COMPARATIVE EVALUATION OF INTRAVITREAL DICLOFENAC PLUS BEVACIZUMAB VERSUS BEVACIZUMAB ALONE IN THE TREATMENT OF NAÏVE DIABETIC MACULAR EDEMA: A RANDOMIZED CONTROLLED TRIAL

OBJECTIVE: To evaluate the therapeutic effects of intra-vitreal injection Bevacizumab combined with Diclofenac-Na versus intra-vitreal Bevacizumab alone in the treatment of naïve diabetic macular edema. METHODS:  In this prospective, randomized interventional clinical trial, 40 eyes of 40 participants were enrolled for trial conducted at an Ophthalmology department of Qazi Hussain Medical Complex, Nowshera. Twenty eyes each included in the intra-vitreal Bevacizumab and bevacizumab plus diclofenac group via random sampling technique. The main outcome variable was a change in best-corrected visual acuity (BC-VA) in log MAR at 4th, 12th and 24th week. The secondary outcomes included mean change in central subfield thickness (CSFT) of macula and possible injection-related side effects. RESULTS:    Marked improvement in BC-VA was observed in both therapeutic groups (mean change in Log MAR: 0.324±0.411 and 0.562±0.388 for bevacizumab alone and combination group, respectively). The difference in BC-VA change was in favor of combination group; however, the level didn’t achieve statistical significance (p = 0.08). Significant decrease in CSFT was noted in both groups (mean reductions: 178.02 ± 166.42, 214.55 ± 132.65) for bevacizumab and combination, respectively). Comparison of CSFT changes between groups revealed that combination decreased CSFT more than bevacizumab, but the difference was statistically insignificant (p = 0.07). Neither injection related side effects nor any marked change in intraocular pressure was observed in either groups. CONCLUSION:       In diabetic macular edema, superiority of combination therapy over Bevacizumab alone was evident, esp. with regard to structural improvement in macula

Design and Analysis of Hollow Catenoidal Horn Profile for Ultrasonic Machining of Composite Materials

The composite materials Si/AlC, crystal quartz, and ceramic glass are becoming an important part of the present society in many engineering and non-engineering fields. Conventional methods available for machining these materials have many flaws due to which their application on large scale is restricted. A non-conventional process known as ultrasonic machining (USM), can be implemented for machining of these materials effectively. Anyhow machining efficiency of USM greatly depends upon its horn design and therefore in the present study a horn based on a catenoidal profile with aluminum and titanium material for USM was designed and developed by using solid works and ANSYS. Modal and harmonic analysis was done on the horn for computation of various parameters of interest such as resonant frequency, amplitude vibration and equivalent stresses. After the computation of the results, they were analyzed and compared with those available in the literature in terms of stresses and magnification factor for their validation comparison with literature, it was found that an aluminum catenoidal horn shows higher magnification with the least stress magnitude as compared to horns avail-able in the literature and hence can be used in USM as a replacement for existing horns

Recurrent Cervical Neurofibrosarcoma: A Rare Case of Malignant Peripheral Nerve Sheath Tumor of Head and Neck Region

Neurofibrosarcoma is a malignant peripheral nerve sheath tumor (MPNST). The cervical location of the neurofibrosarcoma is very rare and is less than 1% in the literature. MPNSTs are often associated with neurofibromatosis type 1 (NF1).  We are presenting a case report of 31 years old female, with huge recurrent cervical neurofibrosarcoma on the right side of the neck.  To date, surgical excision followed by chemotherapy and radiotherapy is the treatment of choice which requires a multidisciplinary approach. We excised the above-mentioned cervical neurofibrosarcoma in a piecemeal fashion and discharged the patient on follow-up with the oncology department

Disclosures relating to Covid-19 in the Malaysian banking industry: Theory and Practice

Purpose: Covid-19 has impacted all the spheres of human lives and so on the stakeholders’ demands. This paper is aimed to discuss various strategies the banking industry may be asked to perform for coping against the Covid-19. This paper also analyzed the volume of real in-time disclosures created by the banking industry. These disclosures were also differentiated between public and private banks and between lowly and highly disclosing banks as well. Design/methodology/approach: Different strategies were used theoretically under the triple bottom line of sustainability. For empirical analysis, the data was taken from Malaysian listed and non-listed banks. Group differences and correlation analyses were performed. Findings: Banks with bigger size, more profitability, and previous engagements in CSR were more active in disclosing their strategies for Covid-19. Banks were doing least for their disclosures on environmental strategies on Covid-19. Overall, the disclosures about Covid-19 can be taken as a nexus of CSR disclosures of the banks since they have similar correlating variables and have significant correlations. Moreover, the findings were robust against alternative measures of CSR. Originality: This research is the first in time to discuss disclosures about Covid-19 generated by the banking industry. Research limitations/implications: The study was limited from the banking industry of Malaysia and had not been able to run regression analysis due to a limited number of observations. Practical implications: Various aspects of strategies under economic, social and environmental concerns had been discussed. Pertinent examples from different countries around the globe had also been given. These strategies can help practitioners in formulating their Covid-19 strategies to satisfy the stakeholders’ demands

Student counseling: adding value to educational institution

Quality of education has always remained an issue for the educational researchers. It is widely believed that quality of education in Sub-Continent is below the international standards. To find out how educational institutions are adding value towards the knowledge, skills, market value and moral upbringing of students and how much students are satisfied with the environment of educational system and the quality of education they receive.Using this information, how a student counseler can benefit the student as well the organization to add value in the education process. This is a case study where a Govt. Intermediate College was selected for interviews and document analysis purpose. Findings based on general perception of the respondents, All the respondents have matriculated from Public School. The findings showed that students were really concerned about the poor discipline of their schools and generally they were not satisfied with the commitment and capability of their teachers. However, they admitted that school contributed towards their grooming and personality building

Relationship between risk perception and employee investment behavior

nvestment behavior of an investor depends on his/her risk perception and risk attitude. This paper attempts to explore that how the perception of an investor who is also the employee of that organization differs from other investors. Does he/she perceives risk similarly as other common investors or his relationship with the organization as an employee has any impact his/her risk perception, attitude and investment behavior. This research study is conceptual in nature and mainly based on previous literature findings and evidences. Findings of this study suggested that employees risk perception is directly related with investment behavior and there is strong relationship between them. This can help the management to make special offers of shares to employees, this will further strength the bond of employees with the organization

Study of erythrocytes as a novel drug carrier for the delivery of artemether

Resealed erythrocytes have been explored in various dimensions of drug delivery, owing to their high biocompatibility and inability to initiate immune response. The present research was designed to evaluate the drug delivery potential of erythrocytes by loading a hydrophobic anti-malarial drug, Artemether. Three different loading techniques were applied to achieve maximum optimized drug loading. A HPLC method was validated for drug quantification in erythrocytes. The relatively high loading was achieved using hypotonic treatment was 31.39% as compared to other two methods. These, drug loaded erythrocytes were characterized for membrane integrity via ESR showing higher ESR values for drug loaded cells as compared to normal cells. Moreover, microscopic evaluation was done to observe morphological changes in erythrocytes after successful loading which showed swollen cells with slight rough surface as compared to smooth surface of normal cells. Drug release was studied for 8 h which showed more than 80% release within 3-7 h from erythrocytes treated with different hypotonic methods. Overall, the study revealed a potential application of erythrocytes in delivery of hydrophobic drugs using hypotonic treatment as compared to other methods

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations