Hollowing of Delayed Surgical Obturator in Maxillectomy Defects Secondary to Mucormycosis in Post-COVID-19 Era: A Case Series

During the second wave of the coronavirus disease 2019 (COVID-19) pandemic in India, there was an increase in the surge of mucormycosis cases secondary to COVID-19 infection. Aggressive surgical debridement is the most common treatment modality opted for its treatment that leads to extended maxillary defects. Obturating such defects may be very challenging from a prosthodontic point of view, as larger defect sizes and fewer retentive areas make it difficult to retain the prosthesis. A delayed surgical obturator is a prosthesis that is placed 6–10 days after the surgery, mainly used to minimize postoperative complications. It reproduces the contour of the palate and allows the patient to resume a regular diet. It also assists in normal speech. But in large surgical defects, the increased obturator's weight makes it uncomfortable and nonretentive for the patient, compromising its function. Consequently, in this case series, hollow bulb obturators are fabricated to decrease the weight of the prosthesis and to improve the function by establishing palatal contour. In case 1, hollowing was done using thermoplastic polyvinyl chloride (PVC) sheets and in case 2 acrylic shim was used. In both cases two-layer techniques were used, as in large defects if we use a single-layer technique it will either increase the weight of the prosthesis or may fail to create a palatal contour that further compromises the function. The techniques followed here are easy to use and less time-consuming. © The Author(s). 2022.

Modified Extraoral Retentive Technique for Retaining Obturator in Bilateral Maxillectomy Defects Secondary to Mucormycosis in Healing Phase

Background: Our country struggled with a plethora of mucormycosis cases during the second wave of coronavirus disease 2019 (COVID-19). The dental community was burdened with different maxillectomy defects in which bilateral maxillectomy cases posed a significant challenge for rehabilitation. Rehabilitating a patient after maxillectomy with conventional obturator prosthesis to close oronasal communication can be an effective way of restoring speech, deglutition, and mastication, and preventing nasal regurgitation. But the main problem is the retention of an obturator in large defects, and there is sparse literature pertaining to the management of bilateral maxillectomy cases in the surgical obturation phase. Purpose: The purpose of this case was to rehabilitate patients with a bilateral maxillectomy defect in the healing phase with an obturator prosthesis retained using extraoral aid where intraoral retention is not possible. Technique: Two different modification techniques in the extraoral retentive method were tried here to overcome difficulties encountered during the rehabilitation of such cases, with special emphasis on augmenting patient comfort. The customized headgear facebow assembly was used for extraoral retention. In the first case, an orthodontic was used to retain the prosthesis to the customized headgear or extraoral elastic straps through orthodontic elastics. The orthodontic facebow has two parts inner and outer bow. The inner bow was attached to the obturator at the level of the occlusion plane by fabricating bilateral posterior acrylic pillars so that the outer bow passes along the commissures of the mouth, but there was the problem of lip trap and feeding difficulties due to the horizontal connecting bar. To overcome these problems, in the second case, the facebow was customized using a 19 gauge orthodontic wire to eliminate horizontal component. Conclusion: The obturator with extraoral retention in the healing phase is a viable retentive aid in patients with extensive maxillary defects, and it was found that the patient was more comfortable with a customized facebow-retained obturator. © TheAuthor(s). 2022.

Local Pre-Conditioning and Quality Enhancement to Handle Different Data Complexities in Contactless Fingerprint Classification

authentication systems have always been a fascinating approach to meet personalized security. Among the major existing solutions fingerprint-biometrics have gained widespread attention;yet, guaranteeing scalability and reliability over real-time demands remains a challenge. Despite innovations, the recent COVID-19 pandemic has capped the efficacy of the existing touch-based two-dimensional fingerprint detection models. Though, touchless fingerprint detection is considered as a viable alternative;yet, the real-time data complexities like non-linear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and spatio-temporal texture damages can limit its efficacy. Considering these complexities, here, we focused on improving the input image intrinsic feature characteristics. More specifically, applied normalization, ridge orientation estimation, ridge frequency estimation, ridge masking and Gabor filtering over the input touchless fingerprint images. The proposed model mainly focusses on reducing FPR & EER by dividing the input image in to blocks and classify each input block as recoverable and nonrecoverable image block. Finally, an image with higher recoverable blocks with sufficiently large intrinsic features were considered for feature extraction and classification. The Proposed method outperforms when compared with the existing state of the art methods by achieving an accuracy of 94.72%, precision of 98.84%, recall of 97.716%, F-Measure 0.9827, specificity of 95.38% and a reduced EER of about 0.084.

COVID-19 HOSPITALIZATION PATTERNS THROUGHOUT THE PANDEMIC ACROSS 10 US DEPARTMENT OF HEALTH AND HUMAN SERVICES (HHS) REGIONS

SESSION TITLE: COVID-19 Infections: Issues During and After Hospitalization SESSION TYPE: Original Investigations PRESENTED ON: 10/17/2022 01:30 pm - 02:30 pm PURPOSE: COVID-19 pandemic is well studied, but it’s impact on hospitalization pattern is still unclear. We aim to study the hospitalizations pattern throughout the COVID-19 pandemic across 10 US Health and Human Services (HHS) regions. METHODS: This study was conducted using two publically and freely available databases;1. The COVID Tracking Project- manually aggregated data from available sources from official, public state government sites, and 2. The US Department of HHS – state wise patient impact and hospital capacity data. The state wise hospitalization data was extracted and collated by noting hospitalization for the complete time range (from March 1, 2020 to March 7th, 2021) for dataset-1 and data reported between the dates of March 7th, 2021, to March 12th, 2022, for dataset-2. The HHS wise regional hospitalization data was then calculated by adding the respective daily state statistics and scaled to per 100,000 population. A 7-day moving average filter was finally applied to the data before visualization and analysis, to account for repeated days of missing recordings in the data sources. No patient and hospital identifiers were utilized;thus, study was IRB exempted. RESULTS: Based on proximity of the spikes in each wave, data visualization tools grouped, HHS regions 1, 2, 3, 5 in group A;regions 4, 6, 9 in group B, and regions 7, 8. 10 in group C. The visualization of data determined total 6 spikes till date. The start and end of spikes were determined by placing a threshold (10 cases per day per 100,000 population) on the number of daily hospitalizations. The spikes were further divided when a given start/end date pair has multiple clear peaks. Maximum number of days difference observed between the occurrence of COVID-19 peaks in number of hospitalizations, were 47 days for spike-3 for HHS regions in group A (Compared to 2 and 12 days in group B and C, respectively). For Spike-5 it was highest in group C as 78 days (compared to 18 and 1 day in group A and B, respectively). CONCLUSIONS: In a latest COVID-19 hospitalization data analysis, after normalization of data, states in HHS regions, 4, 6, and 9 have the closest spikes throughout the pandemic. These regions included three most populous states of US (Florida, Texas, California) among others and consisted of 67M (region 4), 42M (region 6) and 51M (region 9) people, total of roughly 50% US population. CLINICAL IMPLICATIONS: The result of this study, first to be presented at CHEST conference will pave the way in adding to public health policy discussion in preparedness and resources allocations for hospitalized patients. A subset-analysis of ICU admission is underway, which will be included at CHEST meeting presentation. DISCLOSURES: No relevant relationships by Ramesh Adhikari no disclosure on file for Keerti Deepika;No relevant relationships by Taru Dutt No relevant relationships by Rahul Kashyap No relevant relationships by Arjun Rajasekar no disclosure on file for Shruti Srivnivasan;No relevant relationships by Salim Surani

Local Pre-Conditioning and Quality Enhancement to Handle Different Data Complexities in Contactless Fingerprint Classification

Biometric authentication systems have always been a fascinating approach to meet personalized security. Among the major existing solutions fingerprint-biometrics have gained widespread attention;yet, guaranteeing scalability and reliability over real-time demands remains a challenge. Despite innovations, the recent COVID-19 pandemic has capped the efficacy of the existing touch-based two-dimensional fingerprint detection models. Though, touchless fingerprint detection is considered as a viable alternative;yet, the real-time data complexities like non-linear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and spatio-temporal texture damages can limit its efficacy. Considering these complexities, here, we focused on improving the input image intrinsic feature characteristics. More specifically, applied normalization, ridge orientation estimation, ridge frequency estimation, ridge masking and Gabor filtering over the input touchless fingerprint images. The proposed model mainly focusses on reducing FPR & EER by dividing the input image in to blocks and classify each input block as recoverable and nonrecoverable image block. Finally, an image with higher recoverable blocks with sufficiently large intrinsic features were considered for feature extraction and classification. The Proposed method outperforms when compared with the existing state of the art methods by achieving an accuracy of 94.72%, precision of 98.84%, recall of 97.716%, F-Measure 0.9827, specificity of 95.38% and a reduced EER of about 0.084. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Effectiveness and satisfaction of technology‐mediated learning during global crisis: understanding the role of pre-developed videos

Purpose: In today’s dynamic situation, innumerable challenges are posited in the education sector because of the COVID-19 pandemic. Higher educational institutes (HEIs) are compelled to adopt digital technologies and technology-mediated learning in the teaching-learning processes. The purpose of this paper is to understand the factors affecting learning effectiveness, learning satisfaction and the mediating role of prerecorded videos from the learners’ perspective. Design/methodology/approach: A self-designed structured questionnaire based on previous similar studies is adopted as a survey instrument. It consists of 22 questions to address the five constructs of the proposed hypothesized conceptual model, developed for the study. Data of 311 students from HEIs of Maharashtra state in India were collected. Confirmatory factor analysis is carried out to test the model fitness, reliability and validity, and structural equation modeling is applied to conduct path analysis and hypotheses testing of the model. Findings: Hypotheses testing reveals that perceived usefulness (PU) significantly affects the perceived learning effectiveness, which again affects the learning satisfaction of the students. In addition, perceived ease of use affects the PU as suggested in the technology acceptance model. The prerecorded videos have a moderating role to play in the computer self-efficacy and the perceived learning effectiveness of the students. This research will provide meaningful acumen to enhance the overall learning process among students in urban as well as rural India. Originality/value: This study explores the technology-mediated learning during the unexpected and dynamic situations of the COVID-19 pandemic in the context of higher education in India. For sustainable use of technology-assisted learning, educators must understand the key factors that influence students’ learning effectiveness and satisfaction. The research outcomes will lead toward developing the human capacities, as the prerecorded videos at the HEIs of India will provide new approaches for effectively adopting digital technologies and technology-mediated learning. © 2022, Emerald Publishing Limited.