Awareness and Knowledge on Epilepsy Among Undergraduate Medical Students in Pakistan

Background:  Epilepsy is a common neurological disorder which affects millions of people throughout the world. However, it has been discovered that there is a great degree of ignorance regarding the science of epilepsy among the general masses as well as the populace of medical students.  Studies have been conducted whose results have shown that, there has been fabrication regarding the clinical presentation & treatment of epilepsy among the common man. Numerous appear to link epilepsy to evil spirits and possession that can be healed by spiritual treatments by certain specialists given the designations of ‘demonologists’,’ paranormal investigators’ or ‘mystics’. In such circumstances, medical students can prove to be a major source to educate the society at large. Therefore, it is crucial that their knowledge & attitude towards epilepsy is accurate and thus, must be evaluated at an early stage in their medical career, so that these future physicians may play a pivotal role in the public awareness of epilepsy.  Method:  This study was conducted in Pakistan, at a Government sector medical college, namely, Karachi Medical and Dental College. This is a cross-sectional study. Data was collected between October to December of the year 2014. 270 medical students were given the KAP (knowledge, attitude & practice) form of epilepsy to fill out. The software used to interpret and tabulate the results was SPSS v.16 for Windows. The chi-square test was employed to determine the proportion of knowledge of epilepsy among medical students. The p-value calculated was equal to 0.05.   Findings:  In this study, 270 medical students were recruited, of which 90 were males (33.3%) where as 180 were females (66.6%). The ages of the student participants were within the range of 18 to 24 years. The analysis of our study demonstrates that: 85.1% of the medical students consider epilepsy to be a neurological disorder; 6.66% believe epilepsy is an infectious disease; 4.44% believe it to be a hereditary disease, whereas, 3.7% of the students reckon it is a psychiatric illness. Generalized tonic clonic seizures was deemed to be the most common form of epilepsy (25.5%) with complex partial seizures being the least common form(1.85%) , as well as relatively unknown by the students.             It was discovered through this study that, students were much less acquainted with the knowledge regarding the treatment of the disorder: multiple drug therapy was considered as the treatment by 56.66% of the students, spiritual treatment by 3.7%, spiritual treatment with medication by 20%, surgical treatment by 17.4% & 2.22% deemed epilepsy as a self limiting disease.  Conclusion:  It has been observed with this survey that the medical students of Pakistan are well aware of the knowledge of epilepsy and it being a medical condition. However, the students need to be educated early on in their training to be physicians, about certain aspects and details of the neurological disorder, for instance, the appropriate treatment of epilepsy, where their knowledge is lacking

AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study

Introduction A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. Methods and analysis A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. Ethics and dissemination The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal

AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.

Introduction: A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. Methods and analysis: A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. Ethics and dissemination: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal. Trial registration number NCT06018545

Metronomic Chemotherapy for Advanced Prostate Cancer: A Literature Review

Metastatic castration-resistant prostate cancer (mCRPC) is the ultimately lethal form of prostate cancer. Docetaxel chemotherapy was the first life-prolonging treatment for mCRPC; however, the standard maximally tolerated dose (MTD) docetaxel regimen is often not considered for patients with mCRPC who are older and/or frail due to its toxicity. Low-dose metronomic chemotherapy (LDMC) is the frequent administration of typically oral and off-patent chemotherapeutics at low doses, which is associated with a superior safety profile and higher tolerability than MTD chemotherapy. We conducted a systematic literature review using the PUBMED, EMBASE, and MEDLINE electronic databases to identify clinical studies that examined the impact of LDMC on patients with advanced prostate cancer. The search identified 30 reports that retrospectively or prospectively investigated LDMC, 29 of which focused on mCRPC. Cyclophosphamide was the most commonly used agent integrated into 27/30 (90%) of LDMC regimens. LDMC resulted in a clinical benefit rate of 56.8 ± 24.5% across all studies. Overall, there were only a few non-hematological grade 3 or 4 adverse events reported. As such, LDMC is a well-tolerated treatment option for patients with mCRPC, including those who are older and frail. Furthermore, LDMC is considered more affordable than conventional mCRPC therapies. However, prospective phase III trials are needed to further characterize the efficacy and safety of LDMC in mCRPC before its use in practice

Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to minimize unnecessary toxic NAC and to modify regimens mid-treatment to achieve better efficacy. Machine learning (ML) is increasingly being used in radiology and medicine because it can identify relationships amongst complex data elements to inform outcomes without the need to specify such relationships a priori. One of the most popular deep learning methods that applies to medical images is the Convolutional Neural Networks (CNN). In contrast to supervised ML, deep learning CNN can operate on the whole images without requiring radiologists to manually contour the tumor on images. Although there have been many review papers on supervised ML prediction of pCR, review papers on deep learning prediction of pCR are sparse. Deep learning CNN could also incorporate multiple image types, clinical data such as demographics and molecular subtypes, as well as data from multiple treatment time points to predict pCR. The goal of this study is to perform a systematic review of deep learning methods that use whole-breast MRI images without annotation or tumor segmentation to predict pCR in breast cancer

Integrative Pathogenicity Assay and Operational Taxonomy-Based Detection of New Forma Specialis of <i>Fusarium oxysporum</i> Causing Datepalm Wilt

Pathogenicity-associated genes are highly host-specific and contribute to host-specific virulence. We tailored the traditional Koch’s postulates with integrative omics by hypothesizing that the effector genes associated with host-pathogenicity are determinant markers for virulence, and developed Integrative Pathogenicity (IP) postulates for authenticated pathogenicity testing in plants. To set the criteria, we experimented on datepalm (Phoenix dactylifera) for the vascular wilt pathogen and confirmed the pathogen based on secreted in xylem genes (effectors genes) using genomic and transcriptomic approaches, and found it a reliable solution when pathogenicity is in question. The genic regions ITS, TEF1-α, and RPBII of Fusarium isolates were examined by phylogenetic analysis to unveil the validated operational taxonomy at the species level. The hierarchical tree generated through phylogenetic analysis declared the fungal pathogen as Fusarium oxysporum. Moreover, the Fusarium isolates were investigated at the subspecies level by probing the IGS, TEF1-α, and Pgx4 genic regions to detect the forma specialis of F. oxysporum that causes wilt in datepalm. The phylogram revealed a new forma specialis in F. oxysporum that causes vascular wilt in datepalm

Struvite separation from wastewater and its use with sulfur-oxidizing bacteria improves phosphorus utilization in alkaline soil

A major portion of phosphatic fertilizer comes from the limiting natural resource, rock phosphate, which demands a timely alternative. Struvite, a crystalline mineral of low solubility, is a worthwhile alternative. Evaluation of the local wastewater streams for their ability to precipitate struvite and its capability as phosphatic fertilizer under an alkaline soil environment was studied. Two stirring speeds, a pH range of 8.0–11.0, and a constant molar ratio were used to optimize local wastewater streams for struvite precipitation. Struvite was used in five different combinations to assess the release of phosphorus (P), including control (no P source), single superphosphate, struvite, struvite + sulfur, and rock phosphate with or without inoculation of sulfur-oxidizing bacteria (SOB). For struvite precipitation, low stirring speeds are ideal because the precipitates readily sink to the bottom once they form. Furthermore, the amalgamation of SOB with sulfur significantly improved P use efficiency under alkaline soils through increased phosphorus sources solubility and enabled optimum wheat production due to its low solubility in an alkaline soil condition. Due to its capacity to recycle phosphorus from wastewater, struvite is poised to emerge as a sustainable fertilizer and had an opportunity to capture a share of this expanding market. HIGHLIGHTS Wastewater characteristics and effects of increasing pH levels on P and N removal.; The effects of stirring speeds and pH levels on MAP precipitation.; Relationship of pH levels with N and P removal and struvite production.; Various P sources effects on soil pH, both with and without SOB.; Struvite application and its impact.

Urinary Exosomal MicroRNAs as Biomarkers for Obesity-Associated Chronic Kidney Disease

The early detection of chronic kidney disease (CKD) is key to reducing the burden of disease and rising costs of care. This need has spurred interest in finding new biomarkers for CKD. Ideal bi-omarkers for CKD should be: easy to measure; stable; reliably detected, even when interfering substances are present; site-specific based on the type of injury (tubules vs. glomeruli); and its changes in concentration should correlate with disease risk or outcome. Currently, no single can-didate biomarker fulfills these criteria effectively, and the mechanisms underlying kidney fibrosis are not fully understood; however, there is growing evidence in support of microRNA-mediated pro-cesses. Specifically, urinary exosomal microRNAs may serve as biomarkers for kidney fibrosis. In-creasing incidences of obesity and the recognition of obesity-associated CKD have increased interest in the interplay of obesity and CKD. In this review, we provide: (1) an overview of the current scope of CKD biomarkers within obese individuals to elucidate the genetic pathways unique to obesi-ty-related CKD; (2) a review of microRNA expression in obese individuals with kidney fibrosis in the presence of comorbidities, such as diabetes mellitus and hypertension; (3) a review of thera-peutic processes, such as diet and exercise, that may influence miR-expression in obesity-associated CKD; (4) a review of the technical aspects of urinary exosome isolation; and (5) future areas of research

AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.

Peer reviewed: TrueINTRODUCTION: A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. METHODS AND ANALYSIS: A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers' performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. ETHICS AND DISSEMINATION: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT06018545