ABSTRACT
INTRODUCTION: Hip fractures cause severe pain during positioning for spinal anesthesia (SA). Intravenous systemic analgesics can lead to various complications in elderly patients, hence peripheral nerve blocks are emerging as a standard of care in pain management for hip fractures, among which femoral nerve block (FNB) is widely known and practiced. Pericapsular nerve group (PENG) block is a recently described technique that blocks the articular nerves of the hip with motor-sparing effects and is used to manage positional pain in hip fractures. This study aims to evaluate the analgesic efficacy of PENG block over FNB in managing pain during positioning before SA in hip fractures. MATERIALS AND METHODS: This was a prospective, randomized, double-blinded study. After ethical clearance, 70 patients undergoing hip fracture surgery under SA in a tertiary-care hospital were recruited and randomized to receive either ultrasound-guided PENG block or FNB with 20 ml of 0.25% bupivacaine before performing SA. We compared pain severity using the visual analog scale (VAS) 15 and 30 minutes after the block and during positioning. The sitting angle, requirement of rescue analgesia for positioning, and anesthesiologist and patient satisfaction scores were also analyzed. Continuous data were analyzed with an unpaired t-test while the chi-square test was used for categorical data. RESULTS: There was a significant reduction in VAS scores after PENG block (PENG: 0.66 ± 1.05 and FNB: 1.94 ± 1.90; p = 0.001) with lesser requirement of rescue analgesia for positioning compared to FNB. The anesthesiologist and patient satisfaction scores were also significantly better in the PENG group. CONCLUSION: PENG block offers better analgesia for positioning before SA than FNB without any significant side effects, and improves patient and anesthesiologist satisfaction, thus proving to be an effective analgesic alternative for painful hip fractures.
ABSTRACT
To obtain a correlation between endoscopic findings, radiological findings and clinical features in children with symptomatic Chronic Adenoid hypertrophy. A cross sectional study was conducted in 42 pediatric patients (3-14 years) who visited the Department of Otorhinolaryngology in a tertiary care centre from November 2019 to April 2021 (18 months). The patients were subjected to complete history taking; ENT examination, Rigid nasal endoscopic examination, a lateral X-ray nasopharynx were performed prior to surgery after obtaining consent from the parents of the patients. The endoscopic findings were assessed using ACE grading system and the X-ray nasopharynx was assessed using Adenoid- Nasopharyngeal ratio. A male predominance was noted with mouth breathing, snoring, nasal obstruction and recurrent rhinitis as common presentation. Grade 3 hypertrophy was the most common finding in X-ray Nasopharynx (Mean ANR-0.682). Children with Grade 3 adenoid hypertrophy with more than 50% choanal obstruction and Eustachian tube abutment in nasal endoscopy were noted to be the most symptomatic clinically. A positive correlation between reduced hearing (p value-0.004) and blocked ear sensation (p value- < 0.01) with eustachian tube abutment was noted. The children with more symptoms did not show higher-grade adenoid hypertrophy radiographically in our study. The adenoid- nasopharyngeal ratio on X-ray correlated with endoscopic grading of adenoid hypertrophy (p value-0.006) and degree of choanal obstruction (p value-0.003) but not with the abutment of the eustachian tube. The endoscopic grading correlated with clinical grading, but not the X-ray grading. Hence, endoscopic grading appears to be more accurate in assessing the adenoid size and endoscopic grading is nearer to clinical grading than X-ray grading. Though, the digital X-ray nasopharynx lateral view is a more convenient method, nasal endoscopy is the gold standard method to determine whether the adenoid hypertrophy is clinically significant or not.
ABSTRACT
Covid-19 has posed a serious threat to the existence of the human race. Early detection of the virus is vital to effectively containing the virus and treating the patients. Profound testing methods such as the Real-time reverse transcription-polymerase chain reaction (RT-PCR) test and the Rapid Antigen Test (RAT) are being used for detection, but they have their limitations. The need for early detection has led researchers to explore other testing techniques. Deep Neural Network (DNN) models have shown high potential in medical image classification and various models have been built by researchers which exhibit high accuracy for the task of Covid-19 detection using chest X-ray images. However, it is proven that DNNs are inherently susceptible to adversarial inputs, which can compromise the results of the models. In this paper, the adversarial robustness of such Covid-19 classifiers is evaluated by performing common adversarial attacks, which include the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Using these attacks, it is found that the accuracy of the models for Covid-19 samples decreases drastically. In the medical domain, adversarial training is the most widely explored technique to defend against adversarial attacks. However, using this technique requires replacing the original model and retraining it by including adversarial samples. Another defensive technique, High-Level Representation Guided Denoiser (HGD), overcomes this limitation by employing an adversarial filter which is also transferable across models. Moreover, the HGD architecture, being suitable for high-resolution images, makes it a good candidate for medical image applications. In this paper, the HGD architecture has been evaluated as a potential defensive technique for the task of medical image analysis. Experiments carried out show an increased accuracy of up to 82% in the white box setting. However, in the black box setting, the defense completely fails to defend against adversarial samples.
