Exploring Immune-Related Gene Expression Up To The First 24-Hour For Predicting Sepsis Outcomes Based On Comprehensive Bioinformatics Analysis And Machine Learning (preprint)

Background: Host immune dysregulation participates in the prognosis of sepsis with high morbidity and mortality. Our study aimed to identify the roles of immuneassociated genes during sepsis progression and to predict sepsis survival up to 24 h at diagnosis, which may help plan future individualized treatments. Methods: GSE54514, GSE57065, and GSE95233 datasets were downloaded from the Gene Expression Omnibus (GEO) database for early identification of differentially expressed IRGs between sepsis patients and healthy controls. Candidate IRGs significantly associated with sepsis survival were obtained by univariate logistic regression analysis. Gene signatures of these IRGs were further selected by Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest Algorithm (RFA). The correlation between signature genes and prognosis was analyzed.Furthermore, signature IRGs were further validated by quantitative PCR (qPCR) on the whole blood of septic patients and an external COVID-19 dataset and candidate drug were predicted. Results: HLA.DPA1, IL18RAP, MMP9, RNASE3, S100P, and PTX3 were found significantly differentially expressed starting very early after sepsis infection and persisting for up to 5 days, and their formed IRG score had a satisfactory predictive value on sepsis outcome. Furthermore, our validation showed that these six IRGs were also significantly dysregulated in both an external COVID-19 dataset and sepsis patients. Finally, 10 potential compounds were predicted to have targeted these genes. Conclusion: Our study developed a prognostic modeling tool for sepsis survival based on IRG expression profiles, and has the capacity for early prediction of sepsis outcomes via monitoring the immunogenomic landscape, and possibly the individualized therapies for sepsis survival.

Prevalence and predictors for adverse effects of Sinopharm and Sinovac COVID-19 Vaccines

Objective: To see the prevalence and potential predictors for side effects of Sinopharm and Sinovac COVID-19 vaccines among students of medical, and BS human nutrition and dietetics courses. Study Design: Cross-sectional Proforma-derived. Setting: Sialkot Medical College, Sialkot, Pakistan. Period: July and August, 2021. Material & Methods: Fifty students (MBBS = 30;Dietetics course = 20) with at least one dose of Sinopharm or Sinovac COVID-19 vaccine were recruited, purposively. The subjects were asked to report in an indigenously designed proforma on adverse effects using recall methodology for open time. Results: Forty nine subjects gave complete responses in the proforma. The rate of adverse effects increased from 75% (n = 3) against Sinopharm to 100% (n = 9) against Sinovac vaccine among 13 students of Dietetics who had only 1st dose. For rest of the 36 participants, the rate was found, as: 76.5% (n = 13) against each of the 1st and 2nd dose of Sinopharm;78.9% (n = 15) against 1st and 68.4 (n = 13) against 2nd doses of Sinovac vaccine. The frequency of different adverse effects per individual ranged 1-3 (Sinopharm) or 1-4 (Sinovac). Whereas, lower rate of injection site pain was recorded on 1st dose of Sinopharm than Sinovac (52.9 vs 57.9%, respectively). None of the variables was noted as potential predictor for the side effects (p >.05). Conclusion: Sinopharm, and Sinovac COVID-19 vaccines exhibit almost similar prevalence of self-manageable side effects. Moreover, there is no predictor for the effects. [ FROM AUTHOR]

Sinopharm and Sinovac COVID-19 vaccines;Prevalence and risk factors for post full vaccination weak immunity

Objective: To see the prevalence and risk factors for post full vaccination (Sinopharm and Sinovac COVID-19 vaccines) weak immunity in persons with no history of viral exposure. Study Design: Prospective Observational. Setting: Syed Research Centre, Sialkot. Period: Nov 2021 to Feb 2022. Material & Methods: Eighty two individuals (male/female, aged: 24-70 years) were enrolled from District Sialkot (Pakistan). Two doses of Sinopharm or Sinovac COVID-19 vaccine were administered to each participant before blood sampling. The quantity of COVID-19 spike immunoglobulin G against SARS-CoV-2 (5-Antigen) was detected in serum using chemiluminescence immunoassay technique [threshold titer: 7.1 (detection), 105. 63 (seroprotection), 492.96 BAU/mL (strong humoral response)]. Results: Overall, 96.3% seropositivity, 89% seroprotection, and 37.8% (n = 31) weak immune response were recorded. Whereas, mean antibody titer was found to be 2312.02 (range: 0.0 – 28711.74) BAU/mL. A vaccinated male had approximately 4 folds (95%CI: 1.297 – 11.504;p = .002) more likelihood of weak immune response than females. Similarly, the rate of poor immunological outcome was higher in individuals with >24.9 Kg/m² (69.2%, n = 27) body mass index compared to ≤24.9 Kg/m² . A person with Sinopharm COVID-19 vaccine was more vulnerable to weak immune response than Sinovac vaccinated participants (RR = 2.351;95%CI = 1.327 – 4.167;p .002;57.6% vs. 24.5%, respectively). Co-occurrence of age (>47 years) and BMI (>24.9 Kg/m² ) existed in 11 of 28 males (39.3%) with impaired immunogenicity. Conclusion: The high rate of post full vaccination weak immune response is alarming. Gender, BMI, and vaccine type were amongst the predictors for diminished humoral response. [ FROM AUTHOR] Copyright of Professional Medical Journal is the property of Professional Medical Journal 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 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 . (Copyright applies to all s.)

Nitazoxanide and Azithromycin for the early Treatment of Covid-19: A Multi-Spectroscopic, Biochemical and Computational Drug-Drug Interaction Study (preprint)

Current investigation presented an experimental and theoretical drug-drug interaction study between nitazoxanide (NTZ) and azithromycin (AZT) in aqueous solution. Understudy drugs alone, in combination, and with other drugs are widely prescribed for the early treatment of COVID-19. Interaction was primarily studied by using UV/Vis, fluorescence, ATR-FTIR and CD spectroscopy. While molecular docking studies was performed to established the drugs interaction computationally. Two drugs when allowed to interact, the bright yellow color was observed giving hyper chromic band at 420 nm. The rate of absorbance was linearly increased with increasing drug concentrations and in a time-dependent manner. Stability of the interaction complex (i.e. NTZ: AZT) was measured at variable temperature (25–80 °C), pH (5.0–10.0) and ionic strength (0.05-2.0 M NaCl), and not only proved stable but also retain antimicrobial potential with reduced cellular toxicity. Mole ratio and Job`s method of continuous variation were used to established the binding stoichiometry between NTZ and AZT and found to be 2:1. While, the calculated binding constant (kb = 8400 M− 1) and Gibb’s free energy (ΔG° = -22.4 KJ/mol) were also suggested the energetically favorable interaction. FTIR spectra of NTZ: AZT complex in comparison with two drugs alone revealed significant interactions which was also complemented from molecular docking studies. The interaction was also successfully demonstrated in presence of carrier protein HSA and by spiking the two drugs in real samples of fresh human plasma and urine. Cumulatively, present study provided new protocol for drug’s interaction, functional efficacy and bioavailability in combination therapy.

Ocular Distribution of the Renin-Angiotensin-Aldosterone System in the Context of the SARS-CoV-2 Pandemic

The COVID-19 pandemic has resulted in an unprecedented impact on global health, economy, and way of life. SARS-CoV-2, the virus responsible for the disease, utilizes the ACE2 receptor found on host cells to mediate entry, replication, and infection. Numerous studies have elucidated the presence of many components of the renin-angiotensin-aldosterone system (RAAS) in the eye, including the ACE2 receptor. Considering this, and the anatomical vulnerability that the exposed ocular surface offers with its interconnectedness to the respiratory system, there is a theoretical risk of pathogen entry from the ocular route as well as the development of COVID-19-associated eye disease. Despite this, the actual epidemiological data demonstrates low ocular symptoms, possibly due to differing ACE2 receptor expression across age, ethnicity, and sex coupled with the protective properties of tears. We summarize the current literature on ocular RAAS with specific focus on the ACE2 receptor and its interplay with the SARS-CoV-2 virus.

Ocular Distribution of the Renin-Angiotensin-Aldosterone System in the Context of the SARS-CoV-2 Pandemic.

The COVID-19 pandemic has resulted in an unprecedented impact on global health, economy, and way of life. SARS-CoV-2, the virus responsible for the disease, utilizes the ACE2 receptor found on host cells to mediate entry, replication, and infection. Numerous studies have elucidated the presence of many components of the renin-angiotensin-aldosterone system (RAAS) in the eye, including the ACE2 receptor. Considering this, and the anatomical vulnerability that the exposed ocular surface offers with its interconnectedness to the respiratory system, there is a theoretical risk of pathogen entry from the ocular route as well as the development of COVID-19-associated eye disease. Despite this, the actual epidemiological data demonstrates low ocular symptoms, possibly due to differing ACE2 receptor expression across age, ethnicity, and sex coupled with the protective properties of tears. We summarize the current literature on ocular RAAS with specific focus on the ACE2 receptor and its interplay with the SARS-CoV-2 virus.

Genomic Characterization of SARS-CoV2 from Peshawar Pakistan Using Next-Generation Sequencing.

This study aimed to characterize the whole genome of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) isolated from an oropharyngeal swab specimen of a Pashtun Pakistani patient using next-generation sequencing. Upon comparing the SARS-CoV2 genome to the reference genome, a total of 10 genetic variants were identified. Among the 10 genetic variants, 1 missense mutation (c.1139A > G, p.Lys292Glu) in the Open Reading Frame 1ab (ORF1ab) positioned at 112 in the non-structural protein 2 (NSP2) was found to be unique. Phylogenetic analysis (n = 84) revealed that the current SARS-CoV2 genome was closely clustered with 8 Pakistani strains belonging to Punjab, Federal Capital, Azad Jammu and Kashmir (AJK), and Khyber Pakhtunkhwa (KP). In addition, the current SARS-CoV2 genome was very similar to the genome of SARS-CoV2 reported from Guam, Taiwan, India, the USA, and France. Overall, this study reports a slight mismatch in the SARS-CoV2 genome, indicating the presence of a single unique missense mutation. However, phylogenetic analysis revealed that the current SARS-CoV2 genome was closely clustered with 8 other Pakistani strains.

Fake News Data Exploration and Analytics

Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models;random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.

Optimizing the Performance of Pure ALOHA for LoRa-Based ESL.

(1) Background: The scientific development in the field of industrialization demands the automization of electronic shelf labels (ESLs). COVID-19 has limited the manpower responsible for the frequent updating of the ESL system. The current ESL uses QR (quick response) codes, NFC (near-field communication), and RFID (radio-frequency identification). These technologies have a short range or need more manpower. LoRa is one of the prominent contenders in this category as it provides long-range connectivity with less energy harvesting and location tracking. It uses many gateways (GWs) to transmit the same data packet to a node, which causes collision at the receiver side. The restriction of the duty cycle (DC) and dependency of acknowledgment makes it unsuitable for use by the common person. The maximum efficiency of pure ALOHA is 18.4%, while that of slotted ALOHA is 36.8%, which makes LoRa unsuitable for industrial use. It can be used for applications that need a low data rate, i.e., up to approximately 27 Kbps. The ALOHA mechanism can cause inefficiency by not eliminating fast saturation even with the increasing number of gateways. The increasing number of gateways can only improve the global performance for generating packets with Poisson law having a uniform distribution of payload of 1~51 bytes. The maximum expected channel capacity usage is similar to the pure ALOHA throughput. (2) Methods: In this paper, the improved ALOHA mechanism is used, which is based on the orthogonal combination of spreading factor (SF) and bandwidth (BW), to maximize the throughput of LoRa for ESL. The varying distances (D) of the end nodes (ENs) are arranged based on the K-means machine learning algorithm (MLA) using the parameter selection principle of ISM (industrial, scientific and medical) regulation with a 1% DC for transmission to minimize the saturation. (3) Results: The performance of the improved ALOHA degraded with the increasing number of SFs and as well ENs. However, after using K-mapping, the network changes and the different number of gateways had a greater impact on the probability of successful transmission. The saturation decreased from 57% to 1~2% by using MLA. The RSSI (Received Signal Strength Indicator) plays a key role in determining the exact position of the ENs, which helps to improve the possibility of successful transmission and synchronization at higher BW (250 kHz). In addition, a high BW has lower energy consumption than a low BW at the same DC with a double-bit rate and almost half the ToA (time on-air).

Impact of COVID-19 on agricultural food: A Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis

Abstract The COVID-19 pandemic has put unprecedented stress on agricultural food. Therefore, the goal of this study is to analyze the effect of the COVID-19 outbreak on agricultural food and provide policy recommendations based on SWOT analysis to minimize risks. Data from the Food and Agriculture Organization and the Pakistan Bureau of Statistics were used for this study with a thorough literature review containing policy documents and scientific magazines in the related sector. There is enough evidence to demonstrate that the COVID-19 pandemic has had a massive influence on agricultural food supply, largely impacting food demand as well as food insecurity. The study uses SWOT analysis to identify the highest-priority policies that may remediate the current issues. Key strategies include developing new online resources such as digitized trade and providing a digital platform to strengthen the buyer?supplier collaboration and lower the risk of food wastage and insecurity. The government should invest in technology that contributes to the sustainable flow of agricultural products along the supply chain to prevent food crises during the pandemic.

COVID-19 Pandemic and Ramadan Fasting among Patients with Type 2 Diabetes Mellitus.

BACKGROUND: This year Ramadan occurs during the global coronavirus disease-2019 (COVID-19) pandemic. Data has shown that patients with type 2 diabetes mellitus (T2DM) are prone to severe disease with COVID-19 and with increased mortality. Acute complications such as dehydration, starvation ketosis, ketoacidosis, and the increased risk of coagulopathy and thrombosis should be considered particularly during this pandemic period. Fasting during Ramadan this year and the COVID-19 pandemic is more challenging, not only for patients with T2DM but also for healthcare providers. We present healthcare providers with important aspects to consider during the COVID-19 pandemic for patients with T2DM who intend to fast during Ramadan and other fasting days.

A distinct metabolic profile associated with a fatal outcome in COVID-19 patients during early epidemic in Italy (preprint)

Leveraging the unique biological resource based upon the initial COVID-19 patients in Policlinico di Milano (Italy), our study provides the first metabolic profile associated with a fatal outcome. The identification of potential predictive biomarkers offers a vital opportunity to employ metabolomics in a clinical setting as diagnostic tool of disease prognosis upon hospital admission.

Future liasing of the lockdown during COVID-19 pandemic: The dawn is expected at hand from the darkest hour.

The lockdown during COVID-19 pandemic has converted the world into new experimental laboratories, which may reveal temporal or spatial comparative analysis data. However, some startling information is gathered in terms of reduced premature mortality cases associated with air and water quality improvement, enhanced e-learning on a broader platform, work from home, and successful e-health. The decline in vehicular density on roads and congestion leads to reduced energy consumption and associated greenhouse gases (GHG) and other pollutants emission. The lockdown has also been identified as a possible emergency measure to combat severe air pollution episodes. Similarly, industrial pollution has been recognized as one of the primary causes of water resource pollution and would, therefore, bring change in policy vis-à-vis groundwater pollution control. Our findings suggest that the results of successful e-learning and work from home would be a permanent shift from conventional modes in the near future due to a drastic reduction in socio-economic cost. Our critical analysis also highlights that with such temporary lockdown measures acute/chronic ill-effects of anthropogenic perturbations on planet earth can be effectively estimated through sociocultural, socioeconomical and socio-political/sociotechnological nexus.