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Abstract Background The relationship between epilepsy and depression is bidirectional. One condition exacerbates the other. However, there are no current guidelines for treating depression in epilepsy patients. In some cases, seizures worsen when antidepressants (AD) are prescribed or when they are discontinued due to adverse events. The Shugan Jieyu capsule, composed of Acanthopanax senticosus and Hypericum perforatum, is a widely used herbal medicine for treating depression. This study aimed to explore the effectiveness and safety of Shugan Jieyu capsules (SJC) in relieving depression in patients with epilepsy. Methods We searched English, Korean, Japanese, and Chinese databases in October 2023 to collect all relevant randomized clinical trials (RCTs). The primary outcomes were the depression scale scores and seizure frequency. The secondary outcomes were quality of life (QoL) and adverse events. Results Nine RCTs were included in this meta-analysis. Compared with AD, SJC showed significant differences in the improvement of depression (SMD: 3.82, 95% CI: 3.25, 4.39) and reduction in seizure frequency (MD: 0.39 times/month, 95% CI: 0.28, 0.50). SJC showed more beneficial results than antiepileptic drugs (AED) in terms of antidepressant effects (SMD: 1.10, 95% CI: 0.69, 1.51) and QoL (MD: 11.75, 95% CI: 10.55, 12.95). When patients were prescribed AED, the additional administration of SJC improved depression symptoms (SMD: 0.96, 95% CI: 0.28, 1.63). The SJC treatment group had a lower incidence of side effects than the control group. However, the difference was not statistically significant. Conclusions Our results suggest that SJC may be effective in treating depression in patients with epilepsy. Additionally, SJC has the potential to help reduce seizure frequency in epilepsy patients with depression.

The rapid advancement of artificial intelligence has observed increased application in predicting vehicle interior noise levels within the automotive industry. However, the collection of labeled data for training models in this context involves significant costs. Previous studies in semi-supervised regression (SSR) have effectively mitigated the reliance on labeled data by incorporating unlabeled data. Nonetheless, these approaches often introduce a high computational cost due to the training of multiple models and data sampling. This study introduces SpecRegMatch, a novel SSR method aimed at addressing the computational cost associated with training by leveraging a single model, thus eliminating the need for multiple data samplings. SpecRegMatch integrates consistency regularization and information maximization to robustly train the model, achieved through various augmentations applied to both the embedding vectors and predicted values. Experimental results demonstrate that SpecRegMatch achieves state-of-the-art performance across various scenarios, even when using a single model. It attains a remarkable performance, as indicated by an $R^{2}$ score of 0.434. This is especially noteworthy in scenarios where labeled data is scarce. You can access the code for our proposed method at https://github.com/sejin-sim/SpecRegMatch.

Anisakiasis is a zoonotic parasitic infection associated with consumption of raw fish or uncooked seafood. We report a rare case of suspected esophageal anisakiasis-induced dysphagia. A 66-year-old man patient without any medical history was admitted to our hospital for evaluation of acute dysphagia. Following intake of abalone sashimi for dinner, he experienced sudden difficulty with swallowing the day before his emergency department visit. He developed skin rash and pruritus throughout his body, shortly thereafter. He also had chest and upper abdominal pain and diarrhea. He had experienced similar symptoms after consumption of raw fish on two previous occasions. Dysphagia improved 9 days later. Gastroscopy performed after symptom onset showed small erosions and edematous mucosa in the mid esophagus. Abdominopelvic computed tomography (CT) showed distal esophageal wall thickening and small bowel obstruction with concomitant ascites. We suspected anisakiasis based on the following criteria: 1) Skin rash after the third episode of consumption of raw fish or seafood (anisakiasisspecific immunoglobulin E class 4). 2) CT-documented small intestinal obstruction and ascites. 3) Eosinophil-dominant fluid observed on ascitic fluid analysis. Therefore, it is reasonable to conclude that temporary dysphagia in this patient was attributable to esophageal anisakiasis associated with raw seafood consumption.

Abstract Cancer drug response prediction is a crucial task in precision medicine, but existing models have limitations in effectively representing molecular profiles of cancer cells. Specifically, when these models represent molecular omics data such as gene expression, they employ a one-hot encoding-based approach, where a fixed gene set is selected for all samples and omics data values are assigned to specific positions in a vector. However, this approach restricts the utilization of embedding-vector-based methods, such as attention-based models, and limits the flexibility of gene selection. To address these issues, our study proposes gene embedding-based fully connected neural networks (GEN) that utilizes gene embedding vectors as input data for cancer drug response prediction. The GEN allows for the use of embedding-vector-based architectures and different gene sets for each sample, providing enhanced flexibility. To validate the efficacy of GEN, we conducted experiments on three cancer drug response datasets. Our results demonstrate that GEN outperforms other recently developed methods in cancer drug prediction tasks and offers improved gene representation capabilities. All source codes are available at https://github.com/DMCB-GIST/GEN/ .

Abstract Osteoporosis is a state of bone weakening caused by an imbalance in osteoblast and osteoclast activity. In this study, the anti-osteoporotic effects of three proteins fermented by lactic acid bacteria (LAB) were assessed. Commercial proteins sodium caseinate (SC), whey protein isolate (WPI), and soy protein isolate (SPI) were fermented by LAB strains for 48 h. The fermented products (F-SC, F-WPI, and F-SPI, respectively) were used in an in vitro osteoclast and osteoblast-like cell model to assess their effects on bone health. Despite no difference in the results of TRAP staining of RANKL-induced osteoclastogenesis, F-WPI and F-SPI were effective in normalizing the altered gene expression of osteoclastogenesis markers such as TRAP, Nfatc1, RANK, and ATP6v0d. F-SPI was also effective in modulating osteoblasts by enhancing the expression of the osteoblastogenesis markers T1Col, Col2a, and OSX to levels higher than those in the SPI group, indicating that protein characteristics could be enhanced through bacterial fermentation. Moreover, these boosted effects of F-SPI may be involved with isoflavone-related metabolism during LAB-fermentation of SPI. These results demonstrate the potential of LAB-fermented proteins as dietary supplements to prevent bone loss. However, further understanding of its effects on balancing osteoblasts and osteoclasts and the underlying mechanisms is needed.

In this study, a previously developed fully passive hydrokinetic turbine with two flapping foils was used to directly drive a reciprocating pump, and the performance of this system was investigated at an offshore site in Republic of Korea. The fully passive operation of the turbine worked effectively due to its coupling mechanism, and pumping was successfully carried out during flood tides when the pumping height was consistently maintained using a water level gauge and winch system. Pumping occurred at a height of approximately 9 m when the flow velocity reached 1.8 m/s, at which point the corresponding Reynolds number exceeded one million. In one case where a high pumping flow rate was achieved during offshore trials conducted over a period of time, the pumping efficiency reached up to 34% when the reduced frequency of the turbine was 0.126, falling within the known optimum range. The pump driven by the flapping-foil hydrokinetic turbine, which can be positioned near the shore or in shallow water, could provide a viable solution for off-grid communities needing to pump seawater or generate hydroelectric power.

Background Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge. Methods To address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works. Use cases We have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times. Conclusions The AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge.

Incomplete neutralization refers to phonetic traces of underlying contrasts in phonologically neutralizing contexts. The present study examines one such context: Russian assimilatory palatalization in C+j sequences. Russian contrasts plain and palatalized consonants, (e.g., /p/ vs. /pj/ with the “plain” consonants having a secondary articulation, involving retraction of the tongue dorsum (velarization/uvularization)). However, in the C+j sequences (e.g., /pjot/ ‘drink (3ps pres)’) the contrast between palatalized and plain consonants is neutralized, due to the palatalization of the plain consonants: /pjot/ 🡪 [pʲjot] (assimilatory palatalization). The purpose of the study is to explore whether the neutralization is complete. To do so, we conducted an electromagnetic articulography (EMA) experiment examining temporal coordination and the spatial position of the tongue body in underlyingly palatalized consonants and those derived from assimilatory palatalization. Articulatory results from four native speakers of Russian revealed that gestures in both conditions are coordinated as complex segments (i.e., they are palatalized consonants); however, there are differences across conditions consistent with the residual presence of a tongue dorsum retraction gesture in the “plain” obstruents. We conclude that neutralization of the plain-palatal contrast in Russian is incomplete—consonants in the assimilatory palatalization condition exhibit inter-gestural coordination characteristic of palatalized consonants along with residual evidence of an underlying tongue dorsum retraction (velarization/uvularization) gesture.

BackgroundIn the wake of challenges brought by the COVID-19 pandemic to conventional medical education, the demand for innovative teaching methods has surged. Nurse training, with its focus on hands-on practice and self-directed learning, encountered significant hurdles with conventional approaches. Augmented reality (AR) offers a potential solution to addressing this issue. ObjectiveThe aim of this study was to develop, introduce, and evaluate an AR-based educational program designed for nurses, focusing on its potential to facilitate hands-on practice and self-directed learning. MethodsAn AR-based educational program for nursing was developed anchored by the Kern six-step framework. First, we identified challenges in conventional teaching methods through interviews and literature reviews. Interviews highlighted the need for hands-on practice and on-site self-directed learning with feedback from a remote site. The training goals of the platform were established by expert trainers and researchers, focusing on the utilization of a ventilator and extracorporeal membrane oxygenation system. Intensive care nurses were enrolled to evaluate AR education. We then assessed usability and acceptability of the AR training using the System Usability Scale and Technology Acceptance Model with intensive care nurses who agreed to test the new platform. Additionally, selected participants provided deeper insights through semistructured interviews. ResultsThis study highlights feasibility and key considerations for implementing an AR-based educational program for intensive care unit nurses, focusing on training objectives of the platform. Implemented over 2 months using Microsoft Dynamics 365 Guides and HoloLens 2, 28 participants were trained. Feedback gathered through interviews with the trainers and trainees indicated a positive reception. In particular, the trainees mentioned finding AR particularly useful for hands-on learning, appreciating its realism and the ability for repetitive practice. However, some challenges such as difficulty in adapting to the new technology were expressed. Overall, AR exhibits potential as a supplementary tool in nurse education. ConclusionsTo our knowledge, this is the first study to substitute conventional methods with AR in this specific area of critical care nursing. These results indicate the multiple principal factors to take into consideration when adopting AR education in hospitals. AR is effective in promoting self-directed learning and hands-on practice, with participants displaying active engagement and enhanced skill acquisition. Trial RegistrationClinicalTrials.gov NCT05629663; https://clinicaltrials.gov/study/NCT05629663.

Abstract To save time during transport, where resuscitation quality can degrade in a moving ambulance, it would be prudent to continue the resuscitation on scene if there is a high likelihood of ROSC occurring at the scene. We developed the pre-hospital real-time cardiac arrest outcome prediction (PReCAP) model to predict ROSC at the scene using prehospital input variables with time-adaptive cohort. The patient survival at discharge from the emergency department (ED), the 30-day survival rate, and the final Cerebral Performance Category (CPC) were secondary prediction outcomes in this study. The Pan-Asian Resuscitation Outcome Study (PAROS) database, which includes out-of-hospital cardiac arrest (OHCA) patients transferred by emergency medical service in Asia between 2009 and 2018, was utilized for this study. From the variables available in the PAROS database, we selected relevant variables to predict OHCA outcomes. Light gradient-boosting machine (LightGBM) was used to build the PReCAP model. Between 2009 and 2018, 157,654 patients in the PAROS database were enrolled in our study. In terms of prediction of ROSC on scene, the PReCAP had an AUROC score between 0.85 and 0.87. The PReCAP had an AUROC score between 0.91 and 0.93 for predicting survived to discharge from ED, and an AUROC score between 0.80 and 0.86 for predicting the 30-day survival. The PReCAP predicted CPC with an AUROC score ranging from 0.84 to 0.91. The feature importance differed with time in the PReCAP model prediction of ROSC on scene. Using the PAROS database, PReCAP predicted ROSC on scene, survival to discharge from ED, 30-day survival, and CPC for each minute with an AUROC score ranging from 0.8 to 0.93. As this model used a multi-national database, it might be applicable for a variety of environments and populations.

Thyroid hormone resistance (RTH) is characterized by a decreased sensitivity of target tissues to thyroid hormones due to a defect in the THRα- and THRβ-encoded thyroid hormone receptors (THRs). The clinical manifestations range from no symptoms to simple goiter and hypo- or hyperthyroidism, depending on the receptor subtype distribution in the tissues. Here, we report the case of a thyroid hormone-resistant 12-month-old boy carrying a novel THRβ variant who was initially diagnosed with congenital hypothyroidism. An extensive evaluation revealed increased free T4 level and inappropriately increased thyroid-stimulating hormone (TSH) level; a normal lipid profile, sex hormone-binding globulin, and free alpha subunit of TSH; exaggerated TSH response to THR; and no radiological evidence of pituitary adenoma. A targeted next-generation sequencing panel identified a heterozygote c.993T>G (p.Asn331Lys) mutation in the THRβ gene. During the first year of life, a higher dose of levothyroxine was administered to the patient due to uncompensated RTH. Levothyroxine treatment was continued after 3 years to maintain TSH level

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model’s effectiveness in providing secure and high-performance DApp solutions.

This paper investigates how the sharp increase in time spent at home due to COVID-19-related restrictions shaped parents’ relationship satisfaction with their partners. Drawing on 78 in-depth interviews with heterosexual partnered parents with at least one child aged 18 or under, we find that this experience of what we call “enforced togetherness” had varied effects on couples’ relationships. More than half of the respondents (fifty-five percent) reported improved relationship satisfaction, while fifteen percent reported a decline, and the remaining thirty percent no change. Individuals with higher satisfaction took advantage of enforced togetherness and sought out more frequent and intense communication and leisure activities, underscoring the importance of spending time in strengthening relationships. On the other hand, those who were unable or unwilling to engage in these activities, due to lack of support for increased care needs and their continued uneven distribution across the couple, saw their relationships deteriorate. Finally, individuals experienced stability in their relationships when their prior routines and arrangements remained largely undisrupted by the pandemic. Our findings shed light on the significance of time as a valuable resource for couples’ relationships, while at the same time emphasizing the role of their agency in its utilization.