The accurate and timely detection of biochemical coagulation indicators is pivotal in pulmonary and critical care medicine. Despite their reliability, traditional laboratories often lag in terms of rapid diagnosis. Point-of-care testing (POCT) has emerged as a promising alternative, which is awaiting rigorous validation. We assessed 226 samples from patients at the First Affiliated Hospital of Guangzhou Medical University using a Beckman Coulter AU5821 and a PUSHKANG POCT Biochemistry Analyzer MS100. Furthermore, 350 samples were evaluated with a Stago coagulation analyzer STAR MAX and a PUSHKANG POCT Coagulation Analyzer MC100. Metrics included thirteen biochemical indexes, such as albumin, and five coagulation indices, such as prothrombin time. Comparisons were drawn against the PUSHKANG POCT analyzer. Bland-Altman plots (MS100: 0.8206‒0.9995; MC100: 0.8318‒0.9911) evinced significant consistency between methodologies. Spearman correlation pinpointed a potent linear association between conventional devices and the PUSHKANG POCT analyzer, further underscored by a robust correlation coefficient (MS100: 0.713‒0.949; MC100: 0.593‒0.950). The PUSHKANG POCT was validated as a dependable tool for serum and whole blood biochemical and coagulation diagnostics. This emphasizes its prospective clinical efficacy, offering clinicians a swift diagnostic tool and heralding a new era of enhanced patient care outcomes.
Humans ; Point-of-Care Testing ; Critical Care ; Blood Coagulation Tests/methods* ; Male ; Blood Coagulation ; Female ; Middle Aged ; Reproducibility of Results ; Prothrombin Time ; Aged ; Adult ; Point-of-Care Systems

Early identification of adolescent depression requires objective biomarkers. This study investigated the functional near-infrared spectroscopy (fNIRS) activation patterns and mismatch negativity (MMN) characteristics in adolescents with first-episode mild-to-moderate depression. We enrolled 33 patients and 33 matched healthy controls, measuring oxyhemoglobin (Oxy-Hb) concentration in the frontal cortex during verbal fluency tasks via fNIRS, and recording MMN latency/amplitude at Fz/Cz electrodes using event-related potentials (ERP). Compared with healthy controls, the depression group showed significantly prolonged MMN latency [Fz: (227.88 ± 31.08) ms vs. (208.70 ± 25.35) ms, P < 0.01; Cz: (223.73 ± 29.03) ms vs. (204.18 ± 22.43) ms, P < 0.01], and obviously reduced Fz amplitude [(2.42 ± 2.18) μV vs. (5.65 ± 5.59) μV, P = 0.03]. A significant positive correlation was observed between MMN latencies at Fz and Cz electrodes ( P < 0.01). Oxy-Hb in left frontopolar prefrontal channels (CH15/17) was significantly decreased in patient group ( P < 0.05). Our findings suggest that adolescents with depression exhibit hypofunction in the left prefrontal cortex and impaired automatic sensory processing. The combined application of fNIRS and ERP techniques may provide an objective basis for early clinical identification.
Humans ; Spectroscopy, Near-Infrared/methods* ; Adolescent ; Prefrontal Cortex/physiopathology* ; Evoked Potentials/physiology* ; Depression/physiopathology* ; Female ; Male ; Oxyhemoglobins ; Electroencephalography

Objective To systematically retrieve,evaluate and integrate evidences about the assessment and management of perioperative pain in patients with acute aortic dissection.Methods PIPOST model was used to identify themes of assessment and management of perioperative pain.The literatures in the themes was systematically searched through the databases of UpToDate,JBI,BMJ Best Practice,practice guide REgistration for trans RAREncy(PREPARE),Guidelines International Network(GIN),National Guideline Clearinghouse(NGC),National Institute for Health and Care Excellence(NICE),Scottish Intercollegiate Guidelines Network(SIGN),New Zealand Guidelines Group(NZGG),Registered Nurses'Association of Ontario(RNAO),Australian Clinical Practice Guidelines(ACPG),American Heart Association(AHA),European Society of Cardiology(ESC),the Chinese Cochrane Center,Medlive,Cochrane library,PubMed,SinoMed,CNKI,Wangfan Data,and VIP.The retrieved literatures were evaluated and the evidences that met the inclusive criteria were extracted from the literatures by researchers who had trained for evidence-based study.Results A total of 17 studies,including 5 guidelines,3 expert consensus,6 systematic reviews and 3 randomised controlled trials were included in this study.Totally,29 pieces of best evidence were extracted in the assessment and management of pain in perioperative patients with acute aortic dissection,including pain assessment,basic principles of pain management,medication intervention strategies of pain management,non-medication intervention strategies of pain management,pain evaluation,education of pain management and organising pain management.Conclusion Evidences in assessment and management of pain in perioperative patients with acute aortic dissection can provide references and guidance for clinical practice.

Objective:To develop deep learning models for ulcerative colitis (UC) classification based on Mayo endoscopic score.Methods:A total of 2400 endoscopic images from the Gastrointestinal Endoscopy Centre of the First Affiliated Hospital of Soochow University and the HyperKvasir database were extracted for training classification models, and 200 endoscopic images from Affiliated Jintan Hospital of Jiangsu University were extracted for evaluating the models, both scored by endoscopists according to Mayo endoscopic score (score 0-3). Four deep convolutional neural networks (MobileNetV2, ResNetV2, Xception, EfficientNetV2S), which were pre-trained in the ImageNet database, were used to develop the UC classification models by transfer learning. Models were evaluated in the test set based on the confusion matrix using accuracy, Matthews correlation coefficient (MCC) and Cohen′s kappa, and compared with the performance of senior and junior physicians. Meanwhile, the model was visualized by gradient-weighted class activation mapping.Results:Four deep learning Mayo score models based on UC endoscopic image classification models were successfully developed. In the test set, the accuracy of MobileNetV2, ResNetV2, Xception and EfficientNetV2S was 0.785, 0.800, 0.815, 0.830, respectively (average accuracy 0.808). Amoug them, EfficientNetV2S model was the best, higher than junior physician′s accuracy (accuracy 0.785), and slightly lower than senior physician′s (accuracy 0.870) .Conclusions:The UC endoscopic severity classification models developed by deep learning show good performance, which can be further improved by larger sample size and optimizing the framework.

Objective:To develop deep learning models for ulcerative colitis (UC) classification based on Mayo endoscopic score.Methods:A total of 2400 endoscopic images from the Gastrointestinal Endoscopy Centre of the First Affiliated Hospital of Soochow University and the HyperKvasir database were extracted for training classification models, and 200 endoscopic images from Affiliated Jintan Hospital of Jiangsu University were extracted for evaluating the models, both scored by endoscopists according to Mayo endoscopic score (score 0-3). Four deep convolutional neural networks (MobileNetV2, ResNetV2, Xception, EfficientNetV2S), which were pre-trained in the ImageNet database, were used to develop the UC classification models by transfer learning. Models were evaluated in the test set based on the confusion matrix using accuracy, Matthews correlation coefficient (MCC) and Cohen′s kappa, and compared with the performance of senior and junior physicians. Meanwhile, the model was visualized by gradient-weighted class activation mapping.Results:Four deep learning Mayo score models based on UC endoscopic image classification models were successfully developed. In the test set, the accuracy of MobileNetV2, ResNetV2, Xception and EfficientNetV2S was 0.785, 0.800, 0.815, 0.830, respectively (average accuracy 0.808). Amoug them, EfficientNetV2S model was the best, higher than junior physician′s accuracy (accuracy 0.785), and slightly lower than senior physician′s (accuracy 0.870) .Conclusions:The UC endoscopic severity classification models developed by deep learning show good performance, which can be further improved by larger sample size and optimizing the framework.

Acute pancreatitis （AP） is a gastrointestinal disease that requires early intervention， and when it progresses to moderate-severe AP （MSAP） or severe AP （SAP）， there will be a significant increase in the mortality rate of patients. Machine learning （ML） has achieved great success in the early prediction of AP using clinical data with the help of its powerful computational and learning capabilities. This article reviews the research advances in ML in predicting the severity， complications， and death of AP， so as to provide a theoretical basis and new insights for clinical diagnosis and treatment of AP through artificial intelligence.

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca²⁺ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.
Humans ; Pancreatitis ; Acute Disease ; Retrospective Studies ; Hospitalization ; Algorithms

Marine microorganisms, especially marine fungi, have historically proven their value as a prolific source for structurally novel and pharmacologically active secondary metabolites (Deshmukh et al., 2018; Carroll et al., 2022). The corals constitute a dominant part of reefs with the highest biodiversity, and harbor highly diverse and abundant microbial symbionts in their tissue, skeleton, and mucus layer, with species-specific core members that are spatially partitioned across coral microhabitats (Wang WQ et al., 2022). The coral-associated fungi were very recently found to be vital producers of structurally diverse compounds, terpenes, alkaloids, peptides, aromatics, lactones, and steroids. They demonstrate a wide range of bioactivity such as anticancer, antimicrobial, and antifouling activity (Chen et al., 2022). The genetically powerful genus Emericella (Ascomycota), which has marine and terrestrial sources, includes over 30 species and is distributed worldwide. It is considered a rich source of diverse secondary metabolites with antimicrobial activity or cytotoxicity (Alburae et al., 2020). Notably, Emericella nidulans, the sexual state of a classic biosynthetic strain Aspergillus nidulans, was recently reported as an important source of highly methylated polyketides (Li et al., 2019) and isoindolone-containing meroterpenoids (Zhou et al., 2016) with unusual skeletons.
Animals ; Aspergillus nidulans ; Polyketides/chemistry* ; Anthozoa/microbiology* ; Anti-Infective Agents/pharmacology* ; Alkaloids

The development of high-throughput sequencing techniques enabled a deeper and more comprehensive understanding of environmental microbiology. Specifically, the third-generation sequencing techniques represented by nanopore sequencing have greatly promoted the development of environmental microbiology research due to its advantages such as long sequencing reads, fast sequencing speed, real-time monitoring of sequencing data, and convenient machine carrying, as well as no GC bias and no PCR amplification requirement. This review briefly summarized the technical principle and characteristics of nanopore sequencing, followed by discussing the application of nanopore sequencing techniques in the amplicon sequencing, metagenome sequencing and whole genome sequencing of environmental microorganisms. The advantages and challenges of nanopore sequencing in the application of environmental microbiology research were also analyzed.
Environmental Microbiology ; High-Throughput Nucleotide Sequencing ; Metagenome ; Nanopore Sequencing ; Nanopores

Objective: To explore the pain experiences of adult burn patients so as to lay foundation for practical analgesic measures. Methods: Using phenomenological method in qualitative research, semi-structured interviews were conducted on 12 adult burn patients hospitalized in our burn units from May to November 2015, aiming at pain experiences from immediately after burns to 3 to 7 months after being discharged from hospital. Then the Colaizzi′s analysis method was applied to analyze, induce, and refine themes of interview data. Results: After analysis, pain experiences of adult burn patients were generalized into 6 themes: deep pain experiences, heavy psychological burden, limited daily life, poor assessment and treatment of pain, different attributions of pain, and different ways of coping of pain. Conclusions Burn pain brings harm to the patients′ physiology, mentality, and daily life. Nevertheless, pain processing modes of medical staff and patients themselves are the key factors affecting patients′ pain experiences. Therefore, according to the deficiency of current situation of pain management, the targeted analgesic intervention measures should be carried out from the perspectives of medical staff and patients.