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Abstract To inform clinical trial design and real-world precision pediatric oncology practice, we classified diagnoses, assessed the landscape of mutations, and identified genomic variants matching trials in a large unselected institutional cohort of solid tumors patients sequenced at Dana-Farber / Boston Children’s Cancer and Blood Disorders Center. Tumors were sequenced with OncoPanel, a targeted next-generation DNA sequencing panel. Diagnoses were classified according to the International Classification of Diseases for Oncology (ICD-O-3.2). Over 6.5 years, 888 pediatric cancer patients with 95 distinct diagnoses had successful tumor sequencing. Overall, 33% (n = 289/888) of patients had at least 1 variant matching a precision oncology trial protocol, and 14% (41/289) were treated with molecularly targeted therapy. This study highlights opportunities to use genomic data from hospital-based sequencing performed either for research or clinical care to inform ongoing and future precision oncology clinical trials. Furthermore, the study results emphasize the importance of data sharing to define the genomic landscape and targeted treatment opportunities for the large group of rare pediatric cancers we encounter in clinical practice.

The management of rainwater collection in a practical way is a fundamental need for the management of water resources in a manner that is sustainable. The goal of this research is to determine whether or not remote sensing technology is effective in providing data on precipitation for the purpose of locating rainwater collection tank locations in the province of Karbala. Rainfall patterns fluctuate considerably. Remote sensing may not capture variability enough to estimate the rainfall period and location. Sustainable rainfall harvesting requires accurate rainfall timing and distribution. This information is applied in the modeling of hydrological processes, the management of disasters, and environmental research. Following the completion of a geographical study, it has been established that the city of Karbala may be divided into two basic sections. Through the use of estimation, it is possible to more easily identify the region that is ideal for the location of rainwater-harvesting reservoirs and lakes. On the contrary, it is crucial to keep in mind that a location that was chosen based on average rainfall over a period of two years could not be suitable for other time periods. This is an idea that should be kept in mind several times. To put this into perspective, when choosing a location, it is vital to take into consideration the severity of the rainfall as well as the geographical location of the area. Particularly in locations such as Karbala, the implementation of data visualization systems into water management practices has the potential to improve both the efficiency and sustainability of water management methods. The findings of this study show the significance of implementing precise site selection techniques to enhance rainwater collection systems and encourage activities that are environmentally responsible for water management.

Many people get their news via the Internet and social media platforms, and given the rapid growth of these platforms, fake news may now spread easily and quickly. False information that aims to mislead and harm society and the individual is known as fake news. By deliberately spreading false information, the media distort public opinion and threaten the social order by leading people to believe things that are not true. With the massive expansion of social media networks, the spread of fake news has increased dramatically. Although interesting, it poses some difficulties due to limited resources (such as datasets and published research). This paper presents diverse machine-learning techniques to identify fabricated news by analyzing the textual content. Several techniques were used, including SVM, RF, logistic regression, Naive Bayes, Gradient Boosting, AdaBoost, KNN, DT, and XGBoost. Based on comparing the results, who got the best result with an accuracy rate of 0.9967 and the lowest loss of 0.003 The study includes a variety of methodologies, such as natural language processing (NLP), machine learning, and data mining, which have been found to improve the efficiency of text processing to increase accuracy and can save time and effort by automatically identifying fake news, especially in light of the massive amount from materials available on the Internet.

Using complex language models has been a common strategy as personalized news recommendation systems are adopted by online news sites more and more. Before the epoch of GPT-3, news recommendation systems underwent a progression from rule-based and collaborative filtering approaches in the pre-2010s, through the integration of neural networks in the 2010s, to the emergence of earlier iterations of large language models like GPT-2 in 2019. Pre-trained language models have ushered in a new era of recommendation paradigms, thanks to the emergence of huge language models like GPT-3 and T-5. With its easy-to-use interface, ChatGPT is becoming more and more popular for text-based jobs. Focusing on news provider fairness, individualized news recommendations, and fake news identification, this study starts an inquiry into ChatGPT's efficacy in news recommendations. We acknowledge that ChatGPT's output sensitivity to input phrasing is a limitation, and our goal is to investigate these limitations from each angle. We also investigate whether certain prompt formats can help to alleviate these constraints or if more research is needed. To go beyond set assessments, we create a webpage where ChatGPT's performance on the examined activities and prompts is tracked once a week. Through the use of big language models, this work seeks to improve news recommendation performance and stimulate more research in this area.

The internet's role in the spread of fake news has made it a serious problem that calls for sophisticated techniques for its automatic detection. To counter the quick spread of misleading material on social media sites, including Facebook, Twitter, Instagram, and WhatsApp, this study explores the domain of deep learning methods and various classification strategies. In particular, the study investigates the application of transformer-based models like BERT, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) for the detection of false news. To provide context for the results, the examination covers a wide range of historical and contemporary trends in the spread of fake news. The study uses strict criteria, including precision, recall, and F1 score, to assess how effective the suggested models are. The created methodologies are validated for effectiveness by using benchmark datasets. This study emphasizes how urgent it is to address the growing problem of fake news, especially as it relates to its use in psychological warfare and clickbait to generate revenue. Through the clarification of the methods, duration, and standards of evaluation used, this research adds to the current conversation on dispelling false information in the digital era.

The rise of online social networks, also known as OSNs, has captured the attention of younger generations and made them an integral part of social life. As a result, the use of various social media platforms has increased significantly, greatly impacting individuals' social connections. These platforms offer a wide range of features, such as news distribution, contributing to their widespread use. However, with the rapid growth of social media, the prevalence of fake accounts has become a major problem, posing a threat to both the security of users and the integrity of these platforms. In response, this article explores the effectiveness of machine learning algorithms (ML). Detect and identify fraudulent accounts on popular social media platforms, especially Instagram and Twitter. Our methodology involves analyzing user activity and account information to develop fine-tuned machine-learning models. Our approach takes into account important parameters such as number of followers, number of posts, and engagement.

Introduction: Allograft dysfunction within the first week posttransplant is an uncommon but known complication following liver transplantation. Seventh‐Day Syndrome (7DS) is a rare complication of allograft dysfunction following liver transplantation characterized by the rapid clinical deterioration of a formerly well‐functioning allograft within the first week posttransplant. The etiology of 7DS is unknown, and treatment options remain limited. While cases of graft survival have been reported, the risk of mortality remains exceedingly high without urgent retransplantation. Methods: Patient data was retrospectively analyzed and a literature review performed. Results: We present a unique case of split liver transplantation into two pediatric recipients in which one recipient developed rapidly progressive graft failure approximately 1 week postoperatively requiring urgent retransplantation while the other recipient had an unremarkable postoperative course. Upon clinical manifestation of progressive graft failure, the patient was treated with thymoglobulin, rituximab, intravenous immunoglobulin, and plasmapheresis. Despite this, the patient's clinical status continued to decline and she underwent retransplantation 11 days following her initial liver transplant. Conclusion: Seventh‐Day Syndrome is a rare complication following liver transplantation that is associated with a high risk of morbidity and mortality. Our case adds to the limited literature on 7DS in children and is the first to report a comparative posttransplant clinical course in two recipients who received split grafts from the same donor. [ABSTRACT FROM AUTHOR]

This is a case report of a 39-year-old patient, G5P1031, with monochorionic diamniotic twins at 30 weeks and 1 day of gestation, who developed mirror syndrome without twin-to-twin transfusion syndrome (TTTS) with a unique presentation of maternal and neonatal hyponatremia. Coinciding with severe hyponatremia were maternal symptoms of edema, nausea and vomiting, hypoalbuminemia, elevated uric acid, as well as fetal selective growth restriction, polyhydramnios, umbilical artery absent end diastolic flow and prolonged bradycardia of twin B. Given the poor status of twin B and the risks to twin A, the patient underwent emergent cesarean delivery. Hyponatremia in all three patients resolved in the following 48–72 h. Mirror syndrome is associated with significant maternal and fetal morbidity and mortality. In this case, severe hyponatremia posed additional risks. Therefore, electrolyte monitoring should be considered in both mother and neonate(s).

Tamoxifen (TAM) is the oldest and the most-prescribed selective estrogen receptor modulator (SERM). It is a member of the triphenylethylene group. TAM has been used to treat breast cancer that spreads to other parts of the human body; it is also utilized to decreasing the chances of breast cancer developing in high-risk patients. Recently, some studies focused on the potential antimicrobial action of TAM. Coronaviruses are enveloped positive-sense RNA nucleic acid viruses that have club-like spikes, characterized by a distinctive replication strategy; they are round and sometimes pleomorphic in shape. Coronavirus disease 2019 (COVID-19) is regarding the new genera of coronaviredia that appeared for the first time in Wuhan, China, in early December 2019. Due to the continuous spread of the novel COVID-19 with the exponential rise in death numbers, new therapeutic development is urgent; in general, there are no specific antiviral drugs or vaccines for 2019-nCoV. Hence, this review will discuss the most recent information about the antiviral action of TAM against COVID-19 infection by trying to give a deep understanding of major properties, mechanisms of action, immune system responses, and antimicrobial efficiency of TAM that is regarding the promising way to treat COVID-19 novel infection. The current review may serve as an impetus for researchers working in the field of medical microbiology, vaccination, and antiviral drug design. The review also rationally reports and critically analyzes the available knowledge by focusing and mentioning future steps and strategies trying to find appropriate solutions regarding challenges in COVID-19 management by TAM utilization.

Background and Aim. Biodentine refers to a bioactive material commonly applied for dental restoration in clinical practice, but poor adhesion of the Biodentine to the restorative materials could affect the quality and long-term integrity of the final restoration. The study aimed to assess shear bond strength (SBS) of two resin-based composites to Biodentine using three commercially available 7th generation bonding agents. Methods. Forty-eight acrylic blocks having central holes with a nominal diameter of 4 mm and a depth of 2 mm were prepared. The holes of the acrylic blocks were filled with Biodentine, which was prepared following the guidelines provided by the manufacturer. Then, the specimens were divided into six groups (n = 8). Groups 1, 2, and 3, Tetric N-Ceram composite bonded to Biodentine with Tetric N-bond, Xeno V+, Bond Force bond, respectively. Group 4, 5, and 6, Filtek Z350 bonded to Biodentine with the same three adhesives. The specimens were placed in distilled water for 24 hours and tested for the SBS in a universal testing machine at a crosshead speed of 1 mm/min. The test data were listed in a table and independent samples t-test and analysis of variance (ANOVA) were conducted as a part of the statistical analysis. Results. The Tetric N bonding agent achieved the highest SBS followed by Bond Force, and Xeno V and highly significant difference was found. On the other hand, an overall increase in the SBS values of the Tetric N-Ceram resin was noticed in comparison with the Filtek Z350 and the differences was statistically significant. Although the specimens failed in adhesive, cohesive and mixed fracture modes but the cohesive was found to be the dominant failure mode in all groups. Conclusion. Among the tested bonding agents and resin composites, the Tetric N-Ceram composite bonded by Tetric N-bond self-etch adhesive with the Biodentine showed the highest SBS compared to the other combinations.

Background. Alkaline phosphatase (ALP) is an enzyme produced by the liver, small intestine, bone, and kidneys as well as the placenta during pregnancy. ALP levels may increase up to twice the normal limit during pregnancy secondary to placental release and fetal bone growth. Rare case reports of extremely elevated levels of ALP during pregnancy have demonstrated possible association with adverse pregnancy outcomes. Case. The patient is a 36-year-old G2P1001 who was found to have extremely elevated ALP levels during pregnancy after presenting with bilateral lower leg swelling and rash after receiving the Pfizer COVID-19 vaccine. She subsequently developed intrahepatic cholestasis of pregnancy and preeclampsia. ALP peaked at 2,601 U/L immediately prior to delivery at 36 weeks 1 day. She was followed postpartum, and her ALP levels had nearly normalized by 15 weeks postpartum. Conclusion. Our case demonstrates a rare report of an extremely elevated level of ALP in the setting of multiple adverse pregnancy outcomes, including preterm delivery, preeclampsia without severe features, and intrahepatic cholestasis of pregnancy.

Objective: The purpose of this study was to evaluate the cervical marginal fit of porcelain laminate veneer (PLV) restorations made from two different types of CAD/CAM ceramic laminates: CEREC C PC and E.max (LD). Materials and Methods: This in-vitro experiment used a total of 32 human maxillary first premolars that were clean and free of any cracks or caries, extracted for orthodontic purposes. The samples were divided in a random way into two study groups: A and B (n = 16). Each sample was mounted on a dental surveyor and a silicon impression was made to create a silicone index for each tooth in both groups. Standardized preparation was carried out for all the samples by using preparation bur kit for the ceramic veneer system. Subsequently, digital impressions were made for all the samples by using Trios 3 shape intraoral camera (Sirona Dental Systems). The design of veneer restorations was made using Sirona inLab CAD SW 16.1 with CEREC inLab MC XL (Dentsply, Sirona Dental Systems, Bensheim, Germany). The veneer restorations were cemented using 3M RelyX veneer resin cement (3M ESPE, Seefeld, Germany) and the samples kept in distilled water for two weeks at 37 °C. All the specimens were subjected to thermocycling in a water bath with temperature varying between 5 °C and 55 °C for 500 cycles. The cervical marginal fit of veneers was evaluated by a digital microscope after sectioning the embedded teeth in acrylic resin. Results: The lowest mean of cervical marginal gap was recorded for Group A (91.59431 ± 1.626069) which was restored with CEREC CAD/CAM veneers, while the highest mean value of the gap was recorded for Group B (106.48863 ± 2.506684) which was restored with IPS E.max CAD. The t-test showed that the type of porcelain veneer restoration had a highly significant effect on the cervical marginal fit (p ≤ 0.01). Conclusions: CEREC CAD/CAM veneers showed smaller cervical marginal gaps, indicating a better fit compared to the IPS E.max CAD.