An Efficient Emergency Patient Monitoring based on Mobile Ad Hoc Networks

Medical sensors are implanted within the vital organs of human body to record and monitor the vital signs of pulse rate, heartbeat, electrocardiogram, body mass index, temperature, blood pressure, etc. to ensure their effective functioning. These are monitored to detect patient’s health from anywhere and at any time. The Wireless Sensor Networks are embedded in the form of Body Area Nets and are capable of sensing and storing the information on a digital device. Later this information could be inspected or even sent to a remotely located storage device specifically (server or any public or private cloud for analysis) so that a medical doctor can diagnose the present medical condition of a person or a patient. Such a facility would be of immense help in the event of an emergency such as a sudden disaster or natural calamity where communication is damaged, and the potential sources become inaccessible. The aim of this paper is to create a mobile platform using Mobile Ad hoc Network to support healthcare connectivity and treatment in emergency situations.

eCOVID19 – Development of Ontology-based Clinical Decision Support System for COVID-19

Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.

Thoracic Splenosis - A necessary differential diagnosis for pleural based nodules with history of thoracoabdominal trauma

Thoracic Splenosis (TS) is a rare medical condition, where there is auto-transplantation of the splenic tissue in the thoracic cavity, often leading to pleural based nodules. Our patient is the first ever case of this condition in Pakistan, and underwent extensive diagnostic procedures as well as medical treatments, before receiving the diagnosis of TS. He underwent HRCT for chronic cough that revealed pleural and mediastinal nodules. This coupled with a vague mass in the testes led to the provisional diagnosis of metastasized testicular tumour, and later a diagnosis of pulmonary tuberculosis was made. However, eventually a 99mTc denatured red blood cell scan confirmed the diagnosis of TS. TS is a benign condition, whereas other causes of pleural nodules are relatively malignant, hence its diagnosis is essential in ruling out malignancies. Among the multiple invasive and non-invasive diagnostic modalities, the gold standard remains 99mTc denatured red blood cell scan, which is a sensitive test that provides an accurate diagnosis and bars the need of multiple invasive procedures. doi: https://doi.org/10.12669/pjms.38.3.4563 How to cite this:Mehmood F, Murtaza MM, Ali S, Ashraf A. Thoracic Splenosis - A necessary differential diagnosis for pleural based nodules with history of thoracoabdominal trauma. Pak J Med Sci. 2022;38(3):---------. doi: https://doi.org/10.12669/pjms.38.3.4563 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Public understanding of intersex: an update on recent findings

Surgical interventions on infants with intersex characteristics are considered justified by some on the grounds that they carry a high risk of intolerable stigma. However, public understanding of intersex and its medicalization are under-researched. We review recent qualitative and quantitative studies of the understandings of intersex and its medicalization among people who have no particular professional or public experience of intersex. First, such laypeople reason about clinical dilemmas by drawing on values in similar ways as expert healthcare professionals do. Second, laypeople can over-estimate the utility of current ‘umbrella terms,’ including intersex, for people with direct familial experience of intersex. Third, beliefs about good and bad effects of medical intervention are affected by framing intersex as either a medical condition or the natural basis for a social identity. Fourth, sexual identity is the best evidenced predictor of opinions about early surgical intervention and its legal limitation on human rights grounds. We argue that possible stigmatizing reactions from the public may not be a solid basis on which to justify early surgical intervention on intersex characteristics.

Computational Modeling Intervertebral Disc Pathophysiology: A Review

Lower back pain is a medical condition of epidemic proportion, and the degeneration of the intervertebral disc has been identified as a major contributor. The etiology of intervertebral disc (IVD) degeneration is multifactorial, depending on age, cell-mediated molecular degradation processes and genetics, which is accelerated by traumatic or gradual mechanical factors. The complexity of such intertwined biochemical and mechanical processes leading to degeneration makes it difficult to quantitatively identify cause–effect relationships through experiments. Computational modeling of the IVD is a powerful investigative tool since it offers the opportunity to vary, observe and isolate the effects of a wide range of phenomena involved in the degenerative process of discs. This review aims at discussing the main findings of finite element models of IVD pathophysiology with a special focus on the different factors contributing to physical changes typical of degenerative phenomena. Models presented are subdivided into those addressing role of nutritional supply, progressive biochemical alterations stemming from an imbalance between anabolic and catabolic processes, aging and those considering mechanical factors as the primary source that induces morphological change within the disc. Limitations of the current models, as well as opportunities for future computational modeling work are also discussed.

Medication Changes and Diagnosis of New Chronic Illnesses in COVID-19 Intensive Care Unit Survivors

Background Currently, there is limited literature on the impact of the COVID-19 infection on medications and medical conditions in COVID-19 intensive care unit (ICU) survivors. Our study is, to our knowledge, the first multicenter study to describe the prevalence of new medical conditions and medication changes at hospital discharge in COVID-19 ICU survivors. Objective To determine the number of medical conditions and medications at hospital admission compared to at hospital discharge in COVID-19 ICU survivors. Methods Retrospective multicenter observational study (7 ICUs) evaluated new medical conditions and medication changes at hospital discharge in patients with COVID-19 infection admitted to an ICU between March 1, 2020, to March 1, 2021. Patient and hospital characteristics, baseline and hospital discharge medication and medical conditions, ICU and hospital length of stay, and Charlson comorbidity index were collected. Descriptive statistics were used to describe patient characteristics and number and type of medical conditions and medications. Paired t-test was used to compare number of medical conditions and medications from hospital discharge to admission. Results Of the 973 COVID-19 ICU survivors, 67.4% had at least one new medical condition and 88.2% had at least one medication change. Median number of medical conditions (increased from 3 to 4, P < .0001) and medications (increased from 5 to 8, P < .0001) increased from admission to discharge. Most common new medical conditions at discharge were pulmonary disorders, venous thromboembolism, psychiatric disorders, infection, and diabetes. Most common therapeutic categories associated with medication change were cardiology, gastroenterology, pain, hematology, and endocrinology. Conclusion and Relevance Our study found that the number of medical conditions and medications increased from hospital admission to discharge. Our results provide additional data to help guide providers on using targeted approaches to manage medications and diseases in COVID-19 ICU survivors after hospital discharge.

A registry-based observational study comparing emergency calls assessed by emergency medical dispatchers with and without support by registered nurses

Background The requirement concerning formal education for emergency medical dispatcher (EMD) is debated and varies, both nationally and internationally. There are few studies on the outcomes of emergency medical dispatching in relation to professional background. This study aimed to compare calls handled by an EMD with and without support by a registered nurse (RN), with respect to priority level, accuracy, and medical condition. Methods A retrospective observational study, performed on registry data from specific regions during 2015. The ambulance personnel’s first assessment of the priority level and medical condition was used as the reference standard. Outcomes were: the proportion of calls dispatched with a priority in concordance with the ambulance personnel’s assessment; over- and undertriage; the proportion of most adverse over- and undertriage; sensitivity, specificity and predictive values for each of the ambulance priorities; proportion of calls dispatched with a medical condition in concordance with the ambulance personnel’s assessment. Proportions were reported with 95% confidence intervals. χ2-test was used for comparisons. P-levels < 0.05 were regarded as significant. Results A total of 25,025 calls were included (EMD n = 23,723, EMD + RN n = 1302). Analyses relating to priority and medical condition were performed on 23,503 and 21,881 calls, respectively. A dispatched priority in concordance with the ambulance personnel’s assessment were: EMD n = 11,319 (50.7%) and EMD + RN n = 481 (41.5%) (p < 0.01). The proportion of overtriage was equal for both groups: EMD n = 5904, EMD + RN n = 306, (26.4%) p = 0.25). The proportion of undertriage for each group was: EMD n = 5122 (22.9%) and EMD + RN n = 371 (32.0%) (p < 0.01). Sensitivity for the most urgent priority was 54.6% for EMD, compared to 29.6% for EMD + RN (p < 0.01), and specificity was 67.3% and 84.8% (p < 0.01) respectively. A dispatched medical condition in concordance with the ambulance personnel’s assessment were: EMD n = 13,785 (66.4%) and EMD + RN n = 697 (62.2%) (p = 0.01). Conclusions A higher precision of emergency medical dispatching was not observed when the EMD was supported by an RN. How patient safety is affected by the observed divergence in dispatched priorities is an area for future research.

Combination therapy with oral minoxidil and dutasteride in the treatment of male patterned baldness: A case report

Androgenetic alopecia is a medical condition with a deep social and psychological impact on the affected individuals and is characterized by progressive hair thinning, leading to hair loss over the scalp in both males and females. Minoxidil in oral form is primarily an antihypertensive drug, whose mechanism of action is not completely known. Dutasteride is a 5-alpha reductase inhibitor, acting on both alpha-1 and alpha-2 receptors. The author combined these two agents for the treatment of male patterned baldness and found that this combination imparts a visible increase in hair thickness, density, and new hair growth in the patient, within a short period causing minimal side effects.

Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.