Non-Linear Ground Deformation Detection and Monitoring using Time-Series InSAR along the Coastal Urban Areas of Pakistan (preprint)

Conventional geodetic methods rely on point measurements, which have drawbacks for detecting and tracking geologic disasters at specific locations. In this study, the time-series InSAR approach was incorporated to estimate non-linear surface deformation caused by tectonic, shoreline reclamation, and other anthropogenic activities in economically important urban regions of Pakistan's southern coast, which possesses around 270 km. The shoreline is extended from the low-populated area on the premises of the Hub River in the west to the highly populated Karachi city and Eastern Industrial Zone, where we collected the Sentinel-1A C-band data from 2017 to 2023 to address urban security and threats to human life and property. The main advantage of opting for the non-linear persistent scatterer interferometric SAR (PSInSAR) approach for this study is that it exposes minute movements without any prior consideration of conventional monitoring techniques, making it valid in continuously varying regions. A vertical displacement range of −170 mm to +80 mm per year was found, which was used to investigate the potential correlation with the most effective causative parameters of deformation. The densely populated areas of the study area experience an annual subsidence of 170 mm, and the less populated western region experiences an uplift of 82 mm annually. Land deformation varies along the coast of the study area, where the eastern region is highly reclaimed and is affected by erosion. Groundwater table-depleting regions experienced high levels of land subsidence, and tectonic activities controlled vertical displacement in the region. Major variation was detected after an earthquake occurred along fault lines. This study was designed because a non-linear approach is required to address ground movement activities acutely, and it will make it possible to plan surface infrastructure and handle issues brought on by subsidence more effectively.

Predictive etiology of Long COVID oncogenesis through SARS-CoV-2 proteins and AKT1 molecular docking interactions (preprint)

Background: In 2019, the world witnessed an outbreak of SARS-CoV-2, whose retention for two months or more leads to long COVID. Several long-term staying viruses cause oncogenesis. We intended to find any such potential with SARS-CoV-2. A rigorous systematic analysis of viral oncogenic pathways and long COVID was conducted. SARS-CoV-2 affects glutamatergic and Protein Tyrosine Kinases 1 signalling, leading to molecular interference. AKT1 protein was analyzed for predictive interaction studies with structural and non-structural viral proteins. Molecular docking simulations were also carried out. Methods:Oncogenes were detected in SARS-CoV-2 protein sequence, using TAG database. AKT1 was selected as a high potential oncogenic factor and was modelled using SWISS-MODEL. Viral proteins structures were either downloaded from Protein Data Bank, otherwise modelled. Docking was performed using HDOCK server. Prediction of possible potential active sites was done using Protein Allosteric and Regulatory Site (PARS). Results: AKT1 showed very strong interactive potential with all viral proteins with docking scores less than -200, envelope protein being the most potently reactive. PARS analysis showed that there might be more than one potential active site. All proteins cavities satisfied the requirement for flexibility p-value. NSP5 showed great structural conservation. Conclusion: When SARS-CoV-2 stays in the body of infected person for extended time durations, it has a strong oncogenic potential. Given the host of cellular targets because of angiotensin-converting enzyme type-2 presence, any infected organ harboring the virus for longer terms might be at risk of developing cancer. We propose further molecular and case study investigations to assess this threat to full extent.

A Meta-analysis of the Severity of Acute Pancreatitis (AP) in COVID-19 Infection.

Many studies have reported severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affecting the gastrointestinal tract and causing gastritis, colitis, duodenitis and acute pancreatitis (AP). We conducted a meta-analysis to evaluate if SARS-CoV-2 infection (COVID-19 infection) affects the outcomes and severity of AP. We searched for articles in PubMed (MEDLINE), Cochrane Library, and clinicaltrials.gov databases and included studies comparing the outcomes of AP in patients with and without COVID-19. Our outcomes were the mean age of occurrence of AP, Charlson Comorbidity Index, incidence of idiopathic etiology of AP, severity of AP, incidence of necrotizing pancreatitis, need for intensive care unit (ICU) admission, and mortality between the two cohorts. We included five observational studies with a total population of 2,446 patients. Our results showed that in COVID-19 patients; AP had higher odds of having an idiopathic etiology (odds ratio, OR 3.14, 95% confidence interval, CI 1.36-7.27), be more severe (OR 3.26, 95% CI 1.47-7.49), had higher risk for pancreatic necrosis (OR 2.40, 95% CI 1.62-3.55), require ICU admission (OR 4.28, 95% CI 2.88-6.37) and had higher mortality (OR 5.75, 95% CI 3.62-9.14) than in patients without COVID-19 infection. Our study concluded that SARS-CoV-2 infection does increase the morbidity and mortality associated with AP and further large-scale multi-center studies are needed to confirm these results.

COVID-19 and Clostridioides difficile Coinfection Outcomes among Hospitalized Patients in the United States: An Insight from National Inpatient Database.

The incidence of Clostridioides difficile infection (CDI) has been increasing compared to pre-COVID-19 pandemic levels. The COVID-19 infection and CDI relationship can be affected by gut dysbiosis and poor antibiotic stewardship. As the COVID-19 pandemic transitions into an endemic stage, it has become increasingly important to further characterize how concurrent infection with both conditions can impact patient outcomes. We performed a retrospective cohort study utilizing the 2020 NIS Healthcare Cost Utilization Project (HCUP) database with a total of 1,659,040 patients, with 10,710 (0.6%) of those patients with concurrent CDI. We found that patients with concurrent COVID-19 and CDI had worse outcomes compared to patients without CDI including higher in-hospital mortality (23% vs. 13.4%, aOR: 1.3, 95% CI: 1.12-1.5, p = 0.01), rates of in-hospital complications such as ileus (2.7% vs. 0.8%, p < 0.001), septic shock (21.0% vs. 7.2%, aOR: 2.3, 95% CI: 2.1-2.6, p < 0.001), length of stay (15.1 days vs. 8 days, p < 0.001) and overall cost of hospitalization (USD 196,012 vs. USD 91,162, p < 0.001). Patients with concurrent COVID-19 and CDI had increased morbidity and mortality, and added significant preventable burden on the healthcare system. Optimizing hand hygiene and antibiotic stewardship during in-hospital admissions can help to reduce worse outcomes in this population, and more efforts should be directly made to reduce CDI in hospitalized patients with COVID-19 infection.

A robust tracking system for COVID-19 like pandemic using advanced hybrid technologies

In order to track patients in coronavirus (COVID-19) like pandemic, this paper proposes a novel model based on hybrid advance technologies, which is capable to trace and track COVID-19 affectees with high accuracy. The hybrid technologies include, cellular, cyber and low range wireless technologies. This technique is capable to trace patients through call data record using cellular technology, voice over Internet protocol calls using cyber technology and physical contact without having a call history using low range wireless technologies. The proposed model is also capable to trace COVID-19 suspects. In addition to tracking, the proposed model is capable to provide surveillance capability as well by geo tagging the patients. In case of any violation by the patients an alert is sent to the concerned department. The proposed model is cost effective and privacy preserved as the entire process is carried out under the umbrella of a concerned government department. The potential outcomes of the proposed model are tracking of COVID-19 patients, monitoring of isolated patients, tracking of suspected ones and inform the mass about the safest path to use. [ABSTRACT FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Crowd Control, Planning, and Prediction Using Sentiment Analysis: An Alert System for City Authorities

Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%;with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words.

COVID-19 vaccine acceptance and its drivers among Pakistani population.

Background & Objective: COVID-19 vaccine has become available within a record time but mere availability will not control the pandemic. High vaccine acceptance is required. The objective was to determine COVID-19 vaccine acceptance and its associated factors among Pakistani population. Methods: An online survey using google form, was conducted from January 31st to February 9th, 2021 before the start of the mass vaccination in Pakistan. The questionnaire had questions about demographics plus vaccine hesitancy. We received a total of 1156 responses. Data was analyzed using STATA version 14. We employed descriptive statistics and chi square test. Result: A total of 1156 responses were received. 65% were male and 35% female. Only 6% were uneducated. Thirty percent had tested positive for COVID-19 earlier. Forty-six percent of the respondents would take (acceptance) a vaccine if available. Forty-eight percent and 45% were confident in using USA/UK and Chinese vaccine respectively. Gender and marital status was statistically significantly associated with vaccine acceptance. Concerns about the side effects were 55% while for efficacy it was 69%. Twenty-three percent were concerned about the permissibility of the vaccine on religious grounds. Conclusion: Gender and marital status was significantly associated with vaccine acceptance. Forty-six percent respondents were willing to take the vaccine. Among the vaccine hesitant group, respondents were worried about the side effects, safety and religious permissibility of vaccine. Policy makers and all the relevant stakeholders should consider low vaccine acceptance as a major bottleneck and should devise strategies to address this major issue in the fight against COVID-19.

Comparison of immune response to SARS-COV-2 vaccine in COVID-recovered versus non-infected Individuals.

To determine the antibody levels at 6 months in SARS-CoV-2 vaccinated individuals in COVID-recovered versus non-infected groups to determine the need to administer booster COVID vaccine in each group. Prospective longitudinal study. Pathology Department, Combined Military Hospital, Lahore for a period of eight months from July 2021 to February 2022. Two hundred and thirty three study participants in both COVID recovered and non-infected groups (105 participants in infected group, 128 participants in non-infected group) were subjected to blood sampling at 6 months post-vaccination. Anti-SARS-CoV-2 IgG antibody test was done using Chemiluminescence method. Comparison of antibody levels between COVID-recovered and non-infected groups was made. Results were compiled and statistically analyzed using SPSS version 21. Out of 233 study participants, males were 183 (78%) while females were 50 (22%), mean age being 35.93 years ± 8.298. Mean Anti-SARS-CoV-2 S IgG levels among COVID-recovered group was 1342 U/ml and among non-infected group was 828 U/ml at 6 months post-vaccination. Mean antibody titers in COVID-19 recovered group are higher than in non-infected group at 6 months post-vaccination in both groups.

Point of Care Testing for SARS-COV-2 Antibodies before doing Endoscopy.

Objectives: COVID-19 has taken the world by storm, creating much disparity among both healthcare and non-healthcare centres regarding the provision of services. The purpose of our study was to see the prevalence of the SARS-COV-2 exposure in the asymptomatic patients undergoing the endoscopic procedure, already triaged based on history and examination. Methods: Total 207 patients were enrolled during a time period of five months during October 2020 to April 2021 at Dr. Ziauddin Hospital Clifton campus, Karachi. In this prospective observational study patients undergoing endoscopic procedures were included after taking informed consent. The patients who already tested positive for COVID-19 by PCR were excluded. Patients were tested for Covid serology by immunochromatographic rapid serology test (ICT). Standard Operating Procedures for dealing with endoscopy patients during the COVID era were followed in all patients irrespective of antibody status. Result: Total number of patients included was 207; males were 121 (58.5%). The mean age was 48.5 ± 17.55 (range 13 to 92). Forty eight patients (23.2%) were positive for either antibody suggesting exposure to the COVID-19 virus. Out of these combined IgM and IgG positivity was seen in 24 (11.5%), IgM mono antibody positivity was seen in 7 (3.38%) and 17 (8.21%) of the study population tested positive for IgG only. 15 out of 46 (32.6%) patients with chronic liver disease in the cohort were seropositive for COVID antibodies. Conclusion: About one-fourth of the patients undergoing the endoscopic procedure were tested positive for COVID antibodies of which a significant percentage had chronic liver disease. It stresses the need of observing standard precautions to prevent the spread of infection during these procedures, especially in the vulnerable population.

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.

Mediating role of wellbeing among organizational virtuousness, emotional intelligence and job performance in post-pandemic COVID-19.

It is devastating to people's mental and emotional health to be exposed to the COVID-19 pandemic and the multifaceted response strategies are required to curb it. As a result of social distancing and self-isolation, people have faced many challenges in their lives. The suffering is even greater at the workplace where the employees are working with the fear of getting exposed to the virus and its new variants which is adversely affecting their wellbeing. This study explores and tests a model that extends the wellbeing research across organizational settings and targets the crucial factors that lead to job performance improvement even in the post pandemic COVID-19 situation. To improve both in-role performance and extra-role performance behaviors in the Pakistan banking sector, organizational virtue (also known as organizational virtuousness) and internal virtue (also known as emotional intelligence) are examined. Data were collected from the 416 bank employees using disproportionate stratified sampling technique. In the bank sector of Pakistan, wellbeing was identified as the key psychological factor that relates the in-role performance and extra-role performance to internal and organizational factors. Research findings also determined that conceptualizing subjective wellbeing in the context of work is more meaningful in understanding its relationship with the workplace variables than the general or global subjective wellbeing.

Mutational analysis of the spike protein of SARS-COV-2 isolates revealed atomistic features responsible for higher binding and infectivity

Introduction: The perpetual appearance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), and its new variants devastated the public health and social fabric around the world. Understanding the genomic patterns and connecting them to phenotypic attributes is of great interest to devise a treatment strategy to control this pandemic. Materials and Methods: In this regard, computational methods to understand the evolution, dynamics and mutational spectrum of SARS-CoV-2 and its new variants are significantly important. Thus, herein, we used computational methods to screen the genomes of SARS-CoV-2 isolated from Pakistan and connect them to the phenotypic attributes of spike protein;we used stability-function correlation methods, protein-protein docking, and molecular dynamics simulation. Results: Using the Global initiative on sharing all influenza data (GISAID) a total of 21 unique mutations were identified, among which five were reported as stabilizing while 16 were destabilizing revealed through mCSM, DynaMut 2.0, and I-Mutant servers. Protein-protein docking with Angiotensin-converting enzyme 2 (ACE2) and monoclonal antibody (4A8) revealed that mutation G446V in the receptor-binding domain;R102S and G181V in the N-terminal domain (NTD) significantly affected the binding and thus increased the infectivity. The interaction pattern also revealed significant variations in the hydrogen bonding, salt bridges and non-bonded contact networks. The structural-dynamic features of these mutations revealed the global dynamic trend and the finding energy calculation further established that the G446V mutation increases the binding affinity towards ACE2 while R102S and G181V help in evading the host immune response. The other mutations reported supplement these processes indirectly. The binding free energy results revealed that wild type-RBD has a TBE of −60.55 kcal/mol while G446V-RBD reported a TBE of −73.49 kcal/mol. On the other hand, wild type-NTD reported −67.77 kcal/mol of TBE, R102S-NTD reported −51.25 kcal/mol of TBE while G181V-NTD reported a TBE of −63.68 kcal/mol. Conclusions: In conclusion, the current findings revealed basis for higher infectivity and immune evasion associated with the aforementioned mutations and structure-based drug discovery against such variants.

Risk of Viral Infectious Diseases from Live Bats, Primates, Rodents and Carnivores for Sale in Indonesian Wildlife Markets.

Southeast Asia is considered a global hotspot of emerging zoonotic diseases. There, wildlife is commonly traded under poor sanitary conditions in open markets; these markets have been considered 'the perfect storm' for zoonotic disease transmission. We assessed the potential of wildlife trade in spreading viral diseases by quantifying the number of wild animals of four mammalian orders (Rodentia, Chiroptera, Carnivora and Primates) on sale in 14 Indonesian wildlife markets and identifying zoonotic viruses potentially hosted by these animals. We constructed a network analysis to visualize the animals that are traded alongside each other that may carry similar viruses. We recorded 6725 wild animals of at least 15 species on sale. Cities and markets with larger human population and number of stalls, respectively, offered more individuals for sale. Eight out of 15 animal taxa recorded are hosts of 17 zoonotic virus species, nine of which can infect more than one species as a host. The network analysis showed that long-tailed macaque has the greatest potential for spreading viral diseases, since it is simultaneously the most traded species, sold in 13/14 markets, and a potential host for nine viruses. It is traded alongside pig-tailed macaques in three markets, with which it shares six viruses in common (Cowpox, Dengue, Hepatitis E, Herpes B, Simian foamy, and Simian retrovirus type D). Short-nosed fruit bats and large flying foxes are potential hosts of Nipah virus and are also sold in large quantities in 10/14 markets. This study highlights the need for better surveillance and sanitary conditions to avoid the negative health impacts of unregulated wildlife markets.

Sovereign bonds and flight to safety: Implications of the COVID-19 crisis for sovereign debt markets in the G-7 and E-7 economies

This study investigates the implications of the COVID-19 pandemic for sovereign debt in the G-7 and E-7 economies and explores the notion of sovereign bonds as a safe haven. Using a set of panel regression and dynamic connectedness TVP-VAR approaches, our results reveal that the impact of COVID-19 global case numbers on sovereign bonds has been contingent on the level of the country's financial and economic development. More precisely, our findings suggest that G-7 countries, where economic development is typically higher, have seen a negative effect of the COVID-19 pandemic on sovereign bond yield: sovereign 10-year bond yields declined as the number of COVID-19 global confirmed cases increased in G-7 countries. However, in E-7 countries, where economic growth and development are typically lower, sovereign bond yields responded positively to the initial increase in COVID-19 global confirmed case numbers, but this positive effect is not statistically significant. We also find that the G-7 and E-7 economies have a strong time-varying connectedness in relation to their bond markets and this effect is more pronounced in G-7 economies. Daily Infectious Disease Equity Market Volatility is likely to be the strongest predictor of total connectedness. Concomitantly, we shed new light on the predictive power of the number of COVID-19 confirmed cases and deaths, and the Daily Infectious Disease Equity Market Volatility Tracker on the interdependence of these sovereign bond markets. Overall, this paper highlights the heterogeneous effect of the COVID-19 pandemic on sovereign bond yields in G-7 and E-7 countries and the notion that the developed economies, with their developed sovereign bond markets, are still seen as a safe haven during times of crisis.

A novel group decision-making framework under Pythagorean fuzzy N-soft expert knowledge

Many scholars have been challenged by multi-attribute group decision-making problems that have stimulated the appearance of increasingly general models. Pythagorean fuzzy sets were a reaction by Yager who in 2013, suggested this model to improve the performance of intuitionistic fuzzy sets. Another hybrid model –soft expert sets– deals with uncertain parameterized information. It considers opinions of different experts, improving the single-agent experience of soft sets. N-soft expert sets and their fuzzy version, namely, fuzzy N-soft expert sets, consider the ratings given to objects by more than one expert with respect to relevant parameters. The arguments supporting the need for independent allocation of membership and non-membership degrees apply to the fuzzy expressions imposed on top of the benefits of the N-soft expert environment. These challenges converge on the formulation of a new hybrid model called Pythagorean fuzzy N-soft expert sets that improves upon Pythagorean fuzzy sets with the benefits of N-soft expert sets. We study their scope of application with practical examples. Afterwards we discuss certain basic operators (subsethood, complement, union and intersection), prove some of their remarkable properties, and provide the concepts of equal, agree, and disagree-Pythagorean fuzzy N-soft expert sets. We present an algorithm for group decision-making problems in this framework and we explore three applications of this methodology, namely, to the analysis of wheat varieties, employee selection, and recovery order of patients suffering COVID-19. In the end, we provide a sensitivity analysis comparing the proposed model with some existing models to guarantee its cogency and feasibility.

In Vitro Screening and MD Simulations of Thiourea Derivatives against SARS-CoV-2 in Association with Multidrug Resistance ABCB1 Transporter.

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is considered a global public health concern since it causes high morbidity and mortality. Recently, it has been reported that repurposed anti-COVID-19 drugs might interact with multidrug resistance ABC transporter, particularly ABCB1. In the current study, a series of thiourea derivatives were screened as potential inhibitors against SARS-CoV-2 by targeting the attachment of receptor binding domain (RBD) of spike protein with ACE2 and their interaction with human ABCB1 has also been explored. The results indicated strong impairment of RBD-ACE2 attachment by BB IV-46 with a percentage inhibition of 95.73 ± 1.79% relative to the positive control, while BB V-19 was proven inactive with a percentage inhibition of 50.90 ± 0.84%. The same compound (BB IV-46) interacted with ABCB1 and potentially inhibited cell proliferation of P-gp overexpressing cell line with an IC50 value of 4.651 ± 0.06 µM. BB V-19, which was inactive against SARS-CoV-2, was inactive against ABCB1 with a higher IC50 value of 35.72 ± 0.09 µM. Furthermore, molecular dynamics simulations followed by binding free-energy analysis explored the binding interaction of BB IV-46 and BB V-19 to RBD region of spike protein of SARS-CoV-2. The results confirmed that compound BB IV-46 interacted strongly with RBD with a significant binding energy (-127.0 kJ/mol), while BB V-19 interacted weakly (-29.30 kJ/mol). The key interacting residues of the RBD involved in binding included Leu441, Lys444, and Tyr449. This study highlights the importance of BB IV-46 against SARS-CoV-2; however, further pharmacokinetic and pharmacodynamics studies are needed to be done.

The Efficacy of Conservative Management in Uncomplicated Acute Appendicitis - A Single-Center Retrospective Study.

Background Acute appendicitis remains the most common cause of lower abdominal pain leading to emergency visits. Even though the standard treatment of acute appendicitis remains appendectomy, in recent times, multiple randomized control trials and meta-analyses have deduced conservative treatment as a successful alternative treatment. During the coronavirus disease (COVID) pandemic, with a shortage of staff and resources, treatment with conservative management of uncomplicated acute appendicitis became very beneficial under certain circumstances and conditions. This study aimed to assess whether it is effective to manage patients with uncomplicated acute appendicitis with antibiotic therapy. Methodology This was a single hospital based retrospective, cross-sectional study from Jan 2015 to May 2020. Patients with clinical and radiological features of uncomplicated acute appendicitis with Alvarado's score >6 were included in the study. Patients were kept on antibiotics, intravenous fluids, and analgesia as part of a conservative regime. Those who failed to respond to conservative therapy were managed surgically. The follow-up period was six months. Results One hundred eighty-two cases of uncomplicated acute appendicitis were included and managed conservatively, of which 52.2% were males while 47.8% were females. The median age of the patients was 26 years. Conservative treatment was successful in 26.2% of the patients, with a recurrence of 5.5% in the six-month follow-up period. The mean number of days of hospital stay was three days in patients treated with conservative or surgical treatment. Conclusion Conservative management is gaining popularity, and many centers are inclined towards non-operative management; however, appendectomy remains the gold standard treatment for appendicitis.

Volatility spillovers, hedging and safe‐havens under pandemics: All that glitters is not gold!

In the context of the COVID-19's outbreak and its implications for the financial sector, this study analyses the aspect of hedging and safe-haven under the pandemic. Drawing on the daily data from 02 August 2019 to 17 April 2020, our key findings suggest that the contagious effects in financial assets' returns significantly increased under COVID-19, indicating exacerbated market risk. The connectedness spiked in the middle of March, consistent with lockdown timings in major economies. The effect became severe with the WHO's declaration of a pandemic, confirming negative news effects. The return connectedness suggests that COVID-19 has been a catalyst of contagious effects on the financial markets. The crude oil and the government bonds are however not as much affected by the spillovers as their endogenous innovation. In terms of spillovers, we do find the safe-haven function of Gold and Bitcoin. Comparatively, the safe-haven effectiveness of Bitcoin is unstable over the pandemic. Whereas, GOLD is the most promising hedge and safe-haven asset, as it remains robust during the current crisis of COVID-19 and thus exhibits superiority over Bitcoin and Tether. Our findings are useful for investors, portfolio managers and policymakers interested in spillovers and safe havens during the current pandemic.