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The clustering analysis approach treats multivariate data tuples as objects and groups them into clusters based on their similarities or dissimilarities within the dataset. However, in modern world, a significant volume of data is continuously generated from diverse sources over time. In these dynamic scenarios, the data is not static but continually evolves. Consequently, the interesting patterns and inherent subgroups within the datasets also change and develop over time. The researchers have paid special attention to monitoring changes in cluster solutions of evolving streams. For this matter, several algorithms have been proposed in the literature. However, to date, no study has examined the effect of variability in cluster sizes on the evolution of cluster solutions. Moreover, no guidance is available on determining the impact of cluster sizes on the type of changes they experience in the streams. In the present simulation study using artificial datasets, the evolution of clusters is examined concerning the variability in cluster sizes. The findings are substantial because tracing and monitoring the changes in clustering solutions have a wide range of applications in every field of research. This study determines the minimum sample size required in the clustering of time-stamped datasets. [ABSTRACT FROM AUTHOR]

Clustering analysis' primary purpose is to divide a dataset into a finite number of segments based on the similarities between items. In recent years, a significant amount of study has focused on the spatio-temporal aspects of clustering. However, clusters are no longer regarded as static objects since changes influence them in the underlying population. This paper describes an R package implementing the MONIC framework for tracing the evolution of clusters extracted from temporal datasets. The name of the package is clusTransition, which stands for Cluster Transition. The algorithm is based on re-clustering cumulative datasets that evolve at successive time-points and monitoring the transitions experienced by the clusters in these clustering solutions. This paper's contribution is to demonstrate how the package clusTransition is developed in the R programming language, and its workflow is discussed using hypothetical and real-life datasets. [ABSTRACT FROM AUTHOR]

This study enumerates the evolution of basic human values orientations and the dynamic relationship between them, computed from Schwartz's value survey conducted in European nations. For this purpose, eight datasets related to the human value scale were extracted from the European Social Survey; each corresponds to a single round conducted cross-sectionally every two years since 2001. Change detection algorithm was implemented to the cluster solutions of temporal datasets, and the evolution of important clusters was traced. Finding of the study reveals that Universalism and Benevolence values are on the rise in European societies in the last couple of decades. Most of the European inhabitants believe in the smooth group functioning and form the organismic needs of cooperation. The people prefer anxiety-free life, and love for nature, environment, humanity, and kindness to other beings in society are essential constructs for them. They avoid self-centred behaviour and prefer social physiognomies. [ABSTRACT FROM AUTHOR]

Fucosyltransferase 2 (FUT2) catalyzes the biosynthesis of A, B, and H antigens and other important glycans, such as (Sialyl Lewisx) sLex, and (Sialyl Lewisy) sLey. The production of these glycans is increased in various cancers, hence to design and develop specific inhibitors of FUT2 is a therapeutic strategy. The current study was designed to identify the inhibitors for FUT2. In silico screening of 300 synthetic compounds was performed. Molecular docking studies highlighted the interactions of ligands with critical amino acid residues, present in the active site of FUT2. The epitope mapping in ligands was performed using the STD-NMR experiments to identify the interactions between ligands, and receptor protein. Finally, we have identified 5 lead compounds 4, 5, 26, 27, and 28 that can be studied for further development as cancer therapeutic agents. [ABSTRACT FROM AUTHOR]

Background: Sepsis is one of the major causes of neonatal mortality in Pakistan. This study aimed to investigate the treatment outcomes, antibiotic use and its resistance pattern among neonatal sepsis patients attending a tertiary care hospital in Pakistan. We also aimed to identify the factors affecting mortality in neonatal sepsis patients. Methods: A descriptive, cross-sectional study was conducted in the pediatric wards of the Bahawal Victoria Hospital, Bahawalpur, Pakistan. All eligible neonatal sepsis patients who were registered at the study site from January 1, 2019 to June 30, 2019 were included in the study. The data collection form included information on patient's characteristics, antibiotic use and its sensitivity pattern, laboratory and microbiological data, and final treatment outcomes. Treatment outcomes included, discharged (with treatment success), leave against medical advice (LAMA), discharged on request (DOR) and death. Multivariable binary logistic regression analysis was used to find the independent factors associated with death. A p-value of less than 0.05 was considered statistically significant. Results: Among the total 586 patients, 398 (67.9%) were male, 328 (56%) were preterm, 415 (70.8%) were diagnosed with early onset sepsis, 299 (51%) were born with low birth weight. Most of the patients (n = 484, 82.6%) were treated with amikacin+cefotaxime at the start of treatment. Culture was positive in 52 (8.9%) patients and the most commonly identified bacteria included, Klebsiella species (n = 19, 36.5%) followed by E. coli (n = 15, 28.5%) and Staphylococcus aureus (n = 8, 15.4%). The identified bacterial isolates showed high level of resistance against the antibiotics initiated at the start of the treatment, while resistance against piperacillin+tazobactam, imipenem, vancomycin and linezolid was very low. Just under half of the patients (n = 280, 47.8%) successfully completed the treatment (i.e., discharged with treatment success), while 123 (21%) patients died during treatment. In multivariable binary logistic regression, the factors which still remained significantly associated with neonatal death included, preterm delivery (AOR 9.59; 95% CI 4.41, 20.84), sub-optimal birth weight (AOR 5.13; 95% CI 2.19, 12.04), early onset sepsis (AOR 2.99; 95% CI 1.39, 6.41) and length of hospital stay (AOR 0.76; 95% CI 0.67, 0.88). Conclusion: The mortality rate associated with sepsis was high in our study cohort. The bacterial isolates showed high level of resistance against the antibiotics started as the empiric therapy. Rational use of antibiotics can decrease the adverse outcomes in neonatal sepsis patients. [ABSTRACT FROM AUTHOR]

Background: Patients’ knowledge about their prescribed medicines is one of the most important antecedents of successful therapy. Poor knowledge about medicines can lead to serious consequences such as non-adherence and misunderstanding of the significance of adverse events. The objective of this study is to understand the factors that are responsible for a patients’ lack of knowledge regarding their medicines, by taking the perspective of the patient as well as that of healthcare professionals. Much of the work in this area has been undertaken in the setting of developed or semi-developed countries, and there is a scarcity of information from developing nations such as Pakistan. Methods: This was a large qualitative study set in the hospital outpatient environment in a teaching hospital in the Punjab province of Pakistan. Data were collected from dialogue with patients (n = 19) and healthcare providers (n = 16) i.e., doctors and dispensers (where a dispenser is a person who merely dispenses medicines; i.e. is not a pharmacist) through in-depth semi-structured interviews. Patients having limited knowledge about their dispensed medicines were assessed using a checklist. The healthcare providers were recruited through a convenience sampling strategy, based on their availability and willingness to participate in the study. Based on the objectives of the study, a pilot tested interview protocol was developed, and used to conduct the interviews. The sample size was controlled by using saturation point criteria. All interviews were audio recorded and transcribed verbatim. The data were analyzed to draw conclusions using inductive thematic content analysis. Results: The analysis of data yielded 31 categories (patients = 19, healthcare professionals = 12), 10 subthemes and three themes. The major themes were healthcare professional-related factors, patient-related factors and system-related factors. The health professional related subthemes included: behaviour and attitude and professional liabilities and liaison. The patient related subthemes included: eagerness of the patients and lack of understanding and misconception. The system-related factors included: patients with special needs, perceived role of the pharmacist, prescription and medicines, and staff workload. Conclusion: Healthcare professional related, patient related and system related factors have a significant influence on patients’ knowledge about dispensed medicines. The non-professional behaviour of doctors, increased staff workload, inadequate time and attention provided by healthcare professionals to patients, illiteracy of patients, lack of specialized labelling on medicines for illiterate patients and absence of pharmacists at the hospital, were the major concerns identified in this study. The study points to a need for appropriate patient education and counselling with regards medicines, improved coordination between hospital staff, and provision of some basic system-related facilities which are pivotal for enhancing patients’ knowledge and adherence to their treatment regimens. [ABSTRACT FROM AUTHOR]

Background: Implementation of the Charter to protect patients’ rights is an important criterion to achieve patient-centered approach and receive financial support from the Global Fund. Our study aims to explore the knowledge of tuberculosis (TB) patients about their rights and responsibilities at the Chest Disease Unit of the Bahawal Victoria Hospital, Bahawalpur, Pakistan. Methods: This was a qualitative study. The data from purposefully selected TB patients was collected by in-depth interviews. Eligibility criteria included confirmed diagnosis of TB and enrollment in the TB program. A pilot tested interview protocol was based upon the objectives of the study, and was used uniformly in each interview to maintain the consistency. The sample size was limited by applying the saturation criteria. All interviews were audiotaped and transcribed verbatim. Inductive thematic content analysis was applied to analyze the data and draw conclusions. Results: Out of the total 16 patients, four were female, and seven were illiterate. Eight patients were known cases of multi-drug resistant TB. Analysis of the data yielded seven themes; tuberculosis care services, moral support and stigmatization, dignity and privacy, complaints, fear of losing job, information sharing and compliance to the treatment plan, and contribution to eradicate TB. First five represented the rights section while latter two were related to the responsibilities section of the Charter. Conclusion: Discriminatory access to TB care services and the right to privacy were two major concerns identified in this study. However, the respondents recognized their responsibilities as a TB patient. To ensure uninterrupted investment from the Global Fund, there is a need to implement fair TB care policies which support human rights-based approach. [ABSTRACT FROM AUTHOR]

Among other diseases, Covid 19 creates a critical situation around the world. Five layers have been recorded so far, resulting in the loss of millions of lives in different countries. The virus was thought to be contagious, so the government initially severely forced citizens to keep a distance from each other. Since then, several vaccines have been developed that play an important role in controlling mortality. In the case of Covid-19 mortality, the government should be forced to take significant steps in the form of lockdown, keeping you away or forcing citizens to vaccinate. In this paper, modeling of Covid-19 death rates is discussed via probability distributions. To delineate the performance of the best fitted model, the mortality rate of Pakistan and Afghanistan is considered. Numerical results conclude that the NFW model can be used to predict the mortality rate for Covid-19 patients more accurately than other probability models. [ABSTRACT FROM AUTHOR]