The coronavirus disease 2019 (COVID-19) pandemic is a worldwide threatening health issue. The progression of this viral infection occurs in the airways of the lungs with an exaggerated inflammatory response referred to as the “cytokine storm” that can lead to lethal lung injuries. In the absence of an effective anti-viral molecule and until the formulation of a successful vaccine, anti-inflammatory drugs might offer a complementary tool for controlling the associated complications of COVID-19 and thus decreasing the subsequent fatalities. Drug repurposing for several molecules has emerged as a rapid temporary solution for COVID-19. Among these drugs is Thalidomide; a historically emblematic controversial molecule that harbors an FDA approval for treating erythema nodosum leprosum (ENL) and multiple myeloma (MM). Based on just one-case report that presented positive outcomes in a patient treated amongst others with Thalidomide, two clinical trials on the efficacy and safety of Thalidomide in treating severe respiratory complications in COVID-19 patients were registered. Yet, the absence of substantial evidence on Thalidomide usage in that context along with the discontinued studies on the efficiency of this drug in similar pulmonary diseases, might cause a significant obstacle for carrying out further clinical evaluations. Herein, we will discuss the theoretical effectiveness of Thalidomide in attenuating inflammatory complications that are encountered in COVID-19 patients while pinpointing the lack of the needed evidences to move forward with this drug.

Genetically inherited defects in lipoprotein metabolism affect more than 10 million individuals around the globe with preponderance in some parts where consanguinity played a major role in establishing founder mutations. Mutations in four genes have been so far linked to the dominant and recessive form of the disease. Those players encode major proteins implicated in cholesterol regulation, namely, the low-density lipoprotein receptor (LDLR) and its associate protein 1 (LDLRAP1), the proprotein convertase substilin/kexin type 9 (PCSK9), and the apolipoprotein B (APOB). Single mutations or compound mutations in one of these genes are enough to account for a spectrum of mild to severe phenotypes. However, recently several reports have identified digenic mutations in familial cases that do not necessarily reflect a much severe phenotype. Yet, data in the literature supporting this notion are still lacking. Herein, we review all the reported cases of digenic mutations focusing on the biological impact of gene dosage and the potential protective effects of single-nucleotide polymorphisms linked to hypolipidemia. We also highlight the difficulty of establishing phenotype–genotype correlations in digenic familial hypercholesterolemia cases due to the complexity and heterogeneity of the phenotypes and the still faulty in silico pathogenicity scoring system. We finally emphasize the importance of having a whole exome/genome sequencing approach for all familial cases of familial hyperlipidemia to better understand the genetic and clinical course of the disease.

T-box (TBX) transcription factors are evolutionary conserved genes and master transcriptional regulators. In mammals, TBX2 subfamily (TBX2, TBX3, TBX4, and TBX5) genes are expressed in the developing lung bud and tracheae. Our group previously showed that the expression of TBX2 subfamily was significantly high in human normal lungs, but markedly suppressed in lung adenocarcinoma (LUAD). To further elucidate their role in LUAD pathogenesis, we first confirmed abundant expression of protein products of the four members by immunostaining in adult human normal lung tissues. We also found overall suppressed expression of these genes and their corresponding proteins in a panel of human LUAD cell lines. Transient over-expression of each of the genes in human (NCI-H1299), and mouse (MDA-F471) derived lung cancer cells was found to significantly inhibit growth and proliferation as well as induce apoptosis. Genome-wide transcriptomic analyses on NCI-H1299 cells, overexpressing TBX2 gene subfamily, unraveled novel regulatory pathways. These included, among others, inhibition of cell cycle progression but more importantly activation of the histone demethylase pathway. When using a pattern-matching algorithm, we showed that TBX's overexpression mimic molecular signatures from azacitidine treated NCI-H1299 cells which in turn are inversely correlated to expression profiles of both human and murine lung tumors relative to matched normal lung. In conclusion, we showed that the TBX2 subfamily genes play a critical tumor suppressor role in lung cancer pathogenesis through regulating its methylating pattern, making them putative candidates for epigenetic therapy in LUAD.

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.

In the absence of a universally accepted association between smoking and COVID-19 health outcomes, we investigated this relationship in a representative cohort from one of the world’s highest tobacco consuming regions. This is the first report from the Middle East and North Africa that tackles specifically the association of smoking and COVID-19 mortality while demonstrating a novel sex-discrepancy in the survival rates among patients. Clinical data for 743 hospitalized COVID-19 patients was retrospectively collected from the leading centre for COVID-19 testing and treatment in Lebanon. Logistic regression, Kaplan-Meier survival curves and Cox proportional hazards model adjusted for age and stratified by sex were used to assess the association between the current cigarette smoking status of patients and COVID-19 outcomes. In addition to the high smoking prevalence among our hospitalized COVID-19 patients (42.3%), enrolled smokers tended to have higher reported ICU admissions (28.3% vs 16.6%, p<0.001), longer length of stay in the hospital (12.0 ± 7.8 vs 10.8 days, p<0.001) and higher death incidences as compared to non-smokers (60.5% vs 39.5%, p<0.001). Smokers had an elevated odds ratio for death (OR = 2.3, p<0.001) and for ICU admission (OR = 2.0, p<0.001) which remained significant in a multivariate regression model. Once adjusted for age and stratified by sex, our data revealed that current smoking status reduces survival rate in male patients ([HR] = 1.9 [95% (CI), 1.029–3.616]; p = 0.041) but it does not affect survival outcomes among hospitalized female patients([HR] = 0.79 [95% CI = 0.374–1.689]; p = 0.551). A high smoking prevalence was detected in our hospitalized COVID-19 cohort combined with worse prognosis and higher mortality rate in smoking patients. Our study was the first to highlight potential sex-specific consequences for smoking on COVID-19 outcomes that might further explain the higher vulnerability to death from this disease among men.

CRISPR-Cas9 has earned widespread acclaim for its ability to efficiently and accurately modify DNA sequences. Since its discovery, this genome-editing tool has transformed research and clinical applications in oncology by enabling scientists to directly target and disrupt cancer-causing genes. Preclinical studies have demonstrated its utility in targeting oncogenes, tumor suppressor genes, and immune checkpoint molecules-cornerstones of cancer biology (Sharma & Giri, 2024). By modifying or knocking out specific genes, CRISPR has already shown promise in boosting the efficacy of adoptive cell therapies like CAR-T cells, where editing inhibitory genes can enhance immune responses (Ravichandran & Maddalo, 2023).However, the potential applications of CRISPR-Cas9 extend far beyond gene editing in immune cells. The technology is being used to modify the tumor microenvironment, regulate gene expression in real-time, and identify drug resistance mechanisms (Li et al., 2022;Sharma & Giri, 2024). In cancer immunotherapy, where immune checkpoint inhibitors (ICIs) have achieved remarkable success, CRISPR-based screening approaches are uncovering new targets that could amplify the efficacy of these therapies. For instance, in vivo CRISPR screens have revealed novel gene networks that regulate immune responses to tumors, opening the door for new combination therapies. Additionally, CRISPR activation and interference libraries (CRISPRa and CRISPRi) have helped map the genetic underpinnings of cancer resistance to natural killer (NK) and T cell-mediated cytotoxicity, highlighting new avenues for therapeutic intervention. As these CRISPR techniques continue to evolve, they promise to expand the scope of immunotherapy in ways previously unimaginable (Wang et al., 2023). While CRISPR's ability to target oncogenic mutations with precision is unparalleled, it is not without challenges. Off-target effects, where unintended sections of the genome are edited, remain a significant concern, particularly in long-term therapies. Chromosomal rearrangements and immune reactions to CRISPR components pose additional hurdles. Despite these concerns, emerging techniques such as base editing and prime editing offer more refined methods for precise DNA modifications (Ravichandran & Maddalo, 2023). These advancements are already showing potential for reducing off-target effects and enhancing the specificity of gene edits, thus increasing the safety and efficacy of CRISPR-based therapies in clinical settings.Beyond therapeutic applications, CRISPR is making inroads into cancer diagnostics. The development of CRISPR-based platforms for detecting cancer-specific mutations in blood samples is revolutionizing liquid biopsy approaches. Technologies like SHERLOCK and DETECTR, which use CRISPRassociated proteins to detect DNA or RNA mutations, are providing noninvasive, highly sensitive methods for early cancer detection. These tools are poised to become key assets in the fight against cancer, allowing for earlier diagnosis and more personalized treatment strategies (Ravichandran & Maddalo, 2023).Parallel to the rise of CRISPR is the advent of PROTACs, another technology with transformative potential in precision oncology. Whereas CRISPR modifies DNA, PROTACs target proteins for degradation via the ubiquitin-proteasome system, enabling the selective elimination of oncogenic proteins. By degrading proteins that are traditionally considered "undruggable," this technology have opened up new possibilities for targeting cancer drivers that were previously out of reach. The ability to administer PROTACs in low oral doses and their high specificity for target proteins make them an attractive complement to CRISPR-based therapies. Rather than competing technologies, CRISPR and PROTACs offer synergistic advantages: CRISPR addresses genetic aberrations, while the latter remove the resulting protein products, creating a comprehensive strategy for tackling cancer on multiple fronts (Kanbar et al., 2024).The integration of artificial intelligence (AI) into these approaches further amplifies their potential. AI-driven algorithms are already being used to optimize CRISPR and PROTAC designs, predicting on-target and off-target effects with increasing accuracy. Machine learning models are assisting in refining CRISPR guide RNAs, reducing the risk of unintended gene edits, while also improving the design of PROTAC molecules for better drug permeability and efficacy. AI's role in precision oncology extends beyond computational design-it is accelerating drug discovery, helping identify new therapeutic targets, and enabling more efficient high-throughput screening. By combining AI with DNA and protein editing technologies, the pace of innovation in cancer treatment is set to quicken, providing patients with more personalized and effective treatment options (Kanbar et al., 2024). Despite these advances, significant barriers remain to the full clinical translation of CRISPR and PROTAC technologies. Ethical concerns surrounding gene editing, particularly in relation to germline modifications, continue to provoke debate. Additionally, issues such as delivery mechanisms for CRISPR components, potential immune reactions, and scalability of PROTAC synthesis must be addressed before these technologies can become mainstream clinical tools. Encouragingly, progress is being made on several fronts. Nanoparticle-based delivery systems are showing promise for enhancing the precision of CRISPR-based therapies, while innovations in onchip screening platforms are facilitating faster, more efficient drug development (Kanbar et al., 2024).In conclusion, CRISPR-Cas9 has fundamentally transformed the landscape of precision oncology, offering unprecedented possibilities for targeting cancer at the genetic level. When combined with complementary technologies like PROTACs and AI-driven innovations, CRISPR holds the potential to unlock new frontiers in personalized cancer...