Exploring the effects of acceptable palliative care models on survival time and healthcare expenditure among patients with cancer: a national longitudinal population-based study.

OBJECTIVE: Hospice care ensures better end-of-life quality by relieving terminal symptoms. Prior research has indicated that hospice care could prolong survival and reduce end-of-life medical expenditures among patients with cancer. However, the dearth of studies on the effects of hospice care type and use sequence on survival time and end-of-life medical expenditures substantiates the need for investigation. DATA SOURCES AND STUDY SETTING: Two million random records were obtained from the National Health Insurance Research Database. STUDY DESIGN: We estimated the effects of the type and sequence of hospice care use on survival time and medical expenditures among advanced cancer patients. This was a cross-sectional study. DATA COLLECTION/EXTRACTION METHODS: Patient data were collected from 2 million random records provided by the National Health Insurance Research Database of Taiwan. We included people with cancer and excluded patients under 20 years of age; 2860 patients remained after matching. PRINCIPAL FINDINGS: The results indicated that the average survival time of patients who received inpatient palliative care (1022 days) was significantly shorter than that of patients who did not receive palliative care (P < 0.001), but the health care expenditure during the entire course of cancer therapy was not the lowest. Interestingly, patients who received inpatient palliative care had the lowest health care expenditure at 1 year or month before the end of life (P < 0.001). CONCLUSION: The type and sequence of palliative care affected the survival time and health care expenditures of cancer patients. Receiving palliative care did not prolong survival but rather reduced health care expenditures. The sequence of receiving palliative care significantly affected health care expenditures.

A novel bacterial sulfite dehydrogenase that requires three c-type cytochromes for electron transfer.

c-type Cytochromes (c-Cyts), primarily as electron carriers and oxidoreductases, play a key role in energy transduction processes in virtually all living organisms. Many bacteria, such as Shewanella oneidensis, are particularly rich in c-Cyts, supporting respiratory versatility not seen in eukaryotes. Unfortunately, a large number of c-Cyts are underexplored, and their biological functions remain unknown. In this study, we identify SorCABD of S. oneidensis as a novel sulfite dehydrogenase (SDH), which catalyzes the oxidation of sulfite to sulfate. In addition to catalytic subunit SorA, this enzymatic complex includes three c-Cyt subunits, which all together carry out electron transfer. The electrons extracted from sulfite oxidation are ultimately delivered to oxygen, leading to oxygen reduction, a process relying on terminal oxidase cyt cbb3. Genomic analysis suggests that the homologs of this SDH are present in a small number of bacterial genera, Shewanella and Vibrio in particular. Because these bacteria are generally capable of reducing sulfite under anaerobic conditions, the co-existence of a sulfite oxidation system implies that they may play especially important roles in the transformation of sulfur species in natural environments.Importancec-type Cytochromes (c-Cyts) endow bacteria with high flexibility in their oxidative/respiratory systems, allowing them to extracellularly transform diverse inorganic and organic compounds for survival and growth. However, a large portion of the bacterial c-Cyts remain functionally unknown. Here, we identify three c-Cyts that work together as essential electron transfer partners for the catalytic subunit of a novel SDH in sulfite oxidation in Shewanella oneidensis. This characteristic makes S. oneidensis the first organism known to be capable of oxidizing and reducing sulfite. The findings suggest that Shewanella, along with a small number of other aquatic bacteria, would serve as a particular driving force in the biogeochemical sulfur cycle in nature.

Pleiotropic Effects of Hfq on the Cytochrome c Content and Pyomelanin Production in Shewanella oneidensis.

Shewanella oneidensis is the best understood model microorganism for the study of diverse cytochromes (cytos) c that support its unparallel respiratory versatility. Although RNA chaperone Hfq has been implicated in regulation of cyto c production, little is known about the biological pathways that it affects in this bacterium. In this study, from a spontaneous mutant that secretes pyomelanin and has a lowered cyto c content, we identified Hfq to be the regulator that critically associates with both phenotypes in S. oneidensis. We found that expression of the key genes in biosynthesis and degradation of heme is differentially affected by Hfq at under- and overproduced levels, and through modulating heme levels, Hfq influences the cyto c content. Although Hfq in excess results in overproduction of the enzymes responsible for both generation and removal of homogentisic acid (HGA), the precursor of pyomelanin, it is compromised activity of HmgA that leads to excretion and polymerization of HGA to form pyomelanin. We further show that Hfq mediates HmgA activity by lowering intracellular iron content because HmgA is an iron-dependent enzyme. Overall, our work highlights the significance of Hfq-mediated posttranscriptional regulation in the physiology of S. oneidensis, unraveling unexpected mechanisms by which Hfq affects cyto c biosynthesis and pyomelanin production. IMPORTANCE In bacteria, Hfq has been implicated in regulation of diverse biological processes posttranslationally. In S. oneidensis, Hfq affects the content of cytos c that serve as the basis of its respiratory versatility and potential application in bioenergy and bioremediation. In this study, we found that Hfq differentially regulates heme biosynthesis and degradation, leading to altered cyto c contents. Hfq in excess causes a synthetic effect on HmgA, an enzyme responsible for pyomelanin formation. Overall, the data presented manifest that the biological processes in a given bacterium regulated by Hfq are highly complex, amounting to required coordination among multiple physiological aspects to allow cells to respond to environmental changes promptly.

Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population - a machine learning approach.

BACKGROUND: The proportion of patients with post-traumatic stress disorder (PTSD) that remain undiagnosed may be substantial. Without an accurate diagnosis, these patients may lack PTSD-targeted treatments and experience adverse health outcomes. This study used a machine learning approach to identify and describe civilian patients likely to have undiagnosed PTSD in the US commercial population. METHODS: The IBM® MarketScan® Commercial Subset (10/01/2015-12/31/2018) was used. A random forest machine learning model was developed and trained to differentiate between patients with and without PTSD using non-trauma-based features. The model was applied to patients for whom PTSD status could not be confirmed to identify individuals likely and unlikely to have undiagnosed PTSD. Patient characteristics, symptoms and complications potentially related to PTSD, treatments received, healthcare costs, and healthcare resource utilization were described separately for patients with PTSD (Actual Positive PTSD cohort), patients likely to have PTSD (Likely PTSD cohort), and patients without PTSD (Without PTSD cohort). RESULTS: A total of 44,342 patients were classified in the Actual Positive PTSD cohort, 5683 in the Likely PTSD cohort, and 2,074,471 in the Without PTSD cohort. While several symptoms/comorbidities were similar between the Actual Positive and Likely PTSD cohorts, others, including depression and anxiety disorders, suicidal thoughts/actions, and substance use, were more common in the Likely PTSD cohort, suggesting that certain symptoms may be exacerbated among those without a formal diagnosis. Mean per-patient-per-6-month healthcare costs were similar between the Actual Positive and Likely PTSD cohorts ($11,156 and $11,723) and were higher than those of the Without PTSD cohort ($3616); however, cost drivers differed between cohorts, with the Likely PTSD cohort experiencing more inpatient admissions and less outpatient visits than the Actual Positive PTSD cohort. CONCLUSIONS: These findings suggest that the lack of a PTSD diagnosis and targeted management of PTSD may result in a greater burden among undiagnosed patients and highlights the need for increased awareness of PTSD in clinical practice and among the civilian population.

Dihydrotanshinone I Enhances Cell Adhesion and Inhibits Cell Migration in Osteosarcoma U-2 OS Cells through CD44 and Chemokine Signaling.

In the screening of novel natural products against cancer using an in vitro cancer cell model, we recently found that tanshinones from a traditional Chinese medicine, the rhizome of Salvia miltiorrhiza Bunge (Danshen), had potent effects on cell proliferation and migration. Especially for human osteosarcoma U-2 OS cells, tanshinones significantly enhanced the cell adherence, implying a possible role in cell adhesion and cell migration inhibition. In this work, therefore, we aimed to provide a new insight into the possible molecule mechanisms of dihydrotanshinone I, which had the strongest effects on cell adhesion among several candidate tanshinones. RNA-sequencing-based transcriptome analysis and several biochemical experiments indicated that there were comprehensive signals involved in dihydrotanshinone I-treated U-2 OS cells, such as cell cycle, DNA replication, thermogenesis, tight junction, oxidative phosphorylation, adherens junction, and focal adhesion. First, dihydrotanshinone I could potently inhibit cell proliferation and induce cell cycle arrest in the G0/G1 phase by downregulating the expression of CDK4, CDK2, cyclin D1, and cyclin E1 and upregulating the expression of p21. Second, it could significantly enhance cell adhesion on cell plates and inhibit cell migration, involving the hyaluronan CD44-mediated CXCL8-PI3K/AKT-FOXO1, IL6-STAT3-P53, and EMT signaling pathways. Thus, the increased expression of CD44 and lengthened protrusions around the cell yielded a significant increase in cell adhesion. In summary, these results suggest that dihydrotanshinone I might be an interesting molecular therapy for enhancing human osteosarcoma U-2 OS cell adhesion and inhibiting cell migration and proliferation.

Predicting Clinical Remission of Chronic Urticaria Using Random Survival Forests: Machine Learning Applied to Real-World Data.

INTRODUCTION: The time required to reach clinical remission varies in patients with chronic urticaria (CU). The objective of this study is to develop a predictive model using a machine learning methodology to predict time to clinical remission for patients with CU. METHODS: Adults with ≥ 2 ICD-9/10 relevant CU diagnosis codes/CU-related treatment > 6 weeks apart were identified in the Optum deidentified electronic health record dataset (January 2007 to June 2019). Clinical remission was defined as ≥ 12 months without CU diagnosis/CU-related treatment. A random survival forest was used to predict time from diagnosis to clinical remission for each patient based on clinical and demographic features available at diagnosis. Model performance was assessed using concordance, which indicates the degree of agreement between observed and predicted time to remission. To characterize clinically relevant groups, features were summarized among cohorts that were defined based on quartiles of predicted time to remission. RESULTS: Among 112,443 patients, 73.5% reached clinical remission, with a median of 336 days from diagnosis. From 1876 initial features, 176 were retained in the final model, which predicted a median of 318 days to remission. The model showed good performance with a concordance of 0.62. Patients with predicted longer time to remission tended to be older with delayed CU diagnosis, and have more comorbidities, more laboratory tests, higher body mass index, and polypharmacy during the 12-month period before the first CU diagnosis. CONCLUSIONS: Applying machine learning to real-world data enabled accurate prediction of time to clinical remission and identified multiple relevant demographic and clinical variables with predictive value. Ongoing work aims to further validate and integrate these findings into clinical applications for CU management.

Excess healthcare costs in patients with autosomal dominant polycystic kidney disease by renal dysfunction stage.

AIM: To build upon previous outdated studies by comprehensively assessing the direct healthcare burden of autosomal dominant polycystic kidney disease (ADPKD). MATERIALS AND METHODS: Patients with ≥2 diagnoses for ADPKD (ADPKD cohort) were identified in the US fee-for-use IBM Truven Health Analytics MarketScan Commercial Claims and Encounters and IBM Truven Health Analytics MarketScan Medicare Supplemental databases (01 January 2015-31 December 2017) and matched (1:3) to controls without ADPKD (non-ADPKD cohort). The index date was the last calendar date followed by 12 months continuous enrollment (study period). Patients with ADPKD were stratified into one of seven mutually exclusive groups based on chronic kidney disease (CKD) stages (I-V), end-stage renal disease requiring renal replacement therapy (ESRD-RRT), and unknown stage. RESULTS: During the 12-month study period, patients with ADPKD incurred significantly higher total healthcare costs than those without ADPKD (mean cost difference = $22,879 per patient per year [PPPY]; p < .001). Besides CKD stages I and II, total healthcare cost differences increased as patients progressed beyond CKD stage III, with the greatest difference observed among patients with ESRD-RRT. Total healthcare cost differences between cohorts were more pronounced in subgroups of patients with hypertension ($29,347) and with high risk of rapid progression ($39,976). Similar results were observed in the Medicare Supplemental population, with a total mean cost difference of $42,694 PPPY (p < .001); cost difference was also higher in the hypertension ($46,461 PPPY) and high risk of rapid progression ($45,708 PPPY) subgroups. LIMITATIONS: Results may not be representative of the overall ADPKD US population; CKD stage was based on diagnosis and procedure codes; criteria used to identify ADPKD at risk of rapid progression did not rely on laboratory values; there may be billing inaccuracies and omissions in health insurance claims data. CONCLUSIONS: This study demonstrated the substantial healthcare costs associated with ADPKD, which increased as patients progressed through more severe CKD stages.