Exploring the role of Orexin-A neuropeptide in Parkinson's disease: A systematic review and meta-analysis.

BACKGROUND: Parkinson's disease (PD) is a progressive neurological condition that affects movement and coordination. Orexin-A (OXA) is an excitatory neuropeptide that is found throughout the central nervous system. There is growing interest in investigating the potential diagnostic and therapeutic utility of OXA in PD. To date, studies have reported a wide range of OXA concentrations in patients with PD. In this review, we discuss the current understanding of the dysregulation of OXA in PD and analyze its levels in the CSF. METHODS: We searched six databases (PubMed, Scopus, Web of Science, EMBASE, ProQuest, and EBSCOHost) and preprint servers using a predetermined search strategy through 4th March 4, 2023. The search keywords included "Parkinson's disease", "Orexin-A", "Hypocretin-1", "cerebrospinal fluid", and "CSF". Studies that reported OXA/Hypocretin-1 levels in the CSF of patients with PD were included. Two researchers independently reviewed the records and extracted data. FINDINGS: Eighteen studies involving 244 patients were analyzed. CSF Orexin-A concentrations were lower in patients with Parkinson's disease than in controls, with a mean difference of -59.21 (95 % CI: -89.10 to -29.32). The mean OXA levels were 281.52 (95 % CI: 226.65-336.40). CONCLUSION: Our analysis reveals lower concentrations of orexin-A in the cerebrospinal fluid of Parkinson's disease patients compared to controls, but within the normal range. These findings suggest a potential, but not significant, disruption in the orexinergic system associated with the disease.

Zavegepant for Acute Treatment of Migraine: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

OBJECTIVE: Evaluate the safety and efficacy of zavegepant (BHV-3500), a recently approved nasal spray containing a third-generation calcitonin gene-related peptide receptor antagonist, for treating acute migraine attacks. METHODS: A comprehensive search was conducted across various databases up to 06/26/2023 to identify relevant randomized clinical trials (RCTs) on zavegepant's efficacy and safety in treatment of acute migraine attacks. Primary outcome: freedom from pain at 2 hours postdose. Safety outcomes were evaluated based on adverse events (AEs), with zavegepant 10 mg and placebo groups compared for incidence of AEs. RESULTS: Two RCTs, involving 2061 participants (1014 receiving zavegepant and 1047 receiving placebo), were quantitatively analyzed. An additional trial was included for qualitative synthesis. Zavegepant 10 mg exhibited a significantly higher likelihood of achieving freedom from pain at 2 hours postdose compared with the placebo group (risk ratio [RR] 1.54, 95% confidence interval [CI] 1.28 to 1.84). It also showed superior relief from the most bothersome symptoms at 2 hours postdose compared with placebo (RR 1.26, 95% CI 1.13 to 1.42). However, the zavegepant 10 mg group experienced a higher incidence of AEs compared with placebo (RR 1.78, 95% CI 1.5 to 2.12), with dysgeusia being the most reported AE (RR 4.18, 95% CI 3.05 to 5.72). CONCLUSION: Zavegepant 10 mg is more effective than placebo in treating acute migraine attacks, providing compelling evidence of its efficacy in relieving migraine pain and most bothersome associated symptoms. Further trials are necessary to confirm its efficacy, tolerability, and safety in diverse clinic-based settings with varied patient populations.

The advent of RNA-based therapeutics for metabolic syndrome and associated conditions: a comprehensive review of the literature.

Metabolic syndrome (MetS) is a prevalent and intricate health condition affecting a significant global population, characterized by a cluster of metabolic and hormonal disorders disrupting lipid and glucose metabolism pathways. Clinical manifestations encompass obesity, dyslipidemia, insulin resistance, and hypertension, contributing to heightened risks of diabetes and cardiovascular diseases. Existing medications often fall short in addressing the syndrome's multifaceted nature, leading to suboptimal treatment outcomes and potential long-term health risks. This scenario underscores the pressing need for innovative therapeutic approaches in MetS management. RNA-based treatments, employing small interfering RNAs (siRNAs), microRNAs (miRNAs), and antisense oligonucleotides (ASOs), emerge as promising strategies to target underlying biological abnormalities. However, a summary of research available on the role of RNA-based therapeutics in MetS and related co-morbidities is limited. Murine models and human studies have been separately interrogated to determine whether there have been recent advancements in RNA-based therapeutics to offer a comprehensive understanding of treatment available for MetS. In a narrative fashion, we searched for relevant articles pertaining to MetS co-morbidities such as cardiovascular disease, fatty liver disease, dementia, colorectal cancer, and endocrine abnormalities. We emphasize the urgency of exploring novel therapeutic avenues to address the intricate pathophysiology of MetS and underscore the potential of RNA-based treatments, coupled with advanced delivery systems, as a transformative approach for achieving more comprehensive and efficacious outcomes in MetS patients.

Self-medication prevalence and associated factors among adult population in Northern India: A community-based cross-sectional study.

Objectives: This study aimed to determine self-medication prevalence and its associated factors. Methods: A community-based cross-sectional study was conducted in the urban and rural catchment areas of Uttar Pradesh, India, among 440 adults using a pretested, semistructured questionnaire. The Chi-square test and logistic regression were used to determine the association of self-medication prevalence with various independent variables. The associations were reported as adjusted odds ratios and 95% confidence intervals. Results: The prevalence of medication use was 66.4%. The majority of participants (45%) took medicine for fever, cough (40.1%), and cold (31.8%). Allopathy (83.2%) was the most common medicine system used for self-medication. More than half reported taking medicine such as paracetamol (52%), followed by cough syrup (21%) and antihistaminic (17%). Convenience (46%) and lack of time (35.3%) were commonly cited reasons for self-medication. Also, 64.4% of the respondents practiced self-medication on the pharmacist's recommendation. Urban participants (adjusted odds ratio: 9.85, 95% confidence interval: 5.32-18.23), females (adjusted odds ratio: 2.32, 95% confidence interval: 1.18-4.57), skilled workers (adjusted odds ratio: 5.62, 95% confidence interval: 1.80-17.5), and those who completed primary school (adjusted odds ratio: 2.48, 95% confidence interval: 1.16-5.25) were more likely to self-medicate than rural, male, unemployed, and illiterate participants, respectively. Also, participants whose income was 30,000 Indian rupees (adjusted odds ratio: 3.21, 95% confidence interval: 1.00-10.21) were more likely to self-medicate than those whose income was less than 4000. Conclusions: A high prevalence of self-medication was found, particularly in urban areas. Convenience and lack of time were commonly cited reasons for self-medication. Allopathy was the most widely used medicine system for self-medication. Antipyretics, cough syrups, and antiallergics were most commonly self-medicated. Gender, education, and income were associated with self-medication. The study highlighted the increased usage among females which could be further explored and role of pharmacists' recommendation as a major driver for self-medication.

Recent nanotheranostic approaches in cancer research.

Humanity is suffering from cancer which has become a root cause of untimely deaths of individuals around the globe in the recent past. Nanotheranostics integrates therapeutics and diagnostics to monitor treatment response and enhance drug efficacy and safety. We hereby propose to discuss all recent cancer imaging and diagnostic tools, the mechanism of targeting tumor cells, and current nanotheranostic platforms available for cancer. This review discusses various nanotheranostic agents and novel molecular imaging tools like MRI, CT, PET, SPEC, and PAT used for cancer diagnostics. Emphasis is given to gold nanoparticles, silica, liposomes, dendrimers, and metal-based agents. We also highlight the mechanism of targeting the tumor cells, and the limitations of different nanotheranostic agents in the field of research for cancer treatment. Due to the complexity in this area, multifunctional and hybrid nanoparticles functionalized with targeted moieties or anti-cancer drugs show the best feature for theranostics that enables them to work on carrying and delivering active materials to the desired area of the requirement for early detection and diagnosis. Non-invasive imaging techniques have a specificity of receptor binding and internalization processes of the nanosystems within the cancer cells. Nanotheranostics may provide the appropriate medicine at the appropriate dose to the appropriate patient at the appropriate time.

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection.

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

Silico-tuberculosis amidst COVID-19 pandemic: global scenario and Indian perspective.

Inhalation of crystalline silica-rich dust particles can result in the deadly occupational lung disorder called silicosis. The risk of contracting tuberculosis (TB) and the potential for lung cancer increase due to silicosis. This review article aims to bring to light the state of silicosis and TB scenario in the world and India for evaluating hurdles in the present and future to achieve the elimination road map and assess these conditions in the backdrop of the COVID-19 pandemic. A patient with silicosis has a 2.8-2.9 times higher risk of developing pulmonary TB and 3.7 times that of extrapulmonary TB. Incidences of missed cases when TB was misdiagnosed with silicosis due to indifferent clinical manifestations of the two in the initial stages are not uncommon. The duration of silica exposure and silicosis severity are directly related to the propensity to develop TB. As per a study, an average gap of 7.6 years has been noticed in a South African population for silico-tuberculosis to develop post-silicosis. In a study done on mine workers at Jodhpur, Rajasthan, it was seen that there is no definitive relation between patients with silicosis and the possibility of having COVID-19. There is a significant need to integrate the Silicosis control program with the TB elimination program for the government. A few steps can include assessing the workplaces, periodic monitoring of the workers' health, active case surveillance, identification of hotspots, and introducing reforms to curb the spread of dust and particulate matter from industrialised areas be taken in this regard.

Emerging zoonotic diseases and COVID-19 pandemic: global Perspective and Indian Scenario.

The current coronavirus disease 2019 (COVID-19) pandemic is one example of the scores of zoonotic diseases responsible for various outbreaks resulting in the deaths of millions of people for centuries. The COVID-19 pandemic has broken the age-old healthcare infrastructure and led to utter chaos. In the shadow of this pandemic, other zoonotic infections like the nipah virus, monkeypox, and langya virus, to name a few, have been neglected. Hence, outbreaks caused by such zoonotic viruses are rising in their endemic areas, like the Indian subcontinent. The mortality and morbidity due to such zoonoses are greater than usual due to the shortage of healthcare professionals caused by the COVID-19 crisis. Due to the lack of vaccines and therapeutics directed against this viral infection, treatment of patients is limited to supportive management and prevention, making preparedness for these potential zoonotic viral outbreaks essential. This paper highlights some of these zoonotic infections, which perpetuated and wreaked havoc while the world was occupied with containing the COVID-19 pandemic.

CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image.

The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based on enhanced deep learning can replace the area's currently cumbersome and sluggish traditional approaches. Although it seems straightforward, classifying fish images is a complex procedure. In addition, the scientific study of population distribution and geographic patterns is important for advancing the field's present advancements. The goal of the proposed work is to identify the best performing strategy using cutting-edge computer vision, the Chaotic Oppositional Based Whale Optimization Algorithm (CO-WOA), and data mining techniques. Performance comparisons with leading models, such as Convolutional Neural Networks (CNN) and VGG-19, are made to confirm the applicability of the suggested method. The suggested feature extraction approach with Proposed Deep Learning Model was used in the research, yielding accuracy rates of 100 %. The performance was also compared to cutting-edge image processing models with an accuracy of 98.48 %, 98.58 %, 99.04 %, 98.44 %, 99.18 % and 99.63 % such as Convolutional Neural Networks, ResNet150V2, DenseNet, Visual Geometry Group-19, Inception V3, Xception. Using an empirical method leveraging artificial neural networks, the Proposed Deep Learning model was shown to be the best model.

3D US-CT/MRI registration for percutaneous focal liver tumor ablations.

PURPOSE: US-guided percutaneous focal liver tumor ablations have been considered promising curative treatment techniques. To address cases with invisible or poorly visible tumors, registration of 3D US with CT or MRI is a critical step. By taking advantage of deep learning techniques to efficiently detect representative features in both modalities, we aim to develop a 3D US-CT/MRI registration approach for liver tumor ablations. METHODS: Facilitated by our nnUNet-based 3D US vessel segmentation approach, we propose a coarse-to-fine 3D US-CT/MRI image registration pipeline based on the liver vessel surface and centerlines. Then, phantom, healthy volunteer and patient studies are performed to demonstrate the effectiveness of our proposed registration approach. RESULTS: Our nnUNet-based vessel segmentation model achieved a Dice score of 0.69. In healthy volunteer study, 11 out of 12 3D US-MRI image pairs were successfully registered with an overall centerline distance of 4.03±2.68 mm. Two patient cases achieved target registration errors (TRE) of 4.16 mm and 5.22 mm. CONCLUSION: We proposed a coarse-to-fine 3D US-CT/MRI registration pipeline based on nnUNet vessel segmentation models. Experiments based on healthy volunteers and patient trials demonstrated the effectiveness of our registration workflow. Our code and example data are publicly available in this r epository.

Can ventricular 3D ultrasound of neonates with posthemorrhagic hydrocephalus inform on the need for a ventriculoperitoneal shunt?

OBJECTIVE: Some neonates born prematurely with intraventricular hemorrhage develop posthemorrhagic hydrocephalus and require lifelong treatment to divert the flow of CSF. Early prediction of the eventual need for a ventriculoperitoneal shunt (VPS) is difficult, and early discussions with families are based on statistics and the grade of hemorrhage. The authors hypothesize that change in ventricular volume during ventricular taps that is measured with repeated 3D ultrasound (3D US) imaging of the lateral ventricles could be used to assess the risk of the future requirement of a VPS. METHODS: A total of 92 neonates with intraventricular hemorrhage who were treated in the NICU were recruited between April 2012 and November 2019. Only patients who required ventricular taps (VTs) were included in this study, resulting in the analysis of 19 patients with a total of 61 VTs. Among them, 14 patients were treated with a VPS, and in 5 patients the hydrocephalus resolved spontaneously. Parameters studied were total ventricular volume measured with 3D US, ventricular volume change after VT, the ratio between volume reduction and tap amount, the difference between tap amount and volume reduction after tap, the average tap amount, the average number of days between taps, pre-tap head circumference, and reduction in head circumference after tap. RESULTS: Statistically significant differences were found in ventricular volume reduction after tap (p = 0.007), the ratio between volume reduction and tap amount (p = 0.03), the difference between tap amount and volume reduction after tap (p = 0.05), and the interval of days between taps (p = 0.0115). CONCLUSIONS: Measuring with 3D US before and after VT can be a useful tool for quantifying ventricular volume. The findings in this study showed that neonates who experience a large reduction of ventricular volume after VT are more likely to be treated with a shunt than are neonates who experience a small reduction.

Enhancing bioflavor production by solid-state fermentation using Kluyveromyces marxianus and l-phenylalanine.

This study includes the utilization of sweet lemon peel (SLP) and sugarcane bagasse (SB) in solid-state fermentation using Kluyveromyces marxianus for bioflavor compounds production adopting response surface methodology. The major flavor compounds, 2-phenylethanol (2-PE) and 2-phenylethyl acetate (2-PEA) were quantified using gas chromatography-mass spectrometry with and without adding any supplements. Quantification of flavor compounds indicated that without adding any accessory in the substrate, the concentration of 2-PE using SLP and SB was 0.15 ± 0.003 mg/g and 0.14 ± 0.002 mg/g, respectively. Whereas 2-PEA concentration using SLP and SB was observed as 0.01 ± 0.008 mg/g and 0.02 ± 0.001 mg/g, respectively. The addition of l-phenylalanine (l-phe) in the substrates showed 30%-75% enhancement in the production of 2-PE and 2-PEA. The present study indicates that the K. marxianus is a potential microbial cell factory for the production of 2-PE and 2-PEA with the addition of synthetic l-phe having a plethora of applications in food and pharmaceutical industries.

Bidirectional genome-wide CRISPR screens reveal host factors regulating SARS-CoV-2, MERS-CoV and seasonal HCoVs.

CRISPR knockout (KO) screens have identified host factors regulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication. Here, we conducted a meta-analysis of these screens, which showed a high level of cell-type specificity of the identified hits, highlighting the necessity of additional models to uncover the full landscape of host factors. Thus, we performed genome-wide KO and activation screens in Calu-3 lung cells and KO screens in Caco-2 colorectal cells, followed by secondary screens in four human cell lines. This revealed host-dependency factors, including AP1G1 adaptin and ATP8B1 flippase, as well as inhibitors, including mucins. Interestingly, some of the identified genes also modulate Middle East respiratory syndrome coronavirus (MERS-CoV) and seasonal human coronavirus (HCoV) (HCoV-NL63 and HCoV-229E) replication. Moreover, most genes had an impact on viral entry, with AP1G1 likely regulating TMPRSS2 activity at the plasma membrane. These results demonstrate the value of multiple cell models and perturbational modalities for understanding SARS-CoV-2 replication and provide a list of potential targets for therapeutic interventions.

A Review on Mechanistic Insight of Plant Derived Anticancer Bioactive Phytocompounds and Their Structure Activity Relationship.

Cancer is a disorder that rigorously affects the human population worldwide. There is a steady demand for new remedies to both treat and prevent this life-threatening sickness due to toxicities, drug resistance and therapeutic failures in current conventional therapies. Researchers around the world are drawing their attention towards compounds of natural origin. For decades, human beings have been using the flora of the world as a source of cancer chemotherapeutic agents. Currently, clinically approved anticancer compounds are vincristine, vinblastine, taxanes, and podophyllotoxin, all of which come from natural sources. With the triumph of these compounds that have been developed into staple drug products for most cancer therapies, new technologies are now appearing to search for novel biomolecules with anticancer activities. Ellipticine, camptothecin, combretastatin, curcumin, homoharringtonine and others are plant derived bioactive phytocompounds with potential anticancer properties. Researchers have improved the field further through the use of advanced analytical chemistry and computational tools of analysis. The investigation of new strategies for administration such as nanotechnology may enable the development of the phytocompounds as drug products. These technologies have enhanced the anticancer potential of plant-derived drugs with the aim of site-directed drug delivery, enhanced bioavailability, and reduced toxicity. This review discusses mechanistic insights into anticancer compounds of natural origins and their structural activity relationships that make them targets for anticancer treatments.

Benchmarking of SpCas9 variants enables deeper base editor screens of BRCA1 and BCL2.

Numerous rationally-designed and directed-evolution variants of SpCas9 have been reported to expand the utility of CRISPR technology. Here, we assess the activity and specificity of WT-Cas9 and 10 SpCas9 variants by benchmarking their PAM preferences, on-target activity, and off-target susceptibility in cell culture assays with thousands of guides targeting endogenous genes. To enhance the coverage and thus utility of base editing screens, we demonstrate that the SpCas9-NG and SpG variants are compatible with both A > G and C > T base editors, more than tripling the number of guides and assayable residues. We demonstrate the performance of these technologies by screening for loss-of-function mutations in BRCA1 and Venetoclax-resistant mutations in BCL2, identifying both known and new mutations that alter function. We anticipate that the tools and methodologies described here will facilitate the investigation of genetic variants at a finer and deeper resolution for any locus of interest.

Metagenomics and artificial intelligence in the context of human health.

Human microbiome is ubiquitous, dynamic, and site-specific consortia of microbial communities. The pathogenic nature of microorganisms within human tissues has led to an increase in microbial studies. Characterization of genera, like Streptococcus, Cutibacterium, Staphylococcus, Bifidobacterium, Lactococcus and Lactobacillus through culture-dependent and culture-independent techniques has been reported. However, due to the unique environment within human tissues, it is difficult to culture these microorganisms making their molecular studies strenuous. MGs offer a gateway to explore and characterize hidden microbial communities through a culture-independent mode by direct DNA isolation. By function and sequence-based MGs, Scientists can explore the mechanistic details of numerous microbes and their interaction with the niche. Since the data generated from MGs studies is highly complex and multi-dimensional, it requires accurate analytical tools to evaluate and interpret the data. Artificial intelligence (AI) provides the luxury to automatically learn the data dimensionality and ease its complexity that makes the disease diagnosis and disease response easy, accurate and timely. This review provides insight into the human microbiota and its exploration and expansion through MG studies. The review elucidates the significance of MGs in studying the changing microbiota during disease conditions besides highlighting the role of AI in computational analysis of MG data.

Does the Head Position Affect Neonatal Lateral Ventricular Volume?

OBJECTIVE: This study aimed to determine whether there are differences in the lateral ventricular volumes, measured by three-dimensional ultrasound (3D US) depending on the posture of the neonate (right and left lateral decubitus). STUDY DESIGN: This was a prospective analysis of the lateral ventricular volumes of preterm neonates recruited from Victoria Hospital, London, Ontario (June 2018-November 2019). A total of 24 premature neonates were recruited. The first cohort of 18 unstable premature neonates were imaged with 3D US in their current sides providing 15 right-sided and 16 left-sided 3D US images. The neonates in the second cohort of six relatively stable infants were imaged after positioning in each lateral decubitus position for 30 minutes, resulting in 40 3D US images obtained from 20 posture change sessions. The images were segmented and the ventricle volumes in each lateral posture were compared with determine whether the posture of the head influenced the volume of the upper and lower ventricle. RESULTS: For the first cohort who did not have their posture changed, the mean of the right and left ventricle volumes were 23.81 ± 15.51 and 21.61 ± 16.19 cm3, respectively, for the 15 images obtained in a right lateral posture and 13.96 ± 8.69 and 14.92 ± 8.77 cm3, respectively, for the 16 images obtained in the left lateral posture. Similarly, for the second cohort who had their posture changed, the mean of right and left ventricle volumes were 20.92 ± 17.3 and 32.74 ± 32.33 cm3, respectively, after 30 minutes in the right lateral posture, and 21.25 ± 18.4 and 32.65 ± 31.58 cm3, respectively, after 30 minutes in the left lateral posture. Our results failed to show a statistically significant difference in ventricular volumes dependence on posture. CONCLUSION: Head positioned to any lateral side for 30 minutes does not have any effect on the lateral ventricular volumes of neonates. KEY POINTS: · Three-dimensional cranial ultrasound can measure neonatal ventricle volume.. · Ventricle volume in each lateral ventricle may be affected by posture of the neonate.. · The 30 minutes in any lateral posture is not sufficient to create volume difference in lateral ventricles..

Bidirectional genome-wide CRISPR screens reveal host factors regulating SARS-CoV-2, MERS-CoV and seasonal HCoVs.

Several genome-wide CRISPR knockout screens have been conducted to identify host factors regulating SARS-CoV-2 replication, but the models used have often relied on overexpression of ACE2 receptor. Additionally, such screens have yet to identify the protease TMPRSS2, known to be important for viral entry at the plasma membrane. Here, we conducted a meta-analysis of these screens and showed a high level of cell-type specificity of the identified hits, arguing for the necessity of additional models to uncover the full landscape of SARS-CoV-2 host factors. We performed genome-wide knockout and activation CRISPR screens in Calu-3 lung epithelial cells, as well as knockout screens in Caco-2 intestinal cells. In addition to identifying ACE2 and TMPRSS2 as top hits, our study reveals a series of so far unidentified and critical host-dependency factors, including the Adaptins AP1G1 and AP1B1 and the flippase ATP8B1. Moreover, new anti-SARS-CoV-2 proteins with potent activity, including several membrane-associated Mucins, IL6R, and CD44 were identified. We further observed that these genes mostly acted at the critical step of viral entry, with the notable exception of ATP8B1, the knockout of which prevented late stages of viral replication. Exploring the pro- and anti-viral breadth of these genes using highly pathogenic MERS-CoV, seasonal HCoV-NL63 and -229E and influenza A orthomyxovirus, we reveal that some genes such as AP1G1 and ATP8B1 are general coronavirus cofactors. In contrast, Mucins recapitulated their known role as a general antiviral defense mechanism. These results demonstrate the value of considering multiple cell models and perturbational modalities for understanding SARS-CoV-2 replication and provide a list of potential new targets for therapeutic interventions.