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Effectiveness of azvudine against severe outcomes among hospitalized COVID-19 patients in Xinjiang, China: a single-center, retrospective, matched cohort study

ABSTRACT Background Since the end of 2022, Azvudine was widely used to treat hospitalized novel coronavirus disease 2019 (COVID-19) patients in China. However, data on the clinical effectiveness of Azvudine against severe outcomes and post-COVID-19-conditions (PCC) among patients infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variants was limited. This study evaluates the effectiveness of Azvudine in hospitalized COVID-19 patients during a SARS-CoV-2 Omicron BA.5 dominance period. Methods From 1 November 2022 to 1 July 2023, we conducted a single-center retrospective cohort study based on hospitalized COVID-19 patients from a tertiary hospital in Shihezi, China, recruiting laboratory-confirmed hospitalized patients with SARS-CoV-2 infection. Patients treated with Azvudine and usual care were propensity-score matched (PSM) at a 1:1 ratio to a control group in which patients undergone usual care only, with matching based on covariates such as sex, age, ethnicity, number of preexisting conditions, antibiotic use upon admission, and complete blood cell count. The primary outcomes were all-cause death and PCC at short-term (60 days) post discharge. The secondary outcomes included the initiation of invasive mechanical ventilation and PCC at long-term post discharge (120 days). Cox proportional hazards (PH) regression models were employed to estimate the hazard ratios (HR) for both all-cause death and invasive mechanical ventilation, and logistic regression models were used to estimate the odds ratios (OR) for short-term and long-term PCC. Subgroup analyses were performed based on the matched covariates. Results A total of 2,639 hospitalized patients diagnosed with COVID-19 were initially identified, and 2,069 patients were screened following the exclusion criteria. After matching, 297 Azvudine recipients and 297 matched controls were eligible for analyses. The incidence rate of all-cause death was lower in the Azvudine group than in the control group (0.007 per person, 95% confidence interval [CI]: 0.001, 0.024 vs 0.128, 95% CI: 0.092, 0.171), and the use of Azvudine was associated with a significant lower risk of death and the use of Azvudine was associated with a reduced risk of death (HR: 0.049, 95% CI: 0.012, 0.205). Subgroup analyses indicated a significant effectiveness of Azvudine against the risk of all-cause death among men, age over 65, patients without the preexisting conditions, and patients with antibiotics dispensed at admission. Statistical difference were not observed between Azvudine group and control group in the invasive mechanical ventilation and short-term and long-term PCC. Conclusions The present findings indicate that receipt of Azvudine was associated with lower risk of all-cause death among hospitalized patients with Omicron BA.5 infection a in real-world setting. Further research is urgently needed to validate the effectiveness of Azvudine on the PCC.

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The clinical application of traditional Chinese medicine NRICM101 in hospitalized patients with COVID-19

ABSTRACT Background The aim of this study was to assess the efficacy and safety of NRICM101 in hospitalized patients with COVID-19. Research design and methods We conducted a retrospective study from April 20 to 8 July 2021, and evaluated the safety and outcomes (mortality, hospital stay, mechanical ventilation, oxygen support, diarrhea, serum potassium) in COVID-19 patients. Propensity score matching at a 1:2 ratio was performed to reduce confounding factors. Results A total of 201 patients were analyzed. The experimental group (n = 67) received NRICM101 and standard care, while the control group (n = 134) received standard care alone. No significant differences were observed in mortality (10.4% vs. 14.2%), intubation (13.8% vs. 11%), time to intubation (10 vs. 11 days), mechanical ventilation days (0 vs. 9 days), or oxygen support duration (6 vs. 5 days). However, the experimental group had a shorter length of hospitalization (odds ratio = 0.12, p = 0.043) and fewer mechanical ventilation days (odds ratio = 0.068, p = 0.008) in initially severe cases, along with an increased diarrhea risk (p = 0.035). Conclusion NRICM101 did not reduce in-hospital mortality. However, it shortened length of hospitalization and reduced mechanical ventilation days in initially severe cases. Further investigation is needed.

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Procedural skill maintenance: What it means to physicians, how it motivates them, and what stops them from doing so

Introduction: Maintenance of procedural skills is crucial for paediatric emergency medicine (PEM) physicians to provide high-quality care. A study by Lin-Martore et al. (2021) in the US identified key themes in conceptualising procedural skill maintenance (PSM), its motivations, and barriers to maintenance. However, the difference in culture brings into question the validity of their findings in other contexts. To determine its applicability specifically in an Asian context, this study aims to replicate the study at KK’s Women and Children Hospital (KKH), focusing on PEM physicians. While the findings are limited to a single hospital, they provide valuable insights into challenges encountered by PEM physicians. Methods: A general qualitative approach was used through semi-structured interviews. Participants were recruited through email. Interviews were conducted via Zoom and subsequently de-identified and transcribed. The data was coded manually through thematic analysis, identifying key themes. Results: Fifteen PEM physicians were interviewed. Participants conceptualised PSM through technical aspects and measured competence through objective and subjective measures. General motivation themes found the (1) desire to provide optimal patient care, (2) procedural competence as part of the identity of a PEM physician who teaches and performs procedures, and (3) desire for choice when alternatives are present. Barriers included limited time, support, and opportunities. Conclusion: The study found that the themes from the original study are applicable in KKH, featuring SDT concepts prominently. Practical recommendations for KKH were proposed. Future research can focus on the challenges and gaps in maintaining procedural skills and develop strategies to improve PSM in PEM physicians. Keywords: Procedural Skill Maintenance, Singapore, Emergency Medicine, Qualitative, Paediatric Medicine

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Modal reduction principles: a parametric shift to graphs

Graph-based frames have been introduced as a logical framework which internalises an inherent boundary to knowability (referred to as ‘informational entropy’), due, e.g. to perceptual, evidential or linguistic limits. They also support the interpretation of lattice-based (modal) logics as hyper-constructive logics of evidential reasoning. Conceptually, the present paper proposes graph-based frames as a formal framework suitable for generalising Pawlak's rough set theory to a setting in which inherent limits to knowability exist and need to be considered. Technically, the present paper establishes systematic connections between the first-order correspondents of Sahlqvist modal reduction principles on Kripke frames, and on the more general relational environments of graph-based and polarity-based frames. This work is part of a research line aiming at: (a) comparing and inter-relating the various (first-order) conditions corresponding to a given (modal) axiom in different relational semantics; (b) recognising when first-order sentences in the frame-correspondence languages of different relational structures encode the same ‘modal content’; (c) meaningfully transferring relational properties across different semantic contexts. The present paper develops these results for the graph-based semantics, polarity-based semantics, and all Sahlqvist modal reduction principles. As an application, we study well known modal axioms in rough set theory (such as those corresponding to seriality, reflexivity, and transitivity) on graph-based frames and show that, although these axioms correspond to different first-order conditions on graph-based frames, their intuitive meaning is retained. This allows us to introduce the notion of hyperconstructivist approximation spaces as the subclass of graph-based frames defined by the first-order conditions corresponding to the same modal axioms defining classical generalised approximation spaces, and to transfer the properties and the intuitive understanding of different approximation spaces to the more general framework of graph-based frames. The approach presented in this paper provides a base for systematically comparing and connecting various formal frameworks in rough set theory, and for the transfer of insights across different frameworks.

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Sole-source Lighting of Lettuce that Increased Yield Had No Negative Effect on Postharvest Longevity

The effects of sole-source lighting on the growth and yield of hydroponically grown lettuce have been extensively studied, but research of postharvest performance is limited. We grew frill-leaf lettuce (Lactuca sativa) ‘Green Incised’ and ‘Hydroponic Green Sweet Crisp’ hydroponically in an indoor vertical research farm under daily light integrals (DLIs) of 12 or 18 mol⋅m−2⋅d−1 and the following three ratios of blue (B; 400–499 nm) and red (R; 600–699 nm) light from light-emitting diode fixtures: B5:R95, B20:R80, and B35:R65. We postulated that biomass accumulation would increase with the DLI and decrease with the B light fraction, and that postharvest longevity would increase with the DLI and the B light fraction. As expected, shoot fresh weight, leaf length and width, leaf number, and relative chlorophyll content (SPAD; ‘Green Incised’ only) decreased as the proportion of B light increased from 5% to 35%. Decreasing the DLI from 18 to 12 mol⋅m−2⋅d−1 reduced the shoot fresh weight and leaf number of both cultivars. Leaves of ‘Green Incised’ were up to 27% wider under B5:R95 and 60% longer under B5:R95 at 12 mol⋅m−2⋅d−1 than those under treatments with a higher DLI or more B light. The shoot fresh weight of ‘Hydroponic Green Sweet Crisp’ was greatest when grown under B5:R95 at 18 mol⋅m−2⋅d−1 and decreased as B light increased or DLI decreased. At the time of harvest, leaves of each cultivar and treatment were placed in clamshells and stored at 7 °C in darkness and evaluated for decay. ‘Green Incised’ that grew under B35:R65 and a DLI of 18 mol⋅m−2⋅d−1 had the shortest storage life, with 9.5 d and 11.4 d for replications 1 and 2, respectively, which were ∼2.5 to 4.0 d and 1.4 to 3.6 d earlier, respectively, than the storage life of lettuce grown under other treatments. In contrast, ‘Hydroponic Green Sweet Crisp’ was not influenced by light quality or DLI and had a storage life of 12.6 to 13.3 d and 13.5 to 14.3 d for replications 1 and 2, respectively. Therefore, a B light fraction between 5% and 20% and a DLI of 18 mol⋅m−2⋅d−1 produced high-yielding frill-leaf lettuce with a relatively long storage life.

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Altered morphometric similarity networks in insomnia disorder.

Previous studies on structural covariance network (SCN) suggested that patients with insomnia disorder (ID) show abnormal structural connectivity, primarily affecting the somatomotor network (SMN) and default mode network (DMN). However, evaluating a single structural index in SCN can only reveal direct covariance relationship between two brain regions, failing to uncover synergistic changes in multiple structural features. To cover this research gap, the present study utilized novel morphometric similarity networks (MSN) to examine the morphometric similarity between cortical areas in terms of multiple sMRI parameters measured at each area. With seven T1-weighted imaging morphometric features from the Desikan-Killiany atlas, individual MSN was constructed for patients with ID (N = 87) and healthy control groups (HCs, N = 84). Two-sample t-test revealed differences in MSN between patients with ID and HCs. Correlation analyses examined associations between MSNs and sleep quality, insomnia symptom severity, and depressive symptoms severity in patients with ID. The right paracentral lobule (PCL) exhibited decreased morphometric similarity in patients with ID compared to HCs, mainly manifested by its de-differentiation (meaning loss of distinctiveness) with the SMN, DMN, and ventral attention network (VAN), as well as its decoupling with the visual network (VN). Greater PCL-based de-differentiation correlated with less severe insomnia and fewer depressive symptoms in the patients group. Additionally, patients with less depressive symptoms showed greater PCL de-differentiation from the SMN. As an important pilot step in revealing the underlying morphometric similarity alterations in insomnia disorder, the present study identified the right PCL as a hub region that is de-differentiated with other high-order networks. Our study also revealed that MSN has an important potential to capture clinical significance related to insomnia disorder.

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Maximizing Emotion Recognition Accuracy with Ensemble Techniques on EEG Signals

Background:: Emotion is a strong feeling such as love, anger, fear, etc. Emotion can be recognized in two ways, i.e., External expression and Biomedical data-based. Nowadays, various research is occurring on emotion classification with biomedical data. Aim:: One of the most current studies in the medical sector, gaming-based applications, education sector, and many other domains is EEG-based emotion identification. The existing research on emotion recognition was published using models like KNN, RF Ensemble, SVM, CNN, and LSTM on biomedical EEG data. In general, only a few works have been published on ensemble or concatenation models for emotion recognition on EEG data and achieved better results than individual ones or a few machine learning approaches. Various papers have observed that CNN works better than other approaches for extracting features from the dataset, and LSTM works better on the sequence data. Method:: Our research is based on emotion recognition using EEG data, a mixed-model deep learning methodology, and its comparison with a machine learning mixed-model methodology. In this study, we introduced a mixed model using CNN and LSTM that classifies emotions in valence and arousal on the DEAP dataset with 14 channels across 32 people. Result and Discussion: We then compared it to SVM, KNN, and RF Ensemble, and concatenated these models with it. First preprocessed the raw data, then checked emotion classification using SVM, KNN, RF Ensemble, CNN, and LSTM individually. After that with the mixed model of CNN-LSTM, and SVM-KNN-RF Ensemble results are compared. Proposed model results have better accuracy as 80.70% in valence than individual ones with CNN, LSTM, SVM, KNN, RF Ensemble and concatenated models of SVM, KNN and RF Ensemble. Conclusion:: Overall, this paper concludes a powerful technique for processing a range of EEG data is the combination of CNNs and LSTMs. Ensemble approach results show better performance in the case of valence at 80.70% and 78.24% for arousal compared to previous research.

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