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Minerals are essential to our diet. They affect nerve and muscle function, build materials for our bones, and regulate the body's water balance. The bioaccessibility and bioavailability of cereal minerals depend upon various aspects, such as plant cell wall configuration, storage chemical form of a mineral, plant tissue localization, and processing conditions. Fortification of minerals is done through classical methods and agro-biotechnological techniques to combat micronutrient deficiencies and improve bioavailability. The recent innovative approaches (chelation and encapsulation) overwhelm issues associated with classical fortification methods, specifically those influencing bioavailability and organoleptic properties. Food processing techniques, such as fermenting, soaking, cooking, and germination, significantly improved mineral bioavailability by reducing phytic acid and other antinutritional factors. Minerals, particularly micro-ones, are lost irreversibly during food processing, and their losses can be reduced by changing the processing strategies. The current review explores minerals in cereals, their bioaccessibility, bioavailability, fortification strategies, the impact of food processing on bioavailability, and their therapeutic potential.

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. ---Continue

Abstract 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

Background and objective: Rising incidence of mental health problems is a serious issue all over the world. Adolescents living in orphanages are at a particular risk as they have numerous challenges in their life and coping with them requires adequate life skills. This study aims to assess the emotional and behavioral issues and the coping strategies adopted by adolescent orphans in Pakistan.Methods: This cross-sectional study comprises 109 adolescent orphans living in different orphanages. The Strengths and Difficulties Questionnaire for evaluating the emotional and behavioral problems (EBPs) was used while KIDCOPE scale was adopted to assess the coping strategies.Results: About 34.9% of orphans fell in an abnormal range of EBP and 22.9% were in the borderline zone. The most prevalent problem was conduct (25.7%) followed by peer problems (24.8%), emotional instability (18.3%), hyperactivity (17.4%), and prosocial behavior (11%). A significant and positive correlation was observed between peer problems and maladaptive strategies (r = 0.191, p = 0.047) and between prosocial behavior and adaptive strategies (r = 0.294, p = 0.002).Conclusion: Orphans residing in orphanages suffer from behavioral and emotional problems and are using maladaptive coping strategies. It is highly suggestive to monitor and maintain an optimal psychological health of this vulnerable population in our country.

Background and Objective: All human beings are vulnerable when deprived of their liberty, but the experience of imprisonment has a significant impact on the social, economic, spiritual, intellectual, psychological, and physical health and well-being of different categories of prisoners, particularly children, women, and older people. This study has been conducted to determine the health problems of female prisoners and their level of access to healthcare services.Methods: TThis was a descriptive cross-sectional study. A sample of 108 female prisoners was included by convenience sampling from KotLakh Pat Jail, Lahore. Healthcare access and women’s health problems were assessed by a self-structured questionnaire. Data were codedand analyzed by using Statistical Package for the Social Sciences version 20.0. Results: Out of 108 females, 78.7% were not satisfied with the health facilities. About 11.1% of the prisoners were suffering from hepatitis C and 1 prisoner was diagnosed with human immunodeficiency virus. Preplacement and periodical health examinations were not conducted as a routine, while stigmatization by healthcare providers was another major issue that female inmates faced during imprisonment.Conclusion: Female inmates were not satisfied with the overall quality of healthcare provided to them. Poor healthcare access combined with communicable diseases adversely affects their physical and psychological well-being, leading to compromised reformation, rehabilitation, and integration back into society.

Background and Objective: The elderly face various health challenges in later life as their immune defenses deteriorate leaving them quite vulnerable and frail. The situation becomes all the more desperate when the basic health support system is compromised or simply unavailable. This study has been conducted to assess the perceived health status of residents of local old-age homes and their access to available health services.Methods: This cross-sectional study was carried out on 119 elderly subjects living in old age homes (OAHs) in Lahore city of Pakistan. Using the Short Form 36 Health Survey Questionnaire, health-related outcomes of the geriatric population were assessed under eight domains while a self-structured questionnaire was used to determine the level of available health care facilities.Results: The mean age of the study population was 69.88 ± 5.08 years The lowest mean score is observed in the pain domain i.e., 19.20 ± 12.12, whereas a little better score has been seen for emotional well-being i.e., (58.28 ± 26.58). Insufficient health care services have been regarded as a major issue for the elderly face in OAHs.Conclusion: The elderly population living in OAHs suffer from poor health status and face multiple barriers to accessing basic health care facilities.

The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others. The imbalanced datasets are balanced by applying resampling and various solutions are designed to tackle such datasets that mainly focus on class distribution issues. The imbalanced data is rebalanced using these methods. This paper introduces a technique for balancing data through two stages: first, oversampling methods are utilized in the process of rebalancing such imbalanced dataset using the single-point crossover to generate the new data of minority classes, second, it searches for an optimal subset of the imbalanced and balanced datasets by Jellyfish Search (JS) which is an optimization method. Experiments are performed on 18 real imbalanced datasets, and results are compared with famous oversampling methods and the recently published ACOR (Ant Colony Optimization Resampling) method in terms of different appraisal measurements. Higher performance is recorded by the proposed method and comparability with well-known and recent techniques.

This paper proposes a new meta-heuristic optimization algorithm, namely Mud Ring Algorithm (MRA) that mimics the mud ring feeding behaviour of bottlenose dolphins in the Atlantic coast of Florida. The inspiration of MRA is mainly based on the foraging behaviour of bottlenose dolphins and their mud ring feeding strategy. This strategy is applied by dolphins to trap fish via creating a plume by a single dolphin moving his tail swiftly in the sand and swims around the group of fish. The fishes become disoriented and jump over the surface only to find the waiting mouths of dolphins. MRA optimization algorithm mathematically simulates this feeding strategy and proves its optimization effectiveness through a comprehensive comparison with other meta-heuristic algorithms. Twenty-nine benchmark functions and four commonly used benchmark engineering challenges are used in the comparison. The statistical comparisons and results prove that the proposed MRA has the superiority in dealing with these optimization problems and can obtain the best solutions than other meta-heuristic optimizers.

Feature selection plays a crucial role in order to mitigate the high dimensional feature space in different classification problems. The computational cost is reduced, and the accuracy of the classification is improved by reducing the dimension of feature space. Hence, in the classification task, finding the optimal subset of features is of utmost importance. Metaheuristic techniques have proved their efficacy in solving many real-world optimization issues. One of the recently introduced physics-inspired optimization methods is Archimedes Optimization Algorithm (AOA). This paper proposes an Enhanced Archimedes Optimization Algorithm (EAOA) by adding a new parameter that depends on the step length of each individual while revising the individual location. The EAOA algorithm is proposed to improve the AOA exploration and exploitation balance and enhance the classification performance for the feature selection issue in real-world data sets. Experiments were performed on twenty-three standard benchmark functions and sixteen real-world data sets to investigate the performance of the proposed EAOA algorithm. The experimental results based on the standard benchmark functions show that the EAOA algorithm provides very competitive results compared to the basic AOA algorithm and five well-known optimization algorithms in terms of improved exploitation, exploration, local optima avoidance, and convergence rate. In addition, the results based on sixteen real-world data sets ascertain that reduced feature subset yields higher classification performance when compared with the other feature selection methods.

Since the COVID-19 outbreak has emerged, every nation and every government are trying its best to combat the disease and develop strategies for better prevention, diagnosis, treatment and management of this lethal virus. Though the mortality rate is far less than any other pandemic the world has experienced, still patients with co-morbid conditions and immune system disorders are at high risk of fatal outcome. There are no approved treatments for COVID-19; thus, people with COVID-19 should be referred to clinical trials. Several agents have been touted as treatments for COVID-19, but at this point, the data are insufficient to inform a recommendation for or against the use of these agents outside of clinical trials; well-conducted randomized trials will be critical in determining how COVID-19 should be treated.

In inference, this hazardous Corona virus has a wide-spread terror over the world. This drastic pandemic originates from china and prevailed darkly all over the world. It has damaged all spheres of life in developing and developed countries as well. In Pakistan, It severely has harmed all areas of a country but specifically it demolished the state of economy, health care professionals, education and employment. However, around the globe, Governments have taken serious actions but remain unable to control the situation completely. There is a dire need of proactive management to fight effectively with such unexpected far-reaching pandemic. Along with Governments it is the duty of a common man to cooperate and participate actively in stimulus actions taken by Government.

Sentiment analysis is also known as opinion mining which shows the people’s opinions and emotions about certain products or services. The main problem in sentiment analysis is the sentiment polarity categorization that determines whether a review is positive, negative or neutral. Previous studies proposed different techniques, but still there are some research gaps, i) some studies include only 3 sentiment classes: positive, neutral and negative, but none of them considered more than 3 classes ii) sentiment polarity features were considered on individual basis but none of them considered on both individual and on combined basis iii) No previous technique considered five sentiment classes with 3 sentiment polarity features such as a verb, adverb, adjective and their combinations. In this study, we propose a sentiment polarity categorization technique for a large data set of online reviews of Instant Videos. A comprehensive data set of five hundred thousand online reviews is used in our research. There are five classes (Strongly Negative, Negative, Neutral, Positive and Strongly Positive). We also consider three polarity features Verb, Adverb, Adjective and their combinations with their different senses in review-level categorization. Our experiments for review-level categorization show promising outcomes as the accuracy of our results is 81 percent which is 3 percent better than many previous techniques whose average accuracy is 78 percent.

Objective: To explore the factors contributing to late diagnosis of breast cancer in female population of Punjab, Pakistan. Methodology: Descriptive qualitative study with phenomenological approach was used. The study was carried out in University of Health Sciences, Lahore in collaboration with breast cancer clinics/departments of the Jinnah Hospital, Lahore, Sir Ganga Ram Hospital Lahore and Mayo Hospital Lahore. 15 Females diagnosed more than 4 months after appearance of symptoms of breast cancer were recruited using purposive sampling until saturation of data. Data collected through in depth semi structured face to face interviews was tape-recorded, transcribed and then analysed using thematic analysis framework method. Results: Personal/psychological factors, Sociocultural and Health care system related factors were identified as main themes emerged from data. Lack of knowledge, religious believes, use of Alternative medicine, socioeconomic status cultural myths and poorly facilitated health care facility were the most influential determinants of delay among breast cancer patients. Conclusion: Diagnosis delay is very significant health problem in women with breast cancer linked with multiple determinants. However, educating women for recognition of symptoms and reinforcement to pursue for earlier medical consultation will be helpful in reducing breast cancer diagnosis delay in the country. Key terms: Breast Cancer, Metastasis, Delayed Diagnosis, Prognosis, Advance Stage. Continuous...

Abstract 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.

Instance reduction is a pre-processing step devised to improve the task of classification. Instance reduction algorithms search for a reduced set of instances to mitigate the low computational efficiency and high storage requirements. Hence, finding the optimal subset of instances is of utmost importance. Metaheuristic techniques are used to search for the optimal subset of instances as a potential application. Antlion optimization (ALO) is a recent metaheuristic algorithm that simulates antlion’s foraging performance in finding and attacking ants. However, the ALO algorithm suffers from local optima stagnation and slow convergence speed for some optimization problems. In this study, a new modified antlion optimization (MALO) algorithm is recommended to improve the primary ALO performance by adding a new parameter that depends on the step length of each ant while revising the antlion position. Furthermore, the suggested MALO algorithm is adapted to the challenge of instance reduction to obtain better results in terms of many metrics. The results based on twenty-three benchmark functions at 500 iterations and thirteen benchmark functions at 1000 iterations demonstrate that the proposed MALO algorithm escapes the local optima and provides a better convergence rate as compared to the basic ALO algorithm and some well-known and recent optimization algorithms. In addition, the results based on 15 balanced and imbalanced datasets and 18 oversampled imbalanced datasets show that the instance reduction proposed method can statistically outperform the basic ALO algorithm and has strong competitiveness against other comparative algorithms in terms of four performance measures: Accuracy, Balanced Accuracy (BACC), Geometric mean (G-mean), and Area Under the Curve (AUC) in addition to the run time. MALO algorithm results show increment in Accuracy, BACC, G-mean, and AUC rates up to 7%, 3%, 15%, and 9%, respectively, for some datasets over the basic ALO algorithm while keeping less computational time.

Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous data is involving and continuously growing in the paradigm of big-data. To extract meaningful information adaptively from big educational data, some specific data mining techniques are needed. This paper presents a clustering approach to partition students into different groups or clusters based on their learning behavior. Furthermore, the personalized e-learning system architecture is presented, which detects and responds to teaching contents according to the students’ learning capabilities. The primary objective includes the discovery of optimal settings, in which the learners can improve their learning capabilities. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP-HD) are analyzed using educational data mining. It has been observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally effective in analyzing the big data to make education systems vigorous.

Abstract 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.

OBJECTIVE: To evaluate the antihyperlipedemic effect of Ajwa date (Phoenix dactylifera) seed on serum lipid levels of diet induced hyperlipidemic rabbits. METHODS: This randomized controlled study of six months’ duration was carried out at Postgraduate Medical Institute, Lahore after obtaining permission from institutional ethical committee. Sample size was calculated at 90% power of study and 5% level of significance. Male rabbits, 32 in number weighing between 0.9-1.3 kg were divided into four groups normal control (NC), high fat diet (HFD), HFD+2%Ajwa date seed powder (HFD+2%A) and HFD+4% Ajwa date seed powder (HFD+4%A) having 8 rabbits in each group. Group NC took normal diet while all other groups were given high fat diet for whole 12 weeks. After 4 weeks groups HFD+2%A and HFD+4%A; were given diet containing Ajwa date seed powder 2gm and 4gm per 100 gm of high fat diet respectively for next 8 weeks. Lipid profile was done at 0, 4, 8 and 12 weeks. RESULTS: Serum high density lipoprotein cholesterol (HDL) is increased while serum cholesterol, triglycerides, low density lipoprotein cholesterol (LDL), atherogenic index of plasma and LDL/HDL ratio decreased significantly in both treatment groups. At 8th week HFD+4%A was more effective than HFD+2%A but at 12th week both were equally effective. CONCLUSION: Ajwa date seeds were effective in improving the lipid profile despite high fat diet. KEY WORDS: Diet, High-Fat (MeSH), Dyslipidemias (MeSH), Phoeniceae (MeSH), Ajwa Date Seed (Non-MeSH), Seeds (MeSH), Phoenix dactylifera (MeSH).

The name ambiguity problem affects the accuracy of the web search, document retrieval, and information fusion. A lot of work has been done to solve the name disambiguation problem for publication or paper, but we propose a model to solve this problem for the National Natural Science Foundation of China fund. In this paper, we propose a probabilistic Markov random fields framework to solve the problem of the National Natural Science Foundation of China fund name disambiguation. We define an objective function and use parameters learning algorithm to get the suitable parameters. Experimental results indicate that our approach significantly outperforms other different traditional clustering methods.

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.

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.