A deep learning-powered diagnostic model for acute pancreatitis.

BACKGROUND: Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic model for acute pancreatitis. MATERIALS AND METHODS: In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People's Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model's performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU). RESULTS: Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978-0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P < 0.001) and an AUC of 0.850 (95%CI: 0.790-0.898) in the external validation set (healthy examination patients vs. patients with acute pancreatitis, P < 0.001). Furthermore, the acute pancreatitis lesion segmentation module demonstrated exceptional performance in the validation set. For pancreas segmentation, peripancreatic inflammatory exudation, peripancreatic effusion, and peripancreatic abscess necrosis, the MIOU values were 86.02 (84.52, 87.20), 61.81 (56.25, 64.83), 57.73 (49.90, 68.23), and 66.36 (55.08, 72.12), respectively. These findings underscore the robustness and reliability of the developed models in accurately characterizing and assessing acute pancreatitis. CONCLUSION: The diagnostic model for acute pancreatitis, driven by deep learning, exhibits excellent efficacy in accurately evaluating the severity of the condition. TRIAL REGISTRATION: This is a retrospective study.

Efficacy of taurine-enhanced enteral nutrition in improving the outcomes of critically ill patients: A systematic review and meta-analysis.

BACKGROUND: Taurine is considered an immunomodulatory agent. From current reports on clinical studies, we conducted a systematic review and meta-analysis to investigate the effects of taurine-enhanced enteral nutrition (EN) on the outcomes of critically ill patients to resolve conflicting evidence in literature. METHODS: Literature from PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, SINOMED, and WanFang databases were retrieved, and randomized controlled trials (RCTs) were identified. The time range spanned from January 1, 2000, to January 31, 2024. The Cochrane Collaboration Tool was used to evaluate the risk of bias. We used the GRADE approach to rate the quality of evidence and the I2 test to assess the statistical heterogeneity of the results. Risk ratio (RR), mean difference (MD), and 95% confidence interval (95% CI) were used to analyze measurement data. RESULTS: Four trials involving 236 patients were finally included. The meta-analysis results indicated that taurine-enhanced EN did not reduce mortality (RR = 0.70, p = 0.45, 95% CI [0.28, 1.80], two trials, 176 participants, low quality). There was also no significant difference in length of stay in the intensive care unit (ICU) between the taurine-enhanced EN and control groups. Taurine-enhanced EN may reduce pro-inflammatory factor interleukin-6 (IL-6) levels in critically ill patients（the result about IL-6 cannot be pooled）. However, taurine-enhanced EN had no significant impact on high-sensitivity-C-reactive protein levels (MD = -0.41, p = 0.40, 95% CI [-1.35, 0.54], two trials, 60 participants, low quality). DISCUSSION: Taurine-enhanced EN may reduce IL-6 levels and is not associated with improved clinical outcomes in critically ill patients, which may have potential immunoregulatory effects in critically ill patients. Given that published studies have small samples, the above conclusions need to be verified by more rigorously designed large-sample clinical trials.

Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol.

BACKGROUND: Patients with acute abdomen often experience reduced voluntary intake and a hypermetabolic process, leading to a high occurrence of malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) criteria have rapidly developed into a principal methodological tool for nutritional diagnosis. Additionally, machine learning is emerging to establish artificial intelligent-enabled diagnostic models, but the accuracy and robustness need to be verified. We aimed to establish an intelligence-enabled malnutrition diagnosis model based on GLIM for patients with acute abdomen. METHOD: This study is a single-centre, cross-sectional observational investigation into the prevalence of malnutrition in patients with acute abdomen using the GLIM criteria. Data collection occurs on the day of admission, at 3 and 7 days post-admission, including biochemical analysis, body composition indicators, disease severity scoring, nutritional risk screening, malnutrition diagnosis and nutritional support information. The occurrence rate of malnutrition in patients with acute abdomen is analysed with the GLIM criteria based on the Nutritional Risk Screening 2002 and the Mini Nutritional Assessment Short-Form to investigate the sensitivity and accuracy of the GLIM criteria. After data cleansing and preprocessing, a machine learning approach is employed to establish a predictive model for malnutrition diagnosis in patients with acute abdomen based on the GLIM criteria. ETHICS AND DISSEMINATION: This study has obtained ethical approval from the Ethics Committee of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital on 28 November 2022 (Yan-2022-442). The results of this study will be disseminated in peer-reviewed journals, at scientific conferences and directly to study participants. TRIAL REGISTRATION NUMBER: ChiCTR2200067044.

Efficacy of probiotics or synbiotics for critically ill adult patients: a systematic review and meta-analysis of randomized controlled trials.

Background: Microbial dysbiosis in critically ill patients is a leading cause of mortality and septic complications. Probiotics and synbiotics have emerged as novel therapy on gut microbiota to prevent septic complications. However, current evidence on their effects is conflicting. This work aims to systematically review the impact of probiotics or synbiotics in critically ill adult patients. Methods: A comprehensive search of the PubMed, CBM, Embase, CENTRAL, ISI, and CNKI databases was performed to identify randomized controlled trials that evaluate probiotics or synbiotics in critically ill patients. The quality assessment was based on the modified Jadad's score scale and the Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.1. The major outcome measure was mortality. Secondary outcomes included incidence of septic complications, sepsis incidence, length of intensive care unit (ICU) stay, incidence of non-septic complication, and ventilator day. Data synthesis was conduct by Review Manager 5.4. Results: A total of 25 randomized controlled trials reporting on 5049 critically ill patients were included. In the intervention group, 2520 participants received probiotics or synbiotics, whereas 2529 participants received standard care or placebo. Pooling data from randomized controlled trials demonstrated a significant reduction in the incidence of ventilator-associated pneumonia (VAP) in the treatment group [(risk ratio (RR) 0.86; 95% confidence interval (CI): 0.78-0.95; p < 0.003, I2 = 85%)]. However, in the subgroup analysis, the reduction of incidence of VAP was only significant in patients receiving synbiotics (RR = 0.61, 95% CI: 0.47-0.80, p = 0.0004, I2 = 40%) and not significant in those receiving only probiotics (RR = 0.91, 95% CI: 0.82-1.01, p = 0.07, I2 = 65%). Moreover, sepsis incidence of critically ill patients was only significantly reduced by the addition of synbiotics (RR = 0.41; 95% CI: 0.22-0.72, p = 0.005, I2 = 0%). The incidence of ICU-acquired infections was significantly reduced by the synbiotics therapy (RR = 0.72; 95% CI: 0.58-0.89, p = 0.0007, I2 = 79%). There was no significant difference in mortality, diarrhea, or length of ICU stay between the treatment and control groups. Conclusions: Synbiotics is an effective and safe nutrition therapy in reducing septic complications in critically ill patients. However, in such patients, administration of probiotics alone compared with placebo resulted in no difference in the septic complications.

Is bivalirudin an alternative anticoagulant for extracorporeal membrane oxygenation (ECMO) patients? A systematic review and meta-analysis.

BACKGROUND: Anticoagulation is important for extracorporeal membrane oxygenation (ECMO). Heparin is widely used; however, in some cases, it is not suitable for patients. Bivalirudin has been recently proposed for ECMO patients, and there is no evidence regarding its effectiveness and safety. OBJECTIVE: We aimed to systematically review the effectiveness and safety of bivalirudin in ECMO patients. STUDY DESIGN AND METHODS: PubMed, Web of Science, Cochrane Library, and EMBASE were searched to find relevant research on the use of bivalirudin versus heparin for anticoagulation in ECMO patients. Outcomes included in-hospital mortality, ECMO duration, major bleeding events, thrombosis events and circuit intervention events. Types of studies included randomized control trials (RCTs), cohort studies, and case-control studies. Case reports, studies lacking comparison with heparin, and where patients transitioned between heparin and bivalirudin, were excluded. Publication bias was evaluated when the number of included studies was more than ten. Sensitivity analysis was performed to examine the stability of the results. RESULTS: Ten articles were selected, and nine articles were included in the meta-analysis. The results of the meta-analysis showed hospital mortality [OR = 0.65, 95%CI (0.44, 0.95), P = 0.03] and thrombosis events decreased (OR = 0.55, 95%CI [0.37, 0.83], P = 0.004) in bivalirudin group compared with heparin in adult patients. Major bleeding events (OR = 0.66, 95%CI [0.17, 2.55], P = 0.55), ECMO duration (MD = 18.92, 95%CI [-29.33, 67.17], P = 0.44) and circuit intervention events (OR = 1.67, 95%CI [0.54, 5.18], P = 0.37) in the bivalirudin group was not statistically significant compared with the heparin group. CONCLUSION: Bivalirudin may provide survival benefits and reduce thrombosis in adult patients on ECMO compared with heparin. There is no difference in treating major bleeding events between bivalirudin and heparin group. However, because all included studies were retrospective observational studies, the evidence level of this systematic review is low and heterogeneity could not be avoided. More high-quality clinical studies are urgently needed to confirm these benefits.

Evaluating machine learning models for sepsis prediction: A systematic review of methodologies.

Studies for sepsis prediction using machine learning are developing rapidly in medical science recently. In this review, we propose a set of new evaluation criteria and reporting standards to assess 21 qualified machine learning models for quality analysis based on PRISMA. Our assessment shows that (1.) the definition of sepsis is not consistent among the studies; (2.) data sources and data preprocessing methods, machine learning models, feature engineering, and inclusion types vary widely among the studies; (3.) the closer to the onset of sepsis, the higher the value of AUROC is; (4.) the improvement in AUROC is primarily due to using machine learning as a feature engineering tool; (5.) deep neural networks coupled with Sepsis-3 diagnostic criteria tend to yield better results on the time series data collected from patients with sepsis. The new evaluation criteria and reporting standards will facilitate the development of improved machine learning models for clinical applications.

Gut, metabolism and nutritional Support for COVID-19: Experiences from China.

There is little research that focuses on the relationship between the gut, metabolism, nutritional support and COVID-19. As a group of Chinese physicians, nutritionists and scientists working on the frontline treating COVID-19 patients, we aim to integrate our experiences and the current clinical evidence to address this pressing issue in this article. Based on our clinical observations and available evidence, we recommend the following practice. Firstly, the Nutritional Risk Screening 2002 tool should be used routinely and periodically; for patients with a score ≥3, oral nutritional supplements should be given immediately. Secondly, for patients receiving the antiviral agents lopinavir/ritonavir, gastrointestinal side effects should be monitored for and timely intervention provided. Thirdly, for feeding, the enteral route should be the first choice. In patients undergoing mechanical ventilation, establishing a jejunal route as early as possible can guarantee the feeding target being achieved if gastric dilatation occurs. Fourthly, we suggest a permissive underfeeding strategy for severe/critical patients admitted to the intensive care unit during the first week of admission, with the energy target no more than 20 kcal/kg/day (for those on mechanical ventilation, this target may be lowered to 10-15 kcal/kg/day) and the protein target around 1.0-1.2 g/kg/day. If the inflammatory condition is significantly alleviated, the energy target may be gradually increased to 25-30 kcal/kg/day and the protein target to 1.2-1.5 g/kg/day. Fifthly, supplemental parenteral nutrition should be used with caution. Lastly, omega-3 fatty acids may be used as immunoregulators, intravenous administration of omega-3 fatty emulsion (10 g/day) at an early stage may help to reduce the inflammatory reaction.

Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries.

PURPOSE: To compare twenty-two machine learning (ML) models against logistic regression on survival prediction in severe traumatic brain injury (STBI) patients in a single center study. MATERIALS AND METHODS: Data was collected from STBI patients admitted to the Sichuan Provincial People's Hospital between December 2009 and November 2011. Twenty-two machine learning (ML) models were tested, and their predictive performance compared with logistic regression (LR) model. Receiver operating characteristics (ROC), area under curve (AUC), accuracy, F-score, precision, recall and Decision Curve Analysis (DCA) were used as performance metrics. RESULTS: A total of 117 patients were enrolled. AUC of all ML models ranged from 86.3% to 94%. AUC of LR was 83%, and accuracy was 88%. The AUC of Cubic SVM, Quadratic SVM and Linear SVM were higher than that of LR. The precision ratio of LR was 95% and recall ratio was 91%, both were lower than most ML models. The F-Score of LR was 0.93, which was only slightly better than that of Linear Discriminant and Quadratic Discriminant. CONCLUSIONS: The twenty-two ML models selected have capabilities comparable to classical LR model for outcome prediction in STBI patients. Of these, Cubic SVM, Quadratic SVM, Linear SVM performed significantly better than LR.

Xanthatin inhibits STAT3 and NF-κB signalling by covalently binding to JAK and IKK kinases.

Aberrant activation of the signal transducer and activator of transcription 3 (STAT3) and the nuclear factor-κB (NF-κB) signalling pathways is associated with the development of cancer and inflammatory diseases. JAKs and IKKs are the key regulators in the STAT3 and NF-κB signalling respectively. Therefore, the two families of kinases have been the major targets for developing drugs to regulate the two signalling pathways. Here, we report a natural compound xanthatin from the traditional Chinese medicinal herb Xanthium L. as a potent inhibitor of both STAT3 and NF-κB signalling pathways. Our data demonstrated that xanthatin was a covalent inhibitor and its activities depended on its α-methylene-γ-butyrolactone group. It preferentially interacted with the Cys243 of JAK2 and the Cys412 and Cys464 of IKKß to inactivate their activities. In doing so, xanthatin preferentially inhibited the growth of cancer cell lines that have constitutively activated STAT3 and p65. These data suggest that xanthatin may be a promising anticancer and anti-inflammation drug candidate.

Purification, crystallization and preliminary X-ray crystallographic studies of swine MHC class I complexed with an FMDV CTL epitope Hu64.

Up to now, no crystal structure of swine leukocyte antigen 2 (SLA-2) molecules was reported. In order to elucidate the structure of SLA-2 and to study the cytotoxic T lymphocyte (CTL) epitopes derived from foot-and-mouth disease virus (FMDV), a complex of swine major histocompatibility complex (MHC) class I molecule (SLA-2 haplotype, Hebao allele) with swine ß2-microglobulin and the CTL epitope FMDV-Hu64 (ALLRTATYY) derived from O serotype of FMDV VP1 protein (residues 64-72) was refolded and crystallized. The crystal, which belonged to space group P212121, diffracted to 2.5 Šresolution and had unit cell parameters a = 48.37, b = 97.75, c = 166.163 Å. These results will help to determine the first structure of a SLA-2 molecule in the context of an FMDV CTL epitope.

α-pyrone derivatives from a marine actinomycete Nocardiopsis sp. YIM M13066.

Five new α-pyrones namely nocapyrones O-S (2-6) and nocapyrone F (1) were isolated from a deep-sea sediment strain Nocardiopsis sp. YIM M13066. Their structures were elucidated by their NMR spectroscopic data, HR-ESI-MS and crystal X-ray diffraction. Their cytotoxic activities against H1299, HeLa, HL7702, MCF-7, PC3 and U251 cell lines were evaluated.

Two new sesquiterpenoids produced by halophilic Nocardiopsis chromatogenes YIM 90109.

Two new germacradiene-type sesquiterpenoids, including 1(10)E,5E-germacradiene-9ß,11-diol (or 9ß-hydroxyl germacradienol) (1) and 11-hydroxy-1(10)E,5E-germacradien-2-one (2-oxygermacradienol) (2), together with a known geosmin-type sesquiterpenoid (1ß,4ß,4aß,7α,8aα)-4,8a-dimethyloctahydronaphthalene-1,4a,7(2H)-triol (3), were elucidated by their NMR spectroscopic data, HR-ESI-MS and single-crystal X-ray diffraction from the halophilic strain Nocardiopsis chromatogenes YIM 90109. The antimicrobial activities were evaluated by paper diffusion method.

Two new polyketides from Nocardiopsis lucentensis DSM 44048.

Two new polyketides, namely lucentides A (1) and B (2), together with 19-hydroxyprotylonolide (3) were isolated from Nocardiopsis lucentensis DSM 44048. Their structures were elucidated by analysis of their high-resolution mass spectrometry (HR-MS) and 1D, 2D nuclear magnetic resonance (NMR) spectroscopic data. The antibacterial activities of compounds 1-3 were evaluated.

The dilemma of heterogeneity tests in meta-analysis: a challenge from a simulation study.

INTRODUCTION: After several decades' development, meta-analysis has become the pillar of evidence-based medicine. However, heterogeneity is still the threat to the validity and quality of such studies. Currently, Q and its descendant I(2) (I square) tests are widely used as the tools for heterogeneity evaluation. The core mission of this kind of test is to identify data sets from similar populations and exclude those are from different populations. Although Q and I(2) are used as the default tool for heterogeneity testing, the work we present here demonstrates that the robustness of these two tools is questionable. METHODS AND FINDINGS: We simulated a strictly normalized population S. The simulation successfully represents randomized control trial data sets, which fits perfectly with the theoretical distribution (experimental group: p = 0.37, control group: p = 0.88). And we randomly generate research samples Si that fits the population with tiny distributions. In short, these data sets are perfect and can be seen as completely homogeneous data from the exactly same population. If Q and I(2) are truly robust tools, the Q and I(2) testing results on our simulated data sets should not be positive. We then synthesized these trials by using fixed model. Pooled results indicated that the mean difference (MD) corresponds highly with the true values, and the 95% confidence interval (CI) is narrow. But, when the number of trials and sample size of trials enrolled in the meta-analysis are substantially increased; the Q and I(2) values also increase steadily. This result indicates that I(2) and Q are only suitable for testing heterogeneity amongst small sample size trials, and are not adoptable when the sample sizes and the number of trials increase substantially. CONCLUSIONS: Every day, meta-analysis studies which contain flawed data analysis are emerging and passed on to clinical practitioners as "updated evidence". Using this kind of evidence that contain heterogeneous data sets leads to wrong conclusion, makes chaos in clinical practice and weakens the foundation of evidence-based medicine. We suggest more strict applications of meta-analysis: it should only be applied to those synthesized trials with small sample sizes. We call upon that the tools of evidence-based medicine should keep up-to-dated with the cutting-edge technologies in data science. Clinical research data should be made available publicly when there is any relevant article published so the research community could conduct in-depth data mining, which is a better alternative for meta-analysis in many instances.

Establishment of quantitative severity evaluation model for spinal cord injury by metabolomic fingerprinting.

Spinal cord injury (SCI) is a devastating event with a limited hope for recovery and represents an enormous public health issue. It is crucial to understand the disturbances in the metabolic network after SCI to identify injury mechanisms and opportunities for treatment intervention. Through plasma 1H-nuclear magnetic resonance (NMR) screening, we identified 15 metabolites that made up an "Eigen-metabolome" capable of distinguishing rats with severe SCI from healthy control rats. Forty enzymes regulated these 15 metabolites in the metabolic network. We also found that 16 metabolites regulated by 130 enzymes in the metabolic network impacted neurobehavioral recovery. Using the Eigen-metabolome, we established a linear discrimination model to cluster rats with severe and mild SCI and control rats into separate groups and identify the interactive relationships between metabolic biomarkers in the global metabolic network. We identified 10 clusters in the global metabolic network and defined them as distinct metabolic disturbance domains of SCI. Metabolic paths such as retinal, glycerophospholipid, arachidonic acid metabolism; NAD-NADPH conversion process, tyrosine metabolism, and cadaverine and putrescine metabolism were included. In summary, we presented a novel interdisciplinary method that integrates metabolomics and global metabolic network analysis to visualize metabolic network disturbances after SCI. Our study demonstrated the systems biological study paradigm that integration of 1H-NMR, metabolomics, and global metabolic network analysis is useful to visualize complex metabolic disturbances after severe SCI. Furthermore, our findings may provide a new quantitative injury severity evaluation model for clinical use.

Identification and characterization of the biosynthetic gene cluster of divergolides from Streptomyces sp. W112.

Divergolides are a group of structurally unprecedented ansamacrolactam antibiotics with antibacterial and antitumor activities. A biosynthetic gene cluster predicted to encode the biosynthesis of divergolides was cloned and sequenced from endophytic Streptomyces sp. W112. The gene cluster of divergolides (div) spans a DNA region of 61-kb and consists of 20 open reading frames (ORFs) that encode polyketide synthases (PKSs), enzymes for the synthesis of AHBA and PKS extender units, and post-PKS modifications, proposed regulators, and putative transporters. Disruption of the AHBA synthase gene (divK) completely abolished the production of divergolides proved its involvement in the biosynthesis of divergolides. Bioinformatics analysis suggested that the regulatory gene div8 in div gene cluster might encode a positive regulator for the biosynthesis of divergolides. Constitutive overexpression of div8 improved the production of divergolides E, implying that div gene cluster maybe responsible for the biosynthesis of divergolides. These findings set the stage for fully investigating the biosynthesis of divergolides and rational engineering of new divergolide analogs by genetic modifications, and pave the way to further improve the production of divergolides.

[Evaluation of the heterogeneity of systematic reviews on nutrition support for burn patients with Meta-regression algorithm].

OBJECTIVE: To evaluate the heterogeneity of systematic reviews (SRs) on nutrition support for burn patients with Meta-regression algorithm quantitatively. METHODS: SRs on nutrition support for burn patients since 1980 were searched in 7 databases, including PubMed, EMBASE on CD, Science Citation Index, Cochrane Library, Chinese Biomedicine Database, Chinese Medical Current Contents, and Chinese Journal Full-text Database. SRs were identified with inclusion criteria: the research was interventive SR or Meta analysis of clinical data of burn patients aged equal to or over 18 years who had received clinical nutrition intervention, with regimen including special nutrient given including enterally or parenterally; the outcome indicator was mortality. A two-step method was established to evaluate the heterogeneity of SRs. (1) Qualitative analysis: the publication time of SRs and the trial design, patient type, regimen or route of nutrition therapy, energy intake, and outcome reported by the included clinical trial were analyzed. (2) Quantitative analysis: a mortality Meta-regression model was established by trial design, publication time, regimen or route of nutrition therapy, and energy intake to calculate the expected mortality. Observation/expectation (O/E) ratio and its 95% confidence interval were calculated to identify the heterogeneity among the clinical trials of SRs. RESULTS: (1) Six SRs were retrieved which were published within 10 years. Only three SRs reported two of the three important parameters of nutrition intervention: regimen or route of nutrition therapy, energy intake, and clinical outcome. Totally, 11 clinical trials that gave outcome information were included in the SRs, including 10 RCTs and 1 case control trial. (2) Meta-regression analysis showed that significant heterogeneity existed in 4 RCTs (including five study groups), which were different from the other 7 trials. The patient samples of these 4 RCTs were from different population. No heterogeneity was detected by Meta-regression model among the three RCTs that reported regimen or route of nutrition therapy, energy intake, and outcome. CONCLUSIONS: Quality of SRs on nutrition intervention for burn patients is poor, and significant heterogeneity exists among trials that are included in these SRs. O/E ratio from Meta-regression could be an effective tool to identify heterogeneity and its source.