IMPAK MEDIA SOSIAL TERHADAP TINGKAH LAKU SOSIAL PELAJAR DI KOLEJ VOKASIONAL

Social media networks have created a phenomenon on the internet that helps maintain relationships with other users. However, in recent times there are a handful of teenagers who are too obsessed with the use of social media that result in the phenomenon of social media addiction. Therefore, this study is conducted to examine the impact of social media on student's social behaviour in vocational colleges. The study was quantitative. The sample of the study consisted of 345 vocational college students. The research instrument involved a set of questionnaires that contained 41 items and used the Likert scale as a measure. The mean score analysis and standard deviation are used to see the purpose of using and the impact of social media among students. T-test is used to see the impact of social media impact on gender-based social behaviour. There are social media effects on students such as wasting time, depression and stress as students are exposed to immoral videos and pictures. Furthermore, there was no significant difference in the impact of social media on students based on student gender based on t-test. In conclusion, social media has had a negative impact on the students. Thus, high awareness needs to be applied to students to curb social behaviour problems

Antidiarrhoeal, antisecretory and antispasmodic activities of matricaria chamomilla are mediated predominantly through K+-channels activation

Background: Matricaria chamomilla commonly known as “Chamomile” (Asteraceae) is a popular medicinal herb widely used in indigenous system of medicine for a variety of ailments. However, there is no detailed study available showing its effectiveness in hyperactive gut disorders like, abdominal colic and diarrhoea. This study was designed to determine the pharmacological basis for the folkloric use of Matricaria chamomilla in diarrhoea. Methods: The crude aqueous-methanolic extract of Matricaria chamomilla (Mc.Cr) was studied for its protective effect in mice against castor oil-induced diarrhoea and intestinal fluid accumulation. The isolated rabbit jejunum was selected for the in-vitro experiments using tissue bath assembly coupled with PowerLab data acquisition system. Results: Oral administration of Mc.Cr to mice at 150 and 300 mg/kg showed marked antidiarrhoeal and antisecretory effects against castor oil-induced diarrhoea and intestinal fluid accumulation, simultaneously, similar to the effects of cromakalim and loperamide. These effects of plant extract were attenuated in animals pretreated with K+ channel antagonist, glibenclamide (GB) or 4-aminopyridine (4-AP). When tested in isolated rabbit jejunum, Mc.Cr caused a dose-dependent (0.3-3 mg/ml) relaxation of spontaneous and low K+ (25 mM)-induced contractions, while it exhibited weak inhibitory effect on high K+ (80 mM). The inhibitory effect of Mc.Cr on low K+-induced contractions was partially inhibited in the presence of GB, while completely blocked by 4-AP. Cromakalim, an ATP-sensitive K+ channel opener, caused complete relaxation of low K+-induced contractions with little effect on high K+. Pretreatment of tissues with GB blocked the inhibitory effects of cromakalim on low K+, while the presence of 4-AP did not alter the original effect. Verapamil, a Ca++ channel antagonist, caused complete relaxation of both low and high K+-induced contractions with similar potency. The inhibitory effect of verapamil was insensitive to GB or 4-AP. When assessed for Ca++ antagonist like activity, Mc.Cr at high concentrations caused rightward shift in the Ca++ concentration-response curves with suppression of the maximum response, similar to the effect of verapamil, while cromakalim did not show similar effect. Conclusions: This study indicates that Matricaria chamomilla possesses antidiarrhoeal, antisecretory and antispasmodic activities mediated predominantly through K+-channels activation along with weak Ca++ antagonist effect

Optimasi Prediksi Kematian pada Gagal Jantung Analisis Perbandingan Algoritma Pembelajaran Ensemble dan Teknik Penyeimbangan Data pada Dataset

Penyakit jantung merupakan penyebab utama kematian di seluruh dunia, termasuk di Indonesia. Identifikasi penyakit kardiovaskular (CVD) memerlukan pertimbangan berbagai faktor, seperti tekanan darah tinggi, kadar kolesterol, diabetes, dan lainnya, dengan gejala yang dapat bervariasi antar jenis kelamin. Meskipun angiografi dianggap metode yang akurat, biayanya tinggi dan kurang terjangkau oleh keluarga berpendapatan rendah. Biaya penyakit kardiovaskular juga memberikan dampak finansial signifikan pada sistem kesehatan. Dalam upaya untuk meningkatkan prediksi penyakit jantung, penelitian ini menggunakan metode ensemble learning, seperti Random Forest, Bagging, Adaboost, Gradient Boosting, dan XGBoost, dengan penyetelan hyperparameter. Eksperimen pada dataset gagal jantung menunjukkan bahwa penerapan teknik Synthetic Minority Over-sampling Technique (SMOTE) pada algoritma Extreme Gradient Boosting (XGB) memberikan hasil terbaik, mencapai akurasi 88.9%, F1-score 87.7%, dan Matthews Correlation Coefficient (MCC) 75.8%. Penggunaan metode balancing data, seperti SMOTE, ROS, dan RUS, secara signifikan memengaruhi performa algoritma, menyoroti pentingnya pemilihan metode sesuai dengan karakteristik dataset. Hasil ini memiliki implikasi penting dalam meningkatkan prediksi dan manajemen risiko kematian pada pasien gagal jantung secara dini dan lebih hemat biaya

Towards symbolic representation-based modeling of Temporal Knowledge Graphs

Symbolic representation helps us to represent information in a well-defined rule-driven fashion. Currently, there are several ways to represent Knowledge Graphs in general. However, in this work, we extended the implementation of symbolic representation to model domain-oriented temporal Knowledge Graphs. For symbolic representation, we incorporated Horn rules and SWRL (Semantic Web Rule Language). The presented approach is semi-autonomous: (i) we extracted hand-crafted rules and (ii) we utilized the PSyKE (Platform for Symbolic Knowledge Extraction) package to extract some rules automatically from raw data logs. For domain modeling, we targeted a smart industry environment. To validate the proposed model, we conducted a counterfactual study using Knowledge Graph and network analysis for fact-finding and filtering

Towards the Modelling of Veillance based Citizen Profiling using Knowledge Graphs

In this work we have proposed a model for Citizen Profiling. It uses veillance (Surveillance and Sousveillance) for data acquisition. For representation of Citizen Profile Temporal Knowledge Graph has been used through which we can answer semantic queries. Previously, most of the work lacks representation of Citizen Profile and have used surveillance for data acquisition. Our contribution is towards enriching the data acquisition process by adding sousveillance mechanism and facilitating semantic queries through representation of Citizen Profiles using Temporal Knowledge Graphs. Our proposed solution is storage efficient as we have only stored data logs for Citizen Profiling instead of storing images, audio, and video for profiling purposes. Our proposed system can be extended to Smart City, Smart Traffic Management, Workplace profiling etc. Agent based mechanism can be used for data acquisition where each Citizen has its own agent. Another improvement can be to incorporate a decentralized version of database for maintaining Citizen profile

A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

Knowledge Graphs are widely used to represent knowledge structures in complex domains. In most real-world scenarios, these knowledge structures are dynamic. As a result, measures must be developed to assess the robustness and usability of Knowledge Graphs in temporal settings. Additionally, the explainability of inherent knowledge constituents is crucial for the desired attention of Knowledge Graphs, particularly in temporal settings. In this paper, we developed a framework to understand the robustness of factual explainability of Knowledge Graphs. The method is further verified by using meso-level attributes of the knowledge graph. The complex network analysis along with the community structures are co-evaluated through homophilic and heterophilic properties within the graph to validate the robustness of the factual interpretations. The analysis reveals that symbolic representation could be used as a reasonable metric for extracting link-based communities

Invasive Aspergillosis Involving the Mediastinum in an Immunocompetent Patient: A Case Report

We report a rare case of invasive pulmonary aspergillosis invading the mediastinum and the left atrium. A 38-year-old female was hospitalized for cough, shortness of breath and fever. She had a past medical history of tuberculosis. Computed tomography(CT)scans identified an ill-defined enhancing mediastinal soft tissue density mass encasing the heart and major vessels. The cardiac echocardiography showed global hypokinesia, low ejection fraction and a large echogenic density in the left atrium. The pathology from the bronchoscopic biopsy observed abundant fungal hyphae which were stained with periodic Acid-Schiff and Gomori\u27s methenamine silver. Despite the treatment with antifungal agents, the patient could not be saved. Invasive pulmonary aspergillosis, which involves the mediastinum and the heart, is very rare in immunocompetent patients

DbKB a knowledge graph dataset for diabetes: A system biology approach

Diabetes has emerged as a prevalent disease, affecting millions of individuals annually according to statistics. Numerous studies have delved into identifying key genes implicated in the causal mechanisms of diabetes. This paper specifically concentrates on 20 functional genes identified in various studies contributing to the complexities associated with Type 2 diabetes (T2D), encompassing complications such as nephropathy, retinopathy, cardiovascular disorders, and foot ulcers. These functional genes serve as a foundation for identifying regulatory genes, their regulators, and protein-protein interactions.The current study introduces a multi-layer Knowledge Graph (DbKB based on MSNMD: Multi-Scale Network Model for Diabetes), encompassing biological networks such as gene regulatory networks and protein-protein interaction networks. This Knowledge Graph facilitates the visualization and querying of inherent relationships between biological networks associated with diabetes, enabling the retrieval of regulatory genes, functional genes, interacting proteins, and their relationships.Through the integration of biologically relevant genetic, molecular, and regulatory information, we can scrutinize interactions among T2D candidate genes [1] and ascertain diseased genes [2]. The first layer of regulators comprises direct regulators to the functional genes, sourced from the TRRUST database in the human transcription factors dataset, thereby forming a multi-layered directed graph. A comprehensive exploration of these direct regulators reveals a total of 875 regulatory transcription factors, constituting the initial layer of regulating transcription factors. Moving to the second layer, we identify 550 regulatory genes.These functional genes engage with other proteins to form complexes, exhibiting specific functions. Leveraging these layers, we construct a Knowledge Graph aimed at identifying interaction-driven sub-networks involving (i) regulating functional genes, (ii) functional genes, and (iii) protein-protein interactions

Short versus long duration of dual antiplatelet therapy after second-generation drug-eluting stents implantation in patients with diabetes

Background: Duration of dual antiplatelet therapy (DAPT) in patients undergoing percutaneous coronary intervention (PCI) remains uncertain, with increasing data suggestive of acceptable short-term duration. Metabolically accelerated atherosclerosis associated with diabetes makes it essential to study short-term DAPT in this subgroup. With limited studies determining optimal DAPT strategies after second-generation stents in this subset, we aimed to establish the optimal duration of DAPT in the diabetic population using second-generation stents. Question: To determine optimal DAPT duration in diabetic population undergoing PCI in 2nd generation stents. Data sources: We conducted an electronic database search of randomized controlled trials from PubMed/Medline, Embase, Cochrane, and Web of Science databases. Study design: A meta-analysis was conducted comparing outcomes of short-term (3-6 months) DAPT therapy versus long-term (12 months) DAPT therapy in the diabetic population undergoing PCI with second-generation stents. Results: A total of 5 randomized controlled trials were included with a total of 3117 diabetic patients. Short-term DAPT did not show any statistical difference from long-term DAPT in achieving primary outcomes (relative ratio: 0.96, 95% confidence interval (CI) 0.68-1.35, P = 0.84). Overall mortality (OR 0.92; 95% CI, 0.52-1.63, P = 0.98), myocardial infarction [odds ratio (OR)OR 1.02; 95% CI, 0.53-1.94, P = 0.85], stent thrombosis (OR 1.20; 95% CI, 0.55-2.60, P = 0.55), target vessel revascularization (OR 1.10; 95% CI, 0.45-2.73, P = 0.74), and stroke (OR 0.50; 95% CI, 0.082-2.43, P = 0.81) did not show any statistical difference between the 2 groups. Similarly, a subgroup analysis of study population comparing 6 versus 12 months of DAPT in diabetic population did not show any difference in net primary outcomes (relative ratio: 0.86, 95% CI 0.45-1.45, P = 0.60). There was no significant heterogeneity noted between the 2 groups. Conclusion: This meta-analysis showed no statistically significant benefit of longer DAPT over shorter DAPT therapy in patients undergoing PCI with drug-eluting stent in patients with diabete

Pharmacogenomics in the UK National Health Service: opportunities and challenges

There is increasing interest in pharmacogenomics. However, it is also widely acknowledged that implementation of pharmacogenomics into clinical practice has been slow. Implementation is being undertaken in many centres in the US, but this is not nationwide and often focused on highly specialised academic centres, driven by champions. To date, there has been no implementation on a whole country basis. The UK National Health Service (NHS) is a single integrated healthcare system, which provides free care to all patients at the point of need. Recently, there has been a drive to implement genomic medicine into the NHS, largely spurred on by the success of the 100,000 genomes project. This represents an unprecedented opportunity to implement pharmacogenomics for over 60 million people. In order to discuss the potential for implementing pharmacogenomics into the NHS, the UK Pharmacogenetics and Stratified Medicine Network, NHS England and Genomics England invited experts from academia, the healthcare sector, industry and patient representatives to come together to discuss the opportunities and challenges1. This report highlights the discussions of the workshop with the aim of providing an overview of the issues that need to be considered to enable pharmacogenomic medicine to become mainstream within the NHS