Application of interdisciplinary collaborative hospice care for terminal geriatric cancer patients: a prospective randomized controlled study.

BACKGROUND: Hospice care (HC) is specialized medical care for terminal patients who are nearing the end of life. Interdisciplinary collaborative hospice care (ICHC) is where experts from different disciplines and patients/caregivers form a treatment team to establish shared patient care goals. However, the ICHC efficacy has not been frequently studied in the terminal geriatric cancer patient (TGCP) population. This study aimed to gain insight into ICHC provided to TGCPs by an ICHC team and identify factors to ameliorate multidimensional HC. METHODS: 166 TGCPs were randomized by a computer-generated random number table using an allocation ratio of 1:1. The patients were divided into the ICHC group and life-sustaining treatment (LST) group. The scores of these questionnaires, such as EORTC, QLQ-C30, Hamilton anxiety scale, the median survival time (MST), symptoms improvement, the median average daily cost of drugs (MADDC), the median total cost of drugs (MTDC) in the last 2 days, and medical care satisfaction were observed in both groups. RESULTS: After treatment, the improvement of emotional function and symptoms in the ICHC group were statistically higher than those in the LST group (P < 0.05). The MADDC and the MTDC in the last 2 days were statistically lower in the ICHC group than those in the LSTs group (P < 0.01). In addition, the overall satisfaction situation and the cooperation ability in the ICHC group were statistically higher than those in the LST group (P < 0.01). CONCLUSION: The ICHC could provide TGCPs with coordinated, comfortable, high-quality, and humanistic care.

Construction of shared gene signature between rheumatoid arthritis and lung adenocarcinoma helps to predict the prognosis and tumor microenvironment of the LUAD patients.

Introduction: Rheumatoid arthritis (RA) is a common chronic autoimmune disease with high incidence rate and high disability rate. One of the top complications is cancer, especially lung adenocarcinoma (LUAD). However, the molecular mechanisms linking RA and LUAD are still not clear. Therefore, in this study, we tried to identify the shared genetic signatures and local immune microenvironment between RA and LUAD and construct a clinical model for survival prediction. Methods: We obtained gene expression profiles and clinical information of patients with RA and LUAD from GEO and TCGA datasets. We performed differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to discover the shared genes between RA and LUAD. Then, COX regression and LASSO analysis were employed to figure out genes significantly associated with survival. qRT-PCR and Western blot were utilized to validate the expression level of candidate genes. For clinical application, we constructed a nomogram, and also explored the value of RALUADS in characterizing immune infiltration features by CIBERSORT and xCell. Finally, responses to different drug therapy were predicted according to different RALUADS. Results: Our analysis identified two gene sets from differentially expressed genes and WGCNA gene modules of RA and LUAD. Filtered by survival analysis, three most significant shared genes were selected, CCN6, CDCA4 and ERLIN1, which were all upregulated in tumors and associated with poor prognosis. The three genes constituted RA and LUAD score (RALUADS). Our results demonstrated that RALUADS was higher in tumor patients and predicted poor prognosis in LUAD patients. Clinical nomogram combining RALUADS and other clinicopathological parameters had superior performance in survival prediction (AUC = 0.722). We further explored tumor immune microenvironment (TME) affected by RALUADS and observed RALUADS was closely related to the sensitivity of multiple immune blockades, chemotherapy and targeted drugs. Conclusion: Our findings suggest that there are shared physiopathologic processes and molecular profiles between RA and LUAD. RALUADS represents an excellent prognosis predictor and immune-related biomarker, which can be applied to select potential effective drugs and for LUAD patients with RA.

Construction of a risk model and deep learning network based on patients with active pulmonary tuberculosis and pulmonary inflammation.

Most patients with active pulmonary tuberculosis (TB) are difficult to be differentiated from pneumonia (PN), especially those with acid-fast bacillus smear-negative (AFB-) and interferon-γ release assay-positive (IGRA+) results. Thus, the aim of the present study was to develop a risk model of low-cost and rapid test for the diagnosis of AFB- IGRA+ TB from PN. A total of 41 laboratory variables of 204 AFB- IGRA+ TB and 156 PN participants were retrospectively analyzed. Candidate variables were identified by t-statistic test and univariate logistic model. The logistic regression analysis was used to construct the multivariate risk model and nomogram with internal and external validation. A total of 13 statistically differential variables were compared between AFB- IGRA+ TB and PN by false discovery rate (FDR) and odds ratio (OR). By integrating five variables, including age, uric acid (UA), albumin (ALB), hemoglobin (Hb) and white blood cell counts (WBC), a multivariate risk model with a concordance index (C-index) of 0.7 (95% CI: 0.61, 0.8) was constructed. The nomogram showed that UA and Hb acted as protective factors with an OR <1, while age, WBC and ALB were risk factors for TB occurrence. Internal and external validation revealed that nomogram prediction was consistent with the actual observations. Collectively, it was revealed that an integration of five biomarkers (age, UA, ALB, Hb and WBC) may be used to quickly predict TB in AFB- IGRA+ clinical samples from PN.

Recent Updates in Experimental Research and Clinical Evaluation on Drugs for COVID-19 Treatment.

Since the outbreak of corona virus disease 2019 (COVID-19) in Wuhan (China) in December 2019, the epidemic has rapidly spread to many countries around the world, posing a huge threat to global public health. In response to the pandemic, a number of clinical studies have been initiated to evaluate the effect of various treatments against COVID-19, combining medical strategies and clinical trial data from around the globe. Herein, we summarize the clinical evaluation about the drugs mentioned in this review for COVID-19 treatment. This review discusses the recent data regarding the efficacy of various treatments in COVID-19 patients, to control and prevent the outbreak.