An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers.

PURPOSE: The prognostic and predictive significance of pathologist-read tumor infiltrating lymphocytes (TILs) in head and neck cancers have been demonstrated through multiple studies over the years. TILs have not been broadly adopted clinically, perhaps due to substantial inter-observer variability. In this study, we developed a machine-based algorithm for TIL evaluation in head and neck cancers and validated its prognostic value in independent cohorts. EXPERIMENTAL DESIGN: A network classifier called NN3-17 was trained to identify and calculate tumor cells, lymphocytes, fibroblasts and "other" cells on hematoxylin-eosin stained sections using the QuPath software. These measurements were used to construct three predefined TIL variables. A retrospective collection of 154 head and neck squamous cell cancer cases was used as the discovery set to identify optimal association of TIL variables and survival. Two independent cohorts of 234 cases were used for validation. RESULTS: We found that electronic TIL variables were associated with favorable prognosis in both the HPV-positive and -negative cases. After adjusting for clinicopathologic factors, Cox regression analysis demonstrated that electronic total TILs% (p = 0.025) in the HPV-positive and electronic stromal TILs% (p < 0.001) in the HPV-negative population were independent markers of disease specific outcomes (disease free survival). CONCLUSIONS: Neural network TIL variables demonstrated independent prognostic value in validation cohorts of HPV-positive and HPV-negative head and neck cancers. These objective variables can be calculated by an open-source software and could be considered for testing in a prospective setting to assess potential clinical implications.

Impact of COVID-19 outbreak on healthcare workers in a Tertiary Healthcare Center in India: a cross sectional study.

Numerous speculations have continually emerged, trying to explore the association between COVID-19 infection and a varied range of demographic and clinical factors. Frontline healthcare workers have been the primary group exposed to this infection, and there have been limited global research that examine this cohort. However, while there are a few large studies conducted on Indian healthcare professionals to investigate their potential risk and predisposing factors to COVID-19 infection, to our knowledge there are no studies evaluating the development of long COVID in this population. This cross-sectional study systematically utilized the demographic and clinical data of 3329 healthcare workers (HCW) from a tertiary hospital in India to gain significant insights into the associations between disease prevalence, severity of SARS-Cov-2 infection and long COVID. Most of the study population was found to be vaccinated (2,615, 78.5%), while 654 (19.65%) HCWs were found to be SARS-CoV-2 positive at least once. Of the infected HCWs, 75.1% (491) did not require hospitalization, whereas the rest were hospitalized for an average duration of 9 days. A total of 206 (6.19%) individuals were found to be suffering from long COVID. Persistent weakness/tiredness was the most experienced long-COVID symptom, while females (1.79, 1.25-2.57), individuals who consumed alcohol (1.85, 1.3-2.64) or had blood group B (1.9, 1.33-2.7) were at a significantly higher risk for developing long COVID.

Neonatal Hearing Screening Programme And Challenges Faced By The Developing Country: A Tertiary Care Hospital Experience.

OBJECTIVE: To screen new-borns to diagnose any hearing impairment early. METHODS: The prospective, cross-sectionalstudy was conducted at the Department of Otorhinolaryngology, Head and Neck Surgery Liaquat National Hospital, Karachi, from November 1, 2020, to April 30, 2021, and new- borns of either gender aged >12h born via spontaneous vaginal delivery, induced labour, and Caesarean section. A predesigned questionnaire was used to collect detailed case history, including gestational age, duration of labour, and other prenatal, natal, and postnatal risk factors. Otoacoustic emission test was performed, and infants referred twice were scheduled for complete diagnostic evaluation and brainstem evoked response audiometry. Data was analysed using SPSS 23. RESULTS: Of the 267 neonates, 249(93.3%) passed the first screening. Of the remaining 18(6.7%) neonates, 8(44.4%) passed the second screening, while 10(55.5%) were asked to come for a follow-up after three weeks. Of them, 3(30%) returned for check-up, while 7(70%) did not show up. CONCLUSIONS: Neonatal risk factors associated with hearing loss need to be identified, and a comprehensive hearing screening programme is required for neonates.

DBU-Catalyzed Ring Expansion or Ene-amine Formation Involving δ-Acetoxy Allenoates and N-Sulfonyl Hydrazides.

DBU-catalyzed spiro-annulation and concomitant ring expansion/domino reaction of δ-acetoxy allenoates with cycl-2-ene-N-sulfonyl hydrazides afford ring-expanded (5 → 6, 6 → 7, and 7 → 8) products. By contrast, cycl-3-ene/ane-N-sulfonyl hydrazones under similar conditions deliver pyrazole cores with the same allenoate that involves allylic elimination in which δ-acetoxy allenoate serves as 3C-synthon. The key spirocyclic intermediates, as well as dienyl-amine intermediates, are isolated and characterized. An extension to (R)-(-)-carvone-derived sulfonyl hydrazide also led to ring expansion and gave pyrazoloazepine.

Prediction of monthly dry days with machine learning algorithms: a case study in Northern Bangladesh.

Dry days at varied scale are an important topic in climate discussions. Prolonged dry days define a dry period. Dry days with a specific rainfall threshold may visualize a climate scenario of a locality. The variation of monthly dry days from station to station could be correlated with several climatic factors. This study suggests a novel approach for predicting monthly dry days (MDD) of six target stations using different machine learning (ML) algorithms in Bangladesh. Several rainfall thresholds were used to prepare the datasets of monthly dry days (MDD) and monthly wet days (MWD). A group of ML algorithms, like Bagged Trees (BT), Exponential Gaussian Process Regression (EGPR), Matern Gaussian Process Regression (MGPR), Linear Support Vector Machine (LSVM), Fine Trees (FT) and Linear Regression (LR) were evaluated on building a competitive prediction model of MDD. In validation of the study, EGPR-based models were able to better capture the monthly dry days (MDD) over Bangladesh compared to those by MGPR, LSVM, BT, LR and FT-based models. When MDD were the predictors for all six target stations, EGPR produced highest mean R2 of 0.91 (min. 0.89 and max. 0.92) with a least mean RMSE of 2.14 (min. 1.78 and max. 2.69) compared to other models. An explicit evaluation of the ML algorithms using one-year lead time approach demonstrated that BT and EGPR were the most result-oriented algorithms (R2 = 0.78 for both models). However, having a least RMSE, EGPR was chosen as the best model in one year lead time. The dataset of monthly dry-wet days was the best predictor in the lead-time approach. In addition, sensitivity analysis demonstrated sensitivity of each station on the prediction of MDD of target stations. Monte Carlo simulation was introduced to assess the robustness of the developed models. EGPR model declared its robustness up to certain limit of randomness on the testing data. The output of this study can be referred to the agricultural sector to mitigate the impacts of dry spells on agriculture.

A genomic overview including polyphasic taxonomy of Thalassoroseus pseudoceratinae gen. nov., sp. nov. isolated from a marine sponge, Pseudoceratina sp.

A pink-coloured, salt- and alkali-tolerant planctomycetal strain (JC658T) with oval to pear-shaped, motile, aerobic, Gram-negative stained cells was isolated from a marine sponge, Pseudoceratina sp. Strain JC658T shares the highest 16S rRNA gene sequence identity with Maioricimonas rarisocia Mal4T (< 89.2%) in the family Planctomycetaceae. The genomic analysis of the new strain indicates its biotechnological potential for the production of various industrially important enzymes, notably sulfatases and carbohydrate-active enzymes (CAZymes), and also potential antimicrobial compounds. Several genes encoding restriction-modification (RM) and CRISPR-CAS systems are also present. NaCl is obligate for growth, of which strain JC658T can tolerate a concentration up to 6% (w/v). Optimum pH and temperature for growth are 8.0 (range 7.0-9.0) and 25 ºC (range 10-40 °C), respectively. The major respiratory quinone of strain JC658T is MK6. Major fatty acids are C16:1ω7c/C16:1ω6c, C18:0 and C16:0. Major polar lipids are phosphatidylcholine, phosphatidyl-dimethylethanolamine and phosphatidyl-monomethylethanolamine. The genomic size of strain JC658T is 7.36 Mb with a DNA G + C content of 54.6 mol%. Based on phylogenetic, genomic (ANI, AAI, POCP, dDDH), chemotaxonomic, physiological and biochemical characteristics, we conclude that strain JC658T belongs to a novel genus and constitutes a novel species within the family Planctomycetaceae, for which we propose the name Thalassoroseus pseudoceratinae gen. nov., sp. nov. The novel species is represented by the type strain JC658T (= KCTC 72881 T = NBRC 114371 T).

Pyridine vs DABCO vs TBAB in Annulations of δ-Acetoxy Allenoates with Thioamides Leading to Dihydrothiophene, Thiopyran, and Thiazole Scaffolds.

The same δ-acetoxy allenoates and thioamides, under DABCO, pyridine, or tetra-n-butyl ammonium bromide (TBAB) catalysis, undergo distinctly different annulations giving chemoselective routes to dihydrothiophene, thiopyran, or thiazole motifs. Thus, using pyridine in [3 + 2] annulation, dihydrothiophenes are obtained as essentially single diastereomers. By contrast, under DABCO catalysis, allenoates deliver thiopyran motifs in good to high yields through 6-exo-dig cyclization. In the thiazole forming [3 + 2] annulation, tetra-n-butyl ammonium bromide (TBAB) facilitates addition-elimination and 5-exo-trig cyclization in which ß- and γ-carbons of allenoates participate to deliver thiazole cores exclusively with a Z-isomeric exocyclic double bond. A possible rationale for these observations is delved into.

The effectiveness of different aerobic exercises to improve pain intensity and disability in chronic low back pain patients: a systematic review.

Background: Physical activity, including aerobic exercise, is highly recommended for chronic low back pain (CLBP) patients to improve pain intensity and functional disability. Objectives: To assess the effectiveness of different aerobic exercises to reduce pain intensity and functional disability in patients with CLBP. Methods: A computer-aided search was performed to find Randomised Controlled Trials (RCTs) that evaluated the effectiveness of different aerobic exercises in CLBP. Articles published between January 2007 to December 2020 were included in the review. Quality assessment using the PEDro scale, extraction of relevant information, and evaluation of outcomes were done by two reviewers independently. Results: A total of 17 studies were included that involved 1146 participants. Outcomes suggested that aerobic exercise combined with other interventions was more effective than aerobic exercise alone. Aerobic exercise with higher frequency (≥ 5 days/week) and longer duration (≥ 12 weeks) were effective to gain clinically significant (≥ 30%) improvements. Environment and using pedometer did not seem to influence the outcomes. Conclusions: Pain intensity and functional disability in CLBP patients can be minimized by prescribing aerobic exercise. However, to get better improvements, aerobic exercise should be done in combination with other interventions and at optimum frequency and duration. Further studies should emphasize examining the optimal doses and duration of different aerobic exercises.

BrCNGC gene family in field mustard: genome-wide identification, characterization, comparative synteny, evolution and expression profiling.

CNGCs are ligand-gated calcium signaling channels, which participate in important biological processes in eukaryotes. However, the CNGC gene family is not well-investigated in Brassica rapa L. (i.e., field mustard) that is economically important and evolutionary model crop. In this study, we systematically identified 29 member genes in BrCNGC gene family, and studied their physico-chemical properties. The BrCNGC family was classified into four major and two sub phylogenetic groups. These genes were randomly localized on nine chromosomes, and dispersed into three sub-genomes of B. rapa L. Both whole-genome triplication and gene duplication (i.e., segmental/tandem) events participated in the expansion of the BrCNGC family. Using in-silico bioinformatics approaches, we determined the gene structures, conserved motif compositions, protein interaction networks, and revealed that most BrCNGCs can be regulated by phosphorylation and microRNAs of diverse functionality. The differential expression patterns of BrCNGC genes in different plant tissues, and in response to different biotic, abiotic and hormonal stress types, suggest their strong role in plant growth, development and stress tolerance. Notably, BrCNGC-9, 27, 18 and 11 exhibited highest responses in terms of fold-changes against club-root pathogen Plasmodiophora brassicae, Pseudomonas syringae pv. maculicola, methyl-jasmonate, and trace elements. These results provide foundation for the selection of candidate BrCNGC genes for future breeding of field mustard.

Influence of Solid Lubricant Addition on Friction and Wear Response of 3D Printed Polymer Composites.

In this study, acrylonitrile butadiene styrene (ABS) and graphite powder-a solid lubricant-were filled and characterized for friction and wear responses. The fused deposition modeling (FDM) technique was utilized to synthesize ABS-graphite composites. A twin-screw extrusion approach was employed to create the composite filament of graphite-ABS that is suitable for the FDM process. Three graphite particle ratios ranging from 0% to 5% were explored in the ABS matrix. The wear and friction properties of ABS composites were examined using a pin on disc tribometer at varied sliding velocities and weights. As a result of the graphite addition in the ABS matrix, weight losses for FDM components as well as a decreased coefficient of friction were demonstrated. Furthermore, as the graphite weight percentage in the ABS matrix grows the value of friction and wear loss decreases. The wear mechanisms in graphite filled ABS composites and ABS were extensively examined using scanning electron microscopy and confocal microscopy.

Low back pain and associated risk factors among medical students in Bangladesh: a cross-sectional study.

Background: Low back pain (LBP) is one of the leading causes of disability worldwide. Different studies showed the high prevalence of LBP among medical students. However, no study has been conducted on Bangladeshi medical students to estimate the prevalence of LBP. This study determined the prevalence, characteristics, and associated risk factors of LBP among medical students in Bangladesh. Methods: A cross-sectional study was conducted from October to December 2020 among randomly selected 270 medical students and medical interns in Faridpur Medical College, Bangladesh, using an online questionnaire. In data analysis, chi-square test and binary logistic regression were performed, and a p-value of < 0.05 was regarded as statistically significant. Results: A total of 207 participants responded fully to the survey, and were included in the analysis. The mean age of the participants was 22.4 ± 1.9 years. The point, 6-month, and 12-month prevalence of LBP was 25.6%, 46.9%, and 63.3%, respectively. In most participants, LBP was localized (53.2%), recurrent (64.9%), non-specific (70.8%), affected for a short period (55%), and relieved without receiving any treatment (60.4%). Participants who had a significantly higher 12-month prevalence of LBP included females (72.2% vs 52.2%), with BMI >25 kg/m 2 (73.2% vs 56.7%), those who performed physical activity at low to moderate frequency (72.4% vs 29.5%), those who spent > 6 hours/day by sitting (71.3% vs 45.3%), and those who did not have enough rest time (92.7% vs 56%). Ergonomic features of chairs, such as having back support, adjustable back support, and adjustable sitting surface, significantly (p < 0.05) influenced the outcomes. Conclusion: The prevalence of LBP among medical students in Bangladesh was high, and most of the risk factors associated with the high prevalence of LBP were modifiable. Hence, LBP can be prevented by implementing preventive strategies and providing ergonomic training and physical activity facilities.

Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network

BackgroundThe second wave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. MethodsThe current Deep-Neo-V model is built on our previously statistically rigorous machine learning framework [Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework] that evaluated statistically significant risk factors, generated new combined variables and then supply these risk factors to deep neural network to predict mortality in RT-PCR positive COVID-19 patients in the inpatient setting. We analyzed adult patients ([≥]18 years) admitted to the Aga Khan University Hospital, Pakistan with a working diagnosis of COVID-19 infection (n=1228). We excluded patients that were negative on COVID-19 on RT-PCR, had incomplete or missing health records. The first phase selection of risk factor was done using Cox-regression univariate and multivariate analyses. In the second phase, we generated new variables and tested those statistically significant for mortality and in the third and final phase we applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models and others. ResultsA total of 1228 cases were diagnosed as COVID-19 infection, we excluded 14 patients after the exclusion criteria and (n=)1214 patients were analyzed. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our DNN (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the curve of the receiver-operator curve of 88.5. ConclusionOur novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.

DNA-dependent protein kinase: Epigenetic alterations and the role in genomic stability of cancer.

DNA-dependent protein kinase (DNA-PK), a member of phosphatidylinositol-kinase family, is a key protein in mammalian DNA double-strand break (DSB) repair that helps to maintain genomic integrity. DNA-PK also plays a central role in immune cell development and protects telomerase during cellular aging. Epigenetic deregulation due to endogenous and exogenous factors may affect the normal function of DNA-PK, which in turn could impair DNA repair and contribute to genomic instability. Recent studies implicate a role for epigenetics in the regulation of DNA-PK expression in normal and cancer cells, which may impact cancer progression and metastasis as well as provide opportunities for treatment and use of DNA-PK as a novel cancer biomarker. In addition, several small molecules and biological agents have been recently identified that can inhibit DNA-PK function or expression, and thus hold promise for cancer treatments. This review discusses the impact of epigenetic alterations and the expression of DNA-PK in relation to the DNA repair mechanisms with a focus on its differential levels in normal and cancer cells.

Role of flavonoids in thrombotic, cardiovascular, and inflammatory diseases.

The failure of mechanisms of natural anti-coagulation either due to genetic impairment or due to severe external injuries may result in a condition called thrombosis. This is believed to be the primary cause for a variety of life-threatening conditions such as: heart attack, stroke, pulmonary embolism, thrombophlebitis, and deep venous thrombosis (DVT). The growing number of these incidents requires an alternative anti-coagulant or anti-thrombotic agent that has minimal side effects and improved efficiency. For decades, plant polyphenols, especially flavonoids, were known for their vital role in preventing various diseases such as cancer. Mitigating excessive oxidative stress caused by reactive oxygen species (ROS) with anti-oxidant-rich flavonoids may reduce the risk of hyper-activation of platelets, cardiovascular diseases (CVD), pain, and thrombosis. Furthermore, flavonoids may mitigate endothelial dysfunction (ED), which generally correlates to the development of coronary artery and vascular diseases. Flavonoids also reduce the risk of atherosclerosis and atherothrombotic disease by inhibiting excessive tissue factor (TF) availability in the endothelium. Although the role of flavonoids in CVD is widely discussed, to the best of our knowledge, their role as anti-thrombotic lead has not been discussed. This review aims to focus on the biological uses of dietary flavonoids and their role in the treatment of various coagulation disorders, and may provide some potential lead to the drug discovery process in this area.

Triple-negative breast cancer-derived microvesicles transfer microRNA221 to the recipient cells and thereby promote epithelial-to-mesenchymal transition.

The triple-negative phenotype is the most prevalent form of human breast cancer worldwide and is characterized by poor survival, high aggressiveness, and recurrence. Microvesicles (MV) are shredded plasma membrane components and critically mediate cell-cell communication, but can also induce cancer proliferation and metastasis. Previous studies have revealed that protease-activated receptor 2 (PAR2) contributes significantly to human triple-negative breast cancer (TNBC) progression by releasing nano-size MV and promoting cell proliferation, migration, and invasion. MV isolated from highly aggressive human TNBC cells impart metastatic potential to nonmetastatic cells. Over-expression of microRNA221 (miR221) has also been reported to enhance the metastatic potential of human TNBC, but miR221's relationship to PAR2-induced MV is unclear. Here, using isolated MV, immunoblotting, quantitative RT-PCR, FACS analysis, and enzymatic assays, we show that miR221 is translocated via human TNBC-derived MV, which upon fusion with recipient cells, enhance their proliferation, survival, and metastasis both in vitro and in vivo by inducing the epithelial-to-mesenchymal transition (EMT). Administration of anti-miR221 significantly impaired MV-induced expression of the mesenchymal markers Snail, Slug, N-cadherin, and vimentin in the recipient cells, whereas restoring expression of the epithelial marker E-cadherin. We also demonstrate that MV-associated miR221 targets phosphatase and tensin homolog (PTEN) in the recipient cells, followed by AKT Ser/Thr kinase (AKT)/NF-κB activation, which promotes EMT. Moreover, elevated miR221 levels in MV derived from human TNBC patients' blood could induce cell proliferation and metastasis in recipient cells. In summary, miR221 transfer from TNBC cells via PAR2-derived MV induces EMT and enhances the malignant potential of recipient cells.

Integrating fuzzy logic with Pearson correlation to optimize contaminant detection in water distribution system with uncertainty analyses.

An effective detection algorithm, supervising an online water system, is expected to monitor changes in water quality due to any contamination. However, contemporary event detection methods are often criticized for their high false detection rates as well as for their low true detection rates. This study proposes two new event detection methods for contamination that use multi-objective optimization by investigating the correlation between multiple types of conventional water quality sensors. While the first method incorporates non-dominated sorting genetic algorithm II (NSGA-II) with the Pearson correlation Euclidean distance (PE) method in order to maximize the probability of detection (PD) and to minimize the false alarm rate (FAR), the second method introduces fuzzy logic in order to establish a degree of correlations ranking that replaces the correlation relationship indicator threshold. Optimization is performed by using NSGA-II in the second method. The results of this study show that the incorporation of fuzzy logic with NSGA-II in event detection method have produced better results in event detection. The results also show that both methods detect all true events without producing any false alarm rates. Moreover, an uncertainty analysis on input sensor signals is performed to test the robustness of the fuzzy logic-based event detection method by employing the widely used Monte Carlo simulation (MCS) technique. Four different scenarios of uncertainty are analyzed, in particular, and the findings suggest that the proposed method is very effective in minimizing false alarm rates and maximizing true events detection, and hence, it can be regarded as one of the novel approaches to demonstrate its application in the development of an event detection algorithm.

Matrix metalloproteinase-2: A key regulator in coagulation proteases mediated human breast cancer progression through autocrine signaling.

AIMS: Cell invasion is attributed to the synthesis and secretion of proteolytically active matrix-metalloproteinases (MMPs) by tumor cells to degrade extracellular matrix (ECM) and promote metastasis. The role of protease-activated receptor 2 (PAR2) in human breast cancer migration/invasion via MMP-2 up-regulation remains ill-defined; hence we investigated whether TF-FVIIa/trypsin-mediated PAR2 activation induces MMP-2 expression in human breast cancer. MAIN METHODS: MMP-2 expression and the signaling mechanisms were analyzed by western blotting and RT-PCR. MMP-2 activity was measured by gelatin zymography. Cell invasion was analyzed by transwell invasion assay whereas; wound healing assay was performed to understand the cell migratory potential. KEY FINDINGS: Here, we highlight that TF-FVIIa/trypsin-mediated PAR2 activation leads to enhanced MMP-2 expression in human breast cancer cells contributing to tumor progression. Knock-down of PAR2 abrogated TF-FVIIa/trypsin-induced up-regulation of MMP-2. Again, genetic manipulation of AKT or inhibition of NF-ĸB suggested that PAR2-mediated enhanced MMP-2 expression is dependent on the PI3K-AKT-NF-ĸB pathway. We also reveal that TF, PAR2, and MMP-2 are over-expressed in invasive breast carcinoma tissues as compared to normal. Knock-down of MMP-2 significantly impeded TF-FVIIa/trypsin-induced cell invasion. Further, we report that MMP-2 activates p38 MAPK-MK2-HSP27 signaling axis that leads to actin polymerization and induces cell migration. Pharmacological inhibition of p38 MAPK or MK2 attenuates MMP-2-induced cell migration. SIGNIFICANCE: The study delineates a novel signaling pathway by which PAR2-induced MMP-2 expression regulates human breast cancer cell migration/invasion. Understanding these mechanistic details will certainly help to identify crucial targets for therapeutic interventions in breast cancer metastasis.

Congenital Vallecular Cyst: ARare and Potentially Lethal Condition.

Congenital vallecular cyst is a rare laryngeal lesion, accounting for 10-20% of all laryngeal cysts, with a potential to cause severe upper airway obstruction, which can be fatal. It can cause stridor, apnea, cyanosis, respiratory distress, and feeding difficulties. Diagnosis requires a high level of clinical suspicion and helps in timely intervention. Direct laryngoscopy is gold standard for definitive diagnosis. Treatment options include aspiration, marsupialization, and surgical excision. Here, we report a case of 7-week-old infant with complain of recurrent episodes of cyanosis when agitated, since the age of 3 weeks, admitted with impression of apparent life-threatening events (ALTEs). Extensive investigations were non-conclusive, which were done in local hospital. Direct laryngoscopy was performed in our hospital and showed presence of a vallecular cyst. Thus complete excision of cyst was done in the same setting with dramatic relief of symptoms.