Background: Diabetes mellitus (DM) can worsen the prognosis or survival in prostate cancer (PC) patients. We investigated whether glycemic control impacts mortality in PC patients with existing diabetes. Methods: All PC patients with or without preexisting DM were enrolled from 2006 to 2017. Mean hemoglobin A1c (HbA1c) values (<7%, 7%-9%, ≥9%) were used to represent glycemic control. Major outcomes included all-cause, PC-specific, and non-PC mortalities. Statistical analyses were performed using Cox regression models with adjusted mean HbA1c and other related confounders. Results: A total of 831 PC patients were enrolled (non-DM group, n = 690; DM group with a record of mean HbA1c values, n = 141). Results showed that the DM group with mean HbA1c level ≥ 9% (n = 14) had significantly increased risk for all-cause and non-PC mortality (hazard ratio [HR], 3.09; 95% CIs, 1.15-8.32; p=0.025 and HR, 5.49; 95% CIs, 1.66-18.16; p = 0.005, respectively), but not for PC-specific mortality (HR, 1.03; 95% CIs, 0.13-8.44; p = 0.975), compared with the non-DM group. Conclusion: Our findings indicate that PC patients with DM who had a mean HbA1c level ≥ 9% had higher risks of all-cause and non-PC mortality compared with non-DM subjects. Further large and long-term studies are needed to verify the effect of glycemic control in PC patients with DM.

Background: Due to the potential consequences of post-traumatic epilepsy (PTE) exacerbating secondary injury following traumatic brain injury (TBI), the use of antiepileptic drugs (AEDs) is an accepted option for seizure prophylaxis. However, there is only a paucity of data that can be found regarding outcomes surrounding the use of AEDs. The purpose of this retrospective study is to evaluate whether the prophylactic administration of AEDs significantly decreased the incidence of PTE, when considering the severity of TBI. Methods: All trauma patients who had been newly diagnosed with TBI from January 1, 2010 to December 31, 2017 were retrospectively analyzed. Statistical comparisons were made using the chi-square test, Mann-Whitney U test, and Cox regression modeling. After excluding any exposed subjects with no appropriate match, patients who had received AED prophylaxis were matched by propensity score with those who did not receive AEDs. All of the TBI populations were followed up until June 30, 2018. Results: We identified 1316 patients who met the inclusion and exclusion criteria in our matched cohort through their propensity scores, where 138 patients had been receiving prophylactic AEDs and 138 patients had not. Baseline characteristics were similar in gender, age, Glasgow Coma Scale (GCS) scores, and risk factors of PTE including skull fracture, chronic alcoholism, subdural hematoma, epidural hematoma, and intracerebral hematoma. After adjusting for those risk factors, the relative incidence of seizure was not statistically significant in either of the groups ( p = 0.566). Conclusion: In our cohort analysis, AED prophylaxis was ineffective in preventing seizures, as the rate of seizures was similar whether patients had been receiving the drugs or not. We therefore concluded that the benefits of routine prophylactic anticonvulsant therapy in patients with TBI need to be re-evaluated.

These results support the importance of duration of ruptured membranes as a risk factor for vertical transmission of HIV and suggest that a diagnosis of AIDS in the mother at the time of delivery may potentiate the effect of duration of ruptured membranes.

The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training data to prevent overfitting of the training model. We propose using a pre-trained model and a fine-tuning technique to improve these approaches without additional time-consuming human labeling. Firstly, we show the architecture of Bidirectional Encoder Representations from Transformers (BERT), an approach for pre-training a model on large-scale unstructured text. We then combine BERT with a one-dimensional convolutional neural network (1d-CNN) to fine-tune the pre-trained model for relation extraction. Extensive experiments on three datasets, namely the BioCreative V chemical disease relation corpus, traditional Chinese medicine literature corpus and i2b2 2012 temporal relation challenge corpus, show that the proposed approach achieves state-of-the-art results (giving a relative improvement of 22.2, 7.77, and 38.5% in F1 score, respectively, compared with a traditional 1d-CNN classifier). The source code is available at https://github.com/chentao1999/MedicalRelationExtraction.

Shortening waiting times is the most obvious and effective method of increasing service quality. As the workforce is limited, it is necessary to reform current systems of medical care and improve the efficiency of medical care. After process reengineering was proposed in 1990s, however, this concept has not yet been commonly applied to medical centers. The subject of this study was an outpatient pharmacy in a medical center. This study applied the methods of a time study to measure field observations and as an analytic tool in process reengineering. The results show that the pharmacists were hindered in filling prescriptions for the following reasons: the preparation of certain prescription units, the menial sorting of medicines and also storage issues related to medicines. Improving the process will decrease time wasted by 10.41% and enhance service by 8.95%. The reengineering process resulted not only in a reduction in outpatients' waiting time but also enhanced the quality and competitiveness of the Hospital's medical treatment.

BackgroundCurrent guidelines recommend bismuth-containing quadruple therapy (BQT) and quinolone-containing therapy after failure of first-line Helicobacter pylori eradication therapy. However, the optimum regimen of second-line eradication therapy remains elusive. We conducted a network meta-analysis to compare the relative efficacy of 16 second-line H. pylori eradication regimens.MethodsThree major bibliographic databases were reviewed to enrol relevant randomised controlled trials between January 2000 and September 2018. Network meta-analysis was conducted by STATA software and we performed subgroup analysis in countries with high clarithromycin resistance and high levofloxacin resistance, and in patients with documented failure of first-line triple therapy.ResultsFifty-four studies totalling 8752 participants who received 16 regimens were eligible for analysis. Compared with a 7-day BQT, use of probiotic add-on therapy during, before, and after second-line antibiotic regimens, quinolone-based sequential therapy for 10–14 days, quinolone-based bismuth quadruple therapy for 10–14 days, bismuth quadruple therapy for 10–14 days, and quinolone-based triple therapy for 10–14 days were significantly superior to the other regimens. Subgroup analysis of countries with high clarithromycin resistance and high levofloxacin resistance revealed that the ranking of second-line eradication regimens was distributed similarly in each group, as well as in patients with failure of first-line triple therapy.ConclusionWe conducted a detailed comparison of second-line H. pylori regimens according to different antibiotic resistance rates and the results suggest alternative treatment choices with potential benefits beyond those that could be achieved using salvage therapies recommended by guidelines.

Post-transplant diabetes mellitus (PTDM) is associated with infection, cardiovascular morbidity, and mortality. A retrospective cohort study involving patients who underwent renal transplantation in a transplantation center in Taiwan from January 2000 to December 2018 was conducted to investigate the incidence and risk factors of PTDM and long-term patient and graft survival rates. High age (45–65 vs. <45 years, adjusted odds ratio (aOR) = 2.90, 95% confidence interval (CI) = 1.64–5.13, p < 0.001), high body mass index (>27 vs. <24 kg/m2, aOR = 5.35, 95% CI = 2.75–10.42, p < 0.001), and deceased organ donor (cadaveric vs. living, aOR = 2.01, 95% CI = 1.03–3.93, p = 0.04) were the three most important risk factors for the development of PTDM. The cumulative survival rate of patients and allografts was higher in patients without PTDM than in those with PTDM (p = 0.007 and 0.041, respectively). Concurrent use of calcineurin inhibitors and mammalian target of rapamycin inhibitors (mTORis) decreased the risk of PTDM (tacrolimus vs. tacrolimus with mTORi, aOR = 0.28, 95% CI = 0.14–0.55, p < 0.001). Investigating PTDM risk factors before and modifying immunosuppressant regimens after transplantation may effectively prevent PTDM development.

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