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Abstract Reducing student attrition in tertiary education plays a significant role in the core mission and financial well-being of an educational institution. The availability of big data source from the Learning Management System (LMS) can be analysed to help with the attrition issues. This study aims to use an integrated Design Science Research (DSR) methodology to develop and evaluate a novel Big Data Analytical Solution (BDAS) as an educational decision support artefact. The BDAS as DSR artefact utilises Artificial Intelligence (AI) approaches to predict potential students at risk. Identifying students at risk helps to take timely intervention in the learning process to improve student academic progress for increasing their retention rate. To evaluate the performance of the predictive model, we compare the accuracy of the collection of representational AI algorithms in the literature. The study utilized an integrated DSR methodology founded on the similarities of DSR and design based research (DBR) to design and develop the proposed BDAS employing an specific evaluation framework that works on real data scenarios. The BDAS does not only aimto replace any existing practice but also support educators to implement a variety of pedagogical practices for improving students’ academic performance.

Nikhil Gadiraju,* Nikhil Peterson,* Jessica Shah,* Annabelle Chu, Michael A Larbie II, Amy Bu, Ann Saterbak Pratt School of Engineering, Duke University, Durham, NC, USA*These authors contributed equally to this workCorrespondence: Ann Saterbak, Department of Biomedical Engineering, Duke University, 101 Science Drive, Campus Box 90281, Durham, NC, 27708-0281, USA, Tel +1 919 660 5899, Fax +1 919 684 4488, Email ann.saterbak@duke.eduPurpose: Long-term oxygen therapy involves utilizing stationary oxygen concentrators to allow patients with respiratory illnesses to attain sufficient blood oxygenation via supplemental oxygen. Disadvantages of these devices include their lack of remote adjustability and domiciliary accessibility. To adjust oxygen flow, patients typically walk across their homes – a physically taxing activity – to manually rotate the knob of the concentrator flowmeter. The purpose of this investigation was to develop a control system device that allows patients to remotely adjust the oxygen flow rates on their stationary oxygen concentrator.Methods: The engineering design process was used to develop the novel FLO2 device. The two-part system is composed of 1) a smartphone application and 2) an adjustable concentrator attachment unit that mechanically interfaces with the stationary oxygen concentrator flowmeter.Results: Product testing indicates that users successfully communicated to the concentrator attachment from a maximum distance of 41m in an open field, suggesting usability from anywhere inside a standard home. The calibration algorithm adjusted oxygen flow rates with an accuracy of ± 0.019 LPM and a precision of ± 0.042 LPM.Conclusion: Initial design testing suggests the device as a reliable and accurate method of wirelessly adjusting oxygen flow on a stationary oxygen concentrator, but further testing should be performed on different stationary oxygen concentrator models.Keywords: oxygen therapy, respiratory disease, flow meter, wireless adjustment, medical device

Huzaifa Ahmad Cheema,1 Rustam Shariq Mujtaba,2 Amna Siddiqui,3 Laiba Imran Vohra,4 Abia Shahid,1 Jaffer Shah,5 Abdulqadir J Nashwan,6 Natasha Howard7– 9 1Division of Infectious Diseases, Department of Medicine, King Edward Medical University, Lahore, Pakistan; 2Department of Pharmacy, National University of Singapore, Singapore, Singapore; 3Department of Medicine, Karachi Medical and Dental College, Karachi, Pakistan; 4Department of Medicine, Ziauddin University, Karachi, Pakistan; 5New York State Department of Health, Albany, NY, USA; 6Hamad Medical Corporation, Doha, Qatar; 7Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore; 8Free Aleppo University, Aleppo, Syria; 9Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UKCorrespondence: Abdulqadir J Nashwan, Department of Nursing, Hamad Medical Corporation, P.O. Box 3050, Doha, Qatar, Email anashwan@hamad.qaAbstract: Dengue outbreaks have been documented in Singapore since 1901, occurring almost annually in the 1960s and disproportionately affecting the paediatric population. In January 2020, virological surveillance detected a shift from DENV-2, which was the previous dominant strain, to DENV-3. As of 20 September 2022, 27,283 cases have been reported in 2022. Singapore is currently also responding to the COVID-19 pandemic, overcoming another wave of infections with 281,977 cases recorded in the past two months as of 19 September 2022. While Singapore has adopted several policies and interventions to combat dengue, primarily through environmental control but also innovations such as the Wolbachia mosquito programme, there is a need for further efforts to deal with the dual threat of dengue and COVID-19. Drawing lessons from Singapore’s experience, countries facing such dual epidemics should enact clear policy responses, including establishing a multisectoral dengue action committee and action plan prior to potential outbreaks. Key indicators should be agreed upon and tracked at all healthcare levels as part of dengue surveillance and incorporated into the national health information system. Digitizing dengue monitoring systems and implementing telemedicine solutions are innovative measures that would facilitate the response to dengue in the context of restrictions during the COVID-19 pandemic that hinder the detection and response to new cases. There is a need for greater international collaboration in reducing or eradicating dengue in endemic countries. Further research is also required on how best to establish integrated early warning systems and extend our knowledge of the effects of COVID-19 on dengue transmission in affected countries.Keywords: DENV-3, SARS-CoV-2, infectious disease control, Singapore

Reena Shah,1 Jasmit Shah,1 Nancy Kunyiha,1 Sayed K Ali,1 Shahin Sayed,2 Salim Surani,3 Mansoor Saleh4 1Department of Medicine, Aga Khan University, Nairobi, Kenya; 2Department of Pathology, Aga Khan University, Nairobi, Kenya; 3Department of Medicine & Pharmacology, Texas A&M University, College Station, TX, USA; 4Department of Hematology and Oncology, Aga Khan University, Nairobi, KenyaCorrespondence: Reena Shah, Department of Medicine, Aga Khan University, East Tower Block, 3 rd Parklands Avenue, P.O. Box 30270 – 00100, Nairobi, Kenya, Tel +254 735 338003, Email reena.shah@aku.eduIntroduction: The first documented case of COVID-19 in Kenya was recorded March of 2020. Co-morbidities including hypertension and diabetes have been associated with increased morbidity, hospitalization, and mortality among COVID-19 patients. This retrospective study describes the clinical characteristics, disease severity, and outcomes among the patient population at a tertiary hospital in Kenya.Methods: This was a retrospective descriptive study of COVID-19 patients who were admitted between March 2020 and December 2020 at the Aga Khan University Hospital in Nairobi, Kenya. Data collected include patient demographic and baseline characteristics. Differences between patients who were known to have diabetes and hypertension during admission were compared for statistical significance. Difference between those who survived and those who died were also compared for statistical significance.Results: A total of 913 records of patients were studied with a mean age of 51.2 years (SD = 16.7), 66.5% were male and 80.8% were of African origin. History of diabetes, hypertension, and HIV status were at 27.3%, 33.1%, and 2.3%, respectively. At presentation, 33.1% (302/913) of patients had known hypertension by history, and following admission, this proportion increased to 37.7% (344/913). At presentation, 27.3% (249/913) of patients had known diabetes. During hospital stay, 20.8% (190) more patients were found to have diabetes, raising the overall percent to 48.1% (439/913). When comparing diabetes and hypertension at baseline versus at the end of admission, diabetes increased by 20.8% (p < 0.001) and hypertension by 4.6% (p = 0.049). HIV co-infection was 2.3%, and no patient had tuberculosis.Conclusion: This study showed a high incidence of co-morbidities in patients infected with COVID-19. Diabetes was most common, followed by hypertension. All patients admitted with COVID-19 infection should routinely be tested for diabetes with HbA1c and have regular blood pressure monitoring in order to diagnose occult diabetes and hypertension. Adverse outcomes were found in patients with these co-morbidities and should be monitored and treated appropriately.Keywords: COVID-19, diabetes, hypertension, sub-Saharan Africa

Abstract Big data analytics utilizes different techniques to transform large volumes of big datasets. The analytics techniques utilize various computational methods such as Machine Learning (ML) for converting raw data into valuable insights. The ML assists individuals in performing work activities intelligently, which empowers decision-makers. Since academics and industry practitioners have growing interests in ML, various existing review studies have explored different applications of ML for enhancing knowledge about specific problem domains. However, in most of the cases existing studies suffer from the limitations of employing a holistic, automated approach. While several researchers developed various techniques to automate the systematic literature review process, they also seemed to lack transparency and guidance for future researchers. This research aims to promote the utilization of intelligent literature reviews for researchers by introducing a step-by-step automated framework. We offer an intelligent literature review to obtain in-depth analytical insight of ML applications in the clinical domain to (a) develop the intelligent literature framework using traditional literature and Latent Dirichlet Allocation (LDA) topic modeling, (b) analyze research documents using traditional systematic literature review revealing ML applications, and (c) identify topics from documents using LDA topic modeling. We used a PRISMA framework for the review to harness samples sourced from four major databases (e.g., IEEE, PubMed, Scopus, and Google Scholar) published between 2016 and 2021 (September). The framework comprises two stages—(a) traditional systematic literature review consisting of three stages (planning, conducting, and reporting) and (b) LDA topic modeling that consists of three steps (pre-processing, topic modeling, and post-processing). The intelligent literature review framework transparently and reliably reviewed 305 sample documents.

Reena Shah,1 Jasmit Shah,1 Jaimini Gohil,2 Gunturu Revathi,3 Salim Surani4 1Department of Internal Medicine, The Aga Khan University, Nairobi, Kenya; 2Department of Pharmacy and Therapeutics, The Aga Khan University, Nairobi, Kenya; 3Department of Pathology, The Aga Khan University, Nairobi, Kenya; 4Department of Pulmonology & Internal Medicine, Texas A&M University, Corpus Christi, Texas, USACorrespondence: Reena Shah, Tel +254735338003, Email reena.shah@aku.eduIntroduction: From the first case of SARS-Co-2 in Wuhan, China, to the virus being declared as a pandemic in March 2020, the world has witnessed morbidity and mortality on a global scale. Scientists have worked at a record pace to deliver a vaccine for the prevention of this deadly disease. Tocilizumab, an interleukin-6 (IL-6) blocker, received an emergency use authorization (EUA) by the Federal Drug Agency (FDA) in June 2021.Methods: This retrospective observational cohort study was conducted at the Aga Khan University Hospital, Nairobi, from March 8, 2020, to December 31, 2020. All patients with PCR confirmed COVID-19 pneumonia were included. Data were obtained from the medical records, and the admission registry was used to identify the patients, and both their electronic and paper-based files were retrieved from the medical records. Patient demographic data, medical history, baseline comorbidities, clinical characteristics, and outcome data were collected to study the infectious complications of Tocilizumab in patients affected by COVID-19 pneumonia.Results: A total of 913 patients who were diagnosed with COVID-19 were included. The overall superinfection infection rate among the COVID-19 patients was 6%. Superinfection in patients who received the Tocilizumab was 17.2% and in the non-Tocilizumab group was 4.8%. The superinfection rate among severe and critically ill patients was even higher at 41.8% and 69.9% (Tocilizumab group) and 2.1% and 11.8% (non-Tocilizumab group), respectively (p < 0.001). There was no difference in mortality observed between the groups (p = 0.846). Infection among HIV co-infection was very low at 2.3%.Conclusion: Contrary to some studies, a higher rate of infection was observed among the Tocilizumab group, and no difference in mortality was observed between Tocilizumab and the non-Tocilizumab group. Infection among patients with HIV remains low in this susceptible population.Keywords: COVID-19, SARS-CoV-2, EMPACTA, RECOVERY, Tocilizumab, IL-6, REMDATA, CONVACTA

Mansoor M Aman,1 Ammar Mahmoud,2 Timothy Deer,3 Dawood Sayed,4 Jonathan M Hagedorn,5 Shane E Brogan,6 Vinita Singh,7 Amitabh Gulati,8 Natalie Strand,9 Jacqueline Weisbein,10 Johnathan H Goree,11 Fangfang Xing,12 Ali Valimahomed,13 Daniel J Pak,14 Antonios El Helou,15 Priyanka Ghosh,16 Krishna Shah,17 Vishal Patel,1 Alexander Escobar,18 Keith Schmidt,19 Jay Shah,20 Vishal Varshney,21 William Rosenberg,22 Sanjeet Narang23 1Department of Anesthesiology, Division of Pain Medicine, Advocate Aurora Health, Oshkosh, WI, USA; 2Department of Anesthesiology, Division of Pain Medicine, Northern Light Health Eastern Maine Medical Center, Bangor, ME, USA; 3The Spine and Nerve Center of the Virginias, Charleston, WV, USA; 4Department of Anesthesiology, Pain and Perioperative Medicine, University of Kansas Medical Center, Kansas City, KS, USA; 5Department of Anesthesiology and Perioperative Medicine, Division of Pain Medicine, Mayo Clinic, Rochester, MN, USA; 6Department of Anesthesiology, Division of Pain Medicine, University of Utah, Salt Lake City, UT, USA; 7Department of Anesthesiology, Division of Pain Medicine, Emory University, Atlanta, GA, USA; 8Department of Anesthesiology and Critical Care, Memorial Sloan Kettering Cancer Center, New York, NY, USA; 9Department of Anesthesiology, Division of Pain Medicine, Mayo Clinic, Phoenix, AZ, USA; 10Department of Anesthesiology, Chronic Pain Division, University of Arkansas for Medical Sciences, Little Rock, AR, USA; 11Interventional Pain Medicine, Napa Valley Orthopedic Medical Group, Napa, CA, USA; 12Swedish Pain Services, Swedish Health Services, Seattle, WA, USA; 13Gramercy Pain Center, Holmdel, NJ, & Advanced Orthopedics Sports Medicine Institute, Freehold, NJ, USA; 14Department of Anesthesiology, Division of Pain Medicine, New York-Presbyterian Hospital/Weill Cornell Medical College, New York, NY, USA; 15Department of Neurosciences, Division of Neurosurgery, The Moncton Hospital, Moncton, NB. Assistant Professor, Department of Surgery, Dalhousie University, Halifax, NS, Canada; 16Remedy Medical Group, San Francisco, CA, USA; 17Assistant Professor of Anesthesiology, Baylor St. Luke’s Medical Center, Baylor College of Medicine, Houston, TX, USA; 18Department of Anesthesiology and Pain Medicine, University of Toledo Medical Center, Toledo, OH, USA; 19AMITA Neurosciences Institute, Comprehensive Pain Management Program, St. Alexius Medical Center, Hoffman Estates, IL, USA; 20SamWell Institute for Pain Management, Colonia, NJ, USA; 21Department of Anesthesia, Providence Healthcare, Vancouver, BC, Canada & Department of Anesthesiology, Pharmacology, Therapeutics, University of British Columbia, Vancouver, BC, Canada; 22Center for the Relief of Pain, Midwest Neurosurgery Associates, Kansas City, Missouri, USA; 23Department of Anesthesiology, Pain and Perioperative Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USACorrespondence: Mansoor M AmanInterventional Pain Medicine, Department of Anesthesiology, Advocate Aurora Health, Oshkosh, WI, 54904, USATel +1 920-456-7715Email aman.mansoorm@gmail.comAbstract: Moderate to severe pain occurs in many cancer patients during their clinical course and may stem from the primary pathology, metastasis, or as treatment side effects. Uncontrolled pain using conservative medical therapy can often lead to patient distress, loss of productivity, shorter life expectancy, longer hospital stays, and increase in healthcare utilization. Various publications shed light on strategies for conservative medical management for cancer pain and a few international publications have reviewed limited interventional data. Our multi-institutional working group was assembled to review and highlight the body of evidence that exists for opioid utilization for cancer pain, adjunct medication such as ketamine and methadone and interventional therapies. We discuss neurolysis via injections, neuromodulation including targeted drug delivery and spinal cord stimulation, vertebral tumor ablation and augmentation, radiotherapy and surgical techniques. In the United States, there is a significant variance in the interventional treatment of cancer pain based on fellowship training. As a first of its kind, this best practices and interventional guideline will offer evidenced-based recommendations for reducing pain and suffering associated with malignancy.Keywords: cancer pain, neurolysis, ketamine, pain pump, intrathecal drug delivery, neuromodulation, radiofrequency, spinal cord stimulation, vertebral augmentation

ObjectivesMobile health (m-Health) technologies may provide an appropriate follow-up support service for patient groups with post-treatment conditions. While previous studies have introduced m-Health methods for patient care, a smart system that may provide follow-up communication and decision support remains limited to the management of a few specific types of diseases. This paper introduces an m-Health solution in the current climate of increased demand for electronic information exchange.MethodsAdopting a novel design science research approach, we developed an innovative solution model for post-treatment follow-up decision support interaction for use by patients and physicians and then evaluated it by using convergent interviewing and focus group methods.ResultsThe cloud-based solution was positively evaluated as supporting physicians and service providers in providing post-treatment follow-up services. Our framework provides a model as an artifact for extending care service systems to inform better follow-up interaction and decision-making.ConclusionsThe study confirmed the perceived value and utility of the proposed Clinical Decision Support artifact indicating that it is promising and has potential to contribute and facilitate appropriate interactions and support for healthcare professionals for future follow-up operationalization. While the prototype was developed and tested in a developing country context, where the availability of doctors is limited for public healthcare, it was anticipated that the prototype would be user-friendly, easy to use, and suitable for post-treatment follow-up through mobility in remote locations.

Mahesh Mathur,* Prakash Acharya,* Alina Karki, Nisha KC, Jyoti ShahDepartment of Dermatology, College of Medical Sciences, Bharatpur, Nepal*These authors contributed equally to this workBackground: Pityriasis versicolor (PV) is essentially a clinical diagnosis characterized by hypopigmented or hyperpigmented patches on the skin. Dermoscopy is gaining popularity as a noninvasive procedure for the diagnosis of different pigmentary and inflammatory disorders. However, scarce evidence exists on the dermoscopic pattern of PV.Objective: To describe the dermoscopic features of hypopigmented and hyperpigmented lesions of PV.Methods: Dermoscopic images of PV lesions located on different body sites were retrospectively evaluated for the presence of predefined criteria.Results: A total of 178 lesions from 125 patients were included in the study among which 164 lesions were hypopigmented and 14 lesions were hyperpigmented. Nonuniform pigmentation was the most common dermoscopic feature seen in both hypopigmented lesions (n=152, 92.68%) and hyperpigmented lesions (n=14, 100%). Scales were seen in 142 hypopigmented lesions (86.56%) and 13 hyperpigmented lesions (92.86%). Patchy scaling was more common in hypopigmented lesions (n=95, 57.92%) while scaling in the furrows was more common in the dermoscopy of hyperpigmented lesions (n=5, 35.71%). Inconspicuous ridges and furrows and perilesional hyperpigmentation were other significant features seen in dermoscopy of the lesions.Conclusion: To our knowledge, this is the first study describing the dermoscopic features of PV in such a large number of patients. Description of these new features adds valuable information and may help to establish dermoscopy as an important auxiliary tool for the diagnosis of PV.Keywords: dermoscopy, dermatoscopy, tinea versicolor, diagnosis, hypopigmented patches

This presentation is to define tuberculous sarcoidosis as a distinct clinical en-tity quite separate from tuberculosis or sarcoidosis. Evidence is also presented to postulate that altered mycobacterial infection or its biodegraded products can reasonably be considered as the aetiologic cause of Tuberculous Sarcoidosis.

With the object to strengthen the clinical status of tuberculous sarcoidosis, we present in this article, the case records published in internationally recognized journals by specialists. From review of clinical material, we have also formulated a table that defines diagnostic criteria of tuberculous sarcoidosis.

This is a case report of a 48-year-old man, followed up for nearly 30 years who initially developed sarcoidosis at the age of 18 that went into remission after 8 years of treatment. Ten years later, he developed sputum-positive tuberculosis and was cured with anti-tubercular treatment. Following this, there was progression of sarcoidosis to stage IV fibrocystic disease. Ten years later, he had massive hemoptysis during which time, aspergilloma was detected in a sarcoid cystic cavity.

The optical response of the fluorescent molecule IR144 in solution is probed by pairs of collinear pulses with intensity just above the linear dependence using two different pulse shaping methods. The first approach mimics a Michelson interferometer while the second approach, known as multiple independent comb shaping (MICS), eliminates spectral interference. The comparison of interfering and non-interfering pulses reveals that experimental information can be lost at early delay times because of linear interference between the pulses.

Information governance (IG) embraces underlying necessities to organize information using consistent method for meeting organizational needs. This demand has proliferated over the past few years to any organization, including universities. However, due to the poor nature of information management systems in Nigerian universities, there is a significant need to explore the possibilities of IG implementation. Even though various factors affect IG success, these have not been investigated yet, particularly in the context of the Nigerian university system. The objective of this study is to identify key factors necessary for IG implementation in universities. A qualitative research using a case study approach was used with a semi-structured interview technique to elicit findings from the domain of information and record management in case universities. Findings were qualitatively analyzed using ATLAS-ti 7.0 software for developing meaningful interpretations. Research findings embraced key factors including user expertise, funding, technology; org. facilities, and security, which are the necessities of leading to successful IG implementation.

Recently, machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on the HE sector. However, there is a scarcity of a systematic review of HE literature to gain from the overarching trends and patterns discovered using ML. This paper conducts a systematic review and meta-analyses of research studies that have reported on the application of ML in HE. The differentiating factors of this study are primarily vested in the meta-analyses including a specific focus on student academic performance, at-risk, and attrition in HE. Our detailed investigation adopts an evidence-based framework called PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for reporting the findings of our systematic review and meta-analyses of literature on the use of ML models, algorithms, evaluation metrics, and other criteria, including demographics for assessing student academic performance, at-risk and attrition in HE. After undergoing the PRISMA steps such as selection criteria and filtering, we arrive at a small-scale dataset of 89 relevant studies published from 2010 to 2020 for an in-depth analysis. The results show the outcomes of the quantitative analysis of the application of ML types, models, evaluation metrics, and other related demographics and provide quality insights of publication patterns and future trends towards predicting and monitoring student academic progress in HE.

Abstract Background In March of 2003, an outbreak of Severe Acute Respiratory Syndrome (SARS) occurred in Northern Vietnam. This outbreak began when a traveler arriving from Hong Kong sought medical care at a small hospital (Hospital A) in Hanoi, initiating a serious and substantial transmission event within the hospital, and subsequent limited spread within the community. Methods We surveyed Hospital A personnel for exposure to the index patient and for symptoms of disease during the outbreak. Additionally, serum specimens were collected and assayed for antibody to SARS-associated coronavirus (SARS-CoV) antibody and job-specific attack rates were calculated. A nested case-control analysis was performed to assess risk factors for acquiring SARS-CoV infection. Results One hundred and fifty-three of 193 (79.3%) clinical and non-clinical staff consented to participate. Excluding job categories with Conclusion This study highlights job categories and activities associated with increased risk for SARS-CoV infection and demonstrates that a broad diversity of hospital workers may be vulnerable during an outbreak. These findings may help guide recommendations for the protection of vulnerable occupational groups and may have implications for other respiratory infections such as influenza.