Grading Method Research Articles

Comparison of interrupted and uninterrupted anticoagulation strategies for cardiac implantable electronic device insertion: a systematic review and meta-analysis

Abstract Introduction Up to 35% of patients who are candidates for a cardiac implantable electronic device (CIED) receive chronic anticoagulation. However, guidelines on anticoagulant management in the perioperative period of CIED insertion differ worldwide. It is uncertain whether interrupting or continuing anticoagulation in these settings provides optimal outcomes. Purpose To assess the effects of interrupting versus continuing chronic anticoagulation with either warfarin or a direct oral anticoagulant (DOAC) in the perioperative setting of CIED insertion. Methods CENTRAL, MEDLINE, Embase, Web of Science, and two trial registries were searched from inception to 26 November 2021. Randomised controlled trials (RCTs) assessing interrupting chronic warfarin or DOAC therapy, with or without heparin bridging, versus continuing anticoagulation in adults undergoing CIED insertion were included. Comparisons were divided into two categories: interrupted versus uninterrupted warfarin and interrupted versus uninterrupted DOAC. Outcomes were expressed as risk ratios (RR) with 95% confidence intervals (95%CI). Inverse variance random-effects models were used for the meta-analyses. Risk of bias (RoB) was assessed using the Cochrane RoB2 tool, and certainty of evidence (CoE) per outcome was assessed with GRADE methods. Results Ten RCTs (n=2221) were included: five RCTs (n=1267) used warfarin; four RCTs (n=954) used DOACs. One additional RCT using warfarin was ongoing without any outcome data available. Bleeding events included device-pocket haematoma, haemothorax, cardiac tamponade, and non-tamponade pericardial effusion. Interrupted versus uninterrupted warfarin: The evidence is very uncertain on the effect of interrupted warfarin on device-pocket haematoma (RR 1.89, 95%CI 0.85 to 4.20, 5 RCTs, very low CoE) and bleeding events (RR 1.94, 95%CI 0.86 to 4.41, 5 RCTs, very low CoE). Interrupted warfarin may result in little to no difference in thromboembolic events (RR 0.85, 95%CI 0.18 to 4.11, 5 RCTs, low CoE) and ischaemic stroke (RR 1.01, 95%CI 0.11 to 9.61, 2 RCTs, low CoE). Interrupted warfarin may reduce all-cause mortality (RR 0.35, 95%CI 0.04 to 2.93, 3 RCTs, low CoE). Interrupted versus uninterrupted DOAC: The evidence is very uncertain on the effect of interrupted DOAC on device-pocket haematoma (RR 1.15, 95%CI 0.58 to 2.28, 4 RCTs, very low CoE) and bleeding events (RR 1.14, 95%CI 0.58 to 2.21, 4 RCTs, very low CoE). Interrupted DOAC may result in little to no difference in thromboembolic events (RR 0.98, 95%CI 0.06 to 15.63, 3 RCT, low CoE) and ischaemic stroke (RR 0.98, 95%CI 0.06 to 15.63, 2 RCTs, low CoE). Interrupted DOAC may reduce all-cause mortality slightly (RR 0.49, 95% CI 0.04 to 5.39, 2 RCTs, low CoE). Conclusion Interrupting anticoagulation with either warfarin or DOACs in patients undergoing CIED insertion produced similar outcomes as uninterrupted anticoagulation.Figure 1.Figure 2.

Effects of Yi Jin Jing on enhancing muscle strength and physical performance in older individuals: a systematic review and meta-analysis

BackgroundThe aging population is rapidly increasing, leading to physical decline and higher risks of chronic diseases, including sarcopenia, which adversely affects muscle quality and strength. Yi Jin Jing (YJJ), a traditional Chinese exercise method, can enhance flexibility and strength, but evidence regarding its effectiveness in older adults is conflicting. This meta-analysis aims to systematically evaluate the effects of YJJ on muscle strength and physical performance in this demographic.MethodsWe searched seven electronic databases: China National Knowledge Infrastructure, Wanfang Data, Sinomed, Web of Science, PubMed, Cochrane Library, and EMBASE to identify randomized controlled trials (RCTs). Following PRISMA guidelines, we quantified the effects of YJJ on muscle strength (grip strength, isokinetic strength) and physical performance (chair sit-to-stand, squatting-to-standing, shoulder flexibility, sit-and-reach tests). Treatment effects were calculated using Hedges’g. The Cochrane tool assessed risk of bias, the PEDro scale evaluated methodological quality, and the GRADE method assessed evidence quality. Data analysis was conducted using Stata 17.0 software, utilizing standardized mean differences (SMD) and 95% confidence intervals (CIs).ResultsThis meta-analysis included 10 RCTs involving 590 participants. The overall risk of bias was assessed to be low. The methodological quality of these studies was generally moderate, and the quality of the main results varied from low to moderate. The findings revealed that YJJ had considerable effects on the chair sit-to-stand test (Hedges’g = 1.06), squatting-to-standing test (Hedges’g = 1.08), and small to moderate effects on handgrip strength (Hedges’g = 0.25), 60°/s extensor peak torque (Hedges’g = 0.47), 60°/s extensor average power (Hedges’g = 0.31), 60°/s extensor total work (Hedges’g = 0.29), 60°/s flexor peak torque (Hedges’g = 0.42), 60°/s flexor average power (Hedges’g = 0.37), and 180°/s extensor peak torque (Hedges’g = 0.29), and left shoulder flexibility (Hedges’g = 0.4). However, there were no significant improvement effects in 180°/s extensor average power (Hedges’g = 0.19), 180°/s extensor total work (Hedges’g = 0.11), 180°/s flexor peak torque (Hedges’g = 0.01), 180°/s flexor average power (Hedges’g = −0.08), right shoulder flexibility (Hedges’g = 0.09), and sit-and-reach test (Hedges’g = 0.15).ConclusionYJJ significantly enhances specific aspects of physical performance, particularly chair sit-to-stand and squatting-to-standing tests, while showing small and moderate improvements in handgrip strength and knee muscle strength. However, it had no significant effects on other metrics, including shoulder flexibility and sit-and-reach tests.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024530487, Registration number: CRD42024530487.

Evaluating the Effectiveness of Probiotic Supplementation in Combination With Doxycycline for the Treatment of Moderate Acne: A Randomized Double-Blind Controlled Clinical Trial.

Acne is a chronic inflammatory skin disease that negatively affects patients' quality of life. Increasing antibiotic resistance is making acne less responsive to treatment. Probiotics are live microorganisms that can provide health benefits by fighting pathogens and maintaining intestinal homeostasis and skin microbiome balance. This study investigates the effects of probiotics in the treatment of acne vulgaris. In this randomized controlled clinical trial, 80 patients with moderate acne were divided into two groups of 40. All patients received the same topical treatment, which consisted of a daily antibacterial face wash and Adapalene gel every other night. The control group received one capsule of doxycycline (100 mg) daily, whereas the intervention group received one probiotic capsule daily in addition to doxycycline. Patients underwent photography of facial acne lesions, and treatment response was assessed using the global acne grading system (GAGS) and acne grading method at baseline, as well as during follow-up visits at 1, 2, and 3 months. The global acne grading system indicated that both groups showed improvement. However, analyses revealed that outcomes were significantly better in the doxycycline plus probiotics group for the forehead (p = 0.018), chin (p = 0.021), and nose (p = 0.021). No significant differences were observed for the left and right cheeks, back, and chest areas, with the mean GAGS score reduction between the two groups differing by only 2%. Treatment with probiotics significantly reduced the severity of lesions compared to the control group (p < 0.001). The acne grading method also indicated that the intervention group had a significantly better treatment response than the control group (p < 0.001). Furthermore, treatment with probiotics did not result in any side effects. Probiotics can serve as an effective and safe treatment option, enhancing the outcomes of routine acne treatments, particularly for patients with acne on the forehead, chin, and nose.

Building a pelvic organ prolapse diagnostic model using vision transformer on multi-sequence MRI.

Although the uterus, bladder, and rectum are distinct organs, their muscular fasciae are often interconnected. Clinical experience suggests that they may share common risk factors and associations. When one organ experiences prolapse, it can potentially affect the neighboring organs. However, the current assessment of disease severity still relies on manual measurements, which can yield varying results depending on the physician, thereby leading to diagnostic inaccuracies. This study aims to develop a multilabel grading model based on deep learning to classify the degree of prolapse of three organs in the female pelvis using stress magnetic resonance imaging (MRI) and provide interpretable result analysis. We utilized sagittal MRI sequences taken at rest and during maximum Valsalva maneuver from 662 subjects. The training set included 464 subjects, the validation set included 98 subjects, and the test set included 100 subjects (training set n=464, validation set n=98, test set n=100). We designed a feature extraction module specifically for pelvic floor MRI using the vision transformer architecture and employed label masking training strategy and pre-training methods to enhance model convergence. The grading results were evaluated using Precision, Kappa, Recall, and Area Under the Curve (AUC). To validate the effectiveness of the model, the designed model was compared with classic grading methods. Finally, we provided interpretability charts illustrating the model's operational principles on the grading task. In terms of POP grading detection, the model achieved an average Precision, Kappa coefficient, Recall, and AUC of 0.86, 0.77, 0.76, and 0.86, respectively. Compared to existing studies, our model achieved the highest performance metrics. The average time taken to diagnose a patient was 0.38s. The proposed model achieved detection accuracy that is comparable to or even exceeds that of physicians, demonstrating the effectiveness of the vision transformer architecture and label masking training strategy for assisting in the grading of POP under static and maximum Valsalva conditions. This offers a promising option for computer-aided diagnosis and treatment planning of POP.

Can routine EPA-based assessments predict OSCE performances of undergraduate medical students?

Background Objective structured clinical examination (OSCE) is used worldwide. This study aims to explore potential alternatives to the OSCE by using entrustable professional activities (EPA)-based assessments in the workplace. Methods This study enrolled 265 six-year undergraduate medical students (UGY) from 2021 to 2023. During their rotations, students were assessed using 13 EPAs, with the grading methods modified to facilitate application. Before graduation, they participated in two mock OSCEs and a National OSCE. We used generalized estimating equations to analyze the associations between the EPA assessments and the OSCE scores, adjusting for age and sex, and developed a prediction model. EPA8 and EPA9, which represent advanced abilities that were not significant in the regression models, were removed from the prediction model. Results Most EPAs were significantly correlated with OSCE scores across the three cohorts. The prediction model for forecasting passing in the three OSCEs demonstrated fair predictive capacity (area under curve = 0.82, 0.66, and 0.71 for students graduated in 2021, 2022, and 2023, respectively all p < 0.05). Conclusions The workplace-based assessments (EPA) showed a high correlation with competency-based assessments in simulated settings (OSCE). EPAs may serve as alternative tools to formal OSCE for medical students.

Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method

Background and AimsInflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on computationally assisted diagnosis of IBD based on pathological images. Therefore, based on the UK and Chinese IBD diagnostic guidelines, our study established an artificial intelligence-assisted diagnostic system for histologic grading of inflammatory activity in ulcerative colitis (UC). MethodsWe proposed an efficient deep-learning (DL) method for grading inflammatory activity in whole-slide images (WSIs) of UC pathology. Our model was constructed using 603 UC WSIs from Nanjing Drum Tower Hospital for model train set and internal test set. We collected 212 UC WSIs from Zhujiang Hospital as an external test set. Initially, the pre-trained ResNet50 model on the ImageNet dataset was employed to extract image patch features from UC patients. Subsequently, a multi-instance learning (MIL) approach with embedded self-attention was utilized to aggregate tissue image patch features, representing the entire WSI. Finally, the model was trained based on the aggregated features and WSI annotations provided by senior gastrointestinal pathologists to predict the level of inflammatory activity in UC WSIs. ResultsIn the task of distinguishing the presence or absence of inflammatory activity, the Area Under Curve (AUC) value in the internal test set is 0.863 (95% confidence interval [CI] 0.829, 0.898), with a sensitivity of 0.913 (95% [CI] 0.866, 0.961), and specificity of 0.816 (95% [CI] 0.771, 0.861). The AUC in the external test set is 0.947 (95% confidence interval [CI] 0.939, 0.955), with a sensitivity of 0.889 (905% [CI] 0.837, 0.940), and specificity of 0.858 (95% [CI] 0.777, 0.939). For distinguishing different levels of inflammatory activity in UC, the average Macro-AUC in the internal test set and the external test set are 0.827 (95% [CI] 0.803, 0.850) and 0.908 (95% [CI] 0.882, 0.935). the average Micro-AUC in the internal test set and the external test set are 0.816 (95% [CI] 0.792, 0.840) and 0.898 (95% [CI] 0.869, 0.926). ConclusionsComparative analysis with diagnoses made by pathologists at different expertise levels revealed that the algorithm reached a proficiency comparable to the pathologist with 5 years of experience. Furthermore, our algorithm performed superior to other MIL algorithms.

Prediction and analysis of interior sound quality of high-speed trains based on XGBoost

This paper primarily investigates the objective quantification of subjective perceptions of sound quality in high-speed trains. The study measures and analyzes the sound quality in high-speed trains under high-speed operating conditions and calculates psychoacoustic parameters. A subjective evaluation of the interior sound quality is conducted using the grading scale method. An Extreme Gradient Boosting (XGBoost) algorithm is utilized to construct a predictive model for sound quality in high-speed trains and get high predictive accuracy for sound quality evaluation. Employing AI-based evaluation models to evaluate the sound quality in high-speed trains offers a more effective method for studying the interior sound quality of high-speed trains.

Sound quality evaluation in special vehicles based on multiple linear regression

The issue of interior sound quality in specialized vehicles has received widespread attention, which could directly affect the riding comfort of the vehicle. This research primarily focuses on the objective quantification of subjective perception of sound qualitySQ in special vehicles. The noise of special vehicles under different working conditions is measured and analyzed, and psychoacoustic parameters and A-weighted sound pressure levels are calculated. The subjective evaluation of interior sound quality was conducted using the grading scale method. A multiple linear regression model is used to establish the correspondence between subjective perceptions and objective parameters. It provides a reference basis for evaluating the sound quality of special vehicles.

Prosiding Seminar Nasional Pendidikan Agama Katolik dan Budi Pekerti Peningkatan Sikap Ketaqwaan dan Semangat Gotong Royong dengan Metode PBL Siswa Kelas IV SDN 05 Jelai Hulu Tahun Ajaran 2024/2025

This study aims to determine the Improvement of Attitude of Piety and the Spirit of Mutual Cooperation with the PBL Method of Grade IV Students of SDN 05 Jelai Hulu according to Phase B. This study uses a classroom action research method that is carried out in two cycles. Each cycle consists of four stages, namely planning, implementation, observation and reflection stages. The results of the study during the two cycles showed a significant increase where in the first cycle the average score obtained by students when applying the PBL model was 70% and in the second cycle by 85%. This shows that if the PBL learning method is applied properly and consistently, it will improve student learning outcomes.

New intergovernmentalism: European council and environmental policy

ABSTRACT The article tests the theory of the new intergovernmentalism on environmental policy of the EU with the main objective of applying the alternative approach to European integration beyond its traditional policy focus. The research question is whether the quantity and quality of the engagement of the European Council (EUCO) in environmental policy has increased in the post-Maastricht period examining the EUCO Conclusions from 1993 to 2023 through the qualitative theme content analysis combining deductive and inductive coding and the holistic grading method. The findings did not confirm that the EUCO is a pivotal actor in the further integration of environmental policy. It rather seems that the analyzed policy is driven altogether by supranational and intergovernmental actors. It thus indicates a relatively broad consensus among EU bodies for an ambitious environmental policy. The findings, therefore, challenge the core assumptions of the NI theory.

Guidelines for diagnosis,treatment and prevention of clostridium difficile infection in China(2024)

Clostridium difficile infection(CDI) is an intestinal infection caused by Clostridium difficile,characterized by clinical symptoms such as fever,abdominal pain,watery diarrhea,and loose stools. Mild cases present with diarrhea,while severe cases can progress to pseudomembranous colitis,often accompany by complications such as toxic megacolon,intestinal perforation,and septic shock. CDI is a significant cause of complications and mortality,especially among elderly patients,and is one of the most common hospital-acquired infections. The purpose of this guideline is to provide guidance on the diagnosis,treatment,and prevention of CDI,covering a total of 20 clinical questions across three domains: diagnosis,treatment,and prevention. The guideline was developed by a collaborative effort of a guideline expert group and a guideline working group. The guideline expert group consists of 37 clinical experts in 18 specialties related to the diagnosis and treatment of CDI and healthcare associated infection control. This guideline was developed using the internationally recognized GRADE method. It includes evidence evaluation and recommendation grading. It categorizes the quality of evidence into four levels: high,medium,low and very low. When formulating recommendations,it considers factors such as the balance of benefits and harms of intervention,quality of evidence,values and preferences,as well as cost and resource consumption. The recommendations are classified into strong and weak recommendations(conditional recommendations). The target audience of this guideline includes Chinese doctors and patients.

Automated grading of diabetic retinopathy and Radiomics analysis on ultra-wide optical coherence tomography angiography scans

Diabetic retinopathy (DR), a progressive condition due to diabetes that can lead to blindness, is typically characterized by a number of stages, including non-proliferative (mild, moderate and severe) and proliferative DR. These stages are marked by various vascular abnormalities, such as intraretinal microvascular abnormalities (IRMA), neovascularization (NV), and non-perfusion areas (NPA). Automated detection of these abnormalities and grading the severity of DR are crucial for computer-aided diagnosis. Ultra-wide optical coherence tomography angiography (UW-OCTA) images, a type of retinal imaging, are particularly well-suited for analyzing vascular abnormalities due to their prominence on these images. However, accurate detection of abnormalities and subsequent grading of DR is quite challenging due to noisy data, presence of artifacts, poor contrast and subtle nature of abnormalities. In this work, we aim to develop an automated method for accurate grading of DR severity on UW-OCTA images. Our method consists of various components such as UW-OCTA scan quality assessment, segmentation of vascular abnormalities and grading the scans for DR severity. Applied on publicly available data from Diabetic retinopathy analysis challenge (DRAC 2022), our method shows promising results with a Dice overlap metric and recall values of 0.88 for abnormality segmentation, and the coefficient-of-agreement (κ) value of 0.873 for DR grading. We also performed a radiomics analysis, and observed that the radiomics features are significantly different for increasing levels of DR severity. This suggests that radiomics could be used for multimodal grading and further analysis of DR, indicating its potential scope in this area.

Classifying fabric wrinkle via regularized random vector function link optimized by African vultures optimization algorithm

The wrinkling of fabrics not only affects their aesthetic appeal and comfort but also leads to increased wear and a reduced lifespan. Consequently, wrinkle resistance has become a critical metric in evaluating fabric performance. Traditional wrinkle grading methods rely heavily on expert assessments, which are susceptible to visual fatigue, resulting in subjective and inconsistent classification outcomes. To address this issue, this article proposes an automatic wrinkle grading model based on an improved African Vultures Optimization (AVO) algorithm, which optimizes a regularized random vector functional link (RRVFL) network. The model enhances convergence speed and classification accuracy by incorporating the Harris Hawks Optimization (HHO) algorithm to provide a superior initial population for the AVO algorithm. Additionally, the AVO algorithm’s local search capability is strengthened during the optimization process, improving the model’s stability and resistance to overfitting. Experimental results demonstrate that the proposed HHO-AVO-RRVFL model achieves an average classification accuracy of 97.86% across multiple datasets, significantly outperforming other comparative algorithms while also excelling in classification stability and convergence speed.

Abnormal phenotypic defects detection of jujube using explainable machine learning enhanced computer vision.

Jujube is susceptible to biotic and abiotic adversity stresses resulting in abnormal phenotypic defects. Therefore, abnormal phenotype fruits should be removed during postharvest sorting to increase added value. An improved maximum horizontal diameter linear regression (MHD-LR) method for size grading of jujube prior to detection of abnormal phenotypic defects was developed. The accuracy of the MHD-LR model is 95%, with an error of only 0.95mm. In addition, a method for detecting abnormal phenotypic defects in jujube was established. It can effectively and accurately classify seven kinds of jujube phenotypes (regular, irregular, wrinkled, moldy, hole-broken, skin-broken, and scarred). The data augmentation method based on linear interpolation can effectively expand the dataset with a variance of only 0.0006. Support vector machine-decision tree (SVMDT), logistic regression, back propagation neural network, and long short-term memory network models were established to classify jujube samples with different phenotypes, with accuracies of 99.57%, 99.00%, 99.14%, and 99.29%, respectively. The results showed that the SVMDT model had higher accuracy and explainability. This research is expected to provide a new method to improve the precise classification of abnormal phenotypic defects in postharvest jujube.

Study of adiponectin gene (rs1501299) polymorphism and serum adiponectin level in patients with primary knee osteoarthritis

BackgroundWe aimed to study, for the first time in the Egyptian population, the relationship between the serum adiponectin level in knee osteoarthritis (KOA) patients and its correlation with clinical, radiological, and ultrasonographic characteristics. Additionally, investigate the relationship between the adiponectin (ADIPOQ) gene rs1501299 (+ 276G/T) polymorphism and KOA susceptibility and severity.MethodsThis case-control study enrolled 40 patients with primary KOA and 40 matched controls. All patients underwent physical examination of the knee, pain assessment using the visual analogue scale (VAS), and functional evaluation by Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Severity of KOA was assessed by Kellgren Lawrence (KL) grading scale and ultrasonography grading systems. Serum adiponectin levels and adiponectin (ADIPOQ) gene single nucleotide polymorphism (SNP) (rs1501299) genotyping were done for all patients and controls.ResultsThe study included 40 patients with primary symptomatic KOA and 40 controls with comparable age, sex, and body mass index. The genotype of the rs1501299 (+ 276G/T) polymorphism of the ADIPOQ gene was determined using TaqMan allelic discrimination. An enzyme-linked immunosorbent test was used to measure the level of serum adiponectin. The Western Ontario and McMaster Universities Osteoarthritis (WOMAC) score was used to assess functional capability, while the visual analogue scale was utilised to assess knee pain. Using the Kellgren-Lawrence (KL) grading method and global femoral cartilage (GFC) ultrasound grading, the severity of KOA was assessed. No significant differences between patients and controls as regards the genotype distributions and allele frequencies (p = 0.400, p = 0.507, respectively) of ADIPOQ gene rs1501299 (+ 276G/T) polymorphism. Furthermore, serum adiponectin level was significantly higher in the patients compared to healthy subjects (p < 0.001). Additionally, adiponectin level had a significant negative correlation with disease severity as evaluated by KL and GFC grading (r=-0.351, p = 0.027 and r=-0.397, p = 0.011, respectively).ConclusionsThe ADIPOQ gene rs1501299 (+ 276G/T) polymorphism was not associated with KOA severity or vulnerability. The level of adiponectin considerably reduced as the severity of KOA rose, indicating that adiponectin may have a preventive effect in KOA.

Prediction of Mortality and Outcome of Various Trauma Scores in Polytrauma Patients.

Background In developing nations, the primary cause of death is trauma, and the prevalence of trauma is increasing as more vehicles are driven. Numerous trauma scoring systems have been created in order to predict the mortality rate and patients with trauma's prognosis. The purpose of the current study was to assess the prognostic ability of various trauma scoring systems for patients' mortality and morbidity in cases involving chest and abdominal injuries, as they are common in the surgery department. Methodology At Ranchi, Jharkhand's Rajendra Institute of Medical Sciences, a prospective observational study was conducted from June 2021 to September 2022. All patients who met the requirements for inclusion were older than 18 and reported chest and abdominal trauma totaling 204. Before any essential therapies, primary care and resuscitation, including airway maintenance, breathing, circulation, and hemorrhage control, were established. A comprehensive clinical evaluation was done based on each patient's needs. Radiological evaluation included chest X-ray and ultrasonography (USG) for chest trauma, whereas USG (FAST) and CT scans were for abdominal trauma. Trauma scores, such as the Revised Trauma Score (RTS), the Trauma Revised Injury Severity Score (TRISS), the New Injury Severity Score (NISS), and the Injury Severity Score (ISS), were computed and examined using IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp. Results Of the 204 patients, 14.7% were female and 85.3% were male. The age range of 21-30 years old accounted for the largest percentage of cases (28%). 50 percent of injuries were caused by automobile accidents, while 25% were the result of assaults. At 83.8% versus 16.2%, blunt injuries were substantially more common than penetrating ones. In comparison to the chest, the abdomen was more frequently involved. The study's findings demonstrated that, while every trauma scoring was statistically significant in predicting mortality, the New Injury Severity Score (NISS), as well as the Trauma Revised Injury Severity Score (TRISS), became the most effective in predicting mortality (p < 0.0001). Conclusion According to the results, the most precise trauma grading method for chest and abdominal injuries is the Trauma Revised Injury Severity Score (TRISS), even though all other trauma scoring systems are useful in predicting patient outcomes. Surgeons using these metrics to predict outcomes and make well-informed treatment decisions can benefit greatly.

Quantifying and Rating the Energy Resilience Performance of Buildings Integrated with Renewables in the Nordics under Typical and Extreme Climatic Conditions

The future buildings and society need to be resilient. This article aims to propose a novel concept of the energy resilience framework and implement a color-based rating system to quantify and rate the energy resilience performance of buildings in Nordic climates. The objective is to conduct a comparative analysis between old (1970s) and new (2020s) single-family buildings integrated with renewable energy sources and storage, assessing their energy resilience performance for heating during power outages, under extreme and typical climatic conditions. The study utilizes dynamic simulation of the buildings and renewable energy systems, conducting parametric studies to calculate proposed resilience indicators and rate their resilience performance, employing both passive and active methods. The total costs of the design variables are also calculated for economic evaluation. Given the complexities arising from climate change, the article uses a simplified method to synthesize regional climate to consider extreme climate change impacts on energy resilience performance. For the old building lacking PV, the robustness duration increased from 1 h to 3 h, and the degree of disruption (DoD) varied from 0.545 to 0.3 in extreme cold to warm climate scenarios, with the higher DoD number indicating worse performance. The impact of the season within the same climate scenario is also evident, as the habitability and robustness durations increased during spring compared to winter. The resilience improved with PV and battery. The new building showed that the robustness duration increased from 3 to 15 h, habitability durations increased, and the DoD varied from 0.496 to 0.22 from extreme cold to warm climates without renewables and storage. With the integration of PV and battery, the new building was able to achieve a lower DoD and better performance with lower PV and battery capacity, compared to the old building. Furthermore, utilizing the color grading method (red to green), optimal technical solutions and corresponding design variables were identified for each building type and climate scenario that could support decision-making. The total cost of the optimal solutions varied, as new buildings required lower costs to reach optimal performance. However, for optimal resilience performance during extreme cold climate scenarios, higher costs are required for each building type. The proposed resilience framework, indicators, color grading system, and costing could potentially support improvements in building regulations, ensuring the development of optimally resilient buildings, particularly in the face of extreme climatic conditions.

SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.

As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive treatment methods, there is still a high mortality rate. Arteriosclerosis, as an important factor reflecting the severity of cardiovascular and cerebrovascular diseases, is imperative to detect the arteriosclerotic retinopathy. However, the detection of arteriosclerosis retinopathy requires expensive and time-consuming manual evaluation, while end-to-end deep learning detection methods also need interpretable design to high light task-related features. Considering the importance of automatic arteriosclerotic retinopathy grading, we propose a segmentation and classification interaction network (SCINet). We propose a segmentation and classification interaction architecture for grading arteriosclerotic retinopathy. After IterNet is used to segment retinal vessel from original fundus images, the backbone feature extractor roughly extracts features from the segmented and original fundus arteriosclerosis images and further enhances them through the vessel aware module. The last classifier module generates fundus arteriosclerosis grading results. Specifically, the vessel aware module is designed to highlight the important areal vessel features segmented from original images by attention mechanism, thereby achieving information interaction. The attention mechanism selectively learns the vessel features of segmentation region information under the proposed interactive architecture, which leads to reweighting the extracted features and enhances significant feature information. Extensive experiments have confirmed the effect of our model. SCINet has the best performance on the task of arteriosclerotic retinopathy grading. Additionally, the CNN method is scalable to similar tasks by incorporating segmented images as auxiliary information.

Comparative analysis of vestibular endolymphatic hydrops grading methods and hearing loss in Ménière's disease: a retrospective MRI study using 3D-real inversion recovery sequence.

To compare the correlation between different grading methods of vestibular endolymphatic hydrops (EH) and the severity of hearing loss in Ménière's disease (MD), and evaluate the diagnostic value of these methods in diagnosing MD. This retrospective study included 30 patients diagnosed with MD from June 2021 to August 2023. All patients underwent inner ear MR gadolinium-enhanced imaging using three-dimensional (3D)-real inversion recovery sequences and pure-tone audiometry. The EH levels were independently evaluated according to the classification methods outlined by Nakashima et al. (Acta Otolaryngol Suppl 5-8, 2009. https://doi.org/10.1080/00016480902729827 ) (M1), Fang et al. (J Laryngol Otol 126:454-459, 2012. https://doi.org/10.1017/S0022215112000060 ) (M2), Barath et al. (Am J Neuroradiol 35:1387-1392, 2014. https://doi.org/10.3174/ajnr.A3856 ), (M3), Liu et al. (Front Surg 9:874971, 2022. https://doi.org/10.3389/fsurg.2022.874971 ), (M4), and Bernaerts et al. (Neuroradiology 61:421-429, 2019. https://doi.org/10.1007/s00234-019-02155-7 ) (M5), with a subsequent comparison of interobserver agreement. After achieving a consensus, an analysis was performed to explore the correlations between vestibular EH grading using different methods, the average hearing thresholds at low-mid, high-, and full frequencies and clinical stages. The diagnostic capabilities of these methods for MD were then compared. The interobserver consistency of M2-M5 was superior to that of M1. The EH grading based on M4 showed a significant correlation with the average hearing thresholds at low-mid, high-, and full frequencies and clinical stages. M1, M2, M3, and M5 correlated with some parameters. A receiver operating characteristic curve analysis indicated that M5 significantly outperformed M1, M2, M3, and M4 in terms of diagnostic efficiency for MD. M4 showed the strongest correlation with the degree of hearing loss in patients with MD, whereas M5 showed the highest diagnostic performance.

Telomere-related prognostic signature for survival assessments in lung adenocarcinoma.

Telomere-related genes (TRGs) are important in many different types of cancers. However, there is a lack of research on the relationship between their expression and prognosis in lung adenocarcinoma (LUAD) patients. This study is to investigate the prognostic value of TRGs in LUAD and to develop a TRG signature that can predict patient survival. A total of 2,086 TRGs were obtained from a database of genes involved in telomere maintenance (TelNet), while the clinical information and tumor RNA expression profiles of 513 LUAD patients were acquired from The Cancer Genome Atlas (TCGA) database. Statistical methodologies, such as least absolute shrinkage and selection operator (LASSO)-Cox, were employed to construct a prognostic model with predictive capabilities. We analyzed 1,339 telomere-associated differentially expressed genes and identified a ten-gene predictive signature for LUAD. This signature exhibited effective prognostic classification capabilities across multiple datasets, including GSE3141 (58 samples), GSE8894 (63 samples), GSE50081 (127 samples), and GSE72094 (398 samples). Furthermore, we screened tumor-sensitive drugs targeting this signature. High telomere levels were associated with reduced survival in lung cancer patients who underwent surgery. Compared to the traditional TNM (tumor node metastasis classification) grading method, our telomere-associated gene panel demonstrated superior prediction accuracy. Notably, patients in the high-risk group, defined by the telomere-associated signature, exhibited improved responses to immunotherapy, suggesting potential benefits for this subgroup of patients. This study presents a comprehensive molecular signature comprising TRGs, which holds potential for functional and therapeutic investigations. Additionally, it serves as an integrated tool to identify crucial molecules for immunotherapy in lung cancer.