Efficient destruction of basic organo-nitrogenous compounds in liquid hydrocarbon fuel using ascorbic acid/H2O2 system under ambient condition.

Due to the limitations of the conventional refinery methods, development of a new method such as oxidative denitrogenation (ODN) is highly desirable. This study described a novel ODN to remove organo-nitrogenous compounds (ONCs) in liquid fuel by ascorbic acid (AscH2) and H2O2 redox system under ambient conditions. Seven ONCs including pyridine, quinoline, acridine, 7,8-benzoquinoline, indole, N-methylpyrrolidone (NMP), and N,N-dimethylformamide (DMF) were chosen to assess the fuel-denitrified ability of the AscH2/H2O2 system. The results showed that the basic group of ONCs (pyridine, quinoline, and acridine) can be effectively removed (removal ratio > 95 %) while the removal efficiency of water-soluble compounds (7,8-benzoquinoline, NMP, and DMF) was moderate (61-68 %) under a mild temperature (30 °C) and atmospheric pressure. Free radical quenching and electron paramagnetic resonance experiments confirmed that hydroxyl and AscH2 radicals played a major role in the degradation of ONCs. The degraded products of quinoline were analyzed by gas chromatography-mass spectroscopy and ion chromatography. Based on the identified intermediate products, a putative reaction pathway majorly involving three steps of N-onium formation, transfer hydrogenation, and free radical oxidative ring-opening was suggested for the quinoline degradation. The presented approach can be performed at a normal temperature and pressure and will live up to expectations in the pre-denitrogenation and selective removal of basic ONCs in fuel oils.

Integration analysis of senescence-related genes to predict prognosis and immunotherapy response in soft-tissue sarcoma: evidence based on machine learning and experiments.

Background: Soft tissue sarcoma (STS) is the malignancy that exhibits remarkable histologic diversity. The diagnosis and treatment of STS is currently challenging, resulting in a high lethality. Chronic inflammation has also been identified as a key characteristic of tumors, including sarcomas. Although senescence plays an important role in the progression of various tumors, its molecular profile remains unclear in STS. Methods: We identified the senescence-related genes (SRGs) in database and depicted characteristics of genomic and transcriptomic profiling using cohort within TCGA and GEO database. In order to investigate the expression of SRGs in different cellular subtypes, single-cell RNA sequencing data was applied. The qPCR and our own sequencing data were utilized for further validation. We used unsupervised consensus clustering analysis to establish senescence-related clusters and subtypes. A senescence scoring system was established by using principal component analysis (PCA). The evaluation of clinical and molecular characteristics was conducted among distinct groups. Results: These SRGs showed differences in SCNV, mutation and mRNA expression in STS tissues compared to normal tissues. Across several cancer types, certain shared features of SRGs were identified. Several SRGs closely correlated with immune cell infiltration. Four clusters related to senescence and three subtypes related to senescence, each with unique clinical and biological traits, were established. The senescence scoring system exhibited effectiveness in predicting outcomes, clinical traits, infiltrations of immune cells and immunotherapy responses. Conclusion: Overall, the current study provided a comprehensive review of molecular profiling for SRGs in STS. The SRGs based clustering and scoring model could help guiding the clinical management of STS.

Ultrasound enhanced destruction of tetracycline hydrochloride with peroxydisulfate oxidation over FeS/NBC catalyst: Governing factors, strengthening mechanism and degradation pathway.

In this study, FeS/N-doped biochar (NBC) derived from the co-pyrolysis of birch sawdust and Mohr's salt was applied to evaluate the efficiency of catalyzed peroxydisulfate (PDS) oxidation for tetracycline (TC) degradation. It is found that the combination of ultrasonic irradiation can distinctly enhance the removal of TC. This study investigated the effects of control factors such as PDS dose, solution pH, ultrasonic power, and frequency on TC degradation. Within the applied ultrasound intensity range, TC degradation increases with increasing frequency and power. However, excessive power can lead to a reduced efficiency. Under the optimized experimental conditions, the observed reaction kinetic constant of TC degradation increased from 0.0251 to 0.0474 min-1, with an increase of 89%. The removal ratio of TC also increased from ∼85% to ∼99% and the mineralization level from 45% to 64% within 90 min. Through the decomposition testing of PDS, reaction stoichiometric efficiency calculation, and electron paramagnetic resonance experiments, it is shown that the increase in TC degradation of the ultrasound-assisted FeS/NBC-PDS system was attributed to the increase in PDS decomposition and utilization, as well as the increase in SO4•- concentration. The radical quenching experiments showed that SO4•-, •OH, and O2•- radicals were the dominant active species in TC degradation. TC degradation pathways were speculated according to intermediates from HPLC-MS analysis. The test of simulated actual samples showed that dissolved organic matter, metal ions, and anions in waters can undercut the TC degradation in FeS/NBC-PDS system, but ultrasound can significantly reduce the negative impact of these factors.

Deciphering the role of NETosis-related signatures in the prognosis and immunotherapy of soft-tissue sarcoma using machine learning.

Background: Soft-tissue sarcomas (STSs) are a rare type of cancer, accounting for about 1% of all adult cancers. Treatments for STSs can be difficult to implement because of their diverse histological and molecular features, which lead to variations in tumor behavior and response to therapy. Despite the growing importance of NETosis in cancer diagnosis and treatment, researches on its role in STSs remain limited compared to other cancer types. Methods: The study thoroughly investigated NETosis-related genes (NRGs) in STSs using large cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine Recursive Feature Elimination (SVM-RFE) were employed for screening NRGs. Utilizing single-cell RNA-seq (scRNA-seq) dataset, we elucidated the expression profiles of NRGs within distinct cellular subpopulations. Several NRGs were validated by quantitative PCR (qPCR) and our proprietary sequencing data. To ascertain the impact of NRGs on the sarcoma phenotype, we conducted a series of in vitro experimental investigations. Employing unsupervised consensus clustering analysis, we established the NETosis clusters and respective NETosis subtypes. By analyzing DEGs between NETosis clusters, an NETosis scoring system was developed. Results: By comparing the outcomes obtained from LASSO regression analysis and SVM-RFE, 17 common NRGs were identified. The expression levels of the majority of NRGs exhibited notable dissimilarities between STS and normal tissues. The correlation with immune cell infiltration were demonstrated by the network comprising 17 NRGs. Patients within various NETosis clusters and subtypes exhibited different clinical and biological features. The prognostic and immune cell infiltration predictive capabilities of the scoring system were deemed efficient. Furthermore, the scoring system demonstrated potential for predicting immunotherapy response. Conclusion: The current study presents a systematic analysis of NETosis-related gene patterns in STS. The results of our study highlight the critical role NRGs play in tumor biology and the potential for personalized therapeutic approaches through the application of the NETosis score model in STS patients.

Learning Representative Features by Deep Attention Network for 3D Point Cloud Registration.

Three-dimensional point cloud registration, which aims to find the transformation that best aligns two point clouds, is a widely studied problem in computer vision with a wide spectrum of applications, such as underground mining. Many learning-based approaches have been developed and have demonstrated their effectiveness for point cloud registration. Particularly, attention-based models have achieved outstanding performance due to the extra contextual information captured by attention mechanisms. To avoid the high computation cost brought by attention mechanisms, an encoder-decoder framework is often employed to hierarchically extract the features where the attention module is only applied in the middle. This leads to the compromised effectiveness of the attention module. To tackle this issue, we propose a novel model with the attention layers embedded in both the encoder and decoder stages. In our model, the self-attentional layers are applied in the encoder to consider the relationship between points inside each point cloud, while the decoder utilizes cross-attentional layers to enrich features with contextual information. Extensive experiments conducted on public datasets prove that our model is able to achieve quality results on a registration task.

Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma.

Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear. Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated. Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response. Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.

Integrating bulk and single-cell RNA sequencing data reveals the relationship between intratumor microbiome signature and host metabolic heterogeneity in breast cancer.

Introduction: Nowadays, it has been recognized that gut microbiome can indirectly modulate cancer susceptibility or progression. However, whether intratumor microbes are parasitic, symbiotic, or merely bystanders in breast cancer is not fully understood. Microbial metabolite plays a pivotal role in the interaction of host and microbe via regulating mitochondrial and other metabolic pathways. And the relationship between tumor-resident microbiota and cancer metabolism remains an open question. Methods: 1085 breast cancer patients with normalized intratumor microbial abundance data and 32 single-cell RNA sequencing samples were retrieved from public datasets. We used the gene set variation analysis to evaluate the various metabolic activities of breast cancer samples. Furthermore, we applied Scissor method to identify microbe-associated cell subpopulations from single-cell data. Then, we conducted comprehensive bioinformatic analyses to explore the association between host and microbe in breast cancer. Results: Here, we found that the metabolic status of breast cancer cells was highly plastic, and some microbial genera were significantly correlated with cancer metabolic activity. We identified two distinct clusters based on microbial abundance and tumor metabolism data. And dysregulation of the metabolic pathway was observed among different cell types. Metabolism-related microbial scores were calculated to predict overall survival in patients with breast cancer. Furthermore, the microbial abundance of the specific genus was associated with gene mutation due to possible microbe-mediated mutagenesis. The infiltrating immune cell compositions, including regulatory T cells and activated NK cells, were significantly associated with the metabolism-related intratumor microbes, as indicated in the Mantel test analysis. Moreover, the mammary metabolism-related microbes were related to T cell exclusion and response to immunotherapy. Conclusions: Overall, the exploratory study shed light on the potential role of the metabolism-related microbiome in breast cancer patients. And the novel treatment will be realized by further investigating the metabolic disturbance in host and intratumor microbial cells.

Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance.

Chronic Kidney disease (CKD) is an important yet under-recognized contributor to morbidity and mortality globally. Machine-learning (ML) based decision support tools have been developed across many aspects of CKD care. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression may help to facilitate early disease prevention, assist with early planning of renal replacement therapy, and offer potential clinical and economic benefits to patients and health systems. Clinical implementation can be affected by the uncertainty surrounding the methodological rigor and performance of ML-based models. This systematic review aims to evaluate the application of prognostic and diagnostic ML tools in CKD development and progression. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for CKD prediction and diagnosis have been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022356704, CRD42022372378). A systematic search will be undertaken of PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the Web of Science, and the IEEE Xplore digital library. Studies in which ML has been applied to predict and diagnose CKD development and progression will be included. The primary outcome will be the comparison of the performance of ML-based models with non-ML-based models. Secondary analysis will consist of model use cases, model construct, and model reporting quality. This systematic review will offer valuable insight into the performance and reporting quality of ML-based models in CKD diagnosis and prediction. This will inform clinicians and technical specialists of the current development of ML in CKD care, as well as direct future model development and standardization.

Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?

Given the growing use of machine learning (ML) technologies in health care, regulatory bodies face unique challenges in governing their clinical use. Under the regulatory framework of the Food and Drug Administration, approved ML algorithms are practically locked, preventing their adaptation in the ever-changing clinical environment, defeating the unique adaptive trait of ML technology in learning from real-world feedback. At the same time, regulations must enforce a strict level of patient safety to mitigate risk at a systemic level. Given that ML algorithms often support, or at times replace, the role of medical professionals, we have proposed a novel regulatory pathway analogous to the regulation of medical professionals, encompassing the life cycle of an algorithm from inception, development to clinical implementation, and continual clinical adaptation. We then discuss in-depth technical and nontechnical challenges to its implementation and offer potential solutions to unleash the full potential of ML technology in health care while ensuring quality, equity, and safety. References for this article were identified through searches of PubMed with the search terms "Artificial intelligence," "Machine learning," and "regulation" from June 25, 2017, until June 25, 2022. Articles were also identified through searches of the reference list of the articles. Only papers published in English were reviewed. The final reference list was generated based on originality and relevance to the broad scope of this paper.

Improving nutritional assessment in acute medical admissions at a district general hospital.

BACKGROUND: Malnutrition adversely affects clinical outcomes, necessitating a prompt and accurate assessment of nutritional status on admission. A variety of tools exist to aid nutritional assessment, of which the malnutrition universal screening tool (MUST) is recommended, but remains difficult to implement in practice. OBJECTIVE: The aim of this audit was to improve the utilisation of the malnutrition universal screening tool (MUST) in the Acute Medical Unit (AMU) at Queen Elizabeth Hospital, King's Lynn. Specifically, patients should have a completed and accurate MUST score within 6 hours of arrival to AMU and high-risk patients (MUST score ≥2) should be referred to dieticians within 48 hours of admission. The first cycle was conducted by March 2019 and the second cycle was completed 1 year later to allow assessment of interventions actioned after the first cycle. METHODS: We conducted a two-cycle audit evaluating the MUST completion and dietician referral rate of high-risk patients (defined as MUST ≥2) on the Acute Medical Unit in a district general hospital, with the standards of 80% and 100% respectively. A questionnaire was distributed after the first cycle exploring nurses' current experience and competence in using MUST. RESULTS: In the first cycle, MUST scores were calculated correctly in 111/150 patients (74%) and 1/9 (11%) high-risk patients were referred to dieticians. After interventions, MUST scores were calculated correctly in 77/101 patients (76%) and 2/4 high-risk patients (50%) were referred to dieticians. The nurses (n = 19) who took part in the questionnaire felt confident in MUST completion, but the average score in an objective assessment was 67%. CONCLUSIONS: As per the literature, the first cycle demonstrated the under-utilisation of MUST in clinical practice. In response, we proposed additional face-to-face training for existing staff, the inclusion of an e-learning module within the staff's induction, and provision of ward MUST 'troubleshooting' booklets. MUST utilisation rates improved upon re-auditing, but not to target standards. We will need to consider potential barriers to sustainable change and implement interventions such as identification of nursing champions to overcome them.

Necroptosis-related lncRNA to establish novel prognostic signature and predict the immunotherapy response in breast cancer.

BACKGROUND: Necroptosis is a type of programmed cell death, and recent researches have showed that lncRNAs could regulate the process of necroptosis in multiple cancers. We tried to screen necroptosis-related lncRNAs and investigate the immune landscape in breast cancer (BC). METHODS: The samples of breast normal and cancer tissue were acquired from TCGA and GTEx databases. A risk prognostic model was constructed based on the identified necroptosis-related lncRNAs by Cox regression and least absolute shrinkage and selection operator (LASSO) method. Moreover, the forecast performance of this model was verified and accredited by synthetic approach. Subsequently, an accurate nomogram was constructed to predict the prognosis of BC patients. The biological differences were investigated through GO, GSEA, and immune analysis. The immunotherapy response was estimated through tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) score. RESULTS: A total of 251 necroptosis-related lncRNAs were identified by differential coexpression analysis, and SH3BP5-AS1, AC012073.1, AC120114.1, LINC00377, AL133467.1, AC036108.3, and AC020663.2 were involved in the risk model, which had an excellent concordance with the prediction. The pathway analyses showed that immune-related pathways were relevant to the necroptosis-related lncRNAs risk model. And the risk score was significantly correlated with immune cell infiltration, as well as the ESTIMATE score. Most notably, the patients of higher risk score were characterized with increased TMB and decreased TIDE score, indicating that these patients showed better immune checkpoint blockade response. CONCLUSION: These findings were conducive to understand the function of necroptosis-related lncRNAs in BC and provide a potential promising therapeutic strategy for BC.

Physiological effects and subjective tolerability of prone positioning in COVID-19 and healthy hypoxic challenge.

BACKGROUND: Prone positioning has a beneficial role in coronavirus disease 2019 (COVID-19) patients receiving ventilation but lacks evidence in awake non-ventilated patients, with most studies being retrospective, lacking control populations and information on subjective tolerability. METHODS: We conducted a prospective, single-centre study of prone positioning in awake non-ventilated patients with COVID-19 and non-COVID-19 pneumonia. The primary outcome was change in peripheral oxygenation in prone versus supine position. Secondary outcomes assessed effects on end-tidal CO2, respiratory rate, heart rate and subjective symptoms. We also recruited healthy volunteers to undergo proning during hypoxic challenge. RESULTS: 238 hospitalised patients with pneumonia were screened; 55 were eligible with 25 COVID-19 patients and three non-COVID-19 patients agreeing to undergo proning - the latter insufficient for further analysis. 10 healthy control volunteers underwent hypoxic challenge. Patients with COVID-19 had a median age of 64 years (interquartile range 53-75). Proning led to an increase in oxygen saturation measured by pulse oximetry (SpO2) compared to supine position (difference +1.62%; p=0.003) and occurred within 10 min of proning. There were no effects on end-tidal CO2, respiratory rate or heart rate. There was an increase in subjective discomfort (p=0.003), with no difference in breathlessness. Among healthy controls undergoing hypoxic challenge, proning did not lead to a change in SpO2 or subjective symptom scores. CONCLUSION: Identification of suitable patients with COVID-19 requiring oxygen supplementation from general ward environments for awake proning is challenging. Prone positioning leads to a small increase in SpO2 within 10 min of proning though is associated with increased discomfort.

Invasive Pulmonary Aspergillosis in Hospital and Ventilator-Associated Pneumonias.

Pneumonia is the commonest nosocomial infection complicating hospital stay, with both non-ventilated hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) occurring frequently amongst patients in intensive care. Aspergillus is an increasingly recognized pathogen amongst patients with HAP and VAP, and is associated with significantly increased mortality if left untreated.Invasive pulmonary aspergillosis (IPA) was originally identified in patients who had been profoundly immunosuppressed, however, this disease can also occur in patients with relative immunosuppression such as critically ill patients in intensive care unit (ICU). Patients in ICU commonly have several risk factors for IPA, with the inflamed pulmonary environment providing a niche for aspergillus growth.An understanding of the true prevalence of this condition amongst ICU patients, and its specific rate in patients with HAP or VAP is hampered by difficulties in diagnosis. Establishing a definitive diagnosis requires tissue biopsy, which is seldom practical in critically ill patients, so imperfect proxy measures are required. Clinical and radiological findings in ventilated patients are frequently non-specific. The best-established test is galactomannan antigen level in bronchoalveolar lavage fluid, although this must be interpreted in the clinical context as false positive results can occur. Acknowledging these limitations, the best estimates of the prevalence of IPA range from 0.3 to 5% amongst all ICU patients, 12% amongst patients with VAP and 7 to 28% amongst ventilated patients with influenza.Antifungal triazoles including voriconazole are the first-line therapy choice in most cases. Amphotericin has excellent antimold coverage, but a less advantageous side effect profile. Echinocandins are less effective against IPA, but may play a role in rescue therapy, or as an adjuvant to triazole therapy.A high index of suspicion for IPA should be maintained when investigating patients with HAP or VAP, especially when they have specific risk factors or are not responding to appropriate empiric antibacterial therapy.

Sustainable medicines use in clinical practice: A clinical pharmacological view on eco-pharmaco-stewardship.

Climate change continues to pose a dangerous threat to human health. However, not only is health impacted by this crisis, healthcare itself adds to the problem, through significant contributions to greenhouse gas emissions. In the UK, the National Health Service (NHS) is responsible for an estimated 4% of the overall national carbon footprint. Medicines account for a quarter of this and whilst they are vital for health now, through sustainable use they can also positively influence the environmental health of the future. In this review, we explore how clinical pharmacologists and other health care professionals can practice sustainable medicines use or eco-pharmaco-stewardship. We will discuss current and near future environmental practices within the NHS, which we suspect will resonate with other health systems. We will suggest approaches for championing eco-pharmaco-stewardship in drug manufacturing, clinical practice and patient use, to achieve a more a sustainable healthcare system.

Case report: delayed response after electroconvulsive therapy in a patient with major depressive disorder.

BACKGROUND: Major depressive disorder and associated mood syndromes are amongst the most common psychiatric disorders. To date, electroconvulsive therapy (ECT) is considered the most effective short-term treatment for patients with severe or treatment-resistant depression. In clinical practice, there is considerable variation in the ECT dosing schedule, with the number of sessions typically ranging from 6 to 12, with early antidepressant effects being predictive of increased positive outcomes. We describe here an unusual case of a female patient with severe depression who did not respond to ECT until the 11th session, after which she had shown a drastic improvement in her mental state. CASE PRESENTATION: A 75-year-old female presented to the old age psychiatry inpatient unit with new onset dysphoric mood, anhedonia, and severe negativity. She scored 23 on the 17-item Hamilton Rating Scale for Depression (HAM-D), and was rated 6 on Clinical Global Impression severity (CGIS) by the responsible clinician. She suffered from post-natal depression fifty years ago and was successfully treated with ECT. She was therefore initiated on a course of ECT treatment. Her condition initially deteriorated, displaying features of catatonia and psychosis, unresponsive to ECT treatment or concurrent psychotropic medications. After 11th ECT session, she started to show signs of clinical improvement and returned close to her baseline mental state after a total of 17 ECT sessions. She remained well 3 months post-treatment, scoring 4 on HAM-D, Clinical Global Improvement or change (CGI-C) rated as 1 (very much improved). The diagnosis was ICD-10 F32.3 severe depressive episode with psychotic symptoms. CONCLUSIONS: we describe here an unusual case of delayed response to electroconvulsive therapy in the treatment of severe depressive disorder. Studies have shown the number of acute ECT treatments to be highly variable, affected by a number of factors including treatment frequency, condition treated and its severity, the ECT technical parameters, as well as concurrent use of pharmacological treatment. This may call for re-consideration of the current ECT treatment guidelines, requiring more research to help stratify and standardize the treatment regime.

Managing the overlap between mental and physical health.

The interface between the Mental Capacity Act 2005 and the Mental Health Act 1983 can be complex, particularly in patients with co-existing mental and physical illnesses. The management of these patients requires the involvement of patients, relatives and multidisciplinary teams. This article presents four illustrative patient cases, all of whom suffered from co-existing mental and physical illnesses. In managing these cases, dilemmas had arisen in the provision of treatment encompassing both legal frameworks. These cases helped to emphasise the decision-specific and time-specific nature of assessment of mental capacity, requiring clinicians to assess on a case-by-case basis over a suitable period. Often, principles from both legal frameworks may be applied by the treatment team. These cases help to highlight the significant overlap between mental and physical health, which often cannot be managed independently. This may call for the need to better integrate the current legal frameworks, and the optimal involvement of specialists across both settings.

Gastrointestinal Symptoms Associated With Unfavorable Prognosis of COVID-19 Patients: A Retrospective Study.

Background and Aim: The global pandemic of COVID-19 has posed an enormous threat to the economy and people's lives across various countries. Patients with COVID-19 most commonly present with respiratory symptoms. However, gastrointestinal (GI) symptoms can also occur. We aimed to study the relationship between GI symptoms and disease prognosis in patients with COVID-19. Methods: In a single-center and retrospective cohort study, the outcomes in COVID-19 patients with or without GI symptoms were compared. The propensity score is a conditional probability of having a particular exposure (COVID-19 patients with GI symptoms vs. without GI symptoms) given a set of baseline measured covariates. Survival was estimated using the Kaplan-Meier method, and any differences in survival were evaluated with a stratified log-rank-test. To explore the GI symptoms associated with ARDS, non-invasive ventilator treatment, tracheal intubation, tracheotomy, and CRRT, univariable and multivariable COX regression models were used. Results: Among 1,113 eligible patients, 359 patients with GI symptoms and 718 without GI symptoms had similar propensity scores and were included in the analyses. Patients with GI symptoms, as compared with those without GI symptoms, were associated with a similar risk of death, but with higher risks of ARDS, non-invasive mechanical ventilation in COVID-19 patients, respectively. Conclusions: The presence of GI symptoms was associated with a high risk of ARDS, non-invasive mechanical ventilation and tracheal intubation in patients with COVID-19 but not mortality.

A Novel Nonlinear Dynamic Method for Stroke Rehabilitation Effect Evaluation Using EEG.

Evaluating the effect of stroke rehabilitation based on electroencephalogram (EEG) is still a challenging problem. This paper presents a novel nonlinear dynamic complexity method for the evaluation of stroke rehabilitation effect from EEG signal. Our method calculates the nonlinearly separable degree (NLSD) of EEG signal, and then employs an indicator, called mean nonlinearly separable complexity degree (Mean_NLSD), to efficiently and accurately evaluate therapy effect of stroke patients. This paper under twelve stimuli conditions on eleven patients and eleven control subjects indicates that in general Mean_NLSD is smaller at the lesion regions and that the Mean_NLSD of the control subjects is stochastic. Compared with conventional spectral methods, such as mean power spectral density (PSD), Mean_NLSD is more sensitive and robust. Overall Mean_NLSD may offer a promising approach to facilitate the evaluation of stroke rehabilitation effect.