Prevalence of alpha-1 antitrypsin deficiency in patients with acute exacerbation of chronic obstructive pulmonary disease: Insights from a prospective study.

Background: Alpha-1 antitrypsin (AAT) deficiency is a genetic risk factor for chronic obstructive pulmonary disease (COPD) but prevalence data in acutely exacerbated Indian patients is limited. This study determined AAT deficiency rates and correlations with inflammation and lung function among hospitalized patients with COPD. Methods: A total of 106 patients hospitalized for acute COPD exacerbations were prospectively enrolled from June 2016 to February 2018 in Kerala, India, excluding any with known AAT deficiency. Serum AAT levels were quantified and correlated with C-reactive protein (CRP) levels as well as postbronchodilator spirometry. Results: Mean serum AAT level was 1.48 ± 0.27 g/L. No AAT deficiency cases were identified, although AAT and CRP both significantly increased during flares. AAT levels positively correlated with FEV1, FVC, and FEV1/FVC ratios. Patients with lower AAT had worse pulmonary status. Conclusion: Despite finding no AAT deficiency in this regional Indian cohort, further studies across expanded, more diverse populations are warranted to definitively establish prevalence nationwide. Temporal monitoring of AAT kinetics could help gauge exacerbation trajectories.

Safety and Efficacy of Calcitonin Gene-related Peptide Receptor Antagonists and Selective Serotonin Receptor Agonist in the Management of Migraine: A Systematic Review and Meta-analysis.

BACKGROUND: Recently, US Food and Drug Administration (FDA) has approved calcitonin gene-related peptide receptor antagonists (rimegepant, and ubrogepant), and selective serotonin receptor agonists (lasmiditan) in the management of migraine. However, the exact safety and efficacy profile of these drugs is unclear so far. METHODS: The study's primary objective was to determine the exact safety and efficacy profile. The overall estimate was calculated in terms of risk ratios using a suitable model. The subgroup analysis was also performed to check the effect of individual drugs on the outcome, whereas sensitivity analysis was performed to check the effects of outliers on the outcome. All the analyses were performed using Rev Man 5. The drugs have shown significant improvement in efficacy parameters (pain freedom, most bothersome symptoms, phonophobia, nausea, and photophobia). RESULTS: The subgroup analysis results have shown significant improvement in all efficacy parameters in the rimegepant and ubrogepant groups. The effect of ubrogepant on safety parameters was found to be non-significant, indicating a better safety profile of ubrogepant than lasmiditan. CONCLUSION: The sensitivity analysis results have shown no effect of outliers on the efficacy parameters. Based on the available evidence, recently approved drugs are effective in the treatment of migraine, however, associated with few adverse drug reactions.

Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences.

Depression is one of the predominant common mental illnesses that affects millions of people of all ages worldwide. Random mood changes, loss of interest in routine activities, and prevalent unpleasant senses often characterize this common depreciated mental illness. Subjects with depressive disorders have a likelihood of developing cardiovascular complications, diabesity, and stroke. The exact genesis and pathogenesis of this disease are still questionable. A significant proportion of subjects with clinical depression display inadequate response to antidepressant therapies. Hence, clinicians often face challenges in predicting the treatment response. Emerging reports have indicated the association of depression with metabolic alterations. Metabolomics is one of the promising approaches that can offer fresh perspectives into the diagnosis, treatment, and prognosis of depression at the metabolic level. Despite numerous studies exploring metabolite profiles post-pharmacological interventions, a quantitative understanding of consistently altered metabolites is not yet established. The article gives a brief discussion on different biomarkers in depression and the degree to which biomarkers can improve treatment outcomes. In this review article, we have systemically reviewed the role of metabolomics in depression along with current challenges and future perspectives.

Salivary gland tumors: an audit from a tertiary care centre in Northern India.

Salivary gland tumors are relatively rare and can exhibit various clinical behaviors. The study aims to understand the natural history, pathology, diagnostic workup, and treatment strategies for these tumors to improve patient outcomes. The audit included patients with salivary gland tumors detected through radiology or cytology. Patients underwent surgery, with some receiving adjuvant treatment. Demographic information, treatment interventions, and survival outcomes were analyzed using SPSS software. A total 89 as malignant salivart gland tumours were audited Malignant tumors were predominantly found in the parotid gland, with fewer cases in the minor salivary gland and submandibular gland.The median age of presentation was 47 years, and the majority of patients were male. The study examined various pathological and clinical factors, including tumor stage, nodal status, and the presence of facial palsy. Surgical procedures and histological types of tumors were documented. Adverse histological features like positive margins, lymph node positivity, lympho-vascular invasion, extracapsular spread, and perineural invasion were noted. POSTOP RT was administered to high-risk patients. Most malignant salivary gland tumors were found in the parotid gland, while minor salivary gland tumors were underrepresented in the audit. Surgical practices were diverse. Radiotherapy protocols were relatively standardized. The study found that certain histological features, such as lymph node positivity, margin positivity, lympho-vascular invasion, perineural invasion, and extracapsular spread, were associated with adverse effects on DFS and OS. The findings suggest that specific histological features, including LVI and ECE have emerged as independent prognostic factors for DFS and OS.

From Detection to Protection: Antibodies and Their Crucial Role in Diagnosing and Combatting SARS-CoV-2.

Understanding the antibody response to SARS-CoV-2, the virus responsible for COVID-19, is crucial to comprehending disease progression and the significance of vaccine and therapeutic development. The emergence of highly contagious variants poses a significant challenge to humoral immunity, underscoring the necessity of grasping the intricacies of specific antibodies. This review emphasizes the pivotal role of antibodies in shaping immune responses and their implications for diagnosing, preventing, and treating SARS-CoV-2 infection. It delves into the kinetics and characteristics of the antibody response to SARS-CoV-2 and explores current antibody-based diagnostics, discussing their strengths, clinical utility, and limitations. Furthermore, we underscore the therapeutic potential of SARS-CoV-2-specific antibodies, discussing various antibody-based therapies such as monoclonal antibodies, polyclonal antibodies, anti-cytokines, convalescent plasma, and hyperimmunoglobulin-based therapies. Moreover, we offer insights into antibody responses to SARS-CoV-2 vaccines, emphasizing the significance of neutralizing antibodies in order to confer immunity to SARS-CoV-2, along with emerging variants of concern (VOCs) and circulating Omicron subvariants. We also highlight challenges in the field, such as the risks of antibody-dependent enhancement (ADE) for SARS-CoV-2 antibodies, and shed light on the challenges associated with the original antigenic sin (OAS) effect and long COVID. Overall, this review intends to provide valuable insights, which are crucial to advancing sensitive diagnostic tools, identifying efficient antibody-based therapeutics, and developing effective vaccines to combat the evolving threat of SARS-CoV-2 variants on a global scale.

Repurposing of drugs against bacterial infections: A pharmacovigilance-based data mining approach.

The World Health Organization (WHO) has published a list of priority pathogens that urgently require research to develop new antibiotics. The main aim of the current study is to identify potential marketed drugs that can be repurposed against bacterial infections. A pharmacovigilance-based drug repurposing approach was used to identify potential drugs. OpenVigil 2.1 tool was used to query the FDA Adverse Event Reporting System database. The reporting odds ratio (ROR) < 1, ROR95CI upper bound <1, and no. of cases ≥30 were used for filtering and sorting of drugs. Sunburst plot was used to represent drugs in a hierarchical order using the Anatomical Therapeutic Chemical classification. Molecular docking and dynamics were performed using the Maestro and Desmond modules of Schrodinger 2023 software respectively. A total of 40 drugs with different classes were identified based on the pharmacovigilance approach which has antibacterial potential. The molecular docking results have shown energetically favored binding conformation of lisinopril against 3-deoxy-manno-octulosonate cytidylyltransferase, UDP-2,3-diacylglucosamine hydrolase, and penicillin-binding protein 3 (PBP3) of Pseudomonas aeruginosa; olmesartan, atorvastatin against lipoteichoic acids flippase LtaA and rosiglitazone and varenicline against  d-alanine ligase of Staphylococcus aureus; valsartan against peptidoglycan deacetylase (SpPgdA) and atorvastatin against CDP-activated ribitol for teichoic acid precursors of Streptococcus pneumoniae. Further, molecular dynamic results have shown the stability of identified drugs in the active site of bacterial targets except lisinopril with PBP3. Lisinopril, olmesartan, atorvastatin, rosiglitazone, varenicline, and valsartan have been identified as potential drugs for repurposing against bacterial infection.

Current Landscape of Cancer Immunotherapy: Harnessing the Immune Arsenal to Overcome Immune Evasion.

Cancer immune evasion represents a leading hallmark of cancer, posing a significant obstacle to the development of successful anticancer therapies. However, the landscape of cancer treatment has significantly evolved, transitioning into the era of immunotherapy from conventional methods such as surgical resection, radiotherapy, chemotherapy, and targeted drug therapy. Immunotherapy has emerged as a pivotal component in cancer treatment, harnessing the body's immune system to combat cancer and offering improved prognostic outcomes for numerous patients. The remarkable success of immunotherapy has spurred significant efforts to enhance the clinical efficacy of existing agents and strategies. Several immunotherapeutic approaches have received approval for targeted cancer treatments, while others are currently in preclinical and clinical trials. This review explores recent progress in unraveling the mechanisms of cancer immune evasion and evaluates the clinical effectiveness of diverse immunotherapy strategies, including cancer vaccines, adoptive cell therapy, and antibody-based treatments. It encompasses both established treatments and those currently under investigation, providing a comprehensive overview of efforts to combat cancer through immunological approaches. Additionally, the article emphasizes the current developments, limitations, and challenges in cancer immunotherapy. Furthermore, by integrating analyses of cancer immunotherapy resistance mechanisms and exploring combination strategies and personalized approaches, it offers valuable insights crucial for the development of novel anticancer immunotherapeutic strategies.

A novel multi-epitope peptide vaccine candidate targeting hepatitis E virus: An in silico approach.

Hepatitis E virus (HEV) is a foodborne virus transmitted through the faecal-oral route that causes viral hepatitis in humans worldwide. Ever since its discovery as a zoonotic agent, HEV was isolated from several species with an expanding range of hosts. HEV possesses several features of other RNA viruses but also has certain HEV-specific traits that make its viral-host interactions inimitable. HEV leads to severe morbidity and mortality in immunocompromised people and pregnant women across the world. The situation in underdeveloped countries is even more alarming. Even after creating a menace across the world, we still lack an effective vaccine against HEV. Till date, there is only one licensed vaccine for HEV available only in China. The development of an anti-HEV vaccine that can reduce HEV-induced morbidity and mortality is required. Live attenuated and killed vaccines against HEV are not accessible due to the lack of a tolerant cell culture system, slow viral replication kinetics and varying growth conditions. Thus, the main focus for anti-HEV vaccine development is now on the molecular approaches. In the current study, we have designed a multi-epitope vaccine against HEV through a reverse vaccinology approach. For the first time, we have used viral ORF3, capsid protein and polyprotein altogether for epitope prediction. These are crucial for viral replication and persistence and are major vaccine targets against HEV. The proposed in silico vaccine construct comprises of highly immunogenic and antigenic T-cell and B-cell epitopes of HEV proteins. The construct is capable of inducing an effective and long-lasting host immune response as evident from the simulation results. In addition, the construct is stable, non-allergic and antigenic for the host. Altogether, our findings suggest that the in silico vaccine construct may be useful as a vaccine candidate for preventing HEV infections.

Safety of immune checkpoint inhibitors: An updated comprehensive disproportionality analysis and meta-analysis.

BACKGROUND: The exact safety profile of Immune checkpoint inhibitors (ICIs) is unclear so far. AIM: The aim of the current study is to analyse the safety profile of ICIs in cancer patients. METHODOLOGY: The updated comprehensive disproportionality analysis of post-marketing data using the FAERS database and meta-analysis of randomized clinical trials (RCTs) was conducted. Disproportionality measures were calculated in terms of PRR associated with chi-square value and ROR with 95% confidence intervals whereas overall estimate measures with 95% CIs, publication bias and heterogeneity were calculated using RevMan 5.4. The GRADE analysis was also done to check the quality of evidence for each outcome. RESULTS: Various novel signals such as cholangitis, encephalitis, anuria, myelosuppression, and cachexia related to different system organ class were identified with ICIs. The sensitivity analysis results have indicated the influence of concomitant drugs on the identified signals. The meta-analysis results have shown a good safety profile of atezolizumab in non-small cell lung cancer (NSCLC) and melanoma, pembrolizumab in gastro-oesophageal cancer, urothelial cancer and head and neck squamous cell carcinoma (HNSCC), nivolumab in HNSCC as compared to the non-ICI group. CONCLUSION: The safety of ICIs is dependent on their types as well as on the types of cancer.

Multi-epitope vaccine design using in silico analysis of glycoprotein and nucleocapsid of NIPAH virus.

According to the 2018 WHO R&D Blueprint, Nipah virus (NiV) is a priority disease, and the development of a vaccine against NiV is strongly encouraged. According to criteria used to categorize zoonotic diseases, NiV is a stage III disease that can spread to people and cause unpredictable outbreaks. Since 2001, the NiV virus has caused annual outbreaks in Bangladesh, while in India it has caused occasional outbreaks. According to estimates, the mortality rate for infected individuals ranges from 70 to 91%. Using immunoinformatic approaches to anticipate the epitopes of the MHC-I, MHC-II, and B-cells, they were predicted using the NiV glycoprotein and nucleocapsid protein. The selected epitopes were used to develop a multi-epitope vaccine construct connected with linkers and adjuvants in order to improve immune responses to the vaccine construct. The 3D structure of the engineered vaccine was anticipated, optimized, and confirmed using a variety of computer simulation techniques so that its stability could be assessed. According to the immunological simulation tests, it was found that the vaccination elicits a targeted immune response against the NiV. Docking with TLR-3, 7, and 8 revealed that vaccine candidates had high binding affinities and low binding energies. Finally, molecular dynamic analysis confirms the stability of the new vaccine. Codon optimization and in silico cloning showed that the proposed vaccine was expressed to a high degree in Escherichia coli. The study will help in identifying a potential epitope for a vaccine candidate against NiV. The developed multi-epitope vaccine construct has a lot of potential, but they still need to be verified by in vitro & in vivo studies.

A Direct Link Implicating Loss of SLC26A6 to Gut Microbial Dysbiosis, Compromised Barrier Integrity, and Inflammation.

BACKGROUND & AIMS: Putative anion transporter-1 (PAT1, SLC26A6) plays a key role in intestinal oxalate and bicarbonate secretion. PAT1 knockout (PKO) mice exhibit hyperoxaluria and nephrolithiasis. Notably, diseases such as inflammatory bowel disease are also associated with higher risk of hyperoxaluria and nephrolithiasis. However, the potential role of PAT1 deficiency in gut-barrier integrity and susceptibility to colitis is currently elusive. METHODS: Age-matched PKO and wild-type littermates were administered 3.5% dextran sulfate sodium in drinking water for 6 days. Ileum and colon of control and treated mice were harvested. Messenger RNA and protein expression of tight junction proteins were determined by reverse transcription polymerase chain reaction and western blotting. Severity of inflammation was assessed by measuring diarrheal phenotype, cytokine expression, and hematoxylin and eosin staining. Gut microbiome and associated metabolome were analyzed by 16S ribosomal RNA sequencing and mass spectrometry, respectively. RESULTS: PKO mice exhibited significantly higher loss of body weight, gut permeability, colonic inflammation, and diarrhea in response to dextran sulfate sodium treatment. In addition, PKO mice showed microbial dysbiosis and significantly reduced levels of butyrate and butyrate-producing microbes compared with controls. Co-housing wild-type and PKO mice for 4 weeks resulted in PKO-like signatures on the expression of tight junction proteins in the colons of wild-type mice. CONCLUSIONS: Our data demonstrate that loss of PAT1 disrupts gut microbiome and related metabolites, decreases gut-barrier integrity, and increases host susceptibility to intestinal inflammation. These findings, thus, highlight a novel role of the oxalate transporter PAT1 in promoting gut-barrier integrity, and its deficiency appears to contribute to the pathogenesis of inflammatory bowel diseases.

Exploring TLR signaling pathways as promising targets in cervical cancer: The road less traveled.

Cervical cancer is the leading cause of cancer-related deaths for women globally. Despite notable advancements in prevention and treatment, the identification of novel therapeutic targets remains crucial for cervical cancer. Toll-like receptors (TLRs) play an essential role in innate immunity as pattern-recognition receptors. There are several types of pathogen-associated molecular patterns (PAMPs), including those present in cervical cancer cells, which have the ability to activate toll-like receptors (TLRs). Recent studies have revealed dysregulated toll-like receptor (TLR) signaling pathways in cervical cancer, leading to the production of inflammatory cytokines and chemokines that can facilitate tumor growth and metastasis. Consequently, TLRs hold significant promise as potential targets for innovative therapeutic agents against cervical cancer. This book chapter explores the role of TLR signaling pathways in cervical cancer, highlighting their potential for targeted therapy while addressing challenges such as tumor heterogeneity and off-target effects. Despite these obstacles, targeting TLR signaling pathways presents a promising approach for the development of novel and effective treatments for cervical cancer.

A new unit distribution: properties, estimation, and regression analysis.

This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve insight and application by presenting quantitative and qualitative perceptions. To estimate the three unknown parameters of the model, we carefully examine various methods: the maximum likelihood, least squares, weighted least squares, Anderson-Darling, and Cramér-von Mises. Through a Monte Carlo simulation experiment, we quantitatively evaluate the effectiveness of these estimation methods, extending a robust evaluation framework. A unique part of this research lies in developing a novel regressive analysis based on the proposed distribution. The application of this analysis reveals new viewpoints and improves the benefit of the model in practical situations. As the emphasis of the study is primarily on practical applications, the viability of the proposed model is assessed through the analysis of real datasets sourced from diverse fields.

Cisplatin and Starvation Differently Sensitize Autophagy in Renal Carcinoma: A Potential Therapeutic Pathway to Target Variegated Drugs Resistant Cancerous Cells.

Cisplatin, a powerful chemotherapy medication, has long been a cornerstone in the fight against cancer due to chemotherapeutic failure. The mechanism of cisplatin resistance/failure is a multifaceted and complex issue that consists mainly of apoptosis inhibition through autophagy sensitization. Currently, researchers are exploring ways to regulate autophagy in order to tip the balance in favor of effective chemotherapy. Based on this notion, the current study primarily identifies the differentially expressed genes (DEGs) in cisplatin-treated autophagic ACHN cells through the Illumina Hi-seq platform. A protein-protein interaction network was constructed using the STRING database and KEGG. GO classifiers were implicated to identify genes and their participating biological pathways. ClueGO, David, and MCODE detected ontological enrichment and sub-networking. The network topology was further examined using 12 different algorithms to identify top-ranked hub genes through the Cytoscape plugin Cytohubba to identify potential targets, which established profound drug efficacy under an autophagic environment. Considerable upregulation of genes related to autophagy and apoptosis suggests that autophagy boosts cisplatin efficacy in malignant ACHN cells with minimal harm to normal HEK-293 growth. Furthermore, the determination of cellular viability and apoptosis by AnnexinV/FITC-PI assay corroborates with in silico data, indicating the reliability of the bioinformatics method followed by qRT-PCR. Altogether, our data provide a clear molecular insight into drug efficacy under starved conditions to improve chemotherapy and will likely prompt more clinical trials on this aspect.

Characterization of a hemolytic and antibiotic-resistant Pseudomonas aeruginosa strain S3 pathogenic to fish isolated from Mahananda River in India.

Virulent strain Pseudomonas aeruginosa isolated from Mahananda River exhibited the highest hemolytic activity and virulence factors and was pathogenic to fish as clinical signs of hemorrhagic spots, loss of scales, and fin erosions were found. S3 was cytotoxic to the human liver cell line (WRL-68) in the trypan blue dye exclusion assay. Genotype characterization using whole genome analysis showed that S3 was similar to P. aeruginosa PAO1. The draft genome sequence had an estimated length of 62,69,783 bp, a GC content of 66.3%, and contained 5916 coding sequences. Eight genes across the genome were predicted to be related to hemolysin action. Antibiotic resistance genes such as class C and class D beta-lactamases, fosA, APH, and catB were detected, along with the strong presence of multiple efflux system genes. This study shows that river water is contaminated by pathogenic P. aeruginosa harboring an array of virulence and antibiotic resistance genes which warrants periodic monitoring to prevent disease outbreaks.

Role of Network Pharmacology in Prediction of Mechanism of Neuroprotective Compounds.

Network pharmacology is an emerging pioneering approach in the drug discovery process, which is used to predict the therapeutic mechanism of compounds using various bioinformatic tools and databases. Emerging studies have indicated the use of network pharmacological approaches in various research fields, particularly in the identification of possible mechanisms of herbal compounds/ayurvedic formulations in the management of various diseases. These techniques could also play an important role in the prediction of the possible mechanisms of neuroprotective compounds. The first part of the chapter includes an introduction on neuroprotective compounds based on literature. Further, network pharmacological approaches are briefly discussed. The use of network pharmacology in the prediction of the neuroprotective mechanism of compounds is discussed in detail with suitable examples. Finally, the chapter concludes with the current challenges and future prospectives.

Computational and in vitro screening validates the repositioning potential of Coxibs as anti-fibrotic agents.

Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease with a survival rate of <5 years. The TGF-ß plays a significant role in the progression and severity of IPF. The TGF-ß receptor type1 TGFBR1 antagonists inhibit the process of fibrosis and may have a role in the treatment of IPF. The main objective of the study was to identify promising drug candidates against IPF using In-silico and In-vitro evaluation methods. An in-silico screening was carried out of the marketed Coxibs to find their TGFBR1 inhibitory potential considering their structural resemblance with the JZO-a co-crystalized ligand of the crystal structure of the TGFBR1. The virtual screening yielded rofecoxib as a TGFBR1 ligand with a significant docking score. To further validate the outcome of molecular docking studies, MD simulation of 200 ns was carried out followed by the determination of conformational stability, binding free energy calculation using MMPBSA/MMGBSA, and Free Energy Landscape (FEL). The therapeutic efficacy of rofecoxib was compared with that of nintedanib (a therapeutic agent used in the treatment of IPF) at equimolar concentrations (5 µM). The model of TGF-ß1 (1 ng/ml)-induced EMT of A549 was used to determine the effect of rofecoxib on the EMT markers like cellular morphology, cytokine expressions, fibrosis associated protein, E-cadherin, and α-smooth muscle actin. In vitro results indicated that rofecoxib significantly suppresses the TGF-ß1-induced EMT of A549 cells and validates the possible preventive/protective role of rofecoxib in pulmonary fibrosis. In conclusion, rofecoxib may be considered for repositioning as an anti-fibrotic agent.Communicated by Ramaswamy H. Sarma.

Efficient imputation methods in case of measurement errors.

This manuscript develops few efficient difference and ratio kinds of imputations to handle the situation of missing observations given that these observations are polluted by the measurement errors (ME). The mean square errors of the developed imputations are studied to the primary degree approximation by adopting Taylor series expansion. The proposed imputations are equated with the latest existing imputations presented in the literature. The execution of the proposed imputations is assessed by utilizing a broad empirical study utilizing some real and hypothetically created populations. Appropriate remarks are made for sampling respondents regarding practical applications.

Use of improved memory type control charts for monitoring cancer patients recovery time censored data.

Control charts are a statistical approach for monitoring cancer data that can assist discover patterns, trends, and unusual deviations in cancer-related data across time. To detect deviations from predicted patterns, control charts are extensively used in quality control and process management. Control charts may be used to track numerous parameters in cancer data, such as incidence rates, death rates, survival time, recovery time, and other related indicators. In this study, CDEC chart is proposed to monitor the cancer patients recovery time censored data. This paper presents a composite dual exponentially weighted moving average Cumulative sum (CDEC) control chart for monitoring cancer patients recovery time censored data. This approach seeks to detect changes in the mean recovery time of cancer patients which usually follows Weibull lifetimes. The results are calculated using type I censored data under known and estimated parameter conditions. We combine the conditional expected value (CEV) and conditional median (CM) approaches, which are extensively used in statistical analysis to determine the central tendency of a dataset, to create an efficient control chart. The suggested chart's performance is assessed using the average run length (ARL), which evaluates how efficiently the chart can detect a change in the process mean. The CDEC chart is compared to existing control charts. A simulation study and a real-world data set related to cancer patients recovery time censored data is used for results illustration. The proposed CDEC control chart is developed for the data monitoring when complete information about the patients are not available. So, instead of doping the patients information we can used the proposed chart to monitor the patients information even if it is censored. The authors conclude that the suggested CDEC chart is more efficient than competitor control charts for monitoring cancer patients recovery time censored data. Overall, this study introduces an efficient new approach for cancer patients recovery time censored data, which might have significant effect on quality control and process improvement across a wide range of healthcare and medical studies.