A mixed-method study to develop and evaluate a structured therapeutic communication module on psychological distress and perceived needs among caregivers of critically ill patients in Critical Care Units.

AIMS AND OBJECTIVES: To develop and evaluate the effectiveness of a structured therapeutic communication module on psychological distress and perceived needs among caregivers of critically ill patients. BACKGROUND: Caregivers of critically ill patients experience intense psychological distress, and their needs often go unexpressed or unidentified. Structured therapeutic communication enables nurses to explore and fulfill these needs. METHOD: A mixed-method study was conducted among 30 caregivers of critically ill patients. During phase one, a qualitative interview was conducted, and a structured therapeutic communication module was developed based on Hildegard Peplau's Interpersonal Relations Theory. In the second phase, one group pre-test and post-test design was adopted. The Hospital Anxiety and Depression Scale (HADS) and a Semi-structured interview schedule were used to assess psychological distress and perceived needs, respectively. RESULTS: Half of (50 %) the caregivers reported a high level of anxiety before intervention, with a mean of 11.30 (SD: 4.0), and 66.7 % of them had a high level of depression, with a mean of 12.03 (SD: 0.08). There was a statistically significant difference in anxiety (CI: 0.451-2.016) and depression (CI: 0.261-1.538) before and after the intervention. The qualitative analysis revealed unmet needs perceived by caregivers. CONCLUSION: Using a structured therapeutic communication module helps nurses to alleviate the psychological distress experienced by caregivers of CCU patients. RELEVANCE TO CLINICAL PRACTICE: Nurses need to be sensitive to the unexpressed needs of caregivers of critically ill patients. The structured therapeutic communication modules can be integrated into routine nursing care practice to ensure family-centered care.

Clinical Conundrum: Unveiling a Unique Presentation of Hypopharyngeal Carcinoma.

Dysphagia is a common symptom encountered in clinical practice, typically associated with a wide range of etiologies, including structural abnormalities, inflammatory conditions, neoplasms, and neurological disorders. However, the combination of subcutaneous emphysema, vocal cord palsy, enlarged arytenoids, and pooling of saliva in a dysphagic patient represents a rare and intriguing presentation. A 33-year-old female presented at a tertiary care hospital in Western India with hoarseness of voice, difficulty in swallowing, productive cough, and neck pain for two months with an abrupt increase in the severity of all symptoms in two days. A history of chewable tobacco use for six years was disclosed. Clinical evaluation revealed a thin build with platynychia and conjunctival pallor, dental staining, drooling of saliva, the presence of extensive subcutaneous emphysema on palpation of the neck, and absent laryngeal crepitus. Endoscopic evaluation was suggestive of right vocal cord palsy and enlarged, congested arytenoid cartilages, post-cricoid growth with pooling of saliva in bilateral pyriform fossae. A CT scan of the neck showed a 2x3 cm neoplastic growth in the hypopharynx, with subcutaneous emphysema and free air foci in the head and neck region, prompting an immediate tracheostomy and biopsy of the hypopharyngeal growth with Ryle's tube insertion. Squamous cell carcinoma was confirmed on the biopsy report. Due to its rarity, the possible underlying cause of idiopathic subcutaneous emphysema should be sought whenever encountered in clinical practice since these patients are potentially misdiagnosed. A high index of suspicion among clinicians, along with a consideration of the constellation of other symptoms and clinical features of a possible underlying hypopharyngeal cancer whenever encountering such patients is of key importance for prompting further investigations and treatment.

Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR.

This research manuscript aims to find the most effective epidermal growth factor receptor (EGFR) inhibitors from millions of in house compounds through Machine Learning (ML) techniques. ML-based structure activity relationship (SAR) models were validated to predict biological activity of untested novel molecules. Six ML algorithms, including k nearest neighbour (KNN), decision tree (DT), Logistic Regression, support vector machine (SVM), multilinear regression (MLR), and random forest (RF), were used to build for activity prediction. Among these, RF classifier (accuracy for train and test set is 90% and 81%) and RF regressor (R2 and MSE for trainset is 0.83 and 0.29 and for test set, 0.69 and 0.46) showed good predictive performance. Also, the six most essential features that affect the biological activity parameter and highly contribute to model development were successfully selected by the variable importance technique. RF regression model was used to predict the biological activity expressed as pIC50 of nearly ten million molecules while RF classification model classifies those molecules into active, moderately active, and least active according to their predicted pIC50. Based on two models, thousand molecules from million molecules with higher predicted pIC50 values and classified as active were selected for molecular docking. Based on the docking scores, predicted pIC50, and binding interactions with MET769 residue, compounds, i.e., Zinc257233137, Zinc257232249, and Zinc101379788, were identified as potential EGFR inhibitors with predicted pIC50 7.72, 7.85, and 7.70. Dynamics studies were also performed on Zinc257233137 to illustrate that it has good binding free energy and stable hydrogen bonding interactions with EGFR. These molecules can be used for further research and proved to be the novel drugs for EGFR in cancer treatment.Communicated by Ramaswamy H. Sarma.