Exploring the live experiences of the nursing faculty, working in public sector institutions of Punjab regarding educational transformation from diploma to degree programme.

The objective of the study was to explore the faculty's views regarding nursing education transformation from diploma to degree programme. Phenomenological descriptive qualitative approach was adopted to explore the experiences of 10 nursing faculty members who were teaching for more than two years in two public sector nursing colleges (inclusion criteria) in Punjab-College of Nursing, Jinnah Hospital Lahore, and College of Nursing, Nishtar Hospital, Multan-from 2021 to 2022. Data was collected by in-depth interviews of the 10 participants. It was tape recorded, transcribed, and analysed by using Braun and Clarke`s thematic analysis technique. Seven themes were derived, in which good transition, change in instructional methodology, several strengths of degree programme, higher authorities' ignorance, lack of resources, resources can be managed at government level as well as institutional level, positive impact, improved skills in all dimensions were the main elements. This transition is facing challenges, and authorities need to pay proper attention, while policy formation for smooth implementation is needed.

DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology.

BACKGROUND: Complex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders. RESULTS: Here, we present an interactive and user-friendly application, called DGH-GO. DGH-GO allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may contribute to distinct disease outcome development. It can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix for the input genes by using Gene Ontology (GO). The resultant matrix can be visualized in 2D plots using different dimension reduction methods (T-SNE, Principal component analysis, umap and Principal coordinate analysis). In the next step, clusters of functionally similar genes are identified from genes functional similarities assessed through GO. This is achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and explore their effect on stratification immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying four clusters of genes that were enriched for distinct biological mechanisms and clinical outcome. In the second case study, the analysis of genes shared by different NDDs showed that genes causing multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology. CONCLUSION: DGH-GO is a user-friendly application that allows biologists to study the multi-etiological nature of complex diseases by dissecting their genetic heterogeneity. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GO.

Revealing the Connection Between Hemodialysis and Sexual Physiology in Women With End-Stage Renal Disease.

INTRODUCTION: In the recent past, the procedure of hemodialysis has frequently been opted for patients with end-stage renal disease (ESRD) around the globe. In such patients, the concern of sexual dysfunction is highly prevalent, which causes psychological as well as social deterioration in these patients. Wretchedly, this issue has been ignored in developing countries like Pakistan because of social and cultural constraints.  Objectives: The aim was to measure and compare Female Sexual Functions of Dialysis (FSFI) scores among three comparative groups: healthy controls, pre-dialysis patients, and hemodialysis patients. METHODS: A comparative cross-sectional study was carried out with 60 females aged 22-50 years in which 20 were healthy (controls) and 40 were patients with ESRD; of these 40, 20 were taking only oral medicines (pre-dialysis) and 20 were also receiving hemodialysis (hemodialysis). Married women who could read Urdu and were living with live spouses were included, and those with any psychological or psychiatric illness were excluded. Data was collected through a Likert-scaled questionnaire, Urdu translation of the FSFI questionnaire, and scores of each domain were analyzed. Single-tail one-way ANOVA was used to observe the significant difference among the three comparative groups. RESULTS: A strong statistical difference was observed among the hemodialysis, pre-dialysis, and healthy control groups when these three study groups were compared for the mean scores of all related domains of FSFI questtionarie. In each female sexual domain, i.e. Desire, Arousal, Lubrication, Orgasm, Satisfaction, and Pain, the diseased groups (pre-dialysis and hemodialysis) showed lower sexual scores than the healthy group. The lowest scores were observed in the pre-dialysis group (16.4 ± 6.8) and the highest were noticed in the healthy group (29.9 ± 1.8); the hemodialysis group (23.3 ± 5.0) expressed a moderate pattern of scores in each sexual domain. CONCLUSION: ESRD female patients who were receiving hemodialysis along with routine oral medications showed improved sexual physiology (with better FSFI scores) compared to those who were without hemodialysis.

Multi-Targeting Approach in Glioblastoma Using Computer-Assisted Drug Discovery Tools to Overcome the Blood-Brain Barrier and Target EGFR/PI3Kp110ß Signaling.

The epidermal growth factor receptor (EGFR) is upregulated in glioblastoma, becoming an attractive therapeutic target. However, activation of compensatory pathways generates inputs to downstream PI3Kp110ß signaling, leading to anti-EGFR therapeutic resistance. Moreover, the blood-brain barrier (BBB) limits drugs' brain penetration. We aimed to discover EGFR/PI3Kp110ß pathway inhibitors for a multi-targeting approach, with favorable ADMET and BBB-permeant properties. We used quantitative structure-activity relationship models and structure-based virtual screening, and assessed ADMET properties, to identify BBB-permeant drug candidates. Predictions were validated in in vitro models of the human BBB and BBB-glioma co-cultures. The results disclosed 27 molecules (18 EGFR, 6 PI3Kp110ß, and 3 dual inhibitors) for biological validation, performed in two glioblastoma cell lines (U87MG and U87MG overexpressing EGFR). Six molecules (two EGFR, two PI3Kp110ß, and two dual inhibitors) decreased cell viability by 40-99%, with the greatest effect observed for the dual inhibitors. The glioma cytotoxicity was confirmed by analysis of targets' downregulation and increased apoptosis (15-85%). Safety to BBB endothelial cells was confirmed for three of those molecules (one EGFR and two PI3Kp110ß inhibitors). These molecules crossed the endothelial monolayer in the BBB in vitro model and in the BBB-glioblastoma co-culture system. These results revealed novel drug candidates for glioblastoma treatment.

Exploring the determinants of delayed diagnosis of breast cancer in the female population of Punjab (Pakistan).

Breast Cancer (BC) is a major health issue in women all over the world. Delayed diagnosis of BC is preventable and has major effects on the patients' prognosis and survival. To explore the reasons of delayed diagnosis of BC in women in Punjab, Pakistan, a qualitative phenomenological study was conducted at the Jinnah Hospital, Sir Ganga Ram Hospital, and Mayo Hospital, Lahore. Females diagnosed more than four months after the appearance of symptoms of BC were recruited using purposive sampling until data saturation. Data collected through in-depth interviews of 15 participants using an interview guide was tape-recorded, transcribed and analysed using inductive thematic analysis framework method. Personal/psychological, Sociocultural, and Health care system related factors were the main themes that emerged from the data. Lack of knowledge, religious beliefs, use of Alternative medicine, socioeconomic status, cultural myths, and distant hospitals were the most influential determinants. Delay in diagnosis is a very significant problem in women with breast cancer and is linked with multiple determinants. However, educating women for recognition of symptoms and reinforcement to pursue earlier medical consultation will be helpful in reducing delay in breast cancer diagnosis.

Gauging the Effectiveness of Training Sessions Among Nurses Regarding Biomedical Waste Management: A Quasi-Experimental Study From a Developing Country.

Introduction Biomedical waste management (BWM) plays a crucial role in maintaining human and environmental health. Unfortunately, health-care workers (HCWs) lack the essential awareness concerning BWM and there is a dire need to adopt different strategies to improve their practices. This research aims to evaluate the effectiveness of training sessions among HCWs regarding biomedical waste management using a quasi-experimental study design. Methods This quasi-experimental study included a total of 64 nurses, selected with a systematic random sampling technique. Three days of structured training sessions were organized in the morning and evening shifts. Pertinently, pre-test and post-test were organized before and after the end of training sessions. Practices of nurses regarding BWM were also assessed before the training and after one month of training with the aid of a checklist. Results The low pre-test scores of the study participants elucidated insufficient knowledge regarding various aspects of BWM. After the three days of the structured training sessions, the analysis of post-test scores elucidated a marked improvement in the knowledge of the study participants. The practices of nurses regarding BWM were inappropriate; however, one month after the training sessions, the re-evaluation of practices showed a significant improvement. Conclusion The study showed that nurses had poor knowledge regarding BWM and were significantly improved on teaching interventions. An essential knowledge regarding BWM is therefore very useful for HCWs to protect themselves from infectious diseases. The inclusion of regular training sessions in the teaching curriculum can ensure adherence to guidelines for appropriate BWM. Assurance of ideal practices for BWM plays a key role in the prevention of nosocomial infection among HCWs.

Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling.

The performance of quantitative structure-activity relationship (QSAR) models largely depends on the relevance of the selected molecular representation used as input data matrices. This work presents a thorough comparative analysis of two main categories of molecular representations (vector space and metric space) for fitting robust machine learning models in QSAR problems. For the assessment of these methods, seven different molecular representations that included RDKit descriptors, five different fingerprints types (MACCS, PubChem, FP2-based, Atom Pair, and ECFP4), and a graph matching approach (non-contiguous atom matching structure similarity; NAMS) in both vector space and metric space, were subjected to state-of-art machine learning methods that included different dimensionality reduction methods (feature selection and linear dimensionality reduction). Five distinct QSAR data sets were used for direct assessment and analysis. Results show that, in general, metric-space and vector-space representations are able to produce equivalent models, but there are significant differences between individual approaches. The NAMS-based similarity approach consistently outperformed most fingerprint representations in model quality, closely followed by Atom Pair fingerprints. To further verify these findings, the metric space-based models were fitted to the same data sets with the closest neighbors removed. These latter results further strengthened the above conclusions. The metric space graph-based approach appeared significantly superior to the other representations, albeit at a significant computational cost.

A visual approach for analysis and inference of molecular activity spaces.

BACKGROUND: Molecular space visualization can help to explore the diversity of large heterogeneous chemical data, which ultimately may increase the understanding of structure-activity relationships (SAR) in drug discovery projects. Visual SAR analysis can therefore be useful for library design, chemical classification for their biological evaluation and virtual screening for the selection of compounds for synthesis or in vitro testing. As such, computational approaches for molecular space visualization have become an important issue in cheminformatics research. The proposed approach uses molecular similarity as the sole input for computing a probabilistic surface of molecular activity (PSMA). This similarity matrix is transformed in 2D using different dimension reduction algorithms (Principal Coordinates Analysis ( PCooA), Kruskal multidimensional scaling, Sammon mapping and t-SNE). From this projection, a kernel density function is applied to compute the probability of activity for each coordinate in the new projected space. RESULTS: This methodology was tested over four different quantitative structure-activity relationship (QSAR) binary classification data sets and the PSMAs were computed for each. The generated maps showed internal consistency with active molecules grouped together for all data sets and all dimensionality reduction algorithms. To validate the quality of the generated maps, the 2D coordinates of test molecules were computed into the new reference space using a data transformation matrix. In total sixteen PSMAs were built, and their performance was assessed using the Area Under Curve (AUC) and the Matthews Coefficient Correlation (MCC). For the best projections for each data set, AUC testing results ranged from 0.87 to 0.98 and the MCC scores ranged from 0.33 to 0.77, suggesting this methodology can validly capture the complexities of the molecular activity space. All four mapping functions provided generally good results yet the overall performance of PCooA and t-SNE was slightly better than Sammon mapping and Kruskal multidimensional scaling. CONCLUSIONS: Our result showed that by using an appropriate combination of metric space representation and dimensionality reduction applied over metric spaces it is possible to produce a visual PSMA for which its consistency has been validated by using this map as a classification model. The produced maps can be used as prediction tools as it is simple to project any molecule into this new reference space as long as the similarities to the molecules used to compute the initial similarity matrix can be computed.

An automated framework for QSAR model building.

BACKGROUND: In-silico quantitative structure-activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling. Thus, a fully automated and advanced modeling platform can be an important addition to the QSAR community. RESULTS: In the presented workflow the process from data preparation to model building and validation has been completely automated. The most critical modeling tasks (data curation, data set characteristics evaluation, variable selection and validation) that largely influence the performance of QSAR models were focused. It is also included the ability to quickly evaluate the feasibility of a given data set to be modeled. The developed framework is tested on data sets of thirty different problems. The best-optimized feature selection methodology in the developed workflow is able to remove 62-99% of all redundant data. On average, about 19% of the prediction error was reduced by using feature selection producing an increase of 49% in the percentage of variance explained (PVE) compared to models without feature selection. Selecting only the models with a modelability score above 0.6, average PVE scores were 0.71. A strong correlation was verified between the modelability scores and the PVE of the models produced with variable selection. CONCLUSIONS: We developed an extendable and highly customizable fully automated QSAR modeling framework. This designed workflow does not require any advanced parameterization nor depends on users decisions or expertise in machine learning/programming. With just a given target or problem, the workflow follows an unbiased standard protocol to develop reliable QSAR models by directly accessing online manually curated databases or by using private data sets. The other distinctive features of the workflow include prior estimation of data modelability to avoid time-consuming modeling trials for non modelable data sets, an efficient variable selection procedure and the facility of output availability at each modeling task for the diverse application and reproduction of historical predictions. The results reached on a selection of thirty QSAR problems suggest that the approach is capable of building reliable models even for challenging problems.

Postnatal diagnosis of maternal congenital heart disease: missed opportunities.

A 30-year-old primigravida with no known comorbidities presented to the emergency department at 29+6/40 gestation, with breathlessness. The initial diagnosis was pulmonary embolism, which was later revised following initial investigations and considered to be pre-eclampsia/HELLP (haemolysis, elevated liver enzymes, low platelets) syndrome. Following caesarean section and delivery of a live baby, the patient had episodes of cyanotic hypoxia and was admitted to intensive care. A provisional diagnosis of idiopathic pulmonary hypertension was performed. Decompensation led to transfer to a specialist intensive care unit for extracorporeal membrane oxygenation, where a diagnosis of patent ductus arteriosus and Eisenmenger's syndrome was made. Heart disease is the leading indirect cause of maternal death, and Eisenmenger's syndrome in pregnancy carries a 50-65% mortality. A literature review demonstrated that this is the only reported case of a postnatal diagnosis of Eisenmenger's syndrome. We considered missed opportunities to make an earlier diagnosis, so that patients and doctors will benefit from the lessons we learnt.

Protein profiling analyses of the outer membrane of Burkholderia cenocepacia reveal a niche-specific proteome.

Outer membrane proteins (OMPs) are integral ß-barrel proteins of the Gram-negative bacterial cell wall and are crucial to bacterial survival within the macrophages and for eukaryotic cell invasion. Here, we used liquid chromatography tandem mass spectrometry (LC-MS/MS) to comprehensively assess the outer membrane proteome of Burkholderia cenocepacia, an opportunistic pathogen causing cystic fibrosis (CF), in conditions mimicking four major ecological niches: water, CF sputum, soil, and plant leaf. Bacterial cells were harvested at late log phase, and OMPs were extracted following the separation of soluble proteins by one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (1D-SDS-PAGE). Protein bands were excised and identified by LC-MS/MS analysis. The proteins identified under various growth conditions were further subjected to in silico analysis of gene ontology (subcellular localization, structural, and functional analyses). Overall, 72 proteins were identified as common to the four culture conditions, while 33, 37, 20, and 10 proteins were exclusively identified in the water, CF sputum, soil, and plant leaf environments, respectively. The functional profiles of the majority of these proteins revealed significant diversity in protein expression between the four environments studied and may indicate that the protein expression profiles are unique for every condition. Comparison of OMPs from one strain in four distinct ecological niches allowed the elucidation of proteins that are essential for survival in each niche, while the commonly expressed OMPs, such as RND efflux system protein, TonB-dependent siderophore receptor, and ABC transporter-like protein, represent promising targets for drug or vaccine development.

Comparative molecular docking analysis of cytoplasmic dynein light chain DYNLL1 with pilin to explore the molecular mechanism of pathogenesis caused by Pseudomonas aeruginosa PAO.

Cytoplasmic dynein light chain 1 (DYNLL1) is a component of large protein complex, which is implicated in cargo transport processes, and is known to interact with many cellular and viral proteins through its short consensus motif (K/R)XTQT. Still, it remains to be explored that bacterial proteins also exhibit similar recognition sequences to make them vulnerable to host defense mechanism. We employed multiple docking protocols including AUTODOCK, PatchDock, ZDOCK, DOCK/PIERR and CLUSPRO to explore the DYNLL1 and Pilin interaction followed by molecular dynamics simulation assays. Subsequent structural comparison of the predicted binding site for DYNLL1-Pilin complex against the experimentally verified DYNLL1 binding partners was performed to cross check the residual contributions and to determine the binding mode. On the basis of in silico analysis, here we describe a novel interaction of DYNLL1 and receptor binding domain of Pilin (the main protein constituent of bacterial type IV Pili) of gram negative bacteria Pseudomonas aeruginosa (PAO), which is the third most common nosocomial pathogen associated with the life-threatening infections. Evidently, our results underscore that Pilin specific motif (KSTQD) exhibits a close structural similarity to that of Vaccinia virus polymerase, P protein Rabies and P protein Mokola viruses. We speculate that binding of DYNLL1 to Pilin may trigger an uncontrolled inflammatory response of the host immune system during P. aeruginosa chronic infections thereby opening a new pioneering area to investigate the role of DYNLL1 in gram negative bacterial infections other than viral infections. Moreover, by manifesting a strict correspondence between sequence and function, our study anticipates a novel drug target site to control the complications caused by P. aeruginosa infections.