Purpose
Metabolic syndrome (MetSyn) is an important late effect of childhood cancer. The combination of rising obesity and high prevalence of under-nutrition at diagnosis makes this a unique ...population to study in LMIC (lower middle-income countries).
Methods
Children ≤ 18 years of age at cancer diagnosis, in a single center in a LMIC, who were disease free and had completed treatment at least 2 years prior to study were included. MetSyn was defined using International Federation for Diabetes criteria for Asian Indians. Logistic regression analyses were carried out to evaluate the influence of various risk factors, including delta BMI (increase in body mass index from diagnosis to evaluation), on MetSyn.
Results
A high prevalence of MetSyn (12.2%), central obesity (33%), and dyslipidemia (61.8%) were found in a cohort of 500 Asian Indian childhood cancer survivors (CCS) at a median follow-up age of 17 years. Multivariable analysis revealed older age at diagnosis ≥ 10 years, OR 2.9 (1.6–5); longer survival duration ≥ 10 years, OR 2.2 (1.3–3.8); high BMI at diagnosis, OR 3.2 (1.5–6.9); and large delta BMI ≥ 50, OR 3.15(1.7–5.9) to be independent predictors of MetSyn. Patients who were underweight or normal at diagnosis with large delta BMI ≥ 50 had very high odds (OR, 12.5, 1.7–92) of developing MetSyn compared to those with lower delta BMI.
Conclusions and implications for cancer survivors
A high prevalence of MetSyn was observed in CCS with early age at onset. Timely screening and early intervention are proven to be beneficial and delta BMI could be a useful screening tool for LMIC.
In today's cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches ...against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms-Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)-were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.
Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by isolated thrombocytopenia. There can be multiple underlying causes which need to be addressed. Helicobacter pylori (H. pylori) ...infection is one such cause, but there is paucity of data to support its association with newly diagnosed ITP in children.
We report a case of a child with Down syndrome with Graves’ disease who initially presented with pancytopenia, but later developed isolated thrombocytopenia. There were multiple possible etiologies including parvovirus B19 which resulted in delay in treatment in our patient. On detailed work up, she was found to have H. pylori infection. The patient eventually responded to H. pylori eradication therapy.
Work up for H. pylori in pediatric age group needs to be considered in case of unexplained thrombocytopenia before labeling it as primary ITP. H. pylori eradication therapy as a part of treatment for H. pylori associated newly diagnosed ITP requires to be studied on larger sample size.
Molecular abnormalities in leukemic cells are important determinants of risk stratification in Pediatric acute lymphoblastic leukemia (ALL). TCF3-PBX1 fusion is one of the common aberrations in ALL ...with doubtful prognostic significance. Therefore, aim of our study is to revisit the clinical characteristics and outcome of this abnormality in children with ALL treated at our institute.Demographic, Clinical and treatment related characteristics of 539 newly diagnosed ALL patients from January 2009 and December 2018, < 18 years of age treated on BFM-95 protocol, was abstracted from the medical records. Clinical characteristics and outcome of children with and without TCF3
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PBX1 fusion was compared.Incidence of TCF3-PBX1 fusion was observed in 24/539(4.4%) patients with a median age of 4 years (range 1–17). None of the patients in TCF3-PBX1 group had CNS or testicular disease at presentation. Day -8 prednisolone response and morphological remission at the end of induction was similar in both study groups. 5-year overall and event free survival for those with and without fusion was 75%, 70.1% and 79.5%, 69.5% respectively.The incidence of TCF3-PBX1 fusion in the present study was 4.4% and it does not have an independent prognostic significance.
Current United States Preventive Services Task Force (USPSTF) recommendations include routine screening for breast, cervical, colorectal, and lung cancer; however, two out of every three cancer cases ...occur in other indications, leading to diagnoses in advanced stages of the disease and a higher likelihood of mortality. Blood-based multi-cancer early detection (MCED) tests can impact cancer screening and early detection by monitoring for multiple different cancer types at once, including indications where screening is not performed routinely today. We conducted a survey amongst healthcare providers (HCPs), payers, and patients within the U.S. health system to understand the current utilization of cancer screening tests and the anticipated barriers to widespread adoption of blood-based MCED tests. The results indicated that the community favors the adoption of blood-based MCED tests and that there is broad agreement on the value proposition. Despite this recognition, the survey highlighted that there is limited use today due to the perceived lack of clinical accuracy and utility data, high out-of-pocket patient costs, and lack of payer coverage. To overcome the hurdles for future widespread adoption of blood-based MCED tests, increased investment in data generation, education, and implementation of logistical support for HCPs will be critical.
Personalized medicine (PM) approaches have revolutionized healthcare delivery by offering new insights that enable healthcare providers to select the optimal treatment approach for their patients. ...However, despite the consensus that these approaches have significant value, implementation across the US is highly variable. In order to address barriers to widespread PM adoption, a comprehensive and methodical approach to assessing the current level of PM integration within a given organization and the broader healthcare system is needed. A quantitative framework encompassing a multifactorial approach to assessing PM adoption has been developed and used to generate a rating of PM integration in 153 organizations across the US. The results suggest significant heterogeneity in adoption levels but also some consistent themes in what defines a high-performing organization, including the sophistication of data collected, data sharing practices, and the level of internal funding committed to supporting PM initiatives. A longitudinal approach to data collection will be valuable to track continued progress and adapt to new challenges and barriers to PM adoption as they arise.
Context: Extrapulmonary tuberculosis (EPTB) especially abdominal lymph nodal tuberculosis (LNTB) poses a unique diagnostic challenge. The clinical, cytological, and microbiological profiles, ...especially with respect to the use and role of Auramine -O (AO) stain, are not as well characterized in abdominal LNTB as cervical LNTB and were evaluated in the present comparative study. Subjects and Methods: This study was conducted in the Department of Pathology of a tertiary care hospital in Shillong, Meghalaya in 540 clinical suspected cases of tuberculosis who underwent FNAC. The smears were submitted for Leishman's stain for cytological analysis, along with ZN and Auramine O stain for demonstration of the organism, analyzed, and scored and the results were compared with culture wherever available. The results from abdominal and cervical lymph nodal tuberculosis were compared using Microsoft Excel and SPSS software. Results: Out of 540 cases, most were tuberculosis (266) followed by reactive lymphadenitis (162), malignancy, and acute necrotizing lesion. On comparing, abdominal lymph nodes (n = 163) were more likely to reveal cheesy/purulent material macroscopically, necrotizing lymphadenitis along with ZN stain and Auramine positivity (P < 0.05) while cervical lymph nodes (n = 66) revealed a higher proportion of granulomatous lymphadenitis and culture positivity (P < 0.05). The sensitivity, NPV, and diagnostic accuracy of AO stain (85.9%, 48.0%, and 62.3%) were higher as compared to ZN stain (47.4%, 39.3%, and 51.9%) with culture as the gold standard. The combined sensitivity of Ziehl Neelsen stain and Auramine stain was 92.05%. Conclusion: Cytological and microbiologic features of abdominal LNTB differ from cervical LNTB. Moreover, AO stain increases the smear positivity, is almost twice as sensitive as ZN stain and should be used as an adjunct in cytological material wherever available.
Despite evidence that precision medicine (PM) results in improved patient care, the broad adoption and implementation has been challenging across the United States (US). To better understand the ...perceived barriers associated with PM adoption, a quantitative survey was conducted across five stakeholders including medical oncologists, surgeons, lab directors, payers, and patients. The results of the survey reveal that stakeholders are often not aligned on the perceived challenges with PM awareness, education and reimbursement, with there being stark contrast in viewpoints particularly between clinicians, payers, and patients. The output of this study aims to help raise the awareness that misalignment on the challenges to PM adoption is contributing to broader lack of implementation that ultimately impacts patients. With better understanding of stakeholder viewpoints, we can help alleviate the challenges by focusing on multi-disciplinary education and awareness to ultimately improve patient outcomes.
Timely vaccination for new respiratory infectious diseases like COVID-19 is crucial for controlling pandemics. Accurate vaccination rate prediction is essential for aiding decision-makers and vaccine ...manufacturers in vaccine production and distribution planning, particularly in developing countries with limited resources. However, insufficient historical vaccination records in these countries challenge traditional machine learning models. This study proposes a multi-source window-dependent transfer learning (WDTL) approach integrated with a Convolutional Neural Networks with Long Short-Term Memory (CNN-LSTM) model, enabling target countries with limited vaccination record to learn from multiple source countries with similar COVID-19, policy, and economic factors within a temporal window. A case study is conducted on three developing countries from different continents, each with limited resources and significant challenges regarding vaccine supply and hesitancy during the early stages of the pandemic. The model’s effectiveness was tested against a CNN-LSTM model and a multi-source TL model without window-dependent similarity evaluation, showing significant improvements with average Mean Absolute Percentage Error (MAPE) reductions of 45% and 19%, respectively. These results underscore the importance of selecting appropriate source countries across temporal windows, considering the evolving COVID-19 situation over time.
•Transfer learning approaches consider multi-source data for training prediction models.•Similarity metrics are defined between source and target countries for effective source selection.•Systematic approach is developed to include interval-based source selection for transfer learning models.•Proposed approach outperforms traditional multi-source transfer learning approaches for time series prediction.
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