Effect of donor–recipient size mismatch on long-term graft survival in pediatric kidney transplantation: a multicenter cohort study

Background Donor–recipient size mismatching is commonly occurs in pediatric kidney transplantation (KT). However, its effect on graft survival remains unknown. This study aimed to determine the effect of donor–recipient size mismatch on the long-term survival rate of transplant kidneys in pediatric KT. Methods A total of 241 pediatric patients who received KT were enrolled. The medical records of all patients were retrospectively reviewed, and the correlation between donor–recipient size mismatch and graft function and long-term graft outcome was analyzed according to donor–recipient size mismatch. Results Recipients and donors’ mean body weight at the time of KT were 34.31 ± 16.85 and 56.53 ± 16.73 kg, respectively. The mean follow-up duration was 96.49 ± 52.98 months. A significant positive correlation was observed between donor–recipient body weight ratio (DRBWR) or donor–recipient body surface area ratio (DRBSR) and graft function until 1 year after KT. However, this correlation could not be confirmed at the last follow-up. The results of long-term survival analysis using Fine and Gray’s subdistribution hazard model showed no significant difference of the survival rate of the transplant kidney according to DRBWR or DRBSR. Conclusion Donor–recipient size mismatch in pediatric KT is not an important factor in determining the long-term prognosis of transplant kidneys

Study on Threat Modeling in Smart Greenhouses

In the era of agriculture 4.0, cutting-edge technologies including Information and communication technology (ICT) is being introduced into traditional agriculture. As farm intelligence emerges as a key area of smart agriculture, the scope of agriculture has expanded from the seed industry to distribution and logistics, however the area that is still most directly connected to the physical agricultural environment is smart farming. Cybersecurity incidents or cybercrimes in smart farming can directly damage crops and harm human safety. Research on individual technical elements that constitute smart farming has been ongoing for a long time relatively, however it has not been long since the work of systematically identifying and classifying threats to smart agriculture as a whole. In this study, STRIDE threat modeling is used to identify cyber threats to greenhouse and make system design more robust. Through this work, we have derived 126 threats and have created 4 types of attack trees. It will be the basis to allow systematic threat classification more clearly in smart greenhouse

An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise

Impact of Non-vitamin K Antagonist Oral Anticoagulant Withdrawal on Stroke Outcomes

Introduction: Discontinuation of oral anticoagulants such as non-vitamin K antagonist oral anticoagulants (NOACs) may induce a hypercoagulable state, leading to severe stroke and poor outcomes. This study aimed to compare stroke outcomes between NOACs withdrawal and other prior medication statuses in patients with non-valvular atrial fibrillation (NVAF).Methods: Consecutive patients who had pre-existing NVAF and were admitted for an acute ischemic stroke or transient ischemic attack- at five hospitals between January 2013 and December 2016 were included. Prior medication status was categorized into seven groups such as no antithrombotics, antiplatelet-only, warfarin with subtherapeutic intensity, warfarin with therapeutic intensity, NOAC, warfarin withdrawal, and NOAC withdrawal. We compared initial National Institute of Health Stroke Scale (NIHSS) scores between groupsResults: Among 719 patients with NVAF, The median NIHSS score at admission was 5 (IQR 1-13). The NOAC withdrawal group had the highest median NIHSS scores at stroke onset [16, interquartile range, IQR (1–17)], followed by the warfarin withdrawal group [11, IQR (1–14, 18)], the no antithrombotic group [5, IQR (1–13, 18, 19)], and the warfarin with subtherapeutic intensity group [5, IQR (1–10, 18, 19)]. A Multivariable analysis demonstrated that NOAC withdrawal was independently associated with higher NIHSS scores at stroke onset (B 4.645, 95% confidence interval 0.384–8.906, P = 0.033). The median interval from drug withdrawal to ischemic stroke or TIA was 7 days (IQR 4-15) in the NOAC group.Conclusions: Stroke that occurred after stopping oral anticoagulants, especially NOAC, and was more severe at presentation and associated with poorer outcomes