A Signature of 14 Long Non-Coding RNAs (lncRNAs) as a Step towards Precision Diagnosis for NSCLC

LncRNAs have arisen as new players in the world of non-coding RNA. Disrupted expression of these molecules can be tightly linked to the onset, promotion and progression of cancer. The present study estimated the usefulness of 14 lncRNAs (HAGLR, ADAMTS9-AS2, LINC00261, MCM3AP-AS1, TP53TG1, C14orf132, LINC00968, LINC00312, TP73-AS1, LOC344887, LINC00673, SOX2-OT, AFAP1-AS1, LOC730101) for early detection of non-small-cell lung cancer (NSCLC). The total RNA was isolated from paired fresh-frozen cancerous and noncancerous lung tissue from 92 NSCLC patients diagnosed with either adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC). The expression level of lncRNAs was evaluated by a quantitative real-time PCR (qPCR). Based on Ct and delta Ct values, logistic regression and gradient boosting decision tree classifiers were built. The latter is a novel, advanced machine learning algorithm with great potential in medical science. The established predictive models showed that a set of 14 lncRNAs accurately discriminates cancerous from noncancerous lung tissues (AUC value of 0.98 ± 0.01) and NSCLC subtypes (AUC value of 0.84 ± 0.09), although the expression of a few molecules was statistically insignificant (SOX2-OT, AFAP1-AS1 and LOC730101 for tumor vs. normal tissue; and TP53TG1, C14orf132, LINC00968 and LOC730101 for LUAD vs. LUSC). However for subtypes discrimination, the simplified logistic regression model based on the four variables (delta Ct AFAP1-AS1, Ct SOX2-OT, Ct LINC00261, and delta Ct LINC00673) had even stronger diagnostic potential than the original one (AUC value of 0.88 ± 0.07). Our results demonstrate that the 14 lncRNA signature can be an auxiliary tool to endorse and complement the histological diagnosis of non-small-cell lung cancer.

Evaluation of Mood Changes AmongPeripartum Women at PKDAS Institute of Medical Science, Vaniyamkulam, Palakkad: A Prospective, Observational, Longitudinal Study

Background: The perinatal period is well established as an increased risk for development of serious mood disorders. Maternal mental health in developing countries gets less than its due attention. The present study was undertaken to evaluate mood changes in Peripartum period in our population and to identify demographic, obstetric, social and psychosocial risk factors associated with Peripartum depression using established scales.Material & Methods:A prospective, observational, longitudinal study conducted in PK das institute of medical science, vaniyamkulam, with 387 perinatal women for 12 months (February 2018– January 2019). Various scales EPDS (Edinburgh Postnatal Depression Scale), CMSS (Couple Marital Satisfaction Scale, IMS (Index of Marital Satisfaction), LES (Life Event Scale) were studied in Peripartum Period.Results:Among a total of 387 participants about half 189 (48.8%) were in 19-25 years of Age. Almost 30% and 40% had dissatisfied married life as per the CMS and IMS scales respectively. Just above 42% were screen positive for depression antenatally with EPDS & 39% (n = 151) in the immediate postpartum period. From these 151 screen positive cases in immediate postpartum period, 138 participants were followed up at 4-6 weeks (13 were lost to follow up) and up to 115 of 138 (83.3%) were screen positive for depression (N= 387, 29.7%), which was statistically significant (p<0.001). With EPDS during antenatal period there was no statistically significant relationship of depression with Education (p = 0.195), Occupation (p = 0.651) and pregnancy planned or unplanned (p = 0.223), whereas, Joint family, participants with dissatisfied marital relationship had increased risk of depression as evidenced by IMS and CMSS (p < 0.001). Participants with a previous male gender baby had less risk of developing depression (p< 0.001) & participants with previous 2 female children had increased risk of depression (p< 0.001).Conclusions:This study highlights importance of screening for maternal mental health problems during Peripartum period. Depression in immediate postpartum period is good predictor for increased risk of depression at 4-6 weeks postpartum.

The mediating role of motivated strategies in the relationship between formative classroom assessment and academic well-being in medical students: a path analysis

Background The support of students' academic well-being is one of the main agendas of medical education. For medical students, well-being can help prevent burnout and provides students with grounds for their future healthcare setting. The aim of this study was to examine the mediating role of motivated strategies for learning in the relationship between formative assessment and academic well-being. Method The present cross-sectional study was performed on 391 undergraduate students of medical sciences selected by a convenient sampling method. The measuring instruments used in this study included motivated strategies for learning questionnaire (Pintrich and De Groot), classroom assessment approaches questionnaire (Yousefi Afrashteh et al.) and Academic well-being Questionnaire (Pietarinen et al.). In order to analyze the data, SPSS-26 software was used for descriptive statistics and correlation matrix, and LISREL-10.20 software was used to do path analysis and determine the relationships between variables within the model. Results Findings showed that formative assessment is a significant resource in shaping subscale of motivated strategies for learning (self-efficacy, intrinsic value, test anxiety, cognitive strategies and self-regulation). Moreover, the results demonstrated that the self-regulated learning strategies is a crucial determinant of academic well-being and is a mediator between formative assessment and academic well-being. Conclusion These findings suggest the important value and necessity of formative assessment in medical science classes which can indirectly lead to improve students’ academic well-being.

Post-Intensive Care Syndrome in Survivors from Critical Illness including COVID-19 Patients: A Narrative Review

Current achievements in medical science and technological advancements in intensive care medicine have allowed better support of critically ill patients in intensive care units (ICUs) and have increased survival probability. Post-intensive care syndrome (PICS) is a relatively new term introduced almost 10 years ago, defined as “new or worsening impairments in physical, cognitive, or mental health status arising after critical illness and persisting beyond acute care hospitalization”. A significant percentage of critically ill patients suffer from PICS for a prolonged period of time, with physical problems being the most common. The exact prevalence of PICS is unknown, and many risk factors have been described well. Coronavirus disease 2019 (COVID-19) survivors seem to be at especially high risk for developing PICS. The families of ICU survivors can also be affected as a response to the stress suffered during the critical illness of their kin. This separate entity is described as PICS family (PICS-F). A multidisciplinary approach is warranted for the treatment of PICS, involving healthcare professionals, clinicians, and scientists from different areas. Improving outcomes is both challenging and imperative for the critical care community. The review of the relevant literature and the study of the physical, cognitive, and mental sequelae could lead to the prevention and timely management of PICS and the subsequent improvement of the quality of life for ICU survivors.

Knowledge, Attitude, and Perception on Bequeathing of Bodies for Medical Education and Research among Health Science Students of the University of Health and Allied Sciences.

Abstract Background: Cadaveric dissection is an established effective teaching method in anatomical science education. Cadaver acquisition for dissection is however based on voluntary body bequeathment. As a result of the increasing numbers of medical schools and students intake, the challenges of inadequate bodies for education became visible in most parts of the world as the main cadaver source remains anonymous corpses in the custody of the state. Cultural and religious beliefs or commercial purposes are among several factors that influence the decision about body donations. This study investigates the knowledge, attitude and perception of body bequeathing among health science students who benefitted or are potential beneficiary of cadaveric studies and identified factors influencing the bequest of bodies in Ghana for educational purposes among students in University of Health and Allied Sciences. Method: This was a cross-sectional descriptive study. The study recruited 513 students in the bachelor programmes for medicine, physician assistantship, nursing, midwifery, pharmacy and allied sciences at various levels. Both closed-and open-ended questions contained in a designed Questionnaire were administered. Result: About Seventy-four percent (74.1%) of respondents had heard of body bequeathal. Majority (98.3%) agreed body bequeathal was important. However, only 39.6% knew the requirements and processes of body bequeathal. Most (>90%) had a negative attitude towards body bequeathal. Conclusion: The study concluded that there was a high awareness of the importance of body bequeathal for medical education and research but very low procedural knowledge on bequeathing a body among health science students. Also most were unwillingness to donate their body or even encouraging others to donate their body. It is therefore recommended that the medical schools should set up accessible body bequeathal programmes that provides opportunities for interested individuals to be readily assisted through the process of body bequeathal. Keywords: Body Bequeathal, Medical Science Education, Cadaveric Dissection, Anatomical education

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, biomedical image processing makes it easier to detect and locate brain tumors. In this study, a segmentation and detection method for brain tumors was developed using images from the MRI sequence as an input image to identify the tumor area. This process is difficult due to the wide variety of tumor tissues in the presence of different patients, and, in most cases, the similarity within normal tissues makes the task difficult. The main goal is to classify the brain in the presence of a brain tumor or a healthy brain. The proposed system has been researched based on Berkeley’s wavelet transformation (BWT) and deep learning classifier to improve performance and simplify the process of medical image segmentation. Significant features are extracted from each segmented tissue using the gray-level-co-occurrence matrix (GLCM) method, followed by a feature optimization using a genetic algorithm. The innovative final result of the approach implemented was assessed based on accuracy, sensitivity, specificity, coefficient of dice, Jaccard’s coefficient, spatial overlap, AVME, and FoM.

A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and Applications

Recent advancements in deep learning architecture have increased its utility in real-life applications. Deep learning models require a large amount of data to train the model. In many application domains, there is a limited set of data available for training neural networks as collecting new data is either not feasible or requires more resources such as in marketing, computer vision, and medical science. These models require a large amount of data to avoid the problem of overfitting. One of the data space solutions to the problem of limited data is data augmentation. The purpose of this study focuses on various data augmentation techniques that can be used to further improve the accuracy of a neural network. This saves the cost and time consumption required to collect new data for the training of deep neural networks by augmenting available data. This also regularizes the model and improves its capability of generalization. The need for large datasets in different fields such as computer vision, natural language processing, security and healthcare is also covered in this survey paper. The goal of this paper is to provide a comprehensive survey of recent advancements in data augmentation techniques and their application in various domains.

Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization

Nowadays, many people are suffering from several health related issues in which Coronary Artery Disease (CAD) is an important one. Identification, prevention and diagnosis of diseases is a very challenging task in the field of medical science. This paper proposes a new feature optimization technique known as PSO-Ensemble1 to reduce the number of features from CAD datasets. The proposed model is based on Particle Swarm Optimization (PSO) with Ensemble1 classifier as the objective function and is compared with other optimization techniques like PSO-CFSE and PSO-J48 with two benchmark CAD datasets. The main objective of this research work is to classify CAD with the proposed PSO-Ensemble1 model using the Ensemble Technique.

Effect of Follow-up Telephone by Enterostomal Nurses on Patients with Permanent Colostomy: A Systematic Review

Background: Patients with permanent colostomy experience quality of life (QoL) decrease, complications, and colostomy adjustment problems. Technology-based interventions can be provided with telephone follow-up (TFU) to provide health education and advice on managing symptoms, identifying complications, and providing quality care services. Objective: To systematically describe and assess the effect of TFU on permanent colostomy include population, model and duration, instruments used, and effect of TFU. Materials and Methods: A systematic review was carried out using the Randomized Controlled Trial (RCT) approach in eight databases, including PubMed, Proquest, ScienceDirect, EBSCOhost, CANCERLIT, Wiley, Gray literature, and Scopus, to identify studies reported in English, published in the last ten years, available full text, and about TFU in permanent colostomy patients. Results and Discussion: Based on the 11 RCT articles analyzed, it was found that the TFU duration ranged from 27 days to 3 years. The TFU improved self-efficacy, QoL, colostomy adjustment, self-care, self-management, service satisfaction, and complications. Conclusion: The TFU has more effect on self-efficacy, QoL, and complications in patients with a permanent colostomy, and effective TFU was performed for at least three months. Further research is needed on the frequency or duration of telephone calls. Bangladesh Journal of Medical Science Vol. 21(1) 2022 Page : 54-66