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Standard treatment for GVHD is glucocorticoids, but for glucocorticoid-refractory GVHD, no intervention has emerged as standard second-line treatment. This trial with 329 patients compared ruxolitinib with control (chosen from among 10 possible therapies) in patients with glucocorticoid-refractory chronic GVHD. Response at week 24 was 50% with ruxolitinib as compared with 26% with control therapy.

Recent improvements in cancer treatment have increased the lifespan of pediatric and adult cancer survivors. However, cancer treatments accelerate aging in survivors, which manifests clinically as the premature onset of chronic diseases, such as endocrinopathies, osteoporosis, cardiac dysfunction, subsequent cancers, and geriatric syndromes of frailty, among others. Therefore, cancer treatment-induced early aging accounts for significant morbidity, mortality, and health expenditures among cancer survivors. One major mechanism driving this accelerated aging is cellular senescence; cancer treatments induce cellular senescence in tumor cells and in normal, nontumor tissue, thereby helping mediate the onset of several chronic diseases. Studies on clinical monitoring and therapeutic targeting of cellular senescence have made considerable progress in recent years. Large-scale clinical trials are currently evaluating senotherapeutic drugs, which inhibit or eliminate senescent cells to ameliorate cancer treatment-related aging. In this article, we survey the recent literature on phenotypes and mechanisms of aging in cancer survivors and provide an up-to-date review of the major preclinical and translational evidence on cellular senescence as a mechanism of accelerated aging in cancer survivors, as well as insight into the potential of senotherapeutic drugs. However, only with time will the clinical effect of senotherapies on cancer survivors be visible.

In the era of big data, artificial intelligence (AI), and the Internet of Things (IoT), digital data have become essential for our everyday functioning and in health care services. The sensitive nature of health care data presents several crucial issues such as privacy, security, interoperability, and reliability that must be addressed in any health care data management system. However, most of the current health care systems are still facing major obstacles and are lacking in some of these areas. This is where decentralized, secure, and scalable databases, most notably blockchains, play critical roles in addressing these requirements without compromising security, thereby attracting considerable interest within the health care community. A blockchain can be maintained and widely distributed using a large network of nodes, mostly computers, each of which stores a full replica of the data. A blockchain protocol is a set of predefined rules or procedures that govern how the nodes interact with the network, view, verify, and add data to the ledger. In this article, we aim to explore blockchain technology, its framework, current applications, and integration with other innovations, as well as opportunities in diverse areas of health care and clinical research, in addition to clarifying its future impact on the health care ecosystem. We also elucidate 2 case studies to instantiate the potential role of blockchains in health care. To identify related existing work, terms based on Medical Subject Headings were used. We included studies focusing mainly on health care and clinical research and developed a functional framework for implementation and testing with data. The literature sources for this systematic review were PubMed, Medline, and the Cochrane library, in addition to a preliminary search of IEEE Xplore. The included studies demonstrated multiple framework designs and various implementations in health care including chronic disease diagnosis, management, monitoring, and evaluation. We found that blockchains exhibit many promising applications in clinical trial management such as smart-contract application, participant-controlled data access, trustless protocols, and data validity. Electronic health records (EHRs), patient-centered interoperability, remote patient monitoring, and clinical trial data management were found to be major areas for blockchain usage, which can become a key catalyst for health care innovations. The potential benefits of blockchains are limitless; however, concrete data on long-term clinical outcomes based on blockchains powered and supplemented by AI and IoT are yet to be obtained. Nonetheless, implementing blockchains as a novel way to integrate EHRs nationwide and manage common clinical problems in an algorithmic fashion has the potential for improving patient outcomes, health care experiences, as well as the overall health and well-being of individuals.

Achondroplasia is the most common short stature skeletal dysplasia (1:20,000–30,000), but the risk of adverse health outcomes from cardiovascular diseases, pain, poor function, excess weight, and sleep apnea is unclear. A multicenter retrospective natural history study was conducted to understand medical and surgical practices in achondroplasia. Data from patients with achondroplasia evaluated by clinical geneticists at Johns Hopkins University, A.I. duPont Hospital for Children, McGovern Medical School UTHealth, and University of Wisconsin were populated into a REDCap database. All available retrospective medical records of anthropometry (length/height, weight, occipitofrontal circumference), surgery, polysomnography (PSG), and imaging (e.g., X-ray, magnetic resonance imaging) were included. Data from 1,374 patients (48.8% female; mean age 15.4±13.9 years) constitute the primary achondroplasia cohort (PAC) with 496 subjects remaining clinically active and eligible for prospective studies. Within the PAC, 76.0% had a de novo FGFR3 pathologic variant and 1,094 (79.6%) had one or more achondroplasia-related surgeries. There are ≥37,000 anthropometry values, 1,631 PSGs and 10,727 imaging studies. This is the largest multicenter achondroplasia natural history study, providing a vast array of medical information for use in caring for these patients. This well-phenotyped cohort is a reference population against which future medical and surgical interventions can be compared.

Cellular senescence is a biological aging hallmark that plays a key role in the development of neurodegenerative diseases. Clinical trials are currently underway to evaluate the effectiveness of senotherapies for these diseases. However, the impact of senescence on brain aging and cognitive decline in the absence of neurodegeneration remains uncertain. Moreover, patient populations like cancer survivors, traumatic brain injury survivors, obese individuals, obstructive sleep apnea patients, and chronic kidney disease patients can suffer age-related brain changes like cognitive decline prematurely, suggesting that they may suffer accelerated senescence in the brain. Understanding the role of senescence in neurocognitive deficits linked to these conditions is crucial, especially considering the rapidly evolving field of senotherapeutics. Such treatments could help alleviate early brain aging in these patients, significantly reducing patient morbidity and healthcare costs. This review provides a translational perspective on how cellular senescence plays a role in brain aging and age-related cognitive decline. We also discuss important caveats surrounding mainstream senotherapies like senolytics and senomorphics, and present emerging evidence of hyperbaric oxygen therapy and immune-directed therapies as viable modalities for reducing senescent cell burden.

Recent improvements in cancer treatment have increased the lifespan of pediatric and adult cancer survivors. However, cancer treatments accelerate aging in survivors, which manifests clinically as the premature onset of chronic diseases, such as endocrinopathies, osteoporosis, cardiac dysfunction, subsequent cancers, and geriatric syndromes of frailty, among others. Therefore, cancer treatment-induced early aging accounts for significant morbidity, mortality, and health expenditures among cancer survivors. One major mechanism driving this accelerated aging is cellular senescence; cancer treatments induce cellular senescence in tumor cells and in normal, nontumor tissue, thereby helping mediate the onset of several chronic diseases. BStudies on clinical monitoring and therapeutic targeting of cellular senescence have made considerable progress in recent years. Large-scale clinical trials are currently evaluating senotherapeutic drugs, which inhibit or eliminate senescent cells to ameliorate cancer treatment-related aging. In this article, we survey the recent literature on phenotypes and mechanisms of aging in cancer survivors and provide an up-to-date review of the major preclinical and translational evidence on cellular senescence as a mechanism of accelerated aging in cancer survivors, as well as insight into the potential of senotherapeutic drugs. However, only with time will the clinical effect of senotherapies on cancer survivors be visible.

The objective of this study was to examine the religious/spiritual beliefs of followers of the five major world religions about frequently encountered medical situations at the end of life (EoL). This was a systematic review of observational studies on the religious aspects of commonly encountered EoL situations. The databases used for retrieving studies were: Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Ovid PsycINFO, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus. Observational studies, including surveys from healthcare providers or the general population, and case studies were included for review. Articles written from a purely theoretical or philosophical perspective were excluded. Our search strategy generated 968 references, 40 of which were included for review, while 5 studies were added from reference lists. Whenever possible, we organized the results into five categories that would be clinically meaningful for palliative care practices at the EoL: advanced directives, euthanasia and physician-assisted suicide, physical requirements (artificial nutrition, hydration, and pain management), autopsy practices, and other EoL religious considerations. A wide degree of heterogeneity was observed within religions, depending on the country of origin, level of education, and degree of intrinsic religiosity. Our review describes the religious practices pertaining to major EoL issues and explains the variations in EoL decision making by clinicians and patients based on their religious teachings and beliefs. Prospective studies with validated tools for religiosity should be performed in the future to assess the impact of religion on EoL care.

In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate estimation of multi-class retinal fluid (MRF) is required for the activity prescription and intravitreal dose. This study proposes an end-to-end deep learning-based retinal fluids segmentation network (RFS-Net) to segment and recognize three MRF lesion manifestations, namely, intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), from multi-vendor optical coherence tomography (OCT) imagery. The proposed image analysis tool will optimize anti-VEGF therapy and contribute to reducing the inter- and intra-observer variability. The proposed RFS-Net architecture integrates the atrous spatial pyramid pooling (ASPP), residual, and inception modules in the encoder path to learn better features and conserve more global information for precise segmentation and characterization of MRF lesions. The RFS-Net model is trained and validated using OCT scans from multiple vendors (Topcon, Cirrus, Spectralis), collected from three publicly available datasets. The first dataset consisted of OCT volumes obtained from 112 subjects (a total of 11,334 B-scans) is used for both training and evaluation purposes. Moreover, the remaining two datasets are only used for evaluation purposes to check the trained RFS-Net's generalizability on unseen OCT scans. The two evaluation datasets contain a total of 1572 OCT B-scans from 1255 subjects. The performance of the proposed RFS-Net model is assessed through various evaluation metrics. The proposed RFS-Net model achieved the mean F1 scores of 0.762, 0.796, and 0.805 for segmenting IRF, SRF, and PED. Moreover, with the automated segmentation of the three retinal manifestations, the RFS-Net brings a considerable gain in efficiency compared to the tedious and demanding manual segmentation procedure of the MRF. Our proposed RFS-Net is a potential diagnostic tool for the automatic segmentation of MRF (IRF, SRF, and PED) lesions. It is expected to strengthen the inter-observer agreement, and standardization of dosimetry is envisaged as a result. [Display omitted] •A deep learning based retinal fluids segmentation network (RFS-Net) is proposed.•It segments multi-class retinal fluid (MRF) lesions (IRF, SRF and PED) in OCT scan.•RFS-Net integrates a comprehensive pre-processing mechanism to boost the performance.•Using recurring ASPP modules, RFS-Net learns inherent multiscale MRF features.•RFS-Net outperforms state-of-the-art schemes in segmentation of MRF lesions in OCT.