METHODS: The medical records for NHL patients who had undergone HDT followed by AHSCT from October 1997 to November 2016 from two hospitals in Klang Valley, Malaysia were obtained from the medical record database and analysed retrospectively through statistical analysis.

RESULTS: A total of 148 patients were retrospectively identified post-AHSCT, where the majority of whom had B cell lymphoma (53.4%). Majority of patients (88.5%) were in complete remission before AHSCT. The overall survival (OS) and event-free survival (EFS) at 3 years were 68.9% and 60.8%, respectively. The major cause of death was disease progression at 73.9%, while transplant-related mortality was 15.2%, with a median follow-up period of 179.5 weeks.

METHODS: We systematically reviewed Medline and Embase for population-based studies reporting hospitalization rates for IBD, Crohn's disease (CD), or ulcerative colitis (UC) in the 21st century. Log-linear models were used to calculate the average annual percentage change (AAPC) with associated 95% confidence intervals (95% CIs). Random-effects meta-analysis pooled country-level AAPCs. Data were stratified by the epidemiologic stage of a region: compounding prevalence (stage 3) in North America, Western Europe, and Oceania vs acceleration of incidence (stage 2) in Asia, Eastern Europe, and Latin America vs emergence (stage 1) in developing countries.

RESULTS: Hospitalization rates for a primary diagnosis of IBD were stable in countries in stage 3 (AAPC, -0.13%; 95% CI, -0.72 to 0.97), CD (AAPC, 0.20%; 95% CI, -1.78 to 2.17), and UC (AAPC, 0.02%; 95% CI, -0.91 to 0.94). In contrast, hospitalization rates for a primary diagnosis were increasing in countries in stage 2 for IBD (AAPC, 4.44%; 95% CI, 2.75 to 6.14), CD (AAPC, 8.34%; 95% CI, 4.38 to 12.29), and UC (AAPC, 3.90; 95% CI, 1.29 to 6.52). No population-based studies were available for developing regions in stage 1 (emergence).

METHODS: This is a one-centre, retrospective, observational study.

RESULTS: Eight hundred and ninety-three blastocysts were biopsied; 57.73% were euploid. The euploidy rate was found to be significantly higher for the embryos with good morphology of inner cell mass (ICM) and TE. Between ICM and TE morphology variables, TE was more predictive of the euploidy rate. When broken down into different age groups, the percentage of good morphology embryos remained similar across all age groups, while the percentage of euploid embryos dropped with increasing age. These results suggest that the correlation between blastocyst morphology and ploidy status was present but poor. Faster growing day 5 blastocysts showed a significantly higher euploidy rate than slower growing day 6 or 7 blastocysts. The number of good-quality blastocysts per cycle, euploid blastocysts per cycle and the euploidy rate were strongly associated with maternal age. A trend towards an increased implantation rate was found with euploid embryo transfers compared to the control group without preimplantation genetic test for aneuploidies (PGT-A).

METHODS: Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network.

RESULTS: A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%).