Dynamical behaviour and meta heuristic optimization of a hospital management software system.

The growing demand for easily available healthcare in recent years has fuelled the digitization of healthcare services. The Hospital Management System (HMS) software stands out as a comprehensive solution among the software systems and tools that hospitals and clinics are developing in tandem with this trend. In order to effectively manage many facets of hospital operations, in this paper, we propose an approach for investigating software of this kind. Thus, we characterise the HMS software as a unique sort of batch arrival retrial queueing system (QS) that can handle both ordinary and priority patient demands. Furthermore, it permits patient resistance (balk) and departure (renege) in specific circumstances. The proposed model is additionally deployed within the framework of Bernoulli working vacation. The supplementary variable technique (SVT) has been utilised to obtain the necessary results. ANFIS, a soft computing tool, is used to validate the analytical results as well. Finally, this study seeks to enhance the cost-effectiveness of software creation by employing four unique optimization methods, aiming to achieve optimal efficiency in resource utilization.

Pediatric-type diffuse low-grade gliomas.

The World Health Organization's 5th edition classification of Central Nervous System (CNS) tumors differentiates diffuse gliomas into adult and pediatric variants. Pediatric-type diffuse low-grade gliomas (pDLGGs) are distinct from adult gliomas in their molecular characteristics, biological behavior, clinical progression, and prognosis. Various molecular alterations identified in pDLGGs are crucial for treatment. There are four distinct entities of pDLGGs. All four of these tumor subtypes exhibit diffuse growth and share overlapping histopathological and imaging characteristics. Molecular analysis is essential for differentiating these lesions.

Novel approaches to fake news and fake account detection in OSNs: user social engagement and visual content centric model.

With an increase in the number of active users on OSNs (Online Social Networks), the propagation of fake news became obvious. OSNs provide a platform for users to interact with others by expressing their opinions, resharing content into different networks, etc. In addition to these, interactions with posts are also collected, termed as social engagement patterns. By taking these social engagement patterns (by analyzing infectious disease spread analogy), SENAD(Social Engagement-based News Authenticity Detection) model is proposed, which detects the authenticity of news articles shared on Twitter based on the authenticity and bias of the users who are engaging with these articles. The proposed SENAD model incorporates the novel idea of authenticity score and factors in user social engagement centric measures such as Following-followers ratio, account age, bias, etc. The proposed model significantly improves fake news and fake account detection, as highlighted by classification accuracy of 93.7%. Images embedded with textual data catch more attention than textual messages and play a vital role in quickly propagating fake news. Images published have distinctive features which need special attention for detecting whether it is real or fake. Images get altered or misused to spread fake news. The framework Credibility Neural Network (CredNN) is proposed to assess the credibility of images on OSNs, by utilizing the spatial properties of CNNs to look for physical alterations in an image as well as analyze if the image reflects a negative sentiment since fake images often exhibit either one or both characteristics. The proposed hybrid idea of combining ELA and Sentiment analysis plays a prominent role in detecting fake images with an accuracy of around 76%.

A cognitive IoT-based framework for effective diagnosis of COVID-19 using multimodal data.

The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods for COVID-19 prediction using a single modality. However, none of them predicts with 100% accuracy, as each individual exhibits varied symptoms for the disease. To decrease the rate of misdiagnosis, multiple modalities can be used for prediction. Besides, there is also a need for a self-diagnosis system to narrow down the risk of virus spread in testing centres. Therefore, we propose a robust IoT and deep learning-based multi-modal data classification method for the accurate prediction of COVID-19. Generally, highly accurate models require deep architectures. In this work, we introduce two lightweight models, namely CovParaNet for audio (cough, speech, breathing) classification and CovTinyNet for image (X-rays, CT scans) classification. These two models were identified as the best unimodal models after comparative analysis with the existing benchmark models. Finally, the obtained results of the five independently trained unimodal models are integrated by a novel dynamic multimodal Random Forest classifier. The lightweight CovParaNet and CovTinyNet models attain a maximum accuracy of 97.45% and 99.19% respectively even with a small dataset. The proposed dynamic multimodal fusion model predicts the final result with 100% accuracy, precision, and recall, and the online retraining mechanism enables it to extend its support even in a noisy environment. Furthermore, the computational complexity of all the unimodal models is minimized tremendously and the system functions effectively with 100% reliability even in the absence of any one of the input modalities during testing.

Melanoma in a cohort of organ transplant recipients: Experience from a dedicated transplant dermatology clinic in Victoria, Australia.

BACKGROUND: There is limited information on the profile of melanomas diagnosed in a specialist transplant dermatology clinic. OBJECTIVE: To describe the incidence and characteristics of incident primary melanomas in a cohort of organ transplant recipients (OTRs) attending a specialized transplant dermatology clinic and determine the number of pigmented lesions needed to excise for every melanoma diagnosed. METHODS: A retrospective study of 327 OTRs monitored by an Australian clinic during a 10-year period. RESULTS: There were 11 incident melanomas diagnosed during a total follow-up of 1280 patient-years. The mean interval between the first transplant and diagnosis was 5.5 years. Only 2 melanomas were >1 mm in Breslow thickness. Seven melanomas (64%) arose de novo. A contiguous nevus was present in 4 cases. Metastatic disease did not develop in the melanoma patients during the follow-up period, and all remain alive. The needed to excise for every melanoma diagnosed ratio was 16:1. LIMITATIONS: The crude incidence rates were age standardized, unlike the comparison rates of melanoma in the general population, and the cohort was small. CONCLUSION: Most melanomas diagnosed in OTR patients attending a specialized transplant dermatology service were detected early. Our data suggest early detection may reduce the proportion of OTRs presenting with thick melanomas, thus improving prognosis and patient outcomes. A needed to excise for every melanoma diagnosed ratio of 16:1 is not unreasonable for this cohort of high-risk patients. To our knowledge, this is the first time this ratio has been calculated for a cohort of OTRs.

Oral Tranexamic Acid for the Treatment of Melasma: A Review.

BACKGROUND: Melasma is a common acquired disorder of hyperpigmentation that commonly affects those with skin of color. Tranexamic acid (TXA) is a novel treatment for melasma that has a multimodal mechanism of action. OBJECTIVE: To provide a comprehensive review of the literature regarding the evidence on the mode of action, safety profile, and efficacy of TXA in the treatment of melasma. MATERIALS AND METHODS: The literature was searched for publications on TXA in the treatment of melasma using MEDLINE, Scopus, and Google Scholar. RESULTS: Oral TXA has clearly demonstrated the efficacy for melasma in Asian skin, even in low doses (e.g., 500 mg daily) over short periods (8-12 weeks). It is also a safe therapeutic option, which is easy to administer with few and mild side effects. Studies have shown that TXA does not increase the thromboembolic risk, although patients should be screened carefully for contraindications and risk factors prior to commencement of the therapy. CONCLUSION: Oral TXA is a safe and efficacious treatment for refractory melasma. It should be considered in cases that are unresponsive to topical hydroquinone and combination topical therapy over a period of approximately 12 weeks and without contraindications to oral TXA.

Genetic profile of 81 retinoblastoma patients from a referral hospital in southern India.

PURPOSE: To determine chromosomal abnormalities and inheritance pattern in patients with retinoblastoma from a referral hospital in southern India. MATERIALS AND METHODS: Eighty-one retinoblastoma patients from 78 families were included in this study. Peripheral venous blood was taken for chromosomal analysis and pedigree was ascertained for segregation analysis. RESULTS: Male to female ratio was 1.7:1, 55.56% were bilateral retinoblastoma, the mean age of onset was 12.37 months in bilateral and 33.07 months in unilateral cases (p=0.048). Majority (90.12%) had sporadic inheritance and 6.17% had autosomal dominant inheritance. In chromosomal abnormalities, 8.33% had 13q14 deletion, three cases had de novo balanced translocations. CONCLUSION: The age of onset of the disease was much earlier in the bilateral cases compared to unilateral cases. Sporadic inheritance was predominant while only a small percentage of patients had autosomal dominant inheritance. The percentage of patients with 13q14 deletion was higher than reported in the literature and three novel chromosomal translocations were observed. This is one of the largest series of cases reported from India.