Clinical value of monitoring cytokine levels for assessing the severity of mycoplasma pneumoniae pneumonia in children.

BACKGROUND: To investigate the clinical relevance of cytokine levels in assessment of the severity of mycoplasma pneumoniae pneumonia (MPP) in children. METHODS: A retrospective study was conducted on 150 pediatric cases of MPP admitted to a local hospital in China from November 1, 2022 to October 31, 2023. These MPP cases were divided into mild (n=100) and severe (n=50) groups according to the severity of the disease. Cytokine levels, including Interferon-γ (IFN-γ), Tumor Necrosis Factor-α (TNF-α), C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-2 (IL-2), and D-Dimer (D-D), were compared between the two groups. The diagnostic efficacy of each cytokine in assessing the severity of MPP was analyzed through Receiver Operating Characteristic (ROC) curves, and correlation between cytokine levels and disease severity was assessed using Pearson's correlation coefficient. RESULTS: The IL-2 level was significantly lower, while TNF-α, IL-6, and IFN-γ levels were significantly higher in the severe group compared to the mild group (all P<0.05). TNF-α, IFN-γ, IL-2, IL-6, CRP, and D-D were identified as factors influencing the severity of MPP (all P<0.05). The ROC curve analysis showed that the areas under the curve (AUCs) of TNF-α, IL-2, IL-6, IFN-γ, CRP, and D-D were 0.864, 0.692, 0.874, 0.949, 0.814, and 0.691, respectively (all P<0.001), indicating their diagnostic value in assessing the severity of MPP. There exists a positive correlation between IL-2 and the percentage of normal lung density on Computed Tomography (CT) scan (P<0.05), while TNF-α, IL-6, IFN-γ, CRP, and D-D showed negative correlations with the percentage of normal lung density (P<0.05). CONCLUSION: Cytokines such as TNF-α, IL-2, IL-6, IFN-γ, CRP, and D-D are aberrantly expressed in children with MPP and are associated with the severity of the disease. These cytokines have high diagnostic value and can serve as reference indicators for clinical, especially prognostic assessment of the severity of (pediatric) MPP.

Refining pain management in mice by comparing multimodal analgesia and NSAID monotherapy for neurosurgical procedures.

While neurosurgical interventions are frequently used in laboratory mice, refinement efforts to optimize analgesic management based on multimodal approaches appear to be rather limited. Therefore, we compared the efficacy and tolerability of combinations of the non-steroidal anti-inflammatory drug carprofen, a sustained-release formulation of the opioid buprenorphine, and the local anesthetic bupivacaine with carprofen monotherapy. Female and male C57BL/6J mice were subjected to isoflurane anesthesia and an intracranial electrode implant procedure. Given the multidimensional nature of postsurgical pain and distress, various physiological, behavioral, and biochemical parameters were applied for their assessment. The analysis revealed alterations in Neuro scores, home cage locomotion, body weight, nest building, mouse grimace scales, and fecal corticosterone metabolites. A composite measure scheme allowed the allocation of individual mice to severity classes. The comparison between groups failed to indicate the superiority of multimodal regimens over high-dose NSAID monotherapy. In conclusion, our findings confirmed the informative value of various parameters for assessment of pain and distress following neurosurgical procedures in mice. While all drug regimens were well tolerated in control mice, our data suggest that the total drug load should be carefully considered for perioperative management. Future studies would be of interest to assess potential synergies of drug combinations with lower doses of carprofen.

Developing Causality and Severity Assessment Frameworks for Food Safety Signals Using Social Media Reviews: A Technical Report Based on Data From an Urban Indian Suburb.

Social media reviews are a valuable data source, reflecting consumer experiences and interactions with businesses. This study leverages such data to develop a passive surveillance framework for food safety in urban India. By employing a Bidirectional Encoder Representations from Transformers (BERT)-powered Aspect-Based Sentiment Analysis tool, branded as Eat At Right Place (ERP), the study analyses over 100,000 reviews from 93 restaurants to identify and assess food safety signals. The Causality Assessment Index (CAI) and Severity Assessment Score (SAS) are introduced to systematically evaluate potential risks. The CAI uses pattern recognition and temporal relationships to establish causality while the SAS quantifies severity based on sub-aspects such as cleanliness, food handling, and unintended health outcomes. Results indicate that 40% of the restaurants had a CAI above 1, highlighting significant food safety concerns. The framework successfully prioritizes corrective actions by grading the severity of issues, demonstrating its potential for real-time food safety management. This study underscores the importance of integrating innovative data-driven approaches into public health monitoring systems and suggests future improvements in natural language processing algorithms and data source expansion. The findings pave the way for enhanced food safety surveillance and timely regulatory interventions.

Evidence-Based Severity Assessment of Animal Models for Pancreatic Cancer.

Animal models are crucial to preclinical oncological research and drug development. Animal experiments must be performed in accordance with the 3R principles of replacement and reduction, if possible, and refinement where these procedures remain crucial. In addition, European Union legislations demand a continuous refinement approach, as well as pro- and retrospective severity assessment. In this study, an objective databased severity assessment was performed in murine models for pancreatic cancer induced by orthotopic, subcutaneous, or intravenous injection of Panc02 cells. Parameters such as body weight change, distress score, perianal temperature, mouse grimace scale, burrowing, nesting behavior, and the concentration of corticosterone in plasma and its metabolites in feces were monitored during tumor progression. The most important parameters were combined into a score and mapped against a reference data set by the Relative Severity Assessment procedure (RELSA) to obtain the maximum achieved severity for each animal (RELSAmax). This scoring revealed a significantly higher RELSAmax for the orthotopic model than for the subcutaneous and intravenous models. However, compared to animal models such as pancreatitis and bile duct ligation, the pancreatic cancer models are shown to be less severe. Data-based animal welfare assessment proved to be a valuable tool for comparing the severity of differently induced cancer models.

Exploring dose-response variability and relative severity assessment in STZ-induced diabetes male NSG mice.

NSG mice are among the most immunodeficient mouse model being used in various scientific branches. In diabetelogical research diabetic NSG mice are an important asset as a xenotransplantation model for human pancreatic islets or pluripotent stem cell-derived islets. The treatment with the beta cell toxin streptozotocin is the standard procedure for triggering a chemically induced diabetes. Surprisingly, little data has been published about the reproducibility, stress and animal suffering in these NSG mice during diabetes induction. The 3R rules, however, are a constant reminder that existing methods can be further refined to minimize suffering. In this pilot study the dose-response relationship of STZ in male NSG mice was investigated and additionally animal suffering was charted by applying the novel 'Relative Severity Assessment' algorithm. By this we successfully explored an STZ dose that reliably induced diabetes while reduced stress and pain to the animals to a minimum using evidence-based and objective parameters rather than criteria that might be influenced by human bias.

Knowledge fused latent representation from lung ultrasound examination for COVID-19 pneumonia severity assessment.

COVID-19 pneumonia severity assessment is of great clinical importance, and lung ultrasound (LUS) plays a crucial role in aiding the severity assessment of COVID-19 pneumonia due to its safety and portability. However, its reliance on qualitative and subjective observations by clinicians is a limitation. Moreover, LUS images often exhibit significant heterogeneity, emphasizing the need for more quantitative assessment methods. In this paper, we propose a knowledge fused latent representation framework tailored for COVID-19 pneumonia severity assessment using LUS examinations. The framework transforms the LUS examination into latent representation and extracts knowledge from regions labeled by clinicians to improve accuracy. To fuse the knowledge into the latent representation, we employ a knowledge fusion with latent representation (KFLR) model. This model significantly reduces errors compared to approaches that lack prior knowledge integration. Experimental results demonstrate the effectiveness of our method, achieving high accuracy of 96.4 % and 87.4 % for binary-level and four-level COVID-19 pneumonia severity assessments, respectively. It is worth noting that only a limited number of studies have reported accuracy for clinically valuable exam level assessments, and our method surpass existing methods in this context. These findings highlight the potential of the proposed framework for monitoring disease progression and patient stratification in COVID-19 pneumonia cases.

Radiological and Biochemical Parameters in Assessing Acute Pancreatitis Severity: A Comprehensive Review.

Acute pancreatitis is a dynamic inflammatory condition of the pancreas with a spectrum ranging from mild to severe. Early and accurate assessment of disease severity is crucial for guiding clinical management and improving patient outcomes. This comprehensive review explores the role of radiological and biochemical parameters in assessing the severity of acute pancreatitis. Radiological imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US), play a pivotal role in identifying key features, such as pancreatic necrosis and peripancreatic fluid collections, indicative of severe disease. Additionally, serum markers such as amylase, lipase, and C-reactive protein (CRP) provide valuable prognostic information and aid in risk stratification. Integrating radiological and biochemical parameters allows for a multidimensional evaluation of disease severity, enabling clinicians to make informed decisions regarding patient management. Early identification of severe cases facilitates timely interventions, including intensive care monitoring, nutritional support, and potential surgical interventions. Despite significant advancements in the field, there remain areas for further research, including the validation of emerging imaging techniques and biomarkers and the exploration of personalized management approaches. Addressing these research gaps can enhance our understanding of acute pancreatitis and ultimately improve patient care and outcomes.

Telemedicine in Acute Trauma Care: A Review of Quantitative Evaluations on the Impact of Remote Consultation.

BACKGROUND: There is extensive literature describing the application of telemedicine techniques to trauma care. However, there is a paucity of articles showing quantitative assessment of its safety and efficacy. This structured review examines articles with quantitative assessment of telemedicine's impact in acute trauma care. METHODS: Medline and CINAHL databases were searched for peer-reviewed articles that quantitatively assess the impact of telemedicine on diagnostic accuracy, clinical decision-making, emergency department length of stay, transfer rates, and mortality in initial trauma management. RESULTS: Only 9 of the 408 screened articles met the criteria for quantitative assessment. Telemedicine appears to be preferentially used for more severely injured patients. Limited quality evidence supports procedural interventions at remote sites. Telemedicine may help abbreviate pre-transfer length of stay. However, its impact on diagnosis and mortality remains unclear. CONCLUSIONS: Telemedicine's potential to enhance the quality and efficiency of trauma care, especially for resource-scarce areas, warrants continued quantitative research.

Exploration and Evaluation of Adverse Drug Reactions Documented in a Tertiary-Care Hospital in Chennai: An In-Depth Retrospective Observational Study.

INTRODUCTION:  While drugs are intended to benefit patients, adverse drug reactions (ADRs) represent a significant negative outcome of drug consumption. They rank as the sixth leading cause of death among hospitalized patients. Many harmful effects are preventable and can reduce morbidity, mortality, and hospitalization duration. This study is a valuable resource for physicians, aiding in the safe and optimal use of medications. METHODOLOGY:  This retrospective observational study, conducted at the Pharmacovigilance Center of Saveetha Medical College and Hospital, Chennai, India, received approval from the Institutional Ethics Committee. All adverse drug interactions reported in our hospital from January 2019 to February 2024 were included after screening for inclusion and exclusion criteria. The collected reactions were analyzed, assessed, and evaluated between February 2024 and April 2024. Data on the drugs causing adverse reactions, the types of reactions, and the treatments administered were collected and documented. The reactions were categorized using the Rawlins and Thompson classification, while causality and severity were assessed using the standard Naranjo causality and modified Hartwig and Siegel severity assessment scales. RESULTS:  During the study, 252 ADRs were documented by the Central Drugs Standard Control Organization. The gender distribution showed 123 cases (48.8%) in males and 129 cases (51.2%) in females, with a higher prevalence in the 20-40 age group. The departmentwise distribution revealed the highest number of ADRs in Obstetrics and Gynecology (60 cases, 24%), followed by General Surgery (52 cases, 21%), General Medicine (44 cases, 17%), Pediatrics (22 cases, 9%), and Emergency Medicine (20 cases, 8%). Antimicrobial drugs constituted the majority of ADRs (149 cases, 59.1%), followed by vitamins and mineral supplements (21 cases, 13.8%), contrast dye (15 cases, 6%), antituberculosis drugs (15 cases, 6%), analgesics (13 cases, 5.2%), and gastrointestinal (GIT) drugs (8 cases, 3.2%). Cefotaxime was the most commonly reported antibiotic (42 cases, 28.2%), followed by Ciprofloxacin (41 cases, 27.5%). Among vitamins and mineral supplements, iron sucrose was implicated in the highest number of ADRs (15 cases, 71.4%). The parenteral route of drug administration showed the highest incidence of ADRs (229 cases, 91%), followed by oral (20 cases, 8%) and topical routes (3 cases, 1%). Dermatological manifestations were most frequently reported (196 cases, 77.8%), followed by GIT symptoms (27 cases, 10.7%), and other manifestations such as shivering, fever, seizures, and dyspnea (29 cases, 11.5%). Based on the Naranjo causality assessment scale, 179 ADRs (71%) were categorized as probable, 55 (22%) as possible, 10 (4%) as certain, and 8 (3%) as doubtful. Approximately 47.2% of ADRs were self-limiting, while 44.1% required symptomatic treatment and 8.7% necessitated aggressive treatment, leading to a prolonged hospital stay or admission to the intensive care unit. CONCLUSION:  The pattern of ADRs in our hospital aligns with findings from other studies. While many of these reactions are unpredictable and mild, they underscore the importance of raising awareness among clinicians and regulatory authorities to promote safe medication use and prevent potentially serious outcomes.

Choice-based severity scale (CSS): assessing the relative severity of procedures from a laboratory animal's perspective.

One primary goal of laboratory animal welfare science is to provide a comprehensive severity assessment of the experimental and husbandry procedures or conditions these animals experience. The severity, or degree of suffering, of these conditions experienced by animals are typically scored based on anthropocentric assumptions. We propose to (a) assess an animal's subjective experience of condition severity, and (b) not only rank but scale different conditions in relation to one another using choice-based preference testing. The Choice-based Severity Scale (CSS) utilizes animals' relative preferences for different conditions, which are compared by how much reward is needed to outweigh the perceived severity of a given condition. Thus, this animal-centric approach provides a common scale for condition severity based on the animal's perspective. To assess and test the CSS concept, we offered three opportunistically selected male rhesus macaques (Macaca mulatta) choices between two conditions: performing a cognitive task in a typical neuroscience laboratory setup (laboratory condition) versus the monkey's home environment (cage condition). Our data show a shift in one individual's preference for the cage condition to the laboratory condition when we changed the type of reward provided in the task. Two additional monkeys strongly preferred the cage condition over the laboratory condition, irrespective of reward amount and type. We tested the CSS concept further by showing that monkeys' choices between tasks varying in trial duration can be influenced by the amount of reward provided. Altogether, the CSS concept is built upon laboratory animals' subjective experiences and has the potential to de-anthropomorphize severity assessments, refine experimental protocols, and provide a common framework to assess animal welfare across different domains.

Causality, Severity, Preventability and Predictability Assessments Scales for Adverse Drug Reactions: A Review.

The pharmacovigilance program of India (PvPI), after its inception, has been reliably acquiring force in bringing issues to light among the masses, healthcare professionals, the pharma industry, and clinical staff at hospitals. Adverse drug reactions are unintended events that occur after exposure to a drug, biological product, or medical device, and they may result in morbidity and mortality. It is critical to monitor the safety of drugs during the post-marketing phase to find long-term and rare ADRs, as well as ADRs in special populations and patients with co-morbidities that are not usually included during clinical trials. The definitive objective of pharmacovigilance is to collate data and analyze it. Assessing the causality between ADRs and drugs is necessary to decrease the occurrence of ADRs and to reduce the risk of drug-related ADRs. ADRs may lead to increased morbidity, increased hospital stays, and increased cost of treatment, resulting in compromised patient safety. Causality assessment is the evaluation of the likelihood that a particular treatment is the cause of an observed adverse event and establishing a causal association between a drug and a drug reaction is necessary to prevent further recurrences. Numerous methods available for establishing a causal association between the drug and adverse events have been broadly classified into clinical judgment or global introspection, algorithms, and probabilistic methods. These include the Swedish method, World Health Organization-Uppsala Monitoring Centre (WHO-UMC) scale, Naranjo's algorithm, Kramer algorithm, Jones algorithm, Karch algorithm, Bégaud algorithm, Adverse Drug Reactions Advisory Committee guidelines, Bayesian Adverse Reaction Diagnostic Instrument, and so on. Despite various methods available, none of the causality assessment tools have been universally accepted as the gold standard. Naranjo's algorithm and WHO-UMC scales are, however, the most commonly used. Similarly, for preventability and severity assessment of ADRs, the Schumock and Thornton scale and Hartwig and Siegel's scale are most commonly used. Hence, we reviewed different tools and methods available to assess the causality, preventability, and severity of ADRs.

Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining.

Multi-layer complex structures are widely used in large-scale engineering structures because of their diverse combinations of properties and excellent overall performance. However, multi-layer complex structures are prone to interlaminar debonding damage during use. Therefore, it is necessary to monitor debonding damage in engineering applications to determine structural integrity. In this paper, a damage information extraction method with ladder feature mining for Lamb waves is proposed. The method is able to optimize and screen effective damage information through ladder-type damage extraction. It is suitable for evaluating the severity of debonding damage in aluminum-foamed silicone rubber, a novel multi-layer complex structure. The proposed method contains ladder feature mining stages of damage information selection and damage feature fusion, realizing a multi-level damage information extraction process from coarse to fine. The results show that the accuracy of damage severity assessment by the damage information extraction method with ladder feature mining is improved by more than 5% compared to other methods. The effectiveness and accuracy of the method in assessing the damage severity of multi-layer complex structures are demonstrated, providing a new perspective and solution for damage monitoring of multi-layer complex structures.

Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning.

INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study's aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense's N-TidalTM capnometer. METHOD: For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1-4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity. RESULTS: The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1. CONCLUSION: The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring.

Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model.

BACKGROUND: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. OBJECTIVE: This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. METHODS: Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset "Chinese Stroke Clinical Records" (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined "CliRoberta" through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. RESULTS: Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. CONCLUSION: Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.

Comparison of a Novel Score "NOD-ACLF" to Other Established Prognostic Scores for Prediction of Mortality in APASL-ACLF Patients: A Cohort Study from a Tertiary Care Center of North India.

Background: Commonly used prognostic scores for acute on-chronic liver failure (ACLF) have complex calculations. We tried to compare the simple counting of numbers and types of organ dysfunction to these scores, to predict mortality in ACLF patients. Methods: In this prospective cohort study, ACLF patients diagnosed on the basis of Asia Pacific Association for Study of the Liver (APASL) definition were included. Severity scores were calculated. Prognostic factors for outcome were analysed. A new score, the Number of Organ Dysfunctions in Acute-on-Chronic Liver Failure (NOD-ACLF) score was developed. Results: Among 80 ACLF patients, 74 (92.5%) were male, and 6 were female (7.5%). The mean age was 41.0±10.7 (18-70) years. Profile of acute insult was; alcohol 48 (60%), sepsis 30 (37.5%), variceal bleeding 22 (27.5%), viral 8 (10%), and drug-induced 3 (3.8%). Profiles of chronic insults were alcohol 61 (76.3%), viral 20 (25%), autoimmune 3 (3.8%), and non-alcoholic steatohepatitis 2 (2.5%). Thirty-eight (47.5%) were discharged, and 42 (52.5%) expired. The mean number of organ dysfunction (NOD-ACLF score) was ->4.5, simple organ failure count (SOFC) score was >2.5, APASL ACLF Research Consortium score was >11.5, Model for End-Stage Liver Disease-Lactate (MELD-LA) score was >21.5, and presence of cardiovascular and respiratory dysfunctions were significantly associated with mortality. NOD-ACLF and SOFC scores had the highest area under the receiver operating characteristic to predict mortality among all these. Conclusion: The NOD-ACLF score is easy to calculate bedside and is a good predictor of mortality in ACLF patients performing similar or better to other scores.

CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes.

Given the pronounced impact COVID-19 continues to have on society-infecting 700 million reported individuals and causing 6.96 million deaths-many deep learning works have recently focused on the virus's diagnosis. However, assessing severity has remained an open and challenging problem due to a lack of large datasets, the large dimensionality of images for which to find weights, and the compute limitations of modern graphics processing units (GPUs). In this paper, a new, iterative application of transfer learning is demonstrated on the understudied field of 3D CT scans for COVID-19 severity analysis. This methodology allows for enhanced performance on the MosMed Dataset, which is a small and challenging dataset containing 1130 images of patients for five levels of COVID-19 severity (Zero, Mild, Moderate, Severe, and Critical). Specifically, given the large dimensionality of the input images, we create several custom shallow convolutional neural network (CNN) architectures and iteratively refine and optimize them, paying attention to learning rates, layer types, normalization types, filter sizes, dropout values, and more. After a preliminary architecture design, the models are systematically trained on a simplified version of the dataset-building models for two-class, then three-class, then four-class, and finally five-class classification. The simplified problem structure allows the model to start learning preliminary features, which can then be further modified for more difficult classification tasks. Our final model CoSev boosts classification accuracies from below 60% at first to 81.57% with the optimizations, reaching similar performance to the state-of-the-art on the dataset, with much simpler setup procedures. In addition to COVID-19 severity diagnosis, the explored methodology can be applied to general image-based disease detection. Overall, this work highlights innovative methodologies that advance current computer vision practices for high-dimension, low-sample data as well as the practicality of data-driven machine learning and the importance of feature design for training, which can then be implemented for improvements in clinical practices.

Platelet distribution width as an useful indicator of influenza severity in children.

PURPOSE: The present study aims to investigate the potential of platelet distribution width as an useful parameter to assess the severity of influenza in children. METHODS: Baseline characteristics and laboratory results were collected and analyzed. Receiver operating characteristic (ROC) curve analysis was used to joint detection of inflammatory markers for influenza positive children, and the scatter-dot plots were used to compare the differences between severe and non-severe group. RESULTS: Influenza B positive children had more bronchitis and pneumonia (P < 0.05), influenza A infected children had more other serious symptoms (P = 0.007). Neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio (NLR), and platelet parameters performed differently among < 4 years and ≥ 4 years children with influenza. Combined detection of platelet parameters and other indicators could better separate healthy children from influenza infected children than single indicator detection. The levels of platelet distribution width of children with severe influenza (A and B) infection was significantly dropped, compared with non-severe group (P < 0.05). CONCLUSIONS: Platelet distribution width could be a very useful and economic indicator in distinction and severity assessment for children with influenza.

Association of elastic power in mechanical ventilation with the severity of acute respiratory distress syndrome: a retrospective study.

BACKGROUND: Mechanical power (MP) is the total energy released into the entire respiratory system per minute which mainly comprises three components: elastic static power, Elastic dynamic power and resistive power. However, the energy to overcome resistance to the gas flow is not the key factor in causing lung injury, but the elastic power (EP) which generates the baseline stretch of the lung fibers and overcomes respiratory system elastance may be closely related to the ARDS severity. Thus, this study aimed to investigate whether EP is superior to other ventilator variables for predicting the severity of lung injury in ARDS patients. METHODS: We retrieved patient data from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The retrieved data involved adults (≥ 18 years) diagnosed with ARDS and subjected to invasive mechanical ventilation for ≥ 48 h. We employed univariate and multivariate logistic regression analyses to investigate the correlation between EP and development of moderate-severe ARDS. Furthermore, we utilized restricted cubic spline models to assess whether there is a linear association between EP and incidence of moderate-severe ARDS. In addition, we employed a stratified linear regression model and likelihood ratio test in subgroups to identify potential modifications and interactions. RESULTS: Moderate-severe ARDS occurred in 73.4% (296/403) of the patients analyzed. EP and MP were significantly associated with moderate-severe ARDS (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.15-1.28, p < 0.001; and OR 1.15, 95%CI 1.11-1.20, p < 0.001; respectively), but EP showed a higher area-under-curve (95%CI 0.72-0.82, p < 0.001) than plateau pressure, driving pressure, and static lung compliance in predicting ARDS severity. The optimal cutoff value for EP was 14.6 J/min with a sensitivity of 75% and specificity of 66%. Quartile analysis revealed that the relationship between EP and ARDS severity remained robust and reliable in subgroup analysis. CONCLUSION: EP is a good ventilator variable associated with ARDS severity and can be used for grading ARDS severity. Close monitoring of EP is advised in patients undergoing mechanical ventilation. Additional experimental trials are needed to investigate whether adjusting ventilator variables according to EP can yield significant improvements in clinical outcomes.

Assessment of oral methotrexate intolerance in Japanese adult patients with rheumatoid arthritis.

AIM: The objective of this study was to assess the gastrointestinal side (GI) effects of oral methotrexate (MTX) in Japanese adult patients with rheumatoid arthritis (RA). METHODS: In this single-center retrospective study, 112 Japanese adult patients (over 18 years old) with RA were examined by Methotrexate Intolerance and Severity assessment in Adults (MISA) questionnaire. RESULTS: Forty-five (40.2%) of patients were MTX intolerant (MISA score ≥1). Twelve patients (11.2%) were moderate-to-severe MTX intolerant (MISA cross-product score ≥4). The most common GI side effects of MTX were gastric discomfort (26.8%), followed by loss of appetite or dysgeusia (14.3%), fatigue and lethargy (12.5%), and nausea (10.7%). CONCLUSIONS: Japanese adult patients with RA showed a high prevalence of MTX intolerance even in low-dose oral MTX. The MISA questionnaire was practical for finding patients with MTX intolerance.

Coarctation duration and severity predict risk of hypertension precursors in a preclinical model and hypertensive status among patients.

Background: Coarctation of the aorta (CoA) often leads to hypertension (HTN) post-treatment. Evidence is lacking for the current >20 mmHg peak-to-peak blood pressure gradient (BPGpp) guideline, which can cause aortic thickening, stiffening and dysfunction. This study sought to find the BPGpp severity and duration that avoid persistent dysfunction in a preclinical model, and test if predictors translate to HTN status in CoA patients. Methods: Rabbits (N=75; 5-12/group) were exposed to mild, intermediate or severe CoA (≤12, 13-19, ≥20 mmHg BPGpp) for ~1, 3 or 22 weeks using dissolvable and permanent sutures with thickening, stiffening, contraction and endothelial function evaluated via multivariate regression. Relevance to CoA patients (N=239; age=0.01-46 years; median 3.7 months) was tested by retrospective review of predictors (preoperative BPGpp, surgical age, etc.) vs follow-up HTN status. Results: CoA duration and severity were predictive of aortic remodeling and active dysfunction in rabbits, and HTN in CoA patients. Interaction between patient age and BPGpp at surgery contributed significantly to HTN, similar to rabbits, suggesting preclinical findings translate to patients. Machine learning decision tree analysis uncovered that pre-operative BPGpp and surgical age predict risk of HTN along with residual post-operative BPGpp. Conclusions: These findings suggest the current BPGpp threshold determined decades ago is likely too high to prevent adverse coarctation-induced aortic remodeling. The results and decision tree analysis provide a foundation for revising CoA treatment guidelines considering the interaction between CoA severity and duration to limit the risk of HTN.