Drug Repurposing for, ENT and Head and Neck, Infectious and Oncologic Diseases: Current Practices and Future Possibilities

The specialty of otolaryngology and head and neck surgery involves various subspecialties, encompassing clinical conditions ranging from medical to surgical issues in infections, noninfectious benign conditions and various benign and malignant tumors. Drug repurposing has proven to be significant in multiple fields and is still investigational in many promising possible solutions to different clinical challenges in this specialty. We discuss some classes of drugs that have been successfully repurposed for ENT pathologies. We also discuss the novel research goals that are being pursued in our department in the context of drug repurposing for airway infectious diseases including COVID-10 and mucormycosis. There has been a silent and underappreciated rise in drug-resistant invasive fungal infections (IFIs). Emerging Mucorales are difficult to diagnose and tolerant to many of the frontline antifungal therapies. There is an urgent need to combat these emerging pathogens and investigate the molecular mechanisms underlying their potentiated virulence traits to identify potential therapeutic targets susceptible to anti-fungal compounds. The drug development process for IFIs remains largely expensive, and is inherently risky. These challenges declare an urgent need for discovery of new antifungal drugs and encourage drug repurposing as alternative approach to fungal control. The understanding of molecular underpinnings behind fungi and human host continue to grow, however, further research endeavors are underway to fully explore the fungal pathogenesis, (including the role of iron) to gather new insights to achieve improved therapeutics. Above all, creative screening tools and out-of-the-box ideas aimed at increasing the possibility of identifying potential first-in-class antifungals are highly encouraged. The recently emerging fungal co-infections in the COVID-19 disease patients has revived the interest in the pathophysiology and clinical management of the IFIs, and identification of potential druggable nodes in olfactory niche to inhibit the spread of COVID-19 and associated co-infections by leveraging in vitro-disease models of host-pathogen interaction. We employed our recently established COVID-19 disease model to decipher potential anti-metabolic molecules that can be repurposed as novel bilateral drugs having anti-fungal and host-directed features with extended applicability in diabetes, COVID-19, and mucormycosis with and without COVID-19. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Anomalies of O3, CO, C2H2, H2CO, and C2H6 detected with multiple ground-based Fourier-transform infrared spectrometers and assessed with model simulation in 2020: COVID-19 lockdowns versus natural variability

Anomalies of tropospheric columns of ozone (O3), carbon monoxide (CO), acetylene (C2H2), formaldehyde (H2CO), and ethane (C2H6) are quantified during the 2020 stringent COVID-19 world-wide lockdown using multiple ground-based Fourier-transform infrared spectrometers covering urban and remote conditions. We applied an exponential smoothing forecasting approach to the data sets to estimate business-as-usual values for 2020, which are then contrasted with actual observations. The Community Atmosphere Model with chemistry (CAM-chem) is used to simulate the same gases using lockdown-adjusted and business-as-usual emissions. The role of meteorology, or natural variability, is assessed with additional CAM-chem simulations. The tropospheric column of O3 declined between March and May 2020 for most sites with a mean decrease of 9.2% ± 4.7%. Simulations reproduce these anomalies, especially under background conditions where natural variability explains up to 80% of the decline for sites in the Northern Hemisphere. While urban sites show a reduction between 1% and 12% in tropospheric CO, the remote sites do not show a significant change. Overall, CAM-chem simulations capture the magnitude of the anomalies and in many cases natural variability and lockdowns have opposite effects. We further used the long-term record of the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument to capture global anomalies of CO. Reductions of CO vary highly across regions but North America and Europe registered lower values in March 2020.The absence of CO reduction in April and May, concomitant with reductions of anthropogenic emissions, is explained by a negative anomaly in the hydroxyl radical (OH) found with CAM-chem.The implications of these findings are discussed for methane (CH4), which shows a positive lifetime anomaly during the COVID-19 lockdown period. The fossil fuel combustion by-product tracer C2H2 shows a mean drop of 13.6% ± 8.3% in urban Northern Hemisphere sites due to the reduction in emissions and in some sites exacerbated by natural variability. For some sites with anthropogenic influence there is a decrease in C2H6.The simulations capture the anomalies but the main cause may be related to natural variability. H2CO declined during the stringent 2020 lockdown in all urban sites explained by reductions in emissions of precursors. Copyright: © 2023 The Author(s).

COVID bell - A smart doorbell solution for prevention of COVID-19

The article introduces a novel strategy for efficiently mitigating COVID-19 distribution at the local level due to contact with any surfaces. Our project aims to be a critical safety shield for the general people in the fight against the epidemic. An ultrasonic sensor is integrated with the automated doorbell system to ring the doorbell with a hand motion. A temperature sensor Mlx90614 is also included in the system, which records the temperature of the person standing in front of the door. The device also includes a camera module that captures the image of the person standing at the front entrance. The captured image is processed through an ML model which runs at over 30 fps to detect whether or not the person is wearing a mask. The image and the temperature of the person standing outside are sent to the owner through the configured iOS application. If the person outside is wearing a mask, one can open the door through the app itself and permit the entry of the person standing outside thereby integrating the edge device with an app for a better user experience. The system helps in reducing physical contact, and the results obtained are at par with the already existing solutions and provide a few advantages over them. © 2023 the author(s), published by De Gruyter.

COVID-19 Associated Mucormycosis.

Mucormycosis is a life-threatening opportunistic fungal infection seen in immunocompromised states. Rising incidence of mucormycosis among Coronavirus Disease-2019 infected individuals is an increasing concern in India. The disease which was endemic has blown out to become an epidemic. The purpose of this research is to study the epidemiology, management and outcome of Coronavirus Disease-2019 Associated Mucormycosis (CAM) cases. Additionally, the role of diabetes and steroids in the causation of CAM was determined. A hospital-based observational study was conducted at a tertiary care centre involving cases with rhino-orbital mucormycosis with recent history of COVID-19 infection. Out of 205,166(81%) cases had Diabetes Mellitus as a comorbid condition. Among them, 75(36.6%) cases were diagnosed with diabetes during COVID-19 treatment. 161/205(78.5%) cases received corticosteroids during COVID-19 treatment. Corticosteroids were notindicated in 43(26.7%) cases. 177/205(85.4%) cases were alive at the end of 12 weeks. 8 out of 10 deaths were seen in cases having diabetes. As the incidence of mucormycosis is increasing, better awareness among general population about the disease, early diagnosis and multidisciplinary approach is required to improve prognosis.

Hypoglycemia Induced by Zinc Supplementation for COVID-19 Prophylaxis: A Case Series.

There has been significant research and therapeutic activity within the healthcare sector in response to the coronavirus disease 2019 (COVID-19). In the United States, a complementary and alternative medicine (CAM) treatment regimen for improving patients' immune systems against COVID-19 prophylaxis includes excess zinc, vitamin C, and vitamin D supplementation administered over a seven-day period. Despite the fact that zinc and other mineral supplements are becoming increasingly popular in Western culture, clinical research on CAM remains limited. This case series examines three patients treated with a surplus of zinc tablets for COVID-19 prophylaxis who presented with moderate-to-severe hypoglycemia. Varying amounts of glucose were administered to these patients to offset their low blood sugar levels. Medical staff noted a positive Whipple's triad in two of the patients but observed no other abnormalities in the laboratory values. All three patients were instructed to cease zinc tablet intake upon discharge. Our findings raise awareness of the potential dangers associated with mineral supplements and serve as a warning for those seeking CAM treatment options.

COVID-19 and Mucormycosis

In early December 2019, several cases of pneumonia of unknown etiology were reported from Wuhan, Hubei province, China. The disease resembles severe acute respiratory syndrome coronavirus (SARS CoV) of 2012 and was subsequently named SARS CoV-2 causing the 2019-novel coronavirus disease (COVID-19) by the World Health Organization (WHO). The first case of COVID-19 in India was reported on January 30, 2020. In the first wave, daily cases peaked in mid-September 2020 and began to drop by January 2021. However, a second wave beginning in March 2021 was experienced which was much larger than the first, with extreme shortages of hospital beds, oxygen cylinders, and other medicines including vaccines in parts of the country. In the second wave, an association of COVID-19 patients with mucormycosis further complicated the situation. COVID-19 associated mucormycosis (CAM) has been increasingly reported particularly among patients with uncontrolled diabetes. An increase in CAM cases could be probably due to immunosuppression caused by the use of steroids, other immunomodulators like tocilizumab. Rhino-orbitalcerebral mucormycosis is the most common presentation. The most common causative agent isolated is Rhizopus arrhizus. Simple KOH examination with broad, ribbon-like, aseptate hyphae that branch at right angles is diagnostic of mucormycosis. This can be further confirmed by culture examination. However, newer tests like MALDI-TOF for species identification are also being explored. The main treatment modality is surgical debridement, removing all the infected, dead, and necrotic tissue followed by simultaneous administration of antifungal antibiotics in the form of Amphotericin B. Liposomal amphotericin B is the drug of choice, however, if not available, amphotericin B deoxycholate, posaconazole, and isavuconazole can be given for the treatment of CAM. Prognosis and clinical improvement depend upon the stage of disease, the surgical management as well as the availability and administration of antifungal drugs. In media, mucormycosis is being projected as black fungus throughout this pandemic, though it is a misnomer and should not be used in the medical literature. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021.

COVID-associated rhinocerebral mucormycosis: a retrospective analysis of presentation and outcomes.

OBJECTIVES: To comprehensively analyse the disease presentation and mortality of COVID-associated rhino-orbito-cerebral mucormycosis. METHODS: A retrospective analysis of the demographics, clinical and radiographic findings was performed. A binary logistic regression analysis was performed to examine the survival of patients with mucormycosis from hypothesised predictors. RESULTS: A total of 202 patients were included in this study. Statistical significance was demonstrated in the predilection to the male gender, recent history of SARS-COV-2, history of use of corticosteroid and hyperglycemia in this cohort of CAM. The mortality rate was 18.31%. Advanced age, raised HbA1c and intra-orbital extension were found to be predictors adversely affecting survival. CONCLUSION: Early diagnosis, aggressive surgical therapy, early and appropriate medical therapy can help improve outcomes. LEVEL OF EVIDENCE: Level 4.

Real Time AI Based Face Mask Detector

The present Covid-19 pandemic, face mask detection identifying significant forward movement in the fields of image and computer observation. Several face detection models were developed utilizing various methods and techniques. The dataset arrangement supplied in this work, which was gathered from multiple sources, could be utilized by other to develop more complex representation such as those for facial identification software, facial positions, and facial component identification. The goal of project 'Real Time AI Based Face Mask Detector', It is develop a tool that really can identify a person image and to affect whether he or she is wearing a mask. COVID makes it necessary to wear a face mask to keep it safe. As the country begins to reopen in stages, face masks have become a crucial part of our everyday life to keep safe. Face masks will be essential for socializing and conducting business. As a result, this software uses a camera to notice whether a person is wearing a mask or not. © 2022 IEEE.

Incidence of COVID-19-Associated Mucormycosis in COVID-19 Patients after Discharge from the COVID-19 Hospital.

Background: There are studies available on the prevalence of coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM) in hospitalized patients but not on the incidence of CAM in post-discharge patients. The aim of our study was to find the incidence of CAM in the patients discharged from a COVID hospital. Material and Methods: Adult patients with COVID discharged between March 1, 2021 and June 30, 2021 were contacted and enquired about sign and symptoms of CAM. Data of all included patients were collected from electronic records. Results: A total of 850 patients responded, among which 59.4% were males, 66.4% patients had co-morbidities, and 24.2% had diabetes mellitus. Around 73% of patients had moderate to severe disease and were given steroids; however, only two patients developed CAM post discharge. Conclusion: The incidence of CAM post discharge was low in our study, which could be attributed to protocolized therapy and intensive monitoring.

The battle against fungi: lessons in antifungal stewardship from COVID 19 times

The COVID-19 pandemic has highlighted the detrimental effect of secondary pathogens in patients with a primary viral insult. In addition to superinfections with bacterial pathogens, invasive fungal infections were increasingly reported. The diagnosis of pulmonary fungal infections has always been challenging; however, it became even more problematic in the setting of COVID-19, particularly regarding the interpretation of radiological findings and mycology test results in patients with these infections. Moreover, prolonged hospitalization in ICU, coupled with underlying host factors. such as preexisting immunosuppression, use of immunomodulatory agents, and pulmonary compromise, caused additional vulnerability to fungal infections in this patient population. In addition, the heavy workload, redeployment of untrained staff, and inconsistent supply of gloves, gowns, and masks during the COVID-19 outbreak made it harder for healthcare workers to strictly adhere to preventive measures for infection control. Taken together, these factors favored patient-to-patient spread of fungal infections, such as those caused by Candida auris, or environment-to-patient transmission, including nosocomial aspergillosis. As fungal infections were associated with increased morbidity and mortality, empirical treatment was overly used and abused in COVID-19-infected patients, potentially contributing to increased resistance in fungal pathogens. The aim of this paper was to focus on essential elements of antifungal stewardship in COVID-19 for three fungal infections, COVID-19-associated candidemia (CAC), -pulmonary aspergillosis (CAPA), and -mucormycosis (CAM).

Intraoperative Scoring Assessment as a Prognostic Tool in Covid-19 Associated Mucormycosis: A Short-Term Observational Study.

To introduce an intraoperative scoring system for covid-19 associatedmucormycosis An observational study conducted among 43 cases of confirmed CAM whichincluded 35 males and 8 females, with an average age of 56 years. The surgicalapproach adopted in our cases included endoscopic surgical debridement withDenker's approach including mandatory pterygopalatine and infratemporal fossaexploration. All cases were intraoperatively scored using our designed intraoperativescoring assessment tool for mucormycosis. Postoperatively patient recovery wasassessed using C reactive protein levels and weekly imaging. Although an early observation in the post op period we observed highermortality among cases reporting with high scores as per our intraoperative reportingsystem. At the end of 2 months of completed treatment we report 6 cases of mortalityamong whom 5 cases were found to have scores (> 25) and one reported with a scoreof 18. This assessment helped us in grading the disease severity and also gaveus an insight about the postoperative prognosis too. Global scientific collaboration andreporting of a validated tool for CAM is of paramount importance to increase theknowledge with regard to this emerging disease.

Neem and Turmeric in the management of Covid Associated Mucormycosis (CAM) derived through network pharmacology.

Mucormycosis or 'Black Fungus' has been known to target immunocompromised individuals even before the emergence of COVID-19. Nevertheless, the present circumstances provide the best opening for Covid Associated Mucormycosis (CAM), as the global pandemic is engulfing a large part of human population making them immunocompromised. This drastic increase in Mucormycosis infections has to be addressed as early as possible. There is a growing tendency of relying upon herbal drugs that have minimal side effects and does not compromise our immune system. Recently, the concept of network pharmacology has grabbed the attention of modern science, especially advanced medical sciences. This is a new discipline that can use computational power to systematically catalogue the molecular interactions between botanical formulations and the human body. In this study, Neem and Turmeric was considered as the target plants and an attempt was made to reveal various aspects through which phytocompounds derived from them may effectively manage CAM menace. We have taken a step-by-step approach for identifying the target proteins and ligands associated with Mucormycosis treatment. Functional network analysis and Molecular docking approaches were applied to validate our findings. Quercetin derived from both Neem and Turmeric was found to be one of the main phytocompounds working against Mucormycosis. Along with that, Caffeic acid, Curcumin, Kaempferol, Tetrahydrocurcumin and Myricetin also play a pivotal role in fighting against Black-Fungus. A thorough analysis of our result suggested a triple-front attack on the fungal pathogens and the approaches are necrosis inhibition, iron chelation and immuno-boosting.Communicated by Ramaswamy H. Sarma.

Automatic approach for mask detection: effective for COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in-person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a deep learning-based system that can detect instances of improper use of face masks. A dual-stage convolutional neural network architecture is used in our system to recognize masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. In this paper, we propose a variant of a multi-face detection model which has the potential to target and identify a group of people whether they are wearing masks or not.

Intensification or diversification: responses by anti health-pass entrepreneurs to French government announcements.

We study the extent to which French entrepreneurs mobilized in an online collective action against the generalization of the health-pass policy in summer 2021. We document the dynamics of registrations on the website Animap.fr where entrepreneurs could claim they would not check the health-pass of their clients. We first note an over-representation of complementary and alternative medicine practitioners among the mobilized people. We also suggest that professionals related to the touristic industry mobilized on the website. Second, we show that the government announcements led to an increase in the mobilization. However, they did not affect the diversity of the entrepreneurs joining the action. This lack of diversity may have restricted the pool of potential participants as well as limited the identification of the "public opinion" to the mobilization.

Detection Covid-19 Infection of Lung CT Scan Slices Images Based on a Transfer Learning and GRAD-CAM

The long timeline of polymerase chain reaction (PCR) tests and the lack of test tool kits in many hospitals lead to fast infection according to the slow diagnosis. The Various experiences of radiologists cause deferent in accurately detection lessons. This research suggested and designed a model based on utilizing the deep learning (DL) algorithms to detect and visualize the infection of covid-19 patients. This work shows how various convolution layers of convolution neural networks (CNN) extract different types of features, which helps us understand how CNN steadily gains spatial information in each layer. As a result, every transition focuses on the region of interest. Understanding how CNN identifies and locates distinct infection areas in an image is easier with a heat map of activation. A gradient class activation map (Grad-CAM) was used to visualize the infected area in the lungs. Transfer learning such as Resnet50, VGG16, and inception V3 have been applied to the dataset to detect the infection area. The result of these models reached an accuracy of 71.01%, 83.51%, and 94.93%, respectively. The VGG16 has been manipulated because the model consumes less training time than the other models to solve the problem. Manipulating on VGG16 has been accomplished to achieve acceptable accuracy. The tuning on the last three layers of VGG16 architecture (dense layers) replaces them with two layers (global average layer and dense layer). The dense layer that is added deals with binary classification problems depending on the sigmoid function. This tuning serves the current study by speeding up the model's prediction and increasing the accuracy. The result of the testing reached 0.9683% of accuracy and 0.0931 loss function without augmentation. This work depends on the current dataset because it contains the mask of lung and infection, which was obtained to localize the infection area. The obtained result proved that the system could help the radiologist accommodate the pandemic. © 2022 IEEE.

COVID 19 Prediction Using Chest X-Ray And CNN Hyperparameter Optimizer

According to mid-June 2020, the abrupt escalation of coronavirus reported widespread fear and crossed 16 million confirmed cases. To fight against this growth, clinical imaging is recommended, and for illustration, X-Ray images can be applied for opinion. This paper categorizes chest X-ray images into three classes- COVID-19 positive, normal, and pneumonia affected. We have used a CNN model for analysis, and hyperparameters are used to train and optimize the CNN layers. Swarm-based artificial intelligent algorithm - Grey Wolf Optimizer algorithm has been used for further analysis. We have tested our proposed methodology, and comparative analysis has been done with two openly accessible dataset containing COVID- 19 affected, pneumonia affected, and normal images. The optimized CNN model features delicacy, insight, values of F1 scores of 97.77, 97.74, 96.24 to 92.86, uniqueness, and perfection, which are better than models at the leading edge of technology. © 2022 IEEE.

Higher proinflammatory responses possibly contributing to suppressed cytotoxicity in patients with COVID-19 associated mucormycosis.

INTRODUCTION: COVID-19 Associated Mucormycosis (CAM), an opportunistic fungal infection, surged during the second wave of SARS Cov-2 pandemic. Since immune responses play an important role in controlling this infection in immunocompetent hosts, it is required to understand immune perturbations associated with this condition for devising immunotherapeutic strategies for its control. We conducted a study to determine different immune parameters altered in CAM cases as compared to COVID-19 patients without CAM. METHODOLOGY: Cytokine levels in serum samples of CAM cases (n = 29) and COVID-19 patients without CAM (n = 20) were determined using luminex assay. Flow cytometric assays were carried out in 20 CAM cases and 10 controls for determination of frequency of NK cells, DCs, phagocytes, T cells and their functionalities. The cytokine levels were analyzed for their association with each other as well as with T cell functionality. The immune parameters were also analyzed with respect to the known risk factors such as diabetes mellitus and steroid treatment. RESULTS: Significant reduction in frequencies of total and CD56 + CD16 + NK cells (cytotoxic subset) was noted in CAM cases. Degranulation responses indicative of cytotoxicity of T cell were significantly hampered in CAM cases as compared to the controls. Conversely, phagocytic functions showed no difference in CAM cases versus their controls except for migratory potential which was found to be enhanced in CAM cases. Levels of proinflammatory cytokines such as IFN-γ, IL-2, TNF-α, IL-17, IL-1ß, IL-18 and MCP-1 were significantly elevated in cases as compared to the control with IFN-γ and IL-18 levels correlating negatively with CD4 T cell cytotoxicity. Steroid administration was associated with higher frequency of CD56 + CD16- NK cells (cytokine producing subset) and higher MCP-1 levels. Whereas diabetic participants had higher phagocytic and chemotactic potential and had higher levels of IL-6, IL-17 and MCP-1. CONCLUSION: CAM cases differed from the controls in terms of higher titers of proinflammatory cytokines, reduced frequency of total and cytotoxic CD56 + CD16 + NK cell. They also had reduced T cell cytotoxicity correlating inversely with IFN-γ and IL-18 levels, possibly indicating induction of negative feedback mechanisms while diabetes mellitus or steroid administration did not affect the responses negatively.

Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images.

COVID-19 has been a global pandemic. Flattening the curve requires intensive testing, and the world has been facing a shortage of testing equipment and medical personnel with expertise. There is a need to automate and aid the detection process. Several diagnostic tools are currently being used for COVID-19, including X-Rays and CT-scans. This study focuses on detecting COVID-19 from X-Rays. We pursue two types of problems: binary classification (COVID-19 and No COVID-19) and multi-class classification (COVID-19, No COVID-19 and Pneumonia). We examine and evaluate several classic models, namely VGG19, ResNet50, MobileNetV2, InceptionV3, Xception, DenseNet121, and specialized models such as DarkCOVIDNet and COVID-Net and prove that ResNet50 models perform best. We also propose a simple modification to the ResNet50 model, which gives a binary classification accuracy of 99.20% and a multi-class classification accuracy of 86.13%, hence cementing the ResNet50's abilities for COVID-19 detection and ability to differentiate pneumonia and COVID-19. The proposed model's explanations were interpreted via LIME which provides contours, and Grad-CAM, which provides heat-maps over the area(s) of interest of the classifier, i.e., COVID-19 concentrated regions in the lungs, and realize that LIME explains the results better. These explanations support our model's ability to generalize. The proposed model is intended to be deployed for free use.

Battling the emerging epidemic of rhino-orbital-cerebral mucormycosis (ROCM) in COVID-19 pandemic: an interventional study.

PURPOSE: The most recent challenge being faced by the healthcare system during the worldwide COVID-19 pandemic is increase in the incidence rate of coinfection or superinfection; one of the most fatal being mucormycosis. This study aimed to estimate the risk factors, symptoms and signs, treatment outcome and prognosis of COVID-19-associated mucormycosis (CAM) patients. METHODS: This is an interventional study of 35 patients diagnosed and managed as CAM at a tertiary care centre in New Delhi, India. RESULTS: The mean age of patients was 40.45 ± 6 years with a male preponderance. CAM did not affect healthy individuals; the major risk factors included diabetes in 65.7% and injudicious steroid use in 51.4% patients. Orbital/facial edema was the most common presenting symptom (25.7%) as well as sign (28.57%). 68.5% patients were stage 3 (involvement of orbit) at presentation; 33.3% showed medial wall involvement. Treatment included intravenous Amphotericin and oral Posaconazole in all patients, paranasal sinus (PNS) debridement in 94.2%, orbital exenteration was done in 8 patients. Adjuvant retrobulbar Amphotericin B injection was administered in 12 patients with radiological resolution seen in 50% after 1 cycle. In patients with Stage 4 disease who underwent exenteration along with PNS debridement, survival rate was 100% at 30 days, and disease reduction occurred in 87.5% patients (P < 0.01). Overall, 68.5% responded to therapy, 8.5% showed progression and mortality rate was 22.85%, at a mean follow up period of 59.5 days. CONCLUSION: A multidisciplinary and aggressive approach is essential in the management of CAM patients.