Negative pressure wound therapy versus standard care for incisional laparotomy subcutaneous wounds in gastrointestinal perforations: A randomized controlled study.

BACKGROUND: Surgical site infections after gastrointestinal perforation with peritonitis have significant morbidity, increased hospital stays, and cost of treatment. The appropriate management of these wounds is still debatable. METHODS: Patients undergoing surgery for gastrointestinal perforation with peritonitis via midline incision were screened for inclusion. After the closure of the midline fascia, patients were randomized into an open negative pressure wound therapy group (application of negative pressure wound therapy and attempted delayed closure at day 4) or a standard care group (no negative pressure wound therapy and attempted delayed closure at day 4). Postoperative outcomes, including surgical site infection till 30 days, were compared between the groups. This was assessed by an independent assessor not involved in the study for delayed closure. Although a priori sample size was calculated, an interim analysis was performed due to slow recruitment during the COVID pandemic. After interim analysis, a continuation of the trial was deemed unethical and terminated. RESULTS: Ninety-six patients were assessed, and 69 were randomized (34 in the negative pressure wound therapy group and 31 in the standard care group). The age, body mass index, comorbidities, blood loss, operative time, and stoma formation were comparable. The surgical site infection was significantly lower in the negative pressure wound therapy group compared to the standard care group (6 [18%] vs 19 [61%], P < .01). The number needed to prevent 1 surgical site infection was 2.3. In a subgroup analysis, the use of negative pressure wound therapy also significantly decreased the rate of surgical site infection in stoma patients (4 [30.7%] vs 9 [69.3%], P = .03). CONCLUSION: Open negative pressure wound therapy significantly decreases the incisional surgical site infection rate in patients with a dirty wound secondary to gastrointestinal perforation with peritonitis.

Early living donor liver transplantation for alcohol-associated hepatitis.

INTRODUCTION AND OBJECTIVES: Lately, there has been a steady increase in early liver transplantation for alcohol-associated hepatitis (AAH). Although several studies have reported favorable outcomes with cadaveric early liver transplantation, the experiences with early living donor liver transplantation (eLDLT) are limited. The primary objective was to assess one-year survival in patients with AAH who underwent eLDLT. The secondary objectives were to describe the donor characteristics, assess the complications following eLDLT, and the rate of alcohol relapse. MATERIALS AND METHODS: This single-center retrospective study was conducted at AIG Hospitals, Hyderabad, India, between April 1, 2020, and December 31, 2021. RESULTS: Twenty-five patients underwent eLDLT. The mean time from abstinence to eLDLT was 92.4 ± 42.94 days. The mean model for end-stage liver disease and discriminant function score at eLDLT were 28.16 ± 2.89 and 104 ± 34.56, respectively. The mean graft-to-recipient weight ratio was 0.85 ± 0.12. Survival was 72% (95%CI, 50.61-88) after a median follow-up of 551 (23-932) days post-LT. Of the 18 women donors,11 were the wives of the recipient. Six of the nine infected recipients died: three of fungal sepsis, two of bacterial sepsis, and one of COVID-19. One patient developed hepatic artery thrombosis and died of early graft dysfunction. Twenty percent had alcohol relapse. CONCLUSIONS: eLDLT is a reasonable treatment option for patients with AAH, with a survival of 72% in our experience. Infections early on post-LT accounted for mortality, and thus a high index of suspicion of infections and vigorous surveillance, in a condition prone to infections, are needed to improve outcomes.

Pandemic preparedness strategies must go beyond vaccines.

Monoclonal antibodies can fill a critical gap to help stop the next infectious disease outbreak from becoming the next pandemic.

Primary Norfloxacin Prophylaxis for APASL-Defined Acute-on-Chronic Liver Failure: A Placebo-Controlled Double-Blind Randomized Trial.

INTRODUCTION: This study aimed to evaluate the role of prophylactic norfloxacin in preventing bacterial infections and its effect on transplant-free survival (TFS) in patients with acute-on-chronic liver failure (ACLF) identified by the Asian Pacific Association for the Study of the Liver criteria. METHODS: Patients with ACLF included in the study were randomly assigned to receive oral norfloxacin 400 mg or matched placebo once daily for 30 days. The incidence of bacterial infections at days 30 and 90 was the primary outcome, whereas TFS at days 30 and 90 was the secondary outcome. RESULTS: A total of 143 patients were included (72 in the norfloxacin and 71 in the placebo groups). Baseline demographics, biochemical variables, and severity scores were similar between the 2 groups. On Kaplan-Meier analysis, the incidence of bacterial infections at day 30 was 18.1% (95% confidence interval [CI], 10-28.9) and 33.8% (95% CI, 23-46) (P = 0.03); and the incidence of bacterial infections at day 90 was 46% (95% CI, 34-58) and 62% (95% CI, 49.67-73.23) in the norfloxacin and placebo groups, respectively (P = 0.02). On Kaplan-Meier analysis, TFS at day 30 was 77.8% (95% CI, 66.43-86.73) and 64.8% (95% CI, 52.54-75.75) in the norfloxacin and placebo groups, respectively (P = 0.084). Similarly, TFS at day 90 was 58.3% (95% CI, 46.11-69.84) and 43.7% (95% CI, 31.91-55.95), respectively (P = 0.058). Thirty percent of infections were caused by multidrug-resistant organisms. More patients developed concomitant candiduria in the norfloxacin group (25%) than in the placebo group (2.63%). DISCUSSION: Primary norfloxacin prophylaxis effectively prevents bacterial infections in patients with ACLF.

Encephalitis or Stroke? A Case Report of Thalamic Stroke in COVID Era

Introduction: Since 2019, COVID-19 pneumonia caused by SARS-CoV-2 virus has led to a worldwide pandemic. Since then, various neurological manifestations of COVID-19 pneumonia have been reported. Neurological manifestations include headache, anosmia, seizures, and altered mental status. In some cases, it presents as stroke, encephalitis, and neuropathy. Artery of Percheron (AOP) is a variant in the posterior circulation. Here, a single artery arises from the posterior cerebral artery p1 segment. It supplies bilateral thalamus with or without midbrain. Thrombosis in this artery leads to clinical symptoms like reduced level of consciousness, altered mental status, and memory impairment. Case Report: Here, we present a case who presented with fever and altered sensorium without any focal neurological deficits and without known risk factors for stroke. His COVID-19 PCR was positive. He was initially diagnosed as COVID-19 pneumonia with encephalitis and was started on treatment for the same. His initial CT brain and lumbar puncture were normal. The next day, when MRI brain with and without contrast was done, the thalamic stroke due to AOP infarction was diagnosed and appropriate treatment for stroke was initiated. Discussion: Many patients miss the window for thrombolysis because of variable presentation in clinical symptoms with negative imaging. It is also difficult to assess the time of onset of stroke in this varied presentation. Our patient had fever and cough for 2 days and had altered mental status since the morning of admission. During hospital stay, he developed bilateral third nerve palsy. This case also highlights the importance of detailed evaluation in COVID-19 patients with neurological complaints. This helps to avoid delays in treatment and to improve clinical outcomes. As our knowledge of COVID-19 and its varied neurological manifestations evolve, we need to be prepared for more atypical presentation to facilitate timely interventions.

Fight against Future Pandemics: UAV-Based Data-Centric Social Distancing, Sanitizing, and Monitoring Scheme

The novel coronavirus disease-2019 (COVID-19) has transformed into a global health concern, which resulted in human containment and isolation to flatten the curve of mortality rates of infected patients. To leverage the massive containment strategy, fifth-generation (5G)-envisioned unmanned aerial vehicles (UAVs) are used to minimize human intervention with the key benefits of ultra-low latency, high bandwidth, and reliability. This allows phased treatment of infected patients via threefold functionalities (3FFs) such as social distancing, proper sanitization, and inspection and monitoring. However, UAVs have to send massive recorded data back to ground stations (GS), which requires a real-time device connection density of 107/km2, which forms huge bottlenecks on 5G ecosystems. A sixth-generation (6G) ecosystem can provide terahertz (THz) frequency bands with massive short beamforming cells, intelligent deep connectivity, and physical- and link-level protocol virtualization. The UAVs form a swarm network to assure 3FFs which requires high-end computations and are data-intensive;thus, these computational tasks can be offloaded to nearby edge servers, which employ local federated learning to train the global models. It synchronizes the UAV task formations and optimizes the network functions. Task optimization of UAV swarms in 6G-assisted channels allows better management and ubiquitous and energy-efficient seamless communication over ground, space, and underwater channels. Thus, a data-centric 3FF approach is essential to fight against future pandemics, with a 6G backdrop channel. The proposed scheme is compared with traditional fourth-generation (4G) and 5G-networks-based schemes to indicate its efficiency in traffic density, processing latency, spectral efficiency, UAV mobility, radio loss, and device connection density.

Challenges, perceptions, and future preferences for post-secondary online education given experiences in the COVID-19 outbreak.

To gain a better understanding of online education status during and after the pandemic outbreak, this paper analyzed the data from a recent survey conducted in the state of Florida in May 2020. In particular, we focused on college students' perception of productivity changes, benefits, challenges, and their overall preference for the future of online education. Our initial exploratory analysis showed that in most cases, students were not fully satisfied with the quality of the online education, and the majority of them suffered a plummet in their productivities. Despite the challenges, around 61% believed that they would prefer more frequent participation in online programs in the future (compared to the normal conditions before the pandemic). A structural equation model was developed to identify and assess the factors that contribute to their productivity and future preferences. The results showed that lack of sufficient communication with other students/ instructor as well as lack of required technology infrastructure significantly reduced students' productivity. On the other hand, productivity was positively affected by perceived benefits such as flexibility and better time management. In addition, productivity played a mediating role for a number of socio-economic, demographic, and attitudinal attributes: including gender, income, technology attitudes, and home environment conflicts. Accordingly, females, high income groups, and those with home environment conflicts experienced lower productivity, which indirectly discouraged their preference for future online education. As expected, a latent pro-online education attitude increased both the productivity and the future online-education preference. Last but not the least, Gen-Xers were more likely to adopt online-education in the post pandemic conditions compared to their peers.

Impacts of COVID-19 on Future Preferences Toward Telework

This paper presents a study in capturing the impacts of the mandatory pandemic-induced telework practice on workers’ perceptions of the benefits, challenges, and difficulties associated with telecommuting and how those might influence their preference for telework in the future. Data was collected through an online survey conducted in South Florida in May 2020. Survey data showed that telework indices (either measured through actual behavior or stated preference) before, during, and after the pandemic were heterogeneous across socio-economic, demographic, and attitudinal segments. Before the outbreak, males, full-time students, those with PhD degrees, and high-income people showed higher percentages of involvement in jobs with a telework option. They also had higher pro-technology, pro-online education, workaholic, and pro-telework attitudes. During the pandemic, professional/managerial/technical jobs as well as jobs with lower physical-proximity measures showed the highest telework frequency. In view of future telework preferences, our analysis showed that those who were more pro-telework, pro-technology, and showed less dislike of telework dislike preferred higher telework frequency. A structural equation model was developed to assess the impacts of different predictors on telework behavior before the pandemic and preferences after the pandemic. While telework frequency before the pandemic was highly affected by the pro-telework attitude, the after-pandemic preferences were influenced by several other attitudes such as dislike telework, enjoy interaction, workaholic, as well as productivity factors. This might confirm the assumption that the mandatory practice through the pandemic has provided employees more experiences with work-from-home arrangements, which could reshape decisions and expectations around telework adoption in the future.

A Real-Time Crowdsensing Framework for Potential COVID-19 Carrier Detection Using Wearable Sensors

Artificial intelligence has been utilized extensively in the healthcare sector for the last few decades to simplify medical procedures, such as diagnosis, prognosis, drug discovery, and many more. With the spread of the COVID-19 pandemic, more methods for detecting and treating COVID-19 infections have been developed. Several projects involving considerable artificial intelligence use have been researched and put into practice. Crowdsensing is an example of an application in which artificial intelligence is employed to detect the presence of a virus in an individual based on their physiological parameters. A solution is proposed to detect the potential COVID-19 carrier in crowded premises of a closed campus area, for example, hospitals, corridors, company premises, and so on. Sensor-based wearable devices are utilized to obtain measurements of various physiological indicators (or parameters) of an individual. A machine-learning-based model is proposed for COVID-19 prediction with these parameters as input. The wearable device dataset was used to train four different machine learning algorithms. The support vector machine, which performed the best, received an F1-score of 96.64% and an accuracy score of 96.57%. Moreover, the wearable device is used to retrieve the coordinates of a potential COVID-19 carrier, and the YOLOv5 object detection method is used to do real-time visual tracking on a closed-circuit television video feed.

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.

Helmet NIV in Acute Hypoxemic Respiratory Failure due to COVID-19: Change in PaO2/FiO2 Ratio a Predictor of Success.

In acute respiratory failure due to severe coronavirus disease 2019 (COVID-19) pneumonia, mechanical ventilation remains challenging and may result in high mortality. The use of noninvasive ventilation (NIV) may delay required invasive ventilation, increase adverse outcomes, and have a potential aerosol risk to caregivers. Data of 30 patients were collected from patient files and analyzed. Twenty-one (70%) patients were weaned successfully after helmet-NIV support (NIV success group), and invasive mechanical ventilation was required in 9 (30%) patients (NIV failure group) of which 8 (26.7%) patients died. In NIV success vs failure patients, the mean baseline PaO2/FiO2 ratio (PFR) (147.2 ± 57.9 vs 156.8 ± 59.0 mm Hg; p = 0.683) and PFR before initiation of helmet (132.3 ± 46.9 vs 121.6 ± 32.7 mm Hg; p = 0.541) were comparable. The NIV success group demonstrated a progressive improvement in PFR in comparison with the failure group at 2 hours (158.8 ± 56.1 vs 118.7 ± 40.7 mm Hg; p = 0.063) and 24 hours (PFR-24) (204.4 ± 94.3 vs 121.3 ± 32.6; p = 0.016). As predictor variables, PFR-24 and change (delta) in PFR at 24 hours from baseline or helmet initiation (dPFR-24) were significantly associated with NIV success in univariate analysis but similar significance could not be reflected in multivariate analysis perhaps due to a small sample size of the study. The PFR-24 cutoff of 161 mm Hg and dPFR-24 cutoff of -1.44 mm Hg discriminate NIV success and failure groups with the area under curve (confidence interval) of 0.78 (0.62-0.95); p = 0.015 and 0.74 (0.55-0.93); p = 0.039, respectively. Helmet interface NIV may be a safe and effective tool for the management of patients with severe COVID-19 pneumonia with acute respiratory failure. More studies are needed to further evaluate the role of helmet NIV especially in patients with initial PFR <150 mm Hg to define PFR/dPFR cutoff at the earliest time point for prediction of helmet-NIV success. How to cite this article Jha OK, Kumar S, Mehra S, Sircar M, Gupta R. Helmet NIV in Acute Hypoxemic Respiratory Failure due to COVID-19: Change in PaO2/FiO2 Ratio a Predictor of Success. Indian J Crit Care Med 2021;25(10):1137-1146.

COVID-19 severity and outcome in multiple sclerosis: Results of a national, registry-based, matched cohort study.

BACKGROUND: Risk factors associated with coronavirus disease 2019 (COVID-19) severity in patients with multiple sclerosis (MS) have been described. Recent improvements in supportive care measures and increased testing capacity may modify the risk of severe COVID-19 outcome in MS patients. This retrospective study evaluates the severity and outcome of COVID-19 in MS and characterizes temporal trends over the course of the pandemic in the United States. METHODS: We conducted a comparative cohort study using de-identified electronic health record (EHR) claims-based data. MS patients diagnosed with COVID-19 between February 2, 2020 and October 13, 2020 were matched (1:2) to a control group using propensity score analysis. The primary outcome was a composite of intensive care unit (ICU) admission, mechanical ventilation, and/or death. RESULTS: A total of 2,529 patients (843 MS and 1,686 matched controls) were included. Non-ambulatory and pre-existing comorbidities were independent risk factors for COVID-19 severity. The risk for the severe composite outcome was lower in the late cohorts compared with the early cohorts. CONCLUSIONS: The majority of MS patients actively treated with a disease-modifying therapy (DMT) had mild disease. The observed trend toward a reduction in severity risk in recent months suggests an improvement in COVID-19 outcome.

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends.

The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.

6G-enabled Edge Intelligence for Ultra -Reliable Low Latency Applications: Vision and Mission

Increasing demand for automation and instant solution leads the technological world towards massive applications such as the Internet of drones, Autonomous Vehicles (AVs), border surveillance, telesurgery, Augmented Reality (AR), which requires a vast upgrade in technology with improved processing and computation power. Centralized Cloud Server (CSS) provides the facility to compute critical tasks at the central data server, but it consumes more time in case of the remote distance between CSS and edge device. In this article, we have discussed the advantages of edge computing over cloud computing to overcome latency and reliability issues in critical applications. Moreover, the idea of processing and analyzing massive applications at the edge comes up with the requirement of building intelligence at the edge to compute complex tasks within a negligible time. Edge intelligence provides intelligence at the edge to process large datasets for critical computations and to overcome storage issues. Also, the performance tolerant connectivity and low-speed rate issues with 4G and 5G can be solved using a 6G wireless network. 6G connected edge intelligence application offers ultra-low-latency, security, and reliability mechanisms that could be helpful in COVID-19 pandemic situations. We have also discussed a demonstration of the aforementioned massive application in the form of a case study on combating COVID-19 situations using 6G-based edge intelligence. The case study depicts the benefits of using 6G (latency: 10−100μs) over 4G (latency: <10ms) and 5G (latency: <5ms) communication networks. The proposed 6G-enabled scheme is compared against the traditional 4G and 5G networks to designate its efficiency in terms of communication latency and network mobility. Eventually, we then analyzed various open issues and research challenges in this emerging research area for future gains and insights.

Early liver transplantation after COVID-19 infection: The first report.

COVID-19 (coronavirus disease 2019) has impacted solid organ transplantation (SOT) in many ways. Transplant centers have initiated SOT despite the COVID-19 pandemic. Although it is suggested to wait for 4 weeks after COVID-19 infection, there are no data to support or refute the timing of liver transplant after COVID-19 infection. Here we describe the course and outcomes of COVID-19-infected candidates and healthy living liver donors who underwent transplantation. A total of 38 candidates and 33 potential living donors were evaluated from May 20, 2020 until October 30, 2020. Ten candidates and five donors were reverse transcriptase-polymerase chain reaction (RT-PCR) positive for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pretransplant. Four candidates succumbed preoperatively. Given the worsening of liver disease, four candidates underwent liver transplant after 2 weeks due to the worsening of liver disease and the other two candidates after 4 weeks. Only one recipient died due to sepsis posttransplant. Three donors underwent successful liver donation surgery after 4 weeks of COVID-19 infection without any postoperative complications, and the other two were delisted (as the candidates expired). This report is the first to demonstrate the feasibility of elective liver transplant early after COVID-19 infection.

Sensing the interactions between carbohydrate-binding agents and N-linked glycans of SARS-CoV-2 spike glycoprotein using molecular docking and simulation studies.

A recent surge in finding new candidate vaccines and potential antivirals to tackle atypical pneumonia triggered by the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) needs new and unexplored approaches in solving this global pandemic. The homotrimeric transmembrane spike (S) glycoprotein of coronaviruses which facilitates virus entry into the host cells is covered with N-linked glycans having oligomannose and complex sugars. These glycans provide a unique opportunity for their targeting via carbohydrate-binding agents (CBAs) which have shown their antiviral potential against coronaviruses and enveloped viruses. However, CBA-ligand interaction is not fully explored in developing novel carbohydrate-binding-based antivirals due to associated unfavorable responses with CBAs. CBAs possess unique carbohydrate-binding specificity, therefore, CBAs like mannose-specific plant lectins/lectin-like mimic Pradimicin-A (PRM-A) can be used for targeting N-linked glycans of S glycoproteins. Here, we report studies on the binding and stability of lectins (NPA, UDA, GRFT, CV-N and wild-type and mutant BanLec) and PRM-A with the S glycoprotein glycans via docking and MD simulation. MM/GBSA calculations were also performed for docked complexes. Interestingly, stable BanLec mutant (H84T) also showed similar docking affinity and interactions as compared to wild-type BanLec, thus, confirming that uncoupling the mitogenic activity did not alter the lectin binding activity of BanLec. The stability of the docked complexes, i.e. PRM-A and lectins with SARS-CoV-2 S glycoprotein showed favorable intermolecular hydrogen-bond formation during the 100 ns MD simulation. Taking these together, our predicted in silico results will be helpful in the design and development of novel CBA-based antivirals for the SARS-CoV-2 neutralization.Communicated by Ramaswamy H. Sarma.