7 research outputs found
Awareness with Recall During General Anesthesia: A Cross-Sectional Study
Get PDFBackground: Accidental awareness and recall a well-known complication of anesthesia and sedation despite advancements in monitoring, drugs, and techniques. This study aimed to determine the prevalence and factors of awareness with recall (AWR) during general anesthesia in a tertiary care hospital.
Methods: A cross-sectional study was conducted at Maqsood Medical Center Peshawar from April to October 2022, with a total of 383 patients ≥18 years, in good neurological health, and having ASA Physical Status I, II, III, IV undergoing elective general anesthesia-based procedures were included through a convenience sampling technique. Data was collected by administering the Brice questionnaire for structured interviews one hour after admission in PACU after assessing responsiveness. Data was analyzed using SPSS 26 with p<0.05 considered significant.
Results: The mean age of the study participants was 40.93+05.2 years. The Incidence of awareness was found 7(1.8%) out of which 8(2.1%) experienced pain, 7(1.8%), being touched, 7(1.8%), hearing sounds, 8(2.1%), unable to speak 8(2.1%), feeling of paralysis, 6(1.6%) experienced tube being inside the throat, 17(4.4%) experienced an abrupt increase in blood pressure, 7(1.8%) experienced sweating, and tear production and 5(1.3%) observed movement and grimacing. No specific association was found between awareness of gender and surgery type. There was a significant association between awareness and ASA classification(p=0.000) and with intraoperative pain(p=0.00), and hemodynamic changes (p=0.04).
Conclusion: In conclusion, there was an unexpectedly high prevalence of anesthetic awareness with recall. During surgery, patients typically experience pain, difficulty in communication, and paralysis.
Keywords: Analgesia, Anesthesia, Operative Surgical Procedure, Metacognition
Evaluation of Currently Available Molecular Assays and Performance of Sampling Approaches for Detection of Sars-Cov-2 RNA
Get PDFOBJECTIVES
This study aims to identify the essential characteristics of diagnostic tests for SARS-CoV-2 and to discuss the limitations of currently available tests and their impact on the test selection process.
METHODOLOGY
The current study was conducted at Mardan Medical Complex (MMC). One hundred nasopharyngeal-positive samples were collected from February to March 2021. Oropharyngeal swab OPS, sputum, and blood samples were collected from the participants to detect SARS-CoV-2 RNA. RNA extraction of SARS-CoV-2 was done using a BigFish auto extractor. A Qiagen Thermal Cycler was used for genome amplification. Five different molecular assays, namely COVSIGN (N gene) Spain, BGI (ORF1ab gene) China, Maccura(ORF1ab, E and N gene) China, R-GENE (RdRp and N genes) France and Genuru (N gene, S gene and ORF ab/1) were used.
RESULTS100 % positivity was recorded in the sputum of all individuals, followed by 91 % OPS and 21% blood. The highest positivity rate for different genes was observed. ROC (Receiver operating characteristic curve) was developed through SPPS version 26.00 to compare the sensitivity and specificity.
CONCLUSION
By comparing the results of different diagnostic kits, it was found that BGI and Maccura are the most sensitive and specific for diagnostic purposes against COVID-19
Continual Learning Objective for Analyzing Complex Knowledge Representations
No full textHuman beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers from catastrophic forgetting due to which its performance on already learned tasks drastically decreases while learning about newer knowledge. Many researchers have proposed promising solutions to eliminate such catastrophic forgetting during the knowledge distillation process. However, to our best knowledge, there is no literature available to date that exploits the complex relationships between these solutions and utilizes them for the effective learning that spans over multiple datasets and even multiple domains. In this paper, we propose a continual learning objective that encompasses mutual distillation loss to understand such complex relationships and allows deep learning models to effectively retain the prior knowledge while adapting to the new classes, new datasets, and even new applications. The proposed objective was rigorously tested on nine publicly available, multi-vendor, and multimodal datasets that span over three applications, and it achieved the top-1 accuracy of 0.9863% and an F1-score of 0.9930
Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning
Get PDFChest X-Rays (CXR) are the most commonly used imaging methodology to diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent upsurge of COVID-19 accompanied by pneumonia and tuberculosis can be fatal and lives could be saved through an early detection and appropriate intervention. Thus CXRs can be used for an automated severity grading that can aid the radiologists in making better and informed diagnosis. In this article, we propose a single framework for disease classification and severity scoring produced by segmenting the lungs into six regions. We present a modified progressive learning technique which caps the amount of augmentations at each step. Our base network is first trained using modified progressive learning and can then be tweaked for new datasets. Furthermore, the segmentation task makes use of attention map generated by the network itself. This attention mechanism achieves segmentation results that are on par with networks having far greater parameters. We also propose severity score grading for 4 thoracic diseases that can provide a single digit score corresponding to the spread of opacity in different lung segments with the help of radiologists. The proposed framework is evaluated using the BRAX data set for segmentation and classification into six classes with severity grading for a subset of the classes. On BRAX, we achieve F1 scores of 0.924 and 0.939 without and with fine-tuning. A mean matching score of 80.8% is obtained for severity score grading while an average AUCROC of 0.88 is achieved for classification
Isolation and Screening of the Heavy Metals, Antibiotics Resistant and Acidophilic Profile of Bacterial strains from Lead Acid Batteries Repairing and Recycling Units
Get PDFBackground: Heavy metal contamination from unregulated waste disposal poses serious risks to ecosystems, soil, water, and human health. Lead acid battery recycling sites are major sources of such pollution. This study aimed to isolate and characterize bacterial strains with potential for bioremediation from lead acid battery workshops in Shoba Bazaar, Peshawar.Methods: Water samples were collected from contaminated sites and cultured in both nutrient agar and LB media. After incubation, 30 bacterial isolates were screened for tolerance to cadmium, lead, zinc, and chromium (50–300 mM), as well as resistance to commonly used antibiotics (ampicillin, amoxicillin, azithromycin, cefixime, and kanamycin). The most tolerant strains, designated LRB1, LRB2, and LRB3, were further analyzed for acid and temperature resistance. Morphological and molecular characterization included Gram staining, microscopic analysis, plasmid isolation and genomic DNA extraction.Results: Isolates LRB1, LRB2, and LRB3 demonstrated high lead tolerance of 270 mM, 300 mM, and 270 mM, respectively. All three strains exhibited resistance to multiple antibiotics like ampicillin (10μg), amoxicillin (30μg), azithromycin (15μg), cefixime (5μg) and kanamycin (30μg), and LRB3 showed growth across a broad pH range (2–8). Plasmid DNA was successfully isolated, indicating potential plasmid-mediated resistance. Gram staining revealed that the isolates were Gram-positive bacilli and cocci. Furthermore, genomic DNA extraction and 16S rDNA PCR with universal primers were used for detection and identification of the bacterial isolates.Conclusion: The isolated bacterial strains demonstrated remarkable tolerance to heavy metals, acids, and antibiotics, suggesting their potential role in the bioremediation of contaminated environments. Further molecular studies are required to elucidate the mechanisms underlying their resistance and to evaluate their suitability for biotechnological applications.Keywords: Heavy metal resistance; Antibiotic resistance; Lead acid batteries; Acidophilic strains; Lead Resistant strain
Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study
Get PDFBackground: Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods: We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung's disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings: We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung's disease) from 264 hospitals (89 in high-income countries, 166 in middle-income countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in low-income countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation: Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between low-income, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030. Funding: Wellcome Trust
Trajectory Analysis Model for Lumbar Puncture in a Simulated Environment
No full textEsta tesis presenta el desarrollo de un entorno de simulación realista para el procedimiento de punción lumbar, integrando retroalimentación háptica y técnicas de aprendizaje automático con el objetivo de fortalecer la formación médica. Se aplicó un modelo de resorte no lineal para caracterizar el comportamiento biomecánico de los tejidos involucrados, incluida la duramadre, utilizando parámetros obtenidos de la literatura científica y refinados mediante cálculos iterativos en MATLAB. Posteriormente, se desarrolló un prototipo de simulador utilizando la librería H3D, lo cual permitió la interacción háptica con dispositivos comerciales.
Asimismo, se diseñó un sistema de evaluación automática de trayectorias de aguja en imágenes de tomografía computarizada, basado en modelos de aprendizaje de máquina. Este sistema permite distinguir entre trayectorias seguras y no seguras para la inserción de la aguja. La aplicación de técnicas de fine-tuning en redes neuronales preentrenadas, como AlexNet, demostró ser eficaz para adaptar modelos generales a la tarea específica de clasificación de trayectorias. El ajuste fino de las capas finales mejoró notablemente la precisión y especialización del modelo en el contexto de imágenes médicas.
La inclusión de trayectorias curvadas en el análisis refleja un esfuerzo por simular errores comunes en la práctica clínica, como inserciones incorrectas de la aguja. Esta estrategia no solo incrementa la robustez del clasificador, sino que también potencia el valor educativo del sistema, al exponer a los estudiantes a escenarios adversos que deben aprender a identificar y evitar. Los resultados preliminares indican que el modelo de evaluación puede integrarse eficazmente en un simulador completo para formación médica.This thesis presents the development of a realistic simulation environment for the lumbar puncture procedure, integrating haptic feedback and machine learning techniques with the aim of strengthening medical training. A nonlinear spring model was applied to characterize the biomechanical behavior of the involved tissues, including the dura mater, using parameters obtained from the scientific literature and refined through iterative calculations in MATLAB. Subsequently, a simulator prototype was developed using the H3D library, enabling haptic interaction with commercial devices.
Additionally, an automatic needle trajectory evaluation system was designed using computed tomography (CT) images and machine learning models. This system allows for the distinction between safe and unsafe needle insertion paths. The application of fine-tuning techniques to pretrained neural networks, such as AlexNet, proved effective in adapting general models to the specific task of trajectory classification. Fine-tuning the final layers significantly improved the model’s accuracy and specialization in the context of medical imaging.
The inclusion of curved trajectories in the analysis reflects an effort to simulate common clinical errors, such as incorrect needle insertions. This strategy not only increases the robustness of the classifier but also enhances the educational value of the system by exposing students to adverse scenarios that they must learn to identify and avoid. Preliminary results indicate that the evaluation model can be effectively integrated into a full-featured simulator for medical training.Índice general
1 Introducción .....................................................................................................................16
1.1 Planteamiento del problema............................................................................................ 16
1.2 Estado del arte.................................................................................................................. 18
1.2.1 Simuladores en neurocirugía de columna........................................................................ 18
1.2.2 Simuladores computacionales.......................................................................................... 18
1.3 Objetivo general ............................................................................................................... 25
1.4 Objetivos específicos........................................................................................................ 25
2 Caracterización de la trayectoria de la aguja......................................................................26
2.1 Dirección de la aguja en punción lumbar......................................................................... 26
2.1.1 La piel................................................................................................................................ 26
2.1.2 Epidermis.......................................................................................................................... 26
2.1.3 Dermis............................................................................................................................... 27
2.2 Propiedades mecánicas de componentes dérmicos........................................................ 28
2.2.1 Colágeno........................................................................................................................... 28
2.2.2 Elastina ............................................................................................................................. 28
2.2.3 Reticulina .......................................................................................................................... 28
2.3 El tejido adiposo ............................................................................................................... 29
2.3.1 Tejido adiposo blanco....................................................................................................... 29
2.3.2 Tejido adiposo pardo........................................................................................................ 29
2.4 Histología y biología celular.............................................................................................. 30
2.4.1 Las propiedades mecánicas de los tejidos adiposos humanos ........................................ 30
2.4.2 Ligamento supraespinoso................................................................................................. 33
2.4.3 Los Ligamentos Interespinosos (LIS)................................................................................. 34
2.4.4 Ligamento amarillo........................................................................................................... 36
6
2.5 Caracterización biomecánica del ligamento flavum ........................................................ 38
2.5.1 Duramadre........................................................................................................................ 39
2.5.2 Caracterización biomecánica............................................................................................ 39
2.5.3 Modelo de fuerza ............................................................................................................. 46
3 Requerimientos de Calidad del Procedimiento ..................................................................49
3.1 Requerimientos................................................................................................................ 49
3.1.1 Estudios de imagen y de laboratorio previos a la punción lumbar.................................. 49
3.1.2 Alimentos y medicamentos.............................................................................................. 49
3.1.3 Anestesia .......................................................................................................................... 49
3.1.4 Ubicación del paciente ..................................................................................................... 50
3.1.5 Procedimiento .................................................................................................................. 50
3.1.6 Simulación háptica............................................................................................................ 53
3.1.7 Dispositivo háptico ........................................................................................................... 56
3.1.8 Simulación en H3D Viewer ............................................................................................... 57
3.1.9 Post punción lumbar ........................................................................................................ 60
4 Modelo de Aprendizaje.....................................................................................................62
4.1 Conjunto de datos generados .......................................................................................... 62
4.1.1 Procesamiento de imagenes en formato Nifti ................................................................. 62
4.1.2 Selección de la red neuronal ............................................................................................ 69
4.1.3 Validación del entrenamiento de la red neuronal ........................................................... 75
4.1.4 Otras posibles trayectorias............................................................................................... 79
4.2 Resultados obtenidos....................................................................................................... 82
Conclusiones..........................................................................................................................84
4.2.1 Trabajos futuros ............................................................................................................... 84
5 Referencias.......................................................................................................................85
6 Anexos …………………………………………………………………………………………………….……………………… 89Maestrí
