A comparative randomized study of USG guided transversus abdominis plane block versus caudal block for post operative analgesia in paediatric unilateral open inguinal hernia repair

Background: Inguinal hernia surgery is associated with moderate to severe post-operative pain especially in paediatric age groups as pain threshold is lesser in children so in this study our aim was to compare usg guided transversus abdominis plane block and caudal block for post-operative analgesia for unilateral inguinal hernia repair surgeries. Materials and Methods: 60 pediatric patients of American society of anaesthesiologist (ASA) I /II aged 1-8 years of either gender, scheduled for elective open unilateral inguinal hernia repair under general anesthesia were divided into two groups. Patients of group 1 were given caudal block group using 0.25% bupivacaine 1ml/kg and group 2 were given ultrasound guided TAP block using 0.25% bupivacaine 0.5ml/kg. The postop analgesic efficacy, hemodynamic stability, degree of motor weakness and Adverse effects if any was noted. Results: There was no significant difference in median CHEOPS score until 6 hours in postoperative period. No Significant difference was observed in hemodynamic parameters in intraoperative and postoperative period. All patients in both the groups were comfortable with a CHEOPS score between 5-6 in the post-operative period with no opiate requirements. No significant haemodynamics changes and adverse effects were observed. Conclusion: We found that children in both the study groups i.e caudal block and USG guided TAP block were having stable intraoperative and post-operative hemodynamic conditions. Post-operative analgesia measured using CHEOPS score was maintained between 5-6 and patients in both groups were comfortable throughout the study period. There was no need of any rescue analgesia during post-operative period for the study period of 6 hours

Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture

Abstract Problem Oral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment. Aim This study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer. Methods A deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture. Results The improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field. Conclusion Integrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies

Combating antibiotic-resistant gram-negative bacteria strains with tetracycline-conjugated carbon nanoparticles

Nontoxic carbon nanoparticle samples prepared by both bottom-up and top-down approaches do not inhibit Gram-negative bacterial growth, indicating excellent biocompatibilities. However, cell growth inhibitory efficacies increase considerably when the carbon nanoparticles are conjugated with the antibiotic tetracycline. In tetracycline-resistant bacteria, these efficacies can approach tenfold higher activities when compared to tetracycline alone. No structural abnormality such as membrane disruptions is evident in the tested bacterial strains; this is in contrast with other nanocarbon systems such as graphene oxides, carbon nanotubes, and amine-functionalized carbon nanoparticles which do exhibit membrane disruptions. In comparison, the tetracycline-conjugated carbon nanoparticles induce membrane perturbations (but not membrane disruptions), inhibiting bacterial efflux mechanisms. It is proposed that when tetracycline is conjugated to the surface of carbon nanoparticles, it functions to direct the nanoparticles to membrane-associated tetracycline efflux pumps, thereby blocking and subsequently inhibiting their function. The conjugation between biocompatible carbon nanoparticles and subtherapeutic but well-established antibiotic molecules may provide hybrid antibiotic assembly strategies resulting in effective multidrug efflux inhibition for combating antibiotic resistance