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Purpose: Accurate prognostication may aid in the selection of patients who will benefit from surgery at recurrent WHO grade 4 glioma. This study aimed to evaluate the role of serial tumour volumetric measurements for prognostication at first tumour recurrence., Methods: We retrospectively analyzed patients with histologically-diagnosed WHO grade 4 glioma at initial and at first tumour recurrence at a tertiary hospital between May 2000 and September 2018. We performed auto-segmentation using ITK-SNAP software, followed by manual adjustment to measure serial contrast-enhanced T1W (CE-T1W) and T2W lesional volume changes on all MRI images performed between initial resection and repeat surgery., Results: Thirty patients met inclusion criteria; the median overall survival using Kaplan-Meier analysis from second surgery was 10.5 months. Seventeen (56.7%) patients received treatment post second surgery. Univariate cox regression analysis showed that greater rate of increase in lesional volume on CE-T1W (HR = 2.57; 95% CI [1.18, 5.57]; p = 0.02) in the last 2 MRI scans leading up to the second surgery was associated with a higher mortality likelihood. Patients with higher Karnofsky Performance Score (KPS) (HR = 0.97; 95% CI [0.95, 0.99]; p = 0.01) and who received further treatment following second surgery (HR = 0.43; 95% CI [0.19, 0.98]; p = 0.04) were shown to have a better survival., Conclusion: Higher rate of CE-T1W lesional growth on the last 2 MRI images prior to surgery at recurrence was associated with increase mortality risk. A larger prospective study is required to determine and validate the threshold to distinguish rapidly progressive tumour with poor prognosis., (© 2024. The Author(s).)

The delivery of CRISPR/Cas systems holds immense potential for revolutionizing cancer treatment, with recent advancements focusing on extracellular vesicles (EVs) and viral vectors. EVs, particularly exosomes, offer promising opportunities for targeted therapy due to their natural cargo transport capabilities. Engineered EVs have shown efficacy in delivering CRISPR/Cas components to tumor cells, resulting in inhibited cancer cell proliferation and enhanced chemotherapy sensitivity. However, challenges such as off-target effects and immune responses remain significant hurdles. Viral vectors, including adeno-associated viruses (AAVs) and adenoviral vectors (AdVs), represent robust delivery platforms for CRISPR/Cas systems. AAVs, known for their safety profile, have already been employed in clinical trials for gene therapy, demonstrating their potential in cancer treatment. AdVs, capable of infecting both dividing and non-dividing cells, offer versatility in CRISPR/Cas delivery for disease modeling and drug discovery. Despite their efficacy, viral vectors present several challenges, including immune responses and off-target effects. Future directions entail refining delivery systems to enhance specificity and minimize adverse effects, heralding personalized and effective CRISPR/Cas-mediated cancer therapies. This article underscores the importance of optimized delivery mechanisms in realizing the full therapeutic potential of CRISPR/Cas technology in oncology. As the field progresses, addressing these challenges will be pivotal for translating CRISPR/Casmediated cancer treatments from bench to bedside. [ABSTRACT FROM AUTHOR]

Patterns are crucial for efficiently scheduling microservice workflow applications to containers in cloud computing scenarios. However, it is challenging to learn patterns of microservice workflows because of their complex precedence constrained structures provided by users with more lightweighted, diversified, and personalized services. In this paper, we propose a graph neural network is designed to identify patterns within a set of microservice workflows by mining the common substructures of workflows. Based on the learned patterns, a pattern-based scheduling algorithm framework is developed for microservice workflows with soft deadline constraints to minimize the average tardiness. A sorting strategy is introduced based on urgency and pattern coverage rate. For simplification of the task sorting process, the pattern-based task sorting algorithm (PB-TS) is devised. Furthermore, a resource selection phase is incorporated to the pattern-based resource selection algorithm (PB-RS) to minimize the candidate resource space. Experimental results demonstrate the proposed method is much efficient as compared to three classical algorithms. [ABSTRACT FROM AUTHOR]

Aim: A systematic review was conducted to summarize the methylated circulating tumor DNA (ctDNA) markers reported over the last decade for early detection of colorectal cancer (CRC) and to identify the main technical challenges that are impeding their clinical implementation. Background: CRC is a major cause of cancer deaths worldwide, but early detection is key for successful treatment. Non-invasive methods such as methylated ctDNA testing show promise for improving detection and monitoring of CRC. Methods: A comprehensive search was performed using Web of Science, PubMed, and Scopus up to December 30, 2023, limited to articles published in the last 10 years (after 2012), while including advanced adenoma/stage 0 or stage I/II samples in biomarker validation. Results: After identifying 694 articles, removing duplicates and screening titles, abstracts, and full texts, a total of 62 articles were found to meet the inclusion criteria. Among the single biomarkers, MYO1 -G, SEPT9, SDC2, and JAM3 revealed the highest sensitivity for polyps and stage I/II CRC. For multi-biomarkers with suitable sensitivity, combinations of SFRP1, SFRP2, SDC2, PRIMA1, or ALX4, BMP3, NPTX2, RARB, SDC2, SEPT9, VIM or ZFHX4, ZNF334, ELOVL2, UNC5C, LOC146880, SFMBT2, GFRA1 were identified for polyps and stage I/II CRC. Conclusion: Enhancing sensitivity and specificity of molecular screening methods is crucial for improving CRC detection. Identifying a select few valuable biomarkers is key to reducing costs, despite challenges posed by low ctDNA levels in plasma, particularly in early-stage cancers. [ABSTRACT FROM AUTHOR]

Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorithms to assist clinicians in the decision-making process for treatment. Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases from 2010 to 2015 were assigned to the developing sets, while cases from 2016 to 2017 were assigned to the testing set. Missing values were addressed using the multiple imputation technique. Four algorithms were utilized to construct the models, comprising traditional logistic regression (LR) and automated machine learning (AutoML) analysis such as gradient boost machine (GBM), deep neural net (DL), and generalized linear model (GLM). We evaluated the models' performance using LR-based metrics, including the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), as well as AutoML-based metrics, such as feature importance, SHapley Additive exPlanation (SHAP) Plots, and Local Interpretable Model Agnostic Explanation (LIME). A total of 6207 patients were included in this study, with 2683, 1780, and 1744 patients allocated to the training, validation, and test sets, respectively. Among the different models evaluated, the GBM model demonstrated the highest performance in the training, validation, and test cohorts, with respective AUC values of 0.805, 0.780, and 0.795. Furthermore, the GBM model outperformed other AutoML models in terms of accuracy, achieving 0.747, 0.700, and 0.706 in the training, validation, and test cohorts, respectively. Additionally, the study revealed that tumor size and tumor location were the most significant predictors influencing the AutoML model's ability to accurately predict LIM. The AutoML model utilizing the GBM algorithm for GIST patients can effectively predict the risk of LIM and provide clinicians with a reference for developing individualized treatment plans. [ABSTRACT FROM AUTHOR]

Monitoring of ingestive activities is critically important for managing the health and wellness of individuals with various health conditions, including the elderly, diabetics, and individuals seeking better weight control. Monitoring swallowing events can be an ideal surrogate for developing streamlined methods for effective monitoring and quantification of eating or drinking events. Swallowing is an essential process for maintaining life. This seemingly simple process is the result of coordinated actions of several muscles and nerves in a complex fashion. In this study, we introduce automated methods for the detection and quantification of various eating and drinking activities. Wireless surface electromyography (sEMG) was used to detect chewing and swallowing from sEMG signals obtained from the sternocleidomastoid muscle, in addition to signals obtained from a wrist-mounted IMU sensor. A total of 4675 swallows were collected from 55 participants in the study. Multiple methods were employed to estimate bolus volumes in the case of fluid intake, including regression and classification models. Among the tested models, neural networks-based regression achieved an R2 of 0.88 and a root mean squared error of 0.2 (minimum bolus volume was 10 ml). Convolutional neural networks-based classification (when considering each bolus volume as a separate class) achieved an accuracy of over 99% using random cross-validation and around 66% using cross-subject validation. Multiple classification methods were also used for solid bolus type detection, including SVM and decision trees (DT), which achieved an accuracy above 99% with random validation and above 94% in cross-subject validation. Finally, regression models with both random and cross-subject validation were used for estimating the solid bolus volume with an R2 value that approached 1 and root mean squared error values as low as 0.00037 (minimum solid bolus weight was 3 gm). These reported results lay the foundation for a cost-effective and non-invasive method for monitoring swallowing activities which can be extremely beneficial in managing various chronic health conditions, such as diabetes and obesity. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing faces growing challenges, furthermore, responding to time-sensitive requests in the traditional cloud computing model is one of the major challenges, considering the growth of the Internet of Things. Recently, the powerful fog computing paradigm has been considered for answering these challenges. But a major challenge in fog computing is managing limited FN resources for correctly responding to IoT requests in environments with heterogeneous latency requirements. This study presented a new resource management framework utilizing a software defined network (SDN) architecture and enhanced reinforcement learning methods. This new framework aims to make optimal use of limited FN resources while satisfying the low-latency requirements of IoT applications. Since SDN is the proper choice to support this intelligent distributed structure, an SDN-based fog architecture was proposed. Moreover, FN must allocate its limited and valuable resources intelligently in a heterogeneous IoT environment with different latency requirements. Consequently, this study formulated the problem of resource allocation in fog computing in the form of a Markov decision process (MDP). The study used different reinforcement learning (RL) techniques to solve the MDP problem. Simulation results in different IoT environments with heterogeneous latency requirements corroborate that RL methods achieve the best possible performance regardless of the IoT environment. [ABSTRACT FROM AUTHOR]

Introduction: Extrapolating the results from the studies using HIPEC, EPIC and normothermic intraperitoneal chemotherapy, we proposed that normothermic chemotherapy instilled after optimal cytoreduction will improve survival in patients who underwent optimal cytoreduction. We undertook this feasibility study with the aim of studying the effectiveness of intraoperative normothermic intraperitoneal chemotherapy in patients with advanced-stage epithelial carcinoma ovary. Methods: This was a single institutional feasibility study. The primary objective was progression-free survival (PFS) following the instillation of normothermic chemotherapy following optimal cytoreduction in cases of advanced ovarian cancer after neoadjuvant chemotherapy. The secondary objective was to study tolerability and toxicity. The study received institutional ethical committee clearance. Results: A total of 24 patients were included in the pilot study over two years. The short-term analysis was made by comparing 45 patients who underwent interval cytoreductive surgery (ICS) with CC 0 and 1 during the same period. The most common side effect was prolonged post-operative ileus which was seen in six patients. The median PFS among patients who received IP chemotherapy was significantly better in comparison to patients who received ICS alone (34.0 vs. 13.0 months, p=0.018). Conclusion: Uniform state-of-the-art management of advanced epithelial ovarian cancer may not be accessible to all due to global resource limitations. Although the evidence is weak due to the small sample size and short follow-up of patients, the findings are encouraging. This feasibility study has provided substantial evidence to work further with this approach in our institute and plan an randomised controlled trial to gain more evidence. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) ranks third in cancer incidence and stands as the second leading cause of cancer-related deaths globally. CRC tumorigenesis results from a cumulative set of genetic and epigenetic alterations, disrupting cancer-regulatory processes like cell proliferation, metabolism, angiogenesis, cell death, invasion, and metastasis. Key epigenetic modifications observed in cancers encompass abnormal DNA methylation, atypical histone modifications, and irregularities in noncoding RNAs, such as microRNAs and long noncoding RNAs. The advancement in genomic technologies has positioned these genetic and epigenetic shifts as potential clinical biomarkers for CRC patients. This review concisely covers the fundamental principles of CRC-associated epigenetic changes, and examines in detail their emerging role as biomarkers for early detection, prognosis, and treatment response prediction. [ABSTRACT FROM AUTHOR]

This manuscript reports the results of an international consensus on technologies of hyperthermic intraperitoneal perioperative chemotherapy (HIPEC) performed with the following goals: To provide recommendations for the technological parameters to perform HIPEC. To identify the role of heat and its application forms in treating peritoneal metastases. To provide recommendations regarding the correct dosimetry of intraperitoneal chemotherapy drugs and their carrier solutions. To identify for each intraperitoneal chemotherapy regimen the best dosimetry and fractionation. To identify areas of future research pertaining to HIPEC technology and regimens. This consensus was performed by the Delphi technique and comprised two rounds of voting. In total, 96 of 102 eligible panelists replied to both Delphi rounds (94.1%) with a consensus of 39/51 questions on HIPEC technical aspects. Among the recommendations that met with the strongest consensus were those concerning the dose of HIPEC drug established in mg/m 2 , a target temperature of at least 42°C, and the use of at least three temperature probes to pursue hyperthermia. Ninety minutes as the ideal HIPEC duration seemed to make consensus. These results should be considered when designing new clinical trials in patients with peritoneal surface malignancies., (© 2024. Society of Surgical Oncology.)

In some areas where routine screening for hepatocellular carcinoma is not available, 30% of HCC patients present with extra-hepatic metastases at the first visit. The most common metastatic organ among them is the lung. The factors influencing the prognosis of this particular subgroup are questions that deserve to be explored. We screened the patients using the SEER database. After exclusion, 989 patients with first diagnosis of hepatocellular carcinoma with lung metastasis were included in this study. Based on Cox regression, the random forest and stepwise methods were applied to screen out risk factors that independently affect the overall survival or disease-specific survival of HCCPM patients and construct prognostic models, respectively. The data were set as training and validation sets, and the reliability and accuracy of the models were verified in different data sets using time-dependent ROC curves with decision curves. We found that the clinical factors affecting the overall survival of HCCPM patients were age grouping, chemotherapy, AJCC T-stage, pathologic grading, and surgery. The clinical factors affecting disease-specific survival in patients with hepatocellular carcinoma pulmonary metastases were age grouping, marital status, AJCC T-stage, pathological grading, and surgery. For the OS model for the training cohort, the 6-month AUC = 0.695, 12-month AUC = 0.692, and 18-month AUC = 0.72. While the DSS model for the training cohort resulted in a 6-month AUC = 0.671, 12-month AUC = 0.671, and 18-month AUC = 0.635. In this study, we developed and validated a model of prognostic risk factors for patients with lung metastases from hepatocellular carcinoma. Our prognostic model can prospectively predict the prognostic status of patients and improve clinical efficiency. [ABSTRACT FROM AUTHOR]

The myelin and lymphocyte protein (MAL) family is a novel gene family first identified and characterized in 2002. This family is comprised of seven members, including MAL, MAL2, plasmolipin, MALL, myeloid differentiation-associated marker (MYADM), MYADML2 and CMTM8, which are located on different chromosomes. In addition to exhibiting extensive activity during transcytosis, the MAL family plays a vital role in the neurological, digestive, respiratory, genitourinary and other physiological systems. Furthermore, the intimate association between MAL and the pathogenesis, progression and metastasis of malignancies, attributable to several mechanisms such as DNA methylation has also been elucidated. In the present review, an overview of the structural and functional properties of the MAL family and the latest research findings regarding the relationship between several MAL members and various cancers is provided. Furthermore, the potential clinical and scientific significance of MAL is discussed and directions for future research are summarized. [ABSTRACT FROM AUTHOR]