Financial Ratios and Stock Return Predictability (Evidence from Pakistan)

The purpose of this research article is to investigate the ability of earning yield (EY), dividend yield (DY) and book-to-market ratio (B/M), to predict stock returns. The sample of the study consists of 100 non-financial companies listed in the “Karachi Stock Exchange”. The duration of the study is 7 years from 2005 to 2011. To find whether EY, DY and B/M ratios can predict stock returns we have used generalized least square and panal data models. The results indicate that DY and EY ratios has direct positive association with stock return where as B/M ratio has significant negative relationship with stock return. Therefore we can say that the above mentioned ratios are able to predict stock returns, furthermore it can be seen that as compare to dividend yield and earning yield the ratio of book to market has the highest predictive power. Moreover when we combine these financial ratios the predictability of stock returns will enhance. Keywords: Financial ratios, Stock return, Karachi Stock Exchange, Dividend Yield, Earning Yield

Evaluation of Prognostic response in HIV positive patients after Antiretroviral Therapy

Objective: The present study was aimed to monitor the prognostic response of antiretroviral therapy in HIV positive patients. Methodology: The study was conducted on confirmed HIV positive patients registered at HIV treatment and care centre, PIMS. Islamabad from January 2013 to December 2015.. Among all HIV positive patients,276 adult cases were selected. There were 263 patients on first-line antiretroviral (ARV) therapy and 13 patients were shifted to 2nd line ARV therapy.CD4 cell counts and viral load (Polymerase chain reaction) monitoring was done after one year of starting ARV therapy. Results: Out of 276 adult patients,  75%(n=207) were male and 25%(n=69) were females. Among 276 adult cases, 95.3% (n=263) patients were on first line ARV therapy. Patients on first line ARV therapy showed good prognostic response. There  were 15.5%(n=40) patients having  CD4+cells less than 350cells/µL. There were 84.5%(n=223) patients having  CD4 +cells count greater than 350cells/µL There were 69%(n=182) patients having viral load <50copies/ml and 31%(n=81) patients who had viral load >50copies/ml. Conclusion: First line ARV therapy given to HIV positive patients proved itself best both in respect of increasing the immunity of HIV positive patients by increasing the number of CD4 cells and also results in effective viral load suppression

Diabetes mellitus does not predict discharge in hospitalized patients with acute pyelonephritis: A study from Karachi, Pakistan

Introduction: The incidence of acute pyelonephritis (APN) in the diabetic population is comparatively higher and tends to be more complicated, with serious outcomes. Although complicated pyelonephritis (PN) needs hospital admission and intravenous antibiotics, the magnitude of hospital stay due to comorbidities is limited. This study\u27s aim was to assess the impact of diabetes mellitus on length of hospital stay among patients with PN.Methods: We did a retrospective data review of 520 randomly selected hospitalized patients of PN from March 2015 to December 2019 from a tertiary care center. Electronic medical records were used for identifying medical conditions through ICD-10 coding. Length of stay (LOS) was categorized as \u3c five days and ≥ five days. Chi‐ squared tests were used to compare categorical parameters. Logistic regression models were used for multivariate analyses.Results: The study included 520 patients with PN; 194 (37.3 %) men and 326 (62.7%) women. Overall, there were 353 (67.8 %) and 167 (32.1 %) patients with LOS \u3c five and ≥ five days respectively. Most of the patients had lower urinary tract symptoms (90%); among them, the majority (92%) were discharged within five days. Likewise, half of the patients had diabetes (51.2); among them, 53% were discharged after five days. Older age (OR:1.7, 95%CI: 1.1 - 2.6), upper urinary tract symptoms (OR:1.6, 95%CI: 1.1 - 2.4), lower urinary tract symptoms (OR:1.9, 95%CI: 1.1 - 3.5), creatinine greater than 1.5 mg/dl (OR:1.6, 95% CI: 1.1 - 2.4) was positively associated with LOS ≥ 5 days after adjusting for other covariates. Diabetes mellitus was not found to be associated with LOS ≥ 5 days (OR: 0.9, 95%CI: 0.8 - 1.5).Conclusion: In patients with acute PN, diabetes mellitus is not independently associated with prolonged hospital stay beyond five days

The relation of ABO blood group to the severity of coronavirus disease: A cross-sectional study from a tertiary care hospital in Karachi

Background: Blood groups are considered to have an impact on the occurrence and severity of coronavirus disease. While among Chinese and Caucasian, blood group O individuals were less and group A were more likely to have severe disease and mortality, data on South Asians aren’t available. Objective: This study aimed to find out the association of disease severity with blood group among coronavirus disease 2019 (COVID-19) patients.Materials and methodology: Data were collected on a predesigned questionnaire containing details of patient demographics, medical comorbidities, clinical presentation, and laboratory parameters. Multiple logistic regression was used to determine the association of the blood group with the severity of coronavirus disease.Result: Among the study participants, blood group B has the highest distribution (39.8%), followed by O (30.0), A (21.9%), and AB (8.1%). About three-fourths (69.9%) had mild to moderate disease while 30.0% had severe disease. Age, gender, hypertension, diabetes mellitus, and hemoglobin level were all associated with disease severity among COVID-19 patients in univariate analysis on P-value for selection (Conclusion: Blood groups don’t have any role in forecasting the severity of coronavirus disease. However, the male gender and diabetics are prone to have severe disease

Development of an Intelligent Real-time Multi-Person Respiratory Illnesses Sensing System using SDR Technology

Respiration monitoring plays a vital role in human health monitoring, as it is an essential indicator of vital signs. Respiration monitoring can help determine the physiological state of the human body and provide insight into certain illnesses. Recently, non-contact respiratory illness sensing methods have drawn much attention due to user acceptance and great potential for real-world deployment. Such methods can reduce stress on healthcare facilities by providing modern digital health technologies. This digital revolution in the healthcare sector will provide inexpensive and unobstructed solutions. Non-contact respiratory illness sensing is effective as it does not require users to carry devices and avoids privacy concerns. The primary objective of this research work is to develop a system for continuous real-time sensing of respiratory illnesses. In this research work, the non-contact software-defined radio (SDR) based RF technique is exploited for respiratory illness sensing. The developed system measures respiratory activity imprints on channel state information (CSI). For this purpose, an orthogonal frequency division multiplexing (OFDM) transceiver is designed, and the developed system is tested for single-person and multi-person cases. Nine respiratory illnesses are detected and classified using machine learning algorithms (ML) with maximum accuracy of 99.7% for a single-person case. Three respiratory illnesses are detected and classified with a maximum accuracy of 93.5% and 88.4% for two- and three-person cases, respectively. The research provides an intelligent, accurate, continuous, and real-time solution for respiratory illness sensing. Furthermore, the developed system can also be deployed in office and home environments

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

ASSESSMENT OF RADIOLOGICAL HEALING IN ELDERLY HIP FRACTURES FIXED WITH INTRAMEDULLARY VERSUS EXTRAMEDULLARY IMPLANTS AT THREE MONTHS

OBJECTIVE: To compare the radiological healing in elderly patients with hip fractures fixed with intramedullary versus extramedullary implants at 3 months by using Radiological Union Score for Hip (RUSH score). METHODS: This quasi-experimental study was conducted at Lady Reading Hospital, Peshawar from September 2020 to March 2021, in elderly patients (50-80 years) with hip fractures. Out of 238 patients, 119 were non-randomly assigned to Group-A undergoing intra- medullary implants and 119 to Group-B undergoing fixation with an extra-medullary implant. After the surgery, the patients were followed up periodically at 2nd week, 6th week and 12th week after surgery and assessed for radiological healing through RUSH score. The data was analyzed using SPSS version 23. RESULTS: Out of 238 patients, 96 were males and 142 were females. In Group-A, 51 (42.9%) were males and 68 (57.1%) were females. In Group-B, 45 (37.8%) were males and 74 (62.2%) were females. Majority (n=135/238: 56.72%) were aging from 50-60 years. Mean±SD of age was 63.1±8.8 years and 61.7±8.1 years in Group-A & Group-B respectively. Mean±SD of RUSH score in Group-A & Group-B was 19.50±6.92 and 22.51±5.60 respectively. Mean RUSH score for males in Group-A and Group-B was 21.52±6.39 and 22.33±6.99 (p=0.354) and for females in Group-A and Group-B was 19.36±7.33 and 22.18±5.75 (p=0.025) respectively. Median and IQR of RUSH score in Group-A & Group-B was 21±10 and 23±8 respectively (p=0.069). CONCLUSION: There was statistically insignificant difference in median RUSH score with use of either intramedullary or extramedullary implants in the management of hip fractures