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Background: Triple-negative breast cancer (TNBC) has a poor prognosis because of frequent recurrence. Androgen receptor (AR) is involved in the pathogenesis of breast cancer, but its role is not clearly defined. The aim of this study was to explore the expression of AR and its relationship with clinicopathologic features in TNBC. Methods: This study investigated 1036 cases of sporadic invasive breast carcinoma. Immunohistochemical assays were performed to determine the expression of AR in 190 TNBC samples. The relationships between AR expression and clinicopathologic data and prognosis were analyzed. Results: In 190 TNBC cases, the prognosis of AR-positive patients was significantly better (p = 0.019, log-rank) than AR-negative patients, and in multivariate analysis, AR expression was an independent indicator of good prognosis (p = 0.039, hazard ratio = 0.36). In patients with disease relapse, AR positivity was significantly correlated with better prognosis (p = 0.034, log-rank). Conclusions: AR expression may be useful as a subclassification marker for prognosis in TNBC.

Objective: Postural tachycardia syndrome (POTS) is one of the major causes of orthostatic intolerance in children. We systematically reviewed the pathogenesis and the progress of individualized treatment for POTS in children. Data Sources: The data analyzed in this review are mainly from articles included in PubMed and EMBASE. Study Selection: The original articles and critical reviews about POTS were selected for this review. Results: Studies have shown that POTS might be related to several factors including hypovolemia, high catecholamine status, abnormal local vascular tension, and decreased skeletal muscle pump activity. In addition to exercise training, the first-line treatments mainly include oral rehydration salts, beta-adrenoreceptor blockers, and alpha-adrenoreceptor agonists. However, reports about the effectiveness of various treatments are diverse. By analyzing the patient's physiological indexes and biomarkers before the treatment, the efficacy of medication could be well predicted. Conclusions: The pathogenesis of POTS is multifactorial, including hypovolemia, abnormal catecholamine state, and vascular dysfunction. Biomarker-directed individualized treatment is an important strategy for the management of POTS children.

There is increasing interest in discovering individualized treatment rules (ITRs) for patients who have heterogeneous responses to treatment. In particular, one aims to find an optimal ITR that is a deterministic function of patient-specific characteristics maximizing expected clinical outcome. In this article, we first show that estimating such an optimal treatment rule is equivalent to a classification problem where each subject is weighted proportional to his or her clinical outcome. We then propose an outcome weighted learning approach based on the support vector machine framework. We show that the resulting estimator of the treatment rule is consistent. We further obtain a finite sample bound for the difference between the expected outcome using the estimated ITR and that of the optimal treatment rule. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data.

One of the main objectives of empirical analysis of experiments and quasi-experiments is to inform policy decisions that determine the allocation of treatments to individuals with different observable covariates. We study the properties and implementation of the Empirical Welfare Maximization (EWM) method, which estimates a treatment assignment policy by maximizing the sample analog of average social welfare over a class of candidate treatment policies. The EWM approach is attractive in terms of both statistical performance and practical implementation in realistic settings of policy design. Common features of these settings include: (i) feasible treatment assignment rules are constrained exogenously for ethical, legislative, or political reasons, (ii) a policy maker wants a simple treatment assignment rule based on one or more eligibility scores in order to reduce the dimensionality of individual observable characteristics, and/or (iii) the proportion of individuals who can receive the treatment is a priori limited due to a budget or a capacity constraint. We show that when the propensity score is known, the average social welfare attained by EWM rules converges at least at n-½ rate to the maximum obtainable welfare uniformly over a minimally constrained class of data distributions, and this uniform convergence rate is minimax optimal. We examine how the uniform convergence rate depends on the richness of the class of candidate decision rules, the distribution of conditional treatment effects, and the lack of knowledge of the propensity score. We offer easily implementable algorithms for computing the EWM rule and an application using experimental data from the National JTPA Study.

Background Precision medicine, targeting treatments to individual genetic and clinical profiles, faces challenges in data collection, costs, and privacy. Generative AI offers a promising solution by creating realistic, privacy-preserving patient data, potentially revolutionizing patient-centric healthcare. Objective This review examines the role of deep generative models (DGMs) in clinical informatics, medical imaging, bioinformatics, and early diagnostics, showcasing their impact on precision medicine. Methods Adhering to PRISMA guidelines, the review analyzes studies from databases such as Scopus and PubMed, focusing on AI's impact in precision medicine and DGMs' applications in synthetic data generation. Results DGMs, particularly Generative Adversarial Networks (GANs), have improved synthetic data generation, enhancing accuracy and privacy. However, limitations exist, especially in the accuracy of foundation models like Large Language Models (LLMs) in digital diagnostics. Conclusion Overcoming data scarcity and ensuring realistic, privacy-safe synthetic data generation are crucial for advancing personalized medicine. Further development of LLMs is essential for improving diagnostic precision. The application of generative AI in personalized medicine is emerging, highlighting the need for more interdisciplinary research to advance this field.

Sorafenib is an oral multikinase inhibitor that suppresses tumor cell proliferation and angiogenesis and promotes tumor cell apoptosis It was approved by the FDA for the treatment of advanced renal cell carcinoma in 2006, and as a unique target drug for advanced hepatocellular carcinoma （HCC） in 2007. Sorafenib can significantly extend the median survival time of patients but only by 3-5 months. Moreover, it is associated with serious adverse side effects, and drug resistance often develops. Therefore, it is of great importance to explore the mechanisms underlying sorafenib resistance and to develop individualized therapeutic strategies for coping with these problems. Recent studies to the primary resistance, mechanisms are underying the acquired resistance to sorafenib, such as crosstalk involving PI3K/Akt and JAK-STAT pathways, the activation of hypoxia-inducible pathways, and epithelial-mesenchymal transition. Here, we briefly describe the function of sorafenib, its clinical application, and the molecular mechanisms for drug resistance, especially for HCC patients.

Behavioural and psychological symptoms of dementia (BPSD) occur in most patients with dementia. They cause great suffering in patients and caregivers, sometimes more so than the cognitive and functional decline inherent to dementia. The clinical features of BPSD include a wide variety of affective, psychotic and behavioural symptoms and signs. The causes and risk factors for BPSD are multiple and include biological, psychological and environmental variables. Frequently, their combination, rather than any specific factor, explains the occurrence of BPSD in an individual patient. Thus, a sound etiopathogenetic investigation including the patient and the family or care team is essential. The aim is to develop an individualized treatment plan using a therapeutic decision tree modified by the individual and environmental risk profile. Still, treatment may be difficult and challenging. Clinical empiricism often steps in where evidence from controlled studies is lacking. Psychosocial treatment approaches are pivotal for successful treatment of BPSD. Often a combination of different non-pharmacological approaches precedes drug treatment (most of which is off-label). Regular assessments of the treatment plan and any prescriptions must be carried out to detect signs of relapse and to stop any medicines that may have become inappropriate. Even with optimal management, BPSD will not disappear completely in some cases and will remain challenging for all involved parties. This article is a narrative review based closely on the interprofessional Swiss recommendations for the treatment of BPSD. To establish the recommendations, a thorough research of the literature has been carried out. Evidence-based data were provided through searches of Medline, Embase, ISI and Cochrane-Database research. Evidence categories of the World Federation of Biological Societies were used. Additionally, the clinical experience of Swiss medical experts was considered.

Many statistical methods have recently been developed for identifying subgroups of patients who may benefit from different available treatments. Compared with the traditional outcome-modeling approaches, these methods focus on modeling interactions between the treatments and covariates while by-pass or minimize modeling the main effects of covariates because the subgroup identification only depends on the sign of the interaction. However, these methods are scattered and often narrow in scope. In this article, we propose a general framework, by weighting and A-learning, for subgroup identification in both randomized clinical trials and observational studies. Our framework involves minimum modeling for the relationship between the outcome and covariates pertinent to the subgroup identification. Under the proposed framework, we may also estimate the magnitude of the interaction, which leads to the construction of scoring system measuring the individualized treatment effect. The proposed methods are quite flexible and include many recently proposed estimators as special cases. As a result, some estimators originally proposed for randomized clinical trials can be extended to observational studies, and procedures based on the weighting method can be converted to an A-learning method and vice versa. Our approaches also allow straightforward incorporation of regularization methods for high-dimensional data, as well as possible efficiency augmentation and generalization to multiple treatments. We examine the empirical performance of several procedures belonging to the proposed framework through extensive numerical studies.

Estimating heterogeneous treatment effects (HTEs) in randomized controlled trials (RCTs) has received substantial attention recently. This has led to the development of several statistical and machine learning (ML) algorithms to assess HTEs through identifying individualized treatment effects. However, a comprehensive review of these algorithms is lacking. We thus aimed to catalog and outline currently available statistical and ML methods for identifying HTEs via effect modeling using clinical RCT data and summarize how they have been applied in practice. We performed a scoping review using prespecified search terms in MEDLINE and Embase, aiming to identify studies that assessed HTEs using advanced statistical and ML methods in RCT data published from 2010 to 2022. Among a total of 32 studies identified in the review, 17 studies applied existing algorithms to RCT data, and 15 extended existing algorithms or proposed new algorithms. Applied algorithms included penalized regression, causal forest, Bayesian causal forest, and other metalearner frameworks. Of these methods, causal forest was the most frequently used (7 studies) followed by Bayesian causal forest (4 studies). Most applications were in cardiology (6 studies), followed by psychiatry (4 studies). We provide example R codes in simulated data to illustrate how to implement these algorithms. This review identified and outlined various algorithms currently used to identify HTEs and individualized treatment effects in RCT data. Given the increasing availability of new algorithms, analysts should carefully select them after examining model performance and considering how the models will be used in practice. •Methods to assess heterogeneous treatment effects (HTEs) are rapidly developing.•This scoping review identified 32 studies applying such methods to RCT until 2022.•Cardiology was the most popular field of application.•The causal forest was the most frequently applied model in healthcare literature.•This review will help researchers apply appropriate algorithms to assess HTEs.