Ocular manifestations and quality of life in patients after hematopoietic stem cell transplantation.

AIM: To explore the relationship between ocular and systemic conditions and the impact of ocular complications on the quality of life (QOL) in patients after allogeneic hematopoietic stem cell transplantation (ALLO-HSCT). METHODS: Forty-four patients with severe hematopoietic disease were enrolled after ALLO-HSCT at our center from July 2018 to October 2020. They completed two questionnaires: the Ocular Surface Disease Index (OSDI) and the quality-of-life scale for Chinese patients with visual impairment (SQOL-DV1). Ocular conditions and systemic conditions were also assessed. RESULTS: Eye damage was correlated with total bilirubin (P=0.005), and gamma-glutamyl transferase (GGT) (P=0.021). There was no significant correlation between the overall QOL score and OSDI (P=0.8226) or SQOL-DV1 (P=0.9526) scores. The OSDI and the overall QOL score were not correlated with ocular conditions, including best-corrected visual acuity (BCVA), intraocular pressure, Schirmer tear test II, sodium fluorescein staining, tear film breakup time, and tear meniscus height. SQOL-DV1 was correlated with BCVA (P=0.0007), sodium fluorescein staining (P=0.007), and tear film breakup time (P=0.0146). CONCLUSION: In some patients, early ocular symptoms are not evident after ALLO-HSCT, while ocular surface complications can be observed after a comprehensive ophthalmological examination. Especially for those with elevated total bilirubin or GGT, regular ophthalmic follow-up visits are essential to diagnose and treat ocular graft versus host disease (oGVHD), especially for patients with elevated total bilirubin or GGT.

A nomogram model for predicting ocular GVHD following allo-HSCT based on risk factors.

OBJECTIVE: To develop and validate a nomogram model for predicting chronic ocular graft-versus-host disease (coGVHD) in patients after allogenic haematopoietic stem cell transplantation (allo-HSCT). METHODS: This study included 61 patients who survived at least 100 days after allo-HSCT. Risk factors for coGVHD were screened using LASSO regression, then the variables selected were subjected to logistic regression. Nomogram was established to further confirm the risk factors for coGVHD. Receiver operating characteristic (ROC) curves were constructed to assess the performance of the predictive model with the training and test sets. Odds ratios and 95% confidence intervals (95% CIs) were calculated by using logistic regression analysis. RESULTS: Among the 61 patients, 38 were diagnosed with coGVHD. We selected five texture features: lymphocytes (LYM) (OR = 2.26), plasma thromboplastin antecedent (PTA) (OR = 1.19), CD3 + CD25 + cells (OR = 1.38), CD3 + HLA-DR + cells (OR = 0.95), and the ocular surface disease index (OSDI) (OR = 1.44). The areas under the ROC curve (AUCs) of the nomogram with the training and test sets were 0.979 (95% CI, 0.895-1.000) and 0.969 (95% CI, 0.846-1.000), respectively.And the Hosmer-Lemeshow test was nonsignificant with the training (p = 0.9949) and test sets (p = 0.9691). CONCLUSION: We constructed a nomogram that can assess the risk of coGVHD in patients after allo-HSCT and help minimize the irreversible loss of vision caused by the disease in high-risk populations.

Dynamic prediction of life-threatening events for patients in intensive care unit.

BACKGROUND: Early prediction of patients' deterioration is helpful in early intervention for patients at greater risk of deterioration in Intensive Care Unit (ICU). This study aims to apply machine learning approaches to heterogeneous clinical data for predicting life-threatening events of patients in ICU. METHODS: We collected clinical data from a total of 3151 patients admitted to the Medical Intensive Care Unit of Peking Union Medical College Hospital in China from January 1st, 2014, to October 1st, 2019. After excluding the patients who were under 18 years old or stayed less than 24 h at the ICU, a total of 2170 patients were enrolled in this study. Multiple machine learning approaches were utilized to predict life-threatening events (i.e., death) in seven 24-h windows (day 1 to day 7) and their performance was compared. RESULTS: Light Gradient Boosting Machine showed the best performance. We found that life-threatening events during the short-term windows can be better predicted than those in the medium-term windows. For example, death in 24 h can be predicted with an Area Under Curve of 0.905. Features like infusion pump related fluid input were highly related to life-threatening events. Furthermore, the prediction power of static features such as age and cardio-pulmonary function increased with the extended prediction window. CONCLUSION: This study demonstrates that the integration of machine learning approaches and large-scale high-quality clinical data in ICU could accurately predict life-threatening events for ICU patients for early intervention.

[Cyclin D1, hTERT expression and telomerase activity in HL-60 and HL-60A cell lines and their significance].

To observe the expression of cyclin D1, hTERT, and telomerase activity in MNC, HL-60, HL-60A and to explore their effects on leukemogenesis and drug-resistance, normal human peripheral blood mononuclear cells, HL-60 cells sensitive to adriamycin and HL-60A cells resistant to adriamycin were investigated. The cell cycle was analyzed by flow cytometry, and the apoptosis was analyzed by Annexin V-FITC(+) PI staining. Expressions of cyclin D1 and hTERT were determined by real-time PCR and Western blot. Telomerase activity was detected by TRAP-ELISA. The results indicated that the percentage of MNC, HL-60 and HL-60A in S phase was (10.21 + 2.11)%, (44.93 + 3.00)%, and (51.38 + 1.10)% respectively; the percentage of apoptosis cells was (16.14 + 2.13)%, (7.53 + 0.92)%, (4.15 + 0.96)% respectively; the expression of mRNA and protein for cyclin D1 and hTERT increased; the telomerase activities of HL-60 and HL-60A were higher (p = 0.000), whereas the difference between HL-60 and HL-60A was no statistically significant (p = 0.232); positive correlation between cyclin D1, hTERT and telomerase activity had been found (p < 0.01). It is concluded that the cells of S phase increased while the apoptotic cells decreased in HL-60 and HL-60A, especially in HL-60A, which may be due to the up-regulation of cyclin D1, hTERT and telomerase activity.