Improving estimation efficiency of case-cohort studies with interval-censored failure time data.

The case-cohort design is a commonly used cost-effective sampling strategy for large cohort studies, where some covariates are expensive to measure or obtain. In this paper, we consider regression analysis under a case-cohort study with interval-censored failure time data, where the failure time is only known to fall within an interval instead of being exactly observed. A common approach to analyzing data from a case-cohort study is the inverse probability weighting approach, where only subjects in the case-cohort sample are used in estimation, and the subjects are weighted based on the probability of inclusion into the case-cohort sample. This approach, though consistent, is generally inefficient as it does not incorporate information outside the case-cohort sample. To improve efficiency, we first develop a sieve maximum weighted likelihood estimator under the Cox model based on the case-cohort sample and then propose a procedure to update this estimator by using information in the full cohort. We show that the update estimator is consistent, asymptotically normal, and at least as efficient as the original estimator. The proposed method can flexibly incorporate auxiliary variables to improve estimation efficiency. A weighted bootstrap procedure is employed for variance estimation. Simulation results indicate that the proposed method works well in practical situations. An application to a Phase 3 HIV vaccine efficacy trial is provided for illustration.

Cutting-Edge Developments and Patent Trends in Microspheres Drug Delivery: A Comprehensive Overview.

Microspheres have emerged as innovative drug delivery platforms with significant potential to improve the therapeutic efficacy of drugs with limited aqueous solubility and prolong their release. This abstract provides an overview of recent developments in microsphere research, highlighting key trends and innovative approaches. Recent studies have focused on various aspects of microspheres, including formulation techniques, materials selection, and their applications in drug delivery. Recent breakthroughs in polymer science have paved the way for the creation of innovative biodegradable and biocompatible materials for microsphere fabrication, improving drug encapsulation effectiveness and release dynamics. Notably, the integration of nanomaterials and functionalized polymers has enabled precise control over drug release rates and enhanced targeting capabilities. The utilization of microspheres for administering a diverse array of therapeutic substances, including anticancer drugs, anti-inflammatory agents, and peptides, has gained significant attention. These microspheres have demonstrated the potential to enhance drug stability, minimize dosing frequency and enhance patient adherence.

asMODiTS: An application of surrogate models to optimize Time Series symbolic discretization through archive-based training set update strategy.

The enhanced multi-objective symbolic discretization for time series (eMODiTS) uses an evolutionary process to identify the appropriate discretization scheme in the Time Series Classification (TSC) task. It discretizes using a unique alphabet cut for each word segment. However, this kind of scheme has a higher computational cost. Therefore, this study implemented surrogate models to minimize this cost. The general procedure is summarized below.•The K-nearest neighbor for regression, the support vector regression model, and the Ra- dial Basis Functions neural networks were implemented as surrogate models to estimate the objective values of eMODiTS, including the discretization process.•An archive-based update strategy was introduced to maintain diversity in the training set.•Finally, the model update process uses a hybrid (fixed and dynamic) approach for the surrogate model's evolution control.

A dynamic attribute reduction algorithm based on relative neighborhood discernibility degree.

This paper addresses the current existence of attribute reduction algorithms for incomplete hybrid decision-making systems, including low attribute reduction efficiency, low classification accuracy and lack of consideration of unlabeled data types. To address these issues, this paper first redefines the weakly labeled relative neighborhood discernibility degree and develops a non-dynamic attribute reduction algorithm. In addition, this paper proposes an incremental update mechanism for weakly tagged relative neighborhood discernibility degree and introduces a new dynamic attribute reduction algorithm for increasing the set of objects based on it. Meanwhile, this paper also compares and analyses the improved algorithm proposed in this study with two existing attribute reduction algorithms using 8 data sets in the UCI database. The results show that the dynamic attribute reduction algorithm proposed in this paper achieves higher attribute reduction efficiency and classification accuracy, which further validates the effectiveness of the algorithm proposed in this paper.

The road ahead: a brief guide to navigating the 2022 WHO classification of endocrine and neuroendocrine tumours.

The most recent WHO classification of endocrine and neuroendocrine tumours has brought about significant changes in the diagnosis and grading of these lesions. For instance, pathologists now have the ability to stratify subsets of thyroid and adrenal neoplasms using various histological features and composite risk assessment models. Moreover, novel recommendations on how to approach endocrine neoplasia involve additional immunohistochemical analyses, and the recognition and implementation of these key markers is essential for modernising diagnostic capabilities. Additionally, an improved understanding of tumour origin has led to the renaming of several entities, resulting in the emergence of terminology not yet universally recognised. The adjustments in nomenclature and prognostication may pose a challenge for the clinical team, and care providers might be eager to engage in a dialogue with the diagnosing pathologist, as treatment guidelines have not fully caught up with these recent changes. Therefore, it is crucial for a surgical pathologist to be aware of the knowledge behind the implementation of changes in the WHO classification scheme. This review article will delve into the most significant diagnostic and prognostic changes related to lesions in the parathyroid, thyroid, adrenal glands and the gastroenteropancreatic neuroendocrine system. Additionally, the author will briefly share his personal reflections on the clinical implementation, drawing from a couple of years of experience with these new algorithms.

Social Network Forensics Analysis Model Based on Network Representation Learning.

The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks.

Updating Prospective Self-Efficacy Beliefs About Cardiac Interoception in Anorexia Nervosa: An Experimental and Computational Study.

Patients with anorexia nervosa (AN) typically hold altered beliefs about their body that they struggle to update, including global, prospective beliefs about their ability to know and regulate their body and particularly their interoceptive states. While clinical questionnaire studies have provided ample evidence on the role of such beliefs in the onset, maintenance, and treatment of AN, psychophysical studies have typically focused on perceptual and 'local' beliefs. Across two experiments, we examined how women at the acute AN (N = 86) and post-acute AN state (N = 87), compared to matched healthy controls (N = 180) formed and updated their self-efficacy beliefs retrospectively (Experiment 1) and prospectively (Experiment 2) about their heartbeat counting abilities in an adapted heartbeat counting task. As preregistered, while AN patients did not differ from controls in interoceptive accuracy per se, they hold and maintain 'pessimistic' interoceptive, metacognitive self-efficacy beliefs after performance. Modelling using a simplified computational Bayesian learning framework showed that neither local evidence from performance, nor retrospective beliefs following that performance (that themselves were suboptimally updated) seem to be sufficient to counter and update pessimistic, self-efficacy beliefs in AN. AN patients showed lower learning rates than controls, revealing a tendency to base their posterior beliefs more on prior beliefs rather than prediction errors in both retrospective and prospective belief updating. Further explorations showed that while these differences in both explicit beliefs, and the latent mechanisms of belief updating, were not explained by general cognitive flexibility differences, they were explained by negative mood comorbidity, even after the acute stage of illness.

The Importance of Extracellular Vesicle Screening in Gastric Cancer: A 2024 Update.

Extracellular vesicles, or EVs, are membrane-bound nanocompartments produced by tumor cells. EVs carry proteins and nucleic acids from host cells to target cells, where they can transfer lipids, proteomes, and genetic material to change the function of target cells. EVs serve as reservoirs for mobile cellular signals. The collection of EVs using less invasive processes has piqued the interest of many researchers. Exosomes carry substances that can suppress the immune system. If the results of exosome screening are negative, immunotherapy will be beneficial for GC patients. In this study, we provide an update on EVs and GC based on ongoing review papers and clinical trials.

Uterine Mesenchymal Tumors: Updates on Pathology, Molecular Landscape, and Therapeutics.

Background: Mesenchymal uterine tumors are a diverse group of neoplasms with varying biological potential. Many of these neoplasms can have overlapping morphologic similarities, which, in some instances, render their diagnosis and categorization thorough histomorphologic examination inconclusive. In the last decade, an exponential amount of molecular data aiming to more accurately characterize and, consequently, treat these tumors have accumulated. Objective: The goal of this narrative review is to provide a pathologic review, a genetic update, and to know the new therapeutic avenues of primary uterine mesenchymal neoplasms.

Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

Dry Eye Disease: An Update on Changing Perspectives on Causes, Diagnosis, and Management.

Dry eye disease is a common clinical problem encountered by ophthalmologists worldwide. Interest in this entity has increased in recent years due to the consequences it has on the ocular surface after any surface procedure. With changing times, several new factors have come to light that can influence this disease. The effect of the COVID-19 pandemic has also been greatly felt, with a range of causes, starting from increased screen work to inflammatory processes, exacerbating the condition in many. With changes in the concepts of the etiopathogenesis of the disease, a paradigm shift has taken place in the approaches to treatment. More researchers are in favor of a new tear film-oriented approach that tries to localize the disease to a single component in the tear film. Innovation of newer techniques for the treatment of meibomian gland disease has also made its foray into clinical ophthalmology. Newer drug formulations and molecules are underway to better treat the inflammatory component of the disease. Many other receptors and targets for the treatment of dry eyes are being researched. This review hopes to provide a succinct, narrative summary of the relevant research on dry eye disease to date to increase awareness about the nature and future course of this disease and its management.

Efficiency and comparability of using new evidence platforms for updating recommendations: Experience with a type-2 diabetes guideline in Colombia.

Introduction: Updating recommendations for guidelines requires a comprehensive and efficient literature search. Although new information platforms are available for developing groups, their relative contributions to this purpose remain uncertain. Methods: As part of a review/update of eight selected evidence-based recommendationsfor type 2 diabetes, we evaluated the following five literature search approaches (targeting systematic reviews, using predetermined criteria): PubMed for MEDLINE, Epistemonikos database basic search, Epistemonikos database using a structured search strategy, Living overview of evidence (L.OVE) platform, and TRIP database. Three reviewers independently classified the retrieved references as definitely eligible, probably eligible, or not eligible. Those falling in the same "definitely" categories for all reviewers were labelled as "true" positives/negatives. The rest went to re-assessment and if found eligible/not eligible by consensus became "false" negatives/positives, respectively. We described the yield for each approach and computed "diagnostic accuracy" measures and agreement statistics. Results: Altogether, the five approaches identified 318 to 505 references for the eight recommendations, from which reviewers considered 4.2 to 9.4% eligible after the two rounds. While Pubmed outperformed the other approaches (diagnostic odds ratio 12.5 versus 2.6 to 5.3), no single search approach returned eligible references for all recommendations. Individually, searches found up to 40% of all eligible references (n = 71), and no combination of any three approaches could find over 80% of them. Kappa statistics for retrieval between searches were very poor (9 out of 10 paired comparisons did not surpass the chance-expected agreement). Conclusion: Among the information platforms assessed, PubMed appeared to be more efficient in updating this set of recommendations. However, the very poor agreement among search approaches in the reference yield demands that developing groups add information from several (probably more than three) sources for this purpose. Further research is needed to replicate our findings and enhance our understanding of how to efficiently update recommendations.

Introducción: La actualización de recomendaciones de las guías de práctica clínica requiere búsquedas bibliográficas exhaustivas y eficientes. Aunque están disponibles nuevas plataformas de información para grupos desarrolladores, su contribución a este propósito sigue siendo incierta. Métodos: Como parte de una revisión/actualización de 8 recomendaciones basadas en evidencia seleccionadas sobre diabetes tipo 2, evaluamos las siguientes cinco aproximaciones de búsqueda bibliográfica (dirigidas a revisiones sistemáticas, utilizando criterios predeterminados): PubMed para MEDLINE; Epistemonikos utilizando una búsqueda básica; Epistemonikos utilizando una estrategia de búsqueda estructurada; plataforma (L.OVE) y TRIP . Tres revisores clasificaron de forma independiente las referencias recuperadas como definitivamente o probablemente elegibles/no elegibles. Aquellas clasificadas en las mismas categorías "definitivas" para todos los revisores, se etiquetaron como "verdaderas" positivas/negativas. El resto se sometieron a una nueva evaluación y, si se consideraban por consenso elegibles/no elegibles, se convirtieron en "falsos" negativos/positivos, respectivamente. Describimos el rendimiento de cada aproximación, junto a sus medidas de "precisión diagnóstica" y las estadísticas de acuerdo. Resultados: En conjunto, las cinco aproximaciones identificaron 318-505 referencias para las 8 recomendaciones, de las cuales los revisores consideraron elegibles el 4,2 a 9,4% tras las dos rondas. Mientras que Pubmed superó a las otras aproximaciones (odds ratio de diagnóstico 12,5 versus 2,6 a 53), ninguna aproximación de búsqueda identificó por sí misma referencias elegibles para todas las recomendaciones. Individualmente, las búsquedas identificaron hasta el 40% de todas las referencias elegibles (n=71), y ninguna combinación de cualquiera de los tres enfoques pudo identificar más del 80% de ellas. Las estadísticas Kappa para la recuperación entre búsquedas fueron muy pobres (9 de cada 10 comparaciones pareadas no superaron el acuerdo esperado por azar). Conclusiones: Entre las plataformas de información evaluadas, Pubmed parece ser la más eficiente para actualizar este conjunto de recomendaciones. Sin embargo, la escasa concordancia en el rendimiento de las referencias exige que los grupos desarrolladores incorporen información de varias fuentes (probablemente más de tres) para este fin. Es necesario seguir investigando para replicar nuestros hallazgos y mejorar nuestra comprensión de cómo actualizar recomendaciones de forma eficiente.

Predictive Factors of Immunotherapy in Gastric Cancer: A 2024 Update.

Many studies on gastric cancer treatment have identified predictors of immunotherapy benefits. This article provides an update on the major developments in research related to predictive factors of immunotherapy for gastric cancer. We used the search term "predictive factors, immunotherapy, gastric cancer" to find the most current publications in the PubMed database related to predictive factors of immunotherapy in gastric cancer. Programmed cell death, genetic, and immunological factors are the main study topics of immunotherapy's predictive factors in gastric cancer. Other preventive factors for immunotherapy in gastric cancer were also found, including clinical factors, tumor microenvironment factors, imaging factors, and extracellular factors. Since there is currently no effective treatment for gastric cancer, we strongly propose that these studies be prioritized.

Advancing perspectives on the off-label use of anticancer drugs: an updated classification and exploration of categories.

To date, the definition that the off-label usage of drugs refers to the unapproved use of approved drugs, which covers unapproved indications, patient populations, doses, and/or routes of administration, has been in existence for many years. Currently, there is a limited frequency and prevalence of research on the off-label use of antineoplastic drugs, mainly due to incomplete definition and classification issues. It is time to embrace new categories for the off-label usage of anticancer drugs. This review provided an insight into an updated overview of the concept and categories of the off-label use of anticancer drugs, along with illustrating specific examples to establish the next studies about the extent of the off-label usage of anticancer drugs in the oncology setting. The scope of the off-label use of current anticancer drugs beyond the previous definitions not only includes off-label uses in terms of indications, patient populations, doses, and/or routes of administration but also off-label use in terms of medication course, combination, sequence of medication, clinical purpose, contraindications scenarios, etc. In addition, the definition of the off-label usage of anticancer drugs should be added to the condition at a given time, and it varies from approval authorities. We presented a new and relatively comprehensive classification, providing extensive analysis and illustrative examples of the off-label usage of antineoplastic drugs for the first time. Such a classification has the potential to promote practical adoption and enhance management strategies for the off-label use of antitumor drugs.

Helicobacter pylori Infection in Patients with Gastric Cancer: A 2024 Update.

Numerous studies have been performed on Helicobacter pylori infection because of the high death rate linked to this illness and gastric cancer. An update on the key developments in recent years in the investigation of Helicobacter pylori and gastric cancer is the goal of this review. Using the search term "Helicobacter pylori, gastric cancer", the PubMed database was searched. Only papers published in 2024 fulfilled the inclusion criteria. Because case report papers were not part of our investigation, they satisfied the exclusion criteria. Most of the research on the variable genes of Helicobacter pylori is guided by genetics to determine potential treatments. Studies on clinical treatments for the eradication of H. pylori with promising therapeutic options are needed. We found the fewest studies related to the immunopathology of H. pylori infection, which is still unknown. In conclusion, priority should be given to this kind of research.

Enabling Navigation and Augmented Reality in the Sitting Position in Posterior Fossa Surgery Using Intraoperative Ultrasound.

Despite its broad use in cranial and spinal surgery, navigation support and microscope-based augmented reality (AR) have not yet found their way into posterior fossa surgery in the sitting position. While this position offers surgical benefits, navigation accuracy and thereof the use of navigation itself seems limited. Intraoperative ultrasound (iUS) can be applied at any time during surgery, delivering real-time images that can be used for accuracy verification and navigation updates. Within this study, its applicability in the sitting position was assessed. Data from 15 patients with lesions within the posterior fossa who underwent magnetic resonance imaging (MRI)-based navigation-supported surgery in the sitting position were retrospectively analyzed using the standard reference array and new rigid image-based MRI-iUS co-registration. The navigation accuracy was evaluated based on the spatial overlap of the outlined lesions and the distance between the corresponding landmarks in both data sets, respectively. Image-based co-registration significantly improved (p < 0.001) the spatial overlap of the outlined lesion (0.42 ± 0.30 vs. 0.65 ± 0.23) and significantly reduced (p < 0.001) the distance between the corresponding landmarks (8.69 ± 6.23 mm vs. 3.19 ± 2.73 mm), allowing for the sufficient use of navigation and AR support. Navigated iUS can therefore serve as an easy-to-use tool to enable navigation support for posterior fossa surgery in the sitting position.

Five-year analysis of neoadjuvant dabrafenib and trametinib for stage III melanoma.

BACKGROUND: Neoadjuvant dabrafenib plus trametinib has a high pathological response rate and impressive short-term survival in patients with resectable stage III melanoma. We report 5-year outcomes from the phase II NeoCombi trial. PATIENTS AND METHODS: NeoCombi (NCT01972347) was a single-arm, open-label, single-centre, phase II trial. Eligible patients were adults (aged ≥18 years) with histologically confirmed, resectable, RECIST-measurable, American Joint Committee on Cancer seventh edition clinical stage IIIB-C BRAF V600E/K-mutant melanoma and Eastern Cooperative Oncology Group performance status ≤1. Patients received 52 weeks of treatment with dabrafenib 150 mg (orally twice per day) plus trametinib 2 mg (orally once per day), with complete resection of the pre-therapy tumour bed at week 12. RESULTS: Between 20 August 2014 and 19 April 2017, 35 patients were enrolled. At data cut-off (17 August 2021), the median follow-up was 60 months [95% confidence interval (CI) 56-72 months]. Overall, 21 of 35 (60%) patients recurred, including 12 (57%) with first recurrence in locoregional sites (followed by later distant recurrence in 6) and 9 (43%) with first recurrence in distant sites, including 3 in the brain. Most recurrences occurred within 2 years, with no recurrences beyond 3 years. At 5 years, recurrence-free survival (RFS) was 40% (95% CI 27% to 60%), distant metastasis-free survival (DMFS) was 57% (95% CI 42% to 76%), and overall survival was 80% (95% CI 67% to 94%). Five-year survival outcomes were stratified by pathological response: RFS was 53% with pathological complete response (pCR) versus 28% with non-pCR (P = 0.087), DMFS was 59% versus 55% (P = 0.647), and overall survival was 88% versus 71% (P = 0.205), respectively. CONCLUSIONS: Neoadjuvant dabrafenib plus trametinib has high pathological response rates in clinical stage III melanoma, but low rates of RFS, similar to those achieved with adjuvant targeted therapy alone. Patients with a pCR to dabrafenib plus trametinib still had a high risk of recurrence, unlike that seen with immunotherapy where recurrences are rare.

New staging of endometrial carcinoma - FIGO 2023.

AIM: To review the changes in the new version of the FIGO 2023 staging system for endometrial cancer. METHODS AND RESULTS: The new FIGO 2023 endometrial cancer staging system provides key updates for the diagnosis and treatment of endometrial cancer. An important step in diagnosis is molecular classification, which allows more accurate risk stratification for recurrence and the identification of targeted therapies. The new staging system, based on the recommendations of the international societies ESGO, ESTRO and ESP, incorporates not only the description of the pathological and anatomical extent of the disease, but also the histopathological characteristics of the tumour, including the histological type and the presence of lymphovascular space invasion. In addition, the staging system uses molecular testing to classify endometrial cancers into four prognostic groups: POLEmut, MMRd, NSMP and p53abn. Each group has its own specific characteristics and prognosis. The most significant changes have occurred in stages I and II, in which the sub-staging better reflects the biological behaviour of the tumour. This update increases the accuracy of prognosis and improves individualized treatment options for patients with endometrial cancer. CONCLUSION: The updated FIGO staging of endometrial cancer for 2023 incorporates different histologic types, tumour features, and molecular classifications to better reflect the current improved understanding of the complex nature of several endometrial cancer types and their underlying bio logic behaviour. The aim of the new endometrial cancer staging system is to better define stages with similar prognosis, allowing for more precise indication of individualised adjuvant radiation or systemic treatment, including the use of immunotherapy.

Idarucizumab in dabigatran-treated patients with acute stroke: a review and clinical update.

Idarucizumab is an antibody fragment specific for the immediate reversal of dabigatran anticoagulation effects. The use of idarucizumab is approved for dabigatran-treated patients suffering from life-threatening or uncontrolled bleeding and those in need of urgent surgery or invasive procedures. Data from randomized controlled clinical trials and real-world experience provide reassuring evidence about the efficacy and safety of idarucizmab use in patients with acute stroke. In this narrative review, we summarize the available real-world evidence and discuss the relevance and importance of idarucizumab treatment in acute stroke patients in everyday clinical practice. In addition, we also discuss special issues like prothrombin complex concentrate application as an alternative to idarucizumab, its application before endovascular therapy, sensitivity of thrombi to lysis, and necessary laboratory examinations.