Geotechnical characteristics of effluent contaminated cohesive soils

In developing countries like Pakistan, raw industrial effluents are usually disposed-off directly into open lands or in water bodies resulting in soil contamination. Leachate formation due to rainfalls in openly dumped solid waste also adds to soil contamination. In this study, engineering behavior of soils contaminated by two industrial effluents, one from paper industry (acidic) and another from textile industry (basic), has been investigated. Laboratory testing revealed significant effects of effluent contamination on engineering behavior of tested soils. Liquid limit, plasticity index, optimum moisture content and compression index of tested soils were found to increase with effluent contaminant, indicating a deterioration in the engineering behavior of soils. Whereas maximum dry density, undrained shear strength and coefficient of consolidation of the contaminated soils showed a decreasing trend. The dilapidation in engineering characteristics of soils due to the addition of industrial effluents could pose serious threats to existing and future foundations in terms of loss of bearing capacity and increase in settlement. Keywords: soil contamination, industrial waste, engineering behavior, effluent waste, leachate. First published online: 28 Nov 201

Outcome of autologous bone graft versus polyetheretherketone cages in anterior cervical discectomy and fusion surgery

Objective:To compare the outcome of autologous bone graft versus PEEK cages in ACDF surgery in terms of clinical performance and radiographic features. Methodology:This study was conducted at Department of Neurosurgery, Punjab Institute of Neurosciences, Lahore, Pakistan from April 2020 to December 2022. In this study patients were randomized into two equal groups i.e. Group A (autologous bone graft)  and Group B (PEEK cage). Results: Total of 98 patients was included in the study. The mean age of cases was 49.88 ± 17.83 years. There were 58(59.18%) male and 40(40.82%) female cases. 25(25.51%) cases who had C3-C4 involved, 48(48.98%) patients had C5-C6 and 25(25.51%) cases had C5 region involved. The mean disc height at 6th months in PEEK group was 6.71 ± 0.46 mm and in bone graft group was 6.33 ± 0.47 mm, p-value < 0.05. The mean operative time in PEEK group (2.07 ± 0.42) was statistically less than bone graft group (3.23 ± 0.36), p-value < 0.05. The average blood loss was also statistically less in PEEK group as compared to bone graft.  The mean hospital stay in PEEK group was 2.92 ± 0.61 days as compared to bone graft was 5.48 ± 1.90 days, p-value < 0.05. Conclusion:Outcome of ACDF surgery PEEK cages are better than autologous bone graft in terms of clinical performance and radiological features. Hence PEEK cages can be opted in future to have better outcome and higher patient’s satisfaction. &nbsp

Carotid intima media thickness evaluation by ultrasound comparison amongst healthy, diabetic and hypertensive Pakistani patients

Objective: To compare carotid Intima media thickness and atherosclerosis burden amongst healthy, diabetic and hypertensive Pakistani patients.Methods: A cross-sectional study was carried out at the Department of radiology and family medicine, Aga Khan University Hospital Karachi from April 2014 to July 2015. Bilateral carotid ultrasound was done in 133 healthy adults, 65 hypertensive, 31 type-2 diabetic and 37 hypertensive with type-2 diabetes patients. Normal adults were matched for age and gender. Mean intimal media thickness was measured for common and internal carotid arteries. Presence or absence of atherosclerotic plaque was also identified. Height, weight, ethnicity, socioeconomic status and other risk factors were also assessed. Ultrasound findings were compared between healthy and diseased patients through statistical tests.Results: A total of 266 patients participated (Controls=133, Hypertensive=65, Diabetic=31, and Diabetes with Hypertension=37). There was no significant difference in the baseline characteristics between the four patients\u27 groups for age (p\u3e0.05) and gender (p\u3e0.05). The mean carotid intima media thickenss of right common carotid artery was significantly higher in patients with diabetes along with hypertension as compared to the control group (p=0.03). For (RICA) Right Internal Carotid Artery, (LCCA) Left Common Carotid Artery and (LICA) Left Internal Carotid Artery, there was a significantly higher thickness among patients with hypertension as compared to the control group with p=0.011, p=0.002, and p=0.039 respectively.Conclusion: Increased CIMT is most likely associated with underlying chronic diseases. Ultrasound is a non-invasive, easily available and useful modality for early detection and prevention of vascular atherosclerosis

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

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

Synthesis, enzyme inhibitory kinetics mechanism and computational study of N-(4-methoxyphenethyl)-N-(substituted)-4-methylbenzenesulfonamides as novel therapeutic agents for Alzheimer’s disease

The present study comprises the synthesis of a new series of sulfonamides derived from 4-methoxyphenethylamine (1). The synthesis was initiated by the reaction of 1 with 4-methylbenzenesulfonyl chloride (2) in aqueous sodium carbonate solution at pH 9 to yield N-(4-methoxyphenethyl)-4-methylbenzensulfonamide (3).This parent molecule 3 was subsequently treated with various alkyl/aralkyl halides, (4a–j), using N,N-dimethylformamide (DMF) as solvent and LiH as activator to produce a series of new N-(4-methoxyphenethyl)-N-(substituted)-4-methylbenzenesulfonamides (5a–j). The structural characterization of these derivatives was carried out by spectroscopic techniques like IR, 1H-NMR, and 13C-NMR. The elemental analysis data was also coherent with spectral data of these molecules. The inhibitory effects on acetylcholinesterase and DPPH were evaluated and it was observed that N-(4-Methoxyphenethyl)-4-methyl-N-(2-propyl)benzensulfonamide (5c) showed acetylcholinesterase inhibitory activity 0.075 ± 0.001 (IC50 0.075 ± 0.001 µM) comparable to Neostigmine methylsulfate (IC50 2.038 ± 0.039 µM).The docking studies of synthesized ligands 5a–j were also carried out against acetylcholinesterase (PDBID 4PQE) to compare the binding affinities with IC50 values. The kinetic mechanism analyzed by Lineweaver-Burk plots demonstrated that compound (5c) inhibits the acetylcholinesterase competitively to form an enzyme inhibitor complex. The inhibition constants Ki calculated from Dixon plots for compound (5c) is 2.5 µM. It was also found from kinetic analysis that derivative 5c irreversible enzyme inhibitor complex. It is proposed on the basis of our investigation that title compound 5c may serve as lead structure for the design of more potent acetylcholinesterase inhibitors

Parasitism in Goats: Husbandry Management, Range Management, Gut Immunity and Therapeutics

Goats play a vital role in the economy of common man. It acts as pivotal point in the uplift of socio-economic status of females. The goats are such delicate and fragile animals that encounter a lot of infectious and non-infectious diseases including viruses, bacteria and gastrointestinal parasites (GIP). The goat being a range animal is selective feeder. It needs a lot of managemental practices which safeguards its health. This chapter focuses on management, impact of gastrointestinal parasites, role of intestinal immunity, various breeds reared in Pakistan, role of plant based phytochemicals to treat against GIT parasites and various models to predict the status of health in animals

Enhancing system performance through objective feature scoring of multiple persons' breathing using non-contact RF approach

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system’s performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system’s performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively