proposes a novel system “IntelliFin” to predict the share market trend. The system uses the various stock market technical indicators along with the company's historical market data trends to predict the share prices. The system employs the sentiment determination of a company's financial and socio-political news for a more accurate prediction. This system is implemented using two models. The first is a hybrid LSTM model optimized by an ADAM optimizer. The other is a hybrid ML model which integrates a Support Vector Regressor, K-Nearest Neighbor classifier, an RF classifier and a Linear Regressor using a Majority Voting algorithm. Both models employ a sentiment analyzer to account for the news impacting the stock prices which is powered by NLP. The models are trained continuously using Reinforcement Learning implemented by the Q-Learning Algorithm to increase the consistency and accuracy. The project aims to support the inexperienced investors, who don't have enough experience in investing in the stock market and help them maximize their profit and minimize or eliminate the losses. The developed system will also serve as a tool for professional investors to help and aid their decision making.

Introduction: The coronavirus comes from the word "corona" which means "crown" in Latin. Coronavirus (CoVs) belongs to a very diverse class of enveloped, single-stranded RNA viruses. It causes many respiratory infections in humans, ranging from a mild cold to severe respiratory distress syndrome. To ensure a better and healthier prognosis in individual infected with COVID-19, a prompt diagnosing technique and treatment is very essential. The role of chest computed tomography (CT) and real-time polymerase chain reaction (RT-PCR) is essential in the early diagnosis of COVID-19 patients. However, because of low efficiency of the detection of viral nucleic acids, this method shows a lack of clinical performance for the diagnosis of COVID-19 disease. The goal of the study is to examine the clinical performance of CT and RT-PCR and assess the diagnostic value of chest CT scan and RT-PCR in COVID-19 patients suspected of having the virus. Methods: The data/information for review article was collected from different literature available on Google Scholar, PubMed, Scihub and Radiopedia. A profound study of all the key words was conducted and relevant information was avidly collected. Technical aspect of the study was cited specifically from radiology literature. Results: Of the 147 studies; 115 were selected based on inclusion and exclusion criteria, we found 60 studies after selecting the title and abstract. A full-text evaluation of 45 accepted studies found 1766 patients. A total of 1,766 patients underwent CT scans, of which 1,452 patients had COVID-19 positive chest CT and 959 of 1713 PCR results were positive. Recent research on diagnostic performance of CT chest and RT-PCR have been widely reported from various countries, according to the findings. Conclusion: In this review article, we compare two diagnostic modalities computed tomography (Radiology) and reverse transcriptase polymerase chain reaction (MLT). The best modality for COVID-19 detection is CT scan because we review different published articles which are mentioned in the result. The sensitivity and specificity CT scan modality is more than PCR.

There is evidence that dietary interventions can reduce antibody levels in patients with autoimmune thyroid disease (ATD), which can have a beneficial effect on the clinical manifestations of the disease. The aim of this study was to review the literature regarding the different types of dietary supplementations and their effectiveness in the management of ATDs.

An efficient route to produce oxanorbornene, a precursor for the production of bio-based trimellitic acid (TMLA) via the Diels–Alder (DA) reaction of biomass-derived dienes and dienophiles has been proposed by utilizing density functional theory (DFT) simulations. It has been suggested that DA reaction of dienes such as 5-hydroxymethyl furfural (HMF), 2,5-dimethylfuran (DMF), furan dicarboxylic acid (FDCA) and biomass-derived dienophiles (ethylene derivatives e.g., acrolein, acrylic acid, etc.) leads to the formation of an intermediate product oxanorbornene, a precursor for the production of TMLA. The activation barriers for the DA reaction were correlated to the type of substituent present on the dienes and dienophiles. Among the dienophiles, acrolein was found to be the best candidate showing a low activation energy (

The transition from non-renewable energy sources to renewable energy is considered a key solution for global warming and a sustainable environment. The eco-innovation is regarding as one of the important sources of this transition; however, the rate of adaptation of renewable energy remains uncomfortably low. The ambiguity still exists whether eco-innovation increase or decrease the energy consumptions in both aggregate and disaggregate level. Therefore, this study aims to investigate the effect of eco-innovation along with human capital, and trade openness on energy consumption at the aggregate (total) and disaggregate levels (renewable and non-renewable). For the above-mentioned objective, this study selects OECD countries' datasets between 1990-2017. The empirical investigation carries out on econometric techniques such as augmented mean group (AMG) and cross-section autoregressive distributed lag (CS-ARDL). The outcomes of the empirical investigation confirm that eco-innovation, human capital, and energy prices play positive roles in increasing the consumption of renewable energy sources in the OECD region. In contrast, economic growth and trade openness are still the sources to promote non-renewable energy consumption in this region. Therefore, the current study suggests promoting eco-innovation and high skilled human capital growth to enhance renewable energy consumption in the region. This study further indicates that a sustainable environment could promote green growth through clean energy production and consumption.

Functional dyspepsia (FD) is a set of relapsing or chronic dyspeptic symptoms in the absence of structural organic lesions. Approximately 15-20% of the general population in many developed countries suffers from dyspeptic symptoms at any time in a year, whereas in Pakistan, 14-22 % of general population suffers from FD. Objective: This study was designed to evaluate the effectiveness of carica papaya extract (CP) in preventing functional dyspepsia in human volunteers.Methodology: A community based, placebo-controlled, double -blind, multi-centered, and randomized clinical trial through systematic random sampling was conducted in Lahore urban community. After initial diagnostic investigations in 200 patients diagnosed as cases of FD fulfilling Rome III criteria were recruitedafter taking written consent and randomly assigned to one of 2 treatment groups (CP extract group or placebo) in equal number. A 7 days medication free period was observed before the start of trial. Each patient received the treatment for 6 weeks. The primary outcome variable was the improvement in gastrointestinalsymptom score (GIS) consisting of evaluation of 10 dyspeptic symptoms rated on Likert scale. Dyspeptic symptoms were assessed at the start of trial then after 2, 4 and 6 weeks. Results: In this trial, 200 patients fully participated in this study (age 36.31±9.711 years, range 18-55, 60% female). Compared with placebo, Carica papaya extract (CP group) showed a clinically significant improvement. The GIS significantly decreased in CP group during the first 2 weeks, compared to the placebo (p < 0.05). During the second and third 2-week period, symptoms further improved in CP group (p < 0.05). After 6 weeks, 95.7% on CP treatment and 3.1% on placebo were completely relieved of FD symptoms (p < 0.001). Conclusions: CP extract was significantly effective gastro-protective as compared with placebo.

Back ground:End-tidal carbon dioxide (ETCO2) is a surrogate, non-invasive measurement of arterial carbon dioxide (PaCO2); however, its clinical applicability in the intensive care unit setting remains unclear in pediatric population. Objectives:This study aims at assessing the effect of severity of lung disease and correlation between ETCO2 and PaCO2 Materials and methods: This was a prospective, nonrandomised study carried in pediatric intensive care unit of a tertiary care unit on 104 children requiring mechanical ventilation. Simultaneous ETCO2 and PaCO2 recording were taken, data on ventilator parameters recorded. Severity of lung disease was estimated by alveolar- arteriolar oxygen gradient, ventilation index and PaO2/FiO2 ratio. The PaCO2 and ETCO2 difference and its variability were studied. Results:208 samples were analysed where the mean PaCO2 value 28.9±9.7 mmhg was higher than mean ETCO2 value of 27.8±9.6 mmHg. The mean difference between PACO2 and ETCO2 was 1.6±3.23 mmHg. There was significant level of correlation between PACO2 and ETCO2 ( r=0.941, r2 = 0.886 , 95% CI= 0.95-0.97) for mild to moderate lung disease than severe lung disease. Systemic diseases, Ventilator index and alveolar arterial oxygen gradient did not have any significant correlation with the PACO2 and ETCO2 difference in our study. Conclusion: Significant correlation was found between the PaCO2 and ETCO2 value irrespective of the disease conditionand ETCO2 can be validated to predict PACO2.

Selecting appropriate surrogate models is crucial. This article upgrades our learning-based surrogate selection paradigm (LEAPS2) to LEAPS2v2. Its key features include: modeling noisy and non-noisy data, avoiding noise-fitting surrogates, powerful data attributes, novel composite metric (Surrogate Quality Score) to assess surrogate accuracy and complexity, and many surrogates and datasets. For any dataset, LEAPS2v2 recommends 3 out of 36 surrogates. A LEAPS2v2 recommendation is successful if it recommends at least one of the top three surrogates based on Surrogate Quality Score. LEAPS2v2 was successful on more than 94 % non-noisy and 89 % noisy datasets. Moreover, it made successful recommendations on 14/16 real industrial datasets. Strong correlation was observed between a surrogate being a true best and being recommended. Our numerical analysis revealed that the best surrogates for noisy data are different from those for non-noisy datasets. No single surrogate outperforms others for all datasets, highlighting the utility of LEAPS2v2. Publication Status: Submitted to Computers & Chemical Engineering.

Today world face one of the biggest problem is hypertension in adults. Hypertension, also known as high blood pressure, increases the risk for heart disease and stroke, two leading causes of death for people in the United States [1]. In the United States, about 77.9 million (1 out of every 3) adults have high blood pressure. A global brief on hypertension by WHO reported that, the prevalence of hypertension in adults aged 25 and above was about 40% around the world in 2008, rising from 600 million in 1980 to 1 billion in 2008 during the past 28 years [2]. Similarly, this prevalence among the adults aged 18 and above in China has risen from 18.8% in 2002 to 25.2% in 2015, presently, there are more than 200 million hypertensive patients in China [3]. Epidemiological studies show a steadily increasing trend in hypertension prevalence over the last 40 years, more in urban than in the rural areas. This is converse to findings reported from developed countries where there is a significant decrease in its prevalence.

People detection and counting is considered as one of the important application in video surveillance. Various computer vision and deep learning based methods have been developed which aim to provide efficient and accurate people detection/counting results using frontal view data sets. Furthermore, there are many challenges which occurs while detecting people including occlusion, perspective distortion, variations in human body pose, size and orientation, these challenges effect the results of developed detection and counting models. In this work, a deep neural network approach i.e. SSD (Single Shot multi-box Detector) is explored for people detection and counting. SSD model is used for detection and counting of people from significantly different viewpoint i.e. top view. To the extent of our knowledge, this is the first attempt to use deep neural network based model for top view people detection and counting. Furthermore, the impact of frontal view trained SSD model on top view test images is also discussed. The experimental results show the effectiveness of deep learning model by achieving promising results with average TPR of 95% and TPR 94.42% for indoor and outdoor environments respectively.

Nowadays skin diseases are considered as one of the common cause of human illness, and if not detected or diagnosed at early stage, it can be more harmful. With advancement in computer vision and image processing various techniques have been developed for diagnoses of skin diseases. This work mainly focused on simple yet effective technique for segmentation of skin lesions. It is also robust for extracting the region of interest whose background and foreground color is bit similar. It automatically segments skin lesion of arbitrary shapes and sizes. The proposed technique is mainly based on three major modules namely preprocessing, segmentation and post-processing. In pre-processing different color models are used for extraction of single channel followed by filtering to remove noise. In the second module, segmentation is preformed using Harris corner detector, seed selection and region growing segmentation. Finally morphological operation is performed as post-processing comprising of region filling and dilation. The experimentation results shows the performance of proposed model by achieving accuracy of 95%.

In this paper we propose a probabilistic approach to synthesize an agent-based heterogeneous semantic model depicting population interaction and analyzing the spatio-temporal dynamics of an airborne epidemic, such as influenza, in a metropolitan area. The methodology is generic in nature and can generate a baseline population for cities for which detailed population summary tables are not available. The joint probabilities of population demographics are estimated using the International Public Use Microsimulation Data (IPUMS) sample set. Agents are assigned various activities based on several characteristics. The agent-based model for the city of Lahore, Pakistan is synthesized and a rule based disease spread model of influenza is simulated. The simulation results are visualized to produce semantic analysis for the spatio-temporal dynamics of the epidemic. The results show that the proposed model can be used by officials and medical experts to simulate an outbreak.