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To solve the problem of real-time measurement error detection of intelligent electric meter, this paper proposes a cloud edge collaborative online detection framework based on CNN-GRU mixed depth model and entropy weight scoring mechanism. The system extracts the local variation characteristics by one-dimensional convolution, and uses two-way GRU to model the time-dependent error evolution. Meanwhile, the dynamic threshold score function is introduced to realize the self-adaptive judgment of error alarm. The special data sets R-Elec, I-Load and S-ErrGen covering residential, industrial and rural scenes are constructed, and pre-processed through standardization, sliding window segmentation and statistical cleaning. The experimental results show that the average identification accuracy of the system in the three types of data is 97.6% and the average response time is 0.62 seconds, which is significantly superior to the performance of traditional models (such as RF, SVM, LSTM) in false alarm control and adaptability. The ablation experiments validated the key role of multi-source feature fusion and entropy scoring mechanisms in performance improvement. The research shows that the fusion of lightweight depth model and cloud edge architecture can achieve efficient, low delay and extensible intelligent meter error online detection, and has the practical engineering deployment value.

In the Internet era, soccer teaching has gotten rid of the previous theoretical teaching mode and paid more attention to the use of video and multimedia for network teaching. In this paper, from the perspective of a fuzzy mathematical algorithm, the mathematical set algorithm is used to construct a fuzzy matrix through algorithm mapping, calculate the NMI value of the equivalent algorithm and the comprehensive rating weights of U1-U10 in the evaluation elements of soccer network teaching, and derive the fuzzy transformation value of the rating result of soccer network teaching platform as 83.7895. The probability distribution value is inferred to be 45.8% on average by creating the affiliation function and then calculating the search accuracy, platform recall, and teaching F1 metrics of the Zadeh mathematical operator. After calculating the weighted average value of B=87.6617 for the Zadeh mathematical operator through the metrics, an empirical analysis of the feasibility of invoking the fuzzy mathematical algorithm in the soccer teaching web platform was conducted. The results showed that the total number of students who wanted to continue using the platform was 36,530, accounting for 91.33% of the total number of students, indicating that the use of fuzzy mathematical algorithms to participate in the teaching of the online platform is significantly better than the traditional teaching model and is conducive to improving the effectiveness of student autonomy.

Background The SF36 Version 2 (SF36V2) is a revision of the SF36 Version 1, and is a widely used health status measure. It is important that guidelines for interpreting scores are available. Method A population sample of Australians (n = 3015) weighted to achieve representativeness was administered the SF36V2. Comparisons between published US weights and sample derived weights were made, and Australian population norms computed and presented. Major findings Significant differences were observed on 7/8 scales and on the mental health summary scale. Possible causes of these findings may include different sampling and data collection procedures, demographic characteristics, differences in data collection time (1998 vs. 2004), differences in health status or differences in cultural perception of the meaning of health. Australian population norms by age cohort, gender and health status are reported by T-score as recommended by the instrument developers. Additionally, the proportions of cases within T-score deciles are presented and show there are important data distribution issues. Principal conclusions The procedures reported here may be used by other researchers where local effects are suspected. The population norms presented may be of interest. There are statistical artefacts associated with T-scores that have implications for how SF36V2 data are analysed and interpreted.

Background Scoring DNA sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding strength between a specific transcription factor and the DNA, these scores are not directly comparable between different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varré J-S. Efficient and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007;2(1510.1186):1748–7188), provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities. Results Here, we provide two different ways to find the scaling parameter λ that allows us to infer binding energy from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimate λ for a specific transcription factor, which we applied to show that λ distributions for different transcription factor families correspond with their DNA binding properties. Our second method can reliably convert λ between different PWMs of the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches. Conclusion These two approaches provide computationally efficient ways to scale PWM scores and estimate the strength of transcription factor binding sites in quantitative studies of binding dynamics. Their results are consistent with each other and previous reports in most of cases.

Background Fournier’s gangrene (FG) is a life-threatening infection associated with high mortality and often necessitates urgent surgical intervention. Vacuum sealing drainage (VSD) has emerged as a promising adjunct to surgical debridement; however, its impact on antibiotic stewardship remains inadequately studied. This study compares the efficacy of VSD and conventional dressings in the management of FG. Methods In this single-center retrospective study, 104 patients with Fournier’s gangrene (FG) treated between January 2022 and December 2023 were analyzed. Patients were categorized into VSD ( n  = 42) and conventional dressing ( n  = 62) groups following surgical debridement. Data included demographics, comorbidities, infection sites, Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) scores, antibiotic duration, and clinical outcomes. Inverse probability of treatment weighting (IPTW) was applied to mitigate confounding biases. Results Before IPTW adjustment, the VSD group exhibited longer durations in key clinical outcomes compared to the control group, including time to leukocyte normalization (+ 1.6 days), antibiotic use (+ 2.0 days), and hospital stay (+ 2.0 days). After IPTW adjustment, all three indicators showed improvement in favor of the VSD group. Generalized weighted regression further confirmed that VSD was associated with significant clinical benefits: shortened time to leukocyte normalization (mean difference: -1.8 days; 90% CI: -3.5 to -0.1), reduced duration of antibiotic therapy (-2.0 days; 90% CI: -3.7 to -0.4), and decreased length of hospitalization (-3.0 days; 90% CI:-5.7 to -0.4).Post-IPTW, VSD emerged as a protective factor, with all standardized mean differences (SMDs) remaining below 0.2, indicating acceptable covariate balance. Conclusions VSD may improve clinical outcomes in patients with Fournier’s gangrene by accelerating recovery and reducing antibiotic use and length of hospitalization. These benefits appear more pronounced in patients with high LRINEC scores or extensive infections. Nevertheless, individualized treatment strategies should consider systemic disease severity, such as the Fournier’s Gangrene Severity Index (FGSI).

Background Up to 70% of horses in domestic equine populations are overweight. Promotion of weight loss is an important component of limiting the detrimental co-morbidities associated with excess adipose tissue. The use of a body condition score (BCS) can help equine caregivers track their horse’s weight-status. However, this single score doesn’t reflect changes in individual adipose depots. The purpose of this study was to characterize morphometric changes (including 8 anatomic BCS measurements that make up the whole-BCS) in horses during 3 different weight-loss programs over 10 weeks. Methods Thirty-two horses were randomly assigned to one of three weight loss groups [exercise (E; fed to 100% digestible energy (DE) requirements, exercise 5x/week), feed restriction (FR; fed to 85% DE requirements, no exercise), or feed restriction and exercise (FRE; 85% DE, exercise 5x/week)], or a weight-maintained control (C; 100% DE, no exercise)] for 10 weeks. Horses were fed individualized diets formulated to meet their nutritional requirements. Outcome measures were recorded at weeks 1, 5 and 10, and included 11 morphometric measurements [body weight (BW) via weight tape, cresty neck score (CNS), and 8 individual BCSs (neck, shoulder, leg, withers, ribs, back, haunches, and tailhead) and a whole-BCS)]. Data were analyzed using a 2-Way RM ANOVA with respect to treatment group and week. Results All 4 groups (including controls) had a significant reduction in BW and whole BCS. At week 10 whole BCS was lower in FRE than controls. All groups had a reduction in back, haunches, leg and shoulder; FR, FRE and E horses had reductions in rib BCS; E and FRE horses had reductions in tailhead BCS and CNS. Conclusion In conclusion, all weight loss strategies produced significant weight loss, but only those strategies which included exercise reduced fat deposits known to be implicated in metabolic dysregulation in obese horses. Future studies should explore the optimum exercise load (duration and intensity) to facilitate loss of fat from these areas.

Ravulizumab and eculizumab are approved terminal complement inhibitor treatments for atypical hemolytic uremic syndrome (aHUS). Ravulizumab was engineered from eculizumab to have an increased half-life allowing for reduced dosing frequency (8-weekly vs. 2-weekly). To account for differences in respective clinical trials, a validated balancing technique was used to enable an indirect comparison of ravulizumab and eculizumab treatment efficacy in aHUS. Patient-level data from four eculizumab clinical trials were available for pooling and comparison with data from two ravulizumab trials. In the primary analysis, adult native kidney data were compared. Propensity scores were calculated from baseline characteristics (dialysis status, estimated glomerular filtration rate, platelet count, serum lactate dehydrogenase). Stabilized inverse probability weighting was used to balance groups. Changes in outcomes from baseline to 26 weeks were compared between treatment groups. Sensitivity and subgroup analyses were conducted to assess the robustness of findings. Overall, 85 patients (46 ravulizumab, 39 eculizumab) were included in the primary analysis. Demographic and clinical characteristics were well balanced after weighting at baseline. At 26 weeks, clinical outcomes (including renal function, hematological markers, and dialysis prevalence), and fatigue and quality of life measures were improved with eculizumab and ravulizumab treatment. No differences between treatment groups reached statistical significance, although confidence intervals were wide. Sensitivity and subgroup analysis results were consistent with those of the primary analysis. Using appropriate methodology for indirect comparison of studies, no differences in outcomes were seen between ravulizumab and eculizumab, although, owing to small sample sizes, confidence intervals were wide.

ObjectivesRespiratory distress is a key contributor to both neonatal morbidity and mortality in low-resource settings.1 However, the identification and management of neonates with respiratory distress in such settings is challenging due to the reduced availability of resources and a smaller evidence base. Neotree, a digital decision-support and data collection tool, currently implemented in Malawi and Zimbabwe, uses an algorithmic approach to assist in diagnosing and treatment of respiratory distress.2 The aim of this study was two-fold:To identify risk factors, clinical features and severity markers associated with respiratory distress in low-resource settings based on current literature.To analyse the clinical profile and outcomes of neonates diagnosed with respiratory distress based on data collected using Neotree.MethodsA mixed methods approach was used. A scoping review was conducted in phase one to identify the clinical profile, risk factors and severity scoring systems listed in the context-specific literature. In phase two, statistical analysis of observational data collected using the Neotree application in KCH between June 2019 and May 2023.ResultsThe most frequently reported risk factors for respiratory distress in low-resource setting were prematurity and low birthweight. Clinical signs and symptoms include tachypnoea, chest recession, cyanosis, grunting and nasal flaring. A respiratory rate >60 and oxygen saturations <90% were also frequently reported. These findings were consistent with the quantitative analysis of data collected at KCH. Of the total admissions during the study period (n=10538), 38% were admitted with respiratory distress. The Neotree diagnostic algorithm, including many of the clinical features identified by this scoping review, identified 93.66% of these neonates. In line with the findings of the scoping review, respiratory distress was more common amongst babies born at lower birthweights and gestations, and these neonates had a higher mortality rate (25.41%) than neonates of similar gestation and birthweight without this condition (14.96%). Several scoring systems for respiratory distress severity were identified. However, varying sensitivities and specificities of these scoring systems may limit their use.ConclusionA diagnosis of neonatal respiratory distress in low-resource settings can be defined according to several clinical signs and symptoms which are well documented in the literature and supported by context-specific observational data collected at KCH. Sensitive diagnostic criteria, such as those contained within Neotree algorithm, can be used to identify neonates requiring management for respiratory distress. Tools for severity assessment may be useful in certain contexts, such as escalation and weaning of respiratory support.ReferencesSivanandan S, R Agarwal, A Sethi. Respiratory distress in term neonates in low-resource settings. Semin Fetal Neonatal Med; 2017;22(4):260–266.Heys M, et al. Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree. Learn Health Syst, 2023;7(1):e10310.

Acute radiation sickness (ARS) following exposure to ionizing irradiation is characterized by radiation-induced multiorgan dysfunction/failure that refers to progressive dysfunction of two or more organ systems, the etiological agent being radiation damage to cells and tissues over time. Radiation sensitivity data on humans and animals has made it possible to describe the signs associated with ARS. A mouse model of total-body irradiation (TBI) has previously been developed that represents the likely scenario of exposure in the human population. Herein, we present the Mouse Intervention Scoring System (MISS) developed at the Veterinary Sciences Department (VSD) of the Armed Forces Radiobiology Research Institute (AFRRI) to identify moribund mice and decrease the numbers of mice found dead, which is therefore a more humane refinement to death as the endpoint. Survival rates were compared to changes in body weights and temperatures in the mouse (CD2F1 male) TBI model (6-14 Gy, 60Co γ-rays at 0.6 Gy min-1), which informed improvements to the Scoring System. Individual tracking of animals via implanted microchips allowed for assessment of criteria based on individuals rather than by group averages. From a total of 132 mice (92 irradiated), 51 mice were euthanized versus only four mice that were found dead (7% of non-survivors). In this case, all four mice were found dead after overnight periods between observations. Weight loss alone was indicative of imminent succumbing to radiation injury, however mice did not always become moribund within 24 hours while having weight loss >30%. Only one survivor had a weight loss of greater than 30%. Temperature significantly dropped only 2-4 days before death/euthanasia in 10 and 14 Gy animals. The score system demonstrates a significant refinement as compared to using subjective assessment of morbidity or death as the endpoint for these survival studies.