Wei-Nung Liu

Background: Hemoglobin (Hb) levels have uncertain associations with blood pressure (BP). In addition, no cohort studies to date have investigated the association between Hb and hypertension among physically fit young adults. Methods: This cohort study involved 2,440 military male and female personnel, aged 18–39 years, free of baseline hypertension in Taiwan in 2014. The individuals were followed up for incident hypertension from baseline to 2020. All participants received military health examinations annually, including BP measurements. Hypertension was defined as a mean systolic BP ≥130 mmHg and/or a mean diastolic BP ≥80 mmHg, in accordance with the latest guidelines. Mild anemia was defined as Hb concentrations of 10.0–13.9 g/dL in men and 10.0–11.9 g/dL in women. Multivariable Cox regression analyses were performed, adjusting for age, sex, BP, body mass index, substance use status, serum uric acid, blood urea nitrogen, estimated glomerular filtration rate, physical activity and fitness to determine the relationships. Results: A total of 915 participants (37.5%) developed new-onset hypertension during the follow-up period. Of the 2,440 participants, 2,148 were men and 292 were women, and new-onset hypertension occurred in 871 men and 44 women. In men, Hb levels (per 1.0 g/dL increase) were positively associated with incident hypertension [hazard ratio (HR) and 95% confidence interval (CI): 1.105 (1.026–1.190)], and mild anemia was linked to a lower risk of incident hypertension [HR: 0.765 (0.597–0.979)]. The association of Hb in men were only significant for isolated diastolic hypertension [HRs: 1.176 (1.047–1.320) and 0.514 (0.325–0.813), respectively]. In young women, the associations of Hb levels and mild anemia with incident hypertension were not statistically significant (HRs: 0.765 and 1.713, both p > 0.05). Conclusions: Hb levels were positively associated with increased risk of hypertension among physically fit young male adults. Such association was specifically for isolated diastolic hypertension. However, among women, neither association reached significance, possibly due to the small number of hypertension events making these results uncertain.

Background and Objectives: Peripheral arterial stiffness (PAS), assessed by brachial-ankle pulse wave velocity (baPWV), is an independent biomarker of cardiovascular diseases (CVD) in patients on maintenance hemodialysis (HD). Malondialdehyde-modified low-density lipoprotein (MDA-LDL), an oxidative stress marker, has been linked to atherosclerosis and CVD. However, the association between serum MDA-LDL and PAS among HD patients has not been fully elucidated. This study aimed to examine the association of serum MDA-LDL with PAS in HD patients and to identify the optimal cutoff value of serum MDA-LDL for predicting PAS. Materials and Methods: A cross-sectional study was conducted in 100 HD patients. Serum MDA-LDL was quantified using an enzyme-linked immunosorbent assay (ELISA), and baPWV was measured using a volume plethysmographic device. Patients were divided into the PAS group (baPWV > 18.0 m/s) and the non-PAS group (baPWV ≤ 18.0 m/s). The associations of baPWV and other clinical and biochemical parameters with serum MDA-LDL were assessed by multivariable logistic regression analyses. A receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cutoff value of serum MDA-LDL for predicting PAS. Results: In multivariable logistic regression analysis, higher serum MDA-LDL, older age, and higher serum C-reactive protein [odds ratios (ORs) and 95% confidence intervals: 1.014 (1.004–1.025), 1.044 (1.004–1.085) and 3.697 (1.149–11.893)] were significantly associated with PAS. In the ROC curve analysis, the optimal cutoff value of MDA-LDL for predicting PAS was 80.91 mg/dL, with a sensitivity of 79.25% and a specificity of 59.57%. Conclusions: Greater serum MDA-LDL levels, particularly ≥80.91 mg/dL, were independently associated with PAS in HD patients. The findings suggest that oxidative stress plays a crucial role in the pathogenesis of PAS, and targeting MDA-LDL may be a potential therapeutic strategy for reducing cardiovascular risk in HD patients.

Background: Bone marrow aspiration and biopsy remain the gold standard for the diagnosis of hematological diseases despite the development of flow cytometry (FCM) and molecular and gene analyses. However, the interpretation of the results is laborious and operator dependent. Furthermore, the obtained results exhibit inter- and intravariations among specialists. Therefore, it is important to develop a more objective and automated analysis system. Several deep learning models have been developed and applied in medical image analysis but not in the field of hematological histology, especially for bone marrow smear applications. Objective: The aim of this study was to develop a deep learning model (BMSNet) for assisting hematologists in the interpretation of bone marrow smears for faster diagnosis and disease monitoring. Methods: From January 1, 2016, to December 31, 2018, 122 bone marrow smears were photographed and divided into a development cohort (N=42), a validation cohort (N=70), and a competition cohort (N=10). The development cohort included 17,319 annotated cells from 291 high-resolution photos. In total, 20 photos were taken for each patient in the validation cohort and the competition cohort. This study included eight annotation categories: erythroid, blasts, myeloid, lymphoid, plasma cells, monocyte, megakaryocyte, and unable to identify. BMSNet is a convolutional neural network with the YOLO v3 architecture, which detects and classifies single cells in a single model. Six visiting staff members participated in a human-machine competition, and the results from the FCM were regarded as the ground truth. Results: In the development cohort, according to 6-fold cross-validation, the average precision of the bounding box prediction without consideration of the classification is 67.4%. After removing the bounding box prediction error, the precision and recall of BMSNet were similar to those of the hematologists in most categories. In detecting more than 5% of blasts in the validation cohort, the area under the curve (AUC) of BMSNet (0.948) was higher than the AUC of the hematologists (0.929) but lower than the AUC of the pathologists (0.985). In detecting more than 20% of blasts, the AUCs of the hematologists (0.981) and pathologists (0.980) were similar and were higher than the AUC of BMSNet (0.942). Further analysis showed that the performance difference could be attributed to the myelodysplastic syndrome cases. In the competition cohort, the mean value of the correlations between BMSNet and FCM was 0.960, and the mean values of the correlations between the visiting staff and FCM ranged between 0.952 and 0.990. Conclusions: Our deep learning model can assist hematologists in interpreting bone marrow smears by facilitating and accelerating the detection of hematopoietic cells. However, a detailed morphological interpretation still requires trained hematologists.

In a retrospective cohort study, we report the current epidemiology of patients with multiple myeloma (MM) and analyze the real-world clinical outcomes of bortezomib-based therapy. This retrospective study was mainly designed to evaluate the characteristics, treatment outcomes, and prognostic factors of patients with MM who received bortezomib-based therapy. We identified 5,726 patients in Taiwan with MM newly diagnosed between 2007 and 2015. Confidential data from the National Health Insurance Research Database (NHIRD) was used under strict guidelines, as it was made available in an electronic format for research purposes. In addition, we analyzed 96 patients who have been diagnosed with MM and were treated at the Tri-Service General Hospital (TSGH) between January 1, 2002, and December 31, 2018. Patients receiving first-line treatment with bortezomib had longer overall survival (OS) compared to those who received non-first-line treatment (ppp = 0.021). Bortezomib as a first-line treatment extended the OS in four-year mortality tracking and lowered the mortality risk according to the NHIRD analysis. In the TSGH analysis, the results indicated that the initial conditions of patients with MM have a lower influence on the OS and PFS after bortezomib-based therapy was administered.