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Background Fibrinogen (FIB) is often elevated in children with Henoch Schonlein purpuric nephritis (HSPN), but the correlation between FIB and renal lesions has been less studied. Objective To explore the correlation between FIB in children with HSPN and the International Study Group on Pediatric Kidney Disease (ISKDC) pathology grading and micropathological changes in parts of renal units, and to clarify whether FIB can assess the severity of renal injury in children with HSPN. Methods In total, 922 children with HSPN who were hospitalized in the First Affiliated Hospital of Henan University of Chinese Medicine in the pediatric nephrology ward and underwent kidney biopsy at the same time from December 2017 to December 2022 were collected, and the clinical information, FIB and renal pathological information during renal biopsy were summarized, and based on the FIB level, the children were categorized into group A (low) 4.98 g/L. The correlation between FIB and ISKDC pathological grades, glomerular mesangial hyperplasia ratio, the crescentic bodies ratio and the nature of the glomerular lesions from acute to chronic was investigated by Spearman rank correlation analysis, and the prediction of FIB on the micropathological changes of renal units was analyzed by the subject's work characteristic (ROC) curve. Results Among 922 children with HSPN who had undergone renal biopsy, the FIB was (3.48±1.01) g/L. 113 cases in group A had a low FIB rate of 12.26%; 734 cases in group B had a standardized FIB rate of 79.61%; and 75 cases in group C had a high FIB rate of 8.13%. The ISKDC pathology classification was type Ⅱa in 173 cases (18.76%), type Ⅱb in 29 cases (3.15%), 466 cases (50.54%) of type Ⅲa, 232 cases (25.16%) of type Ⅲb, and 22 cases (2.39%) of type Ⅳ and above (including 2 cases of type Ⅳa, 18 cases of type Ⅳb, and 2 cases of type Ⅴ). The results of the Spearman's rank correlation analysis showed that the FIB and the grouping of the FIB of the children with HSPN were positively related to the renal pathology ISKDC grading (rs=0.146, P

In the context of rural revitalization and carbon peaking and carbon neutrality goals， a model of township micro-energy network is established. Firstly， the mathematical models of garbage power generation and biogas production from anaerobic digestion of sludge are built in view of the characteristics of energy in rural areas. An electric vehicle load prediction model is established considering the development of rural transportation industry and tourism. Secondly， the carbon emissions from farming， breeding， and purchased energy are modeled， and the carbon reduction from domestic waste treatment， biogas export， and electric vehicle are also considered to illustrate the carbon emission characteristics from production and living in rural areas. Finally， with the lowest energy cost and lowest carbon emission of the township micro-energy network as the objective functions， the multi-objective optimization problem is solved using the normal boundary intersection （NBI） and the TOPSIS. The optimal energy consumption decision of the township under the premise of economy and low carbon is obtained through specific cases. The role of energy equipment with rural characteristics is analyzed to provide a theoretical basis for promoting the construction of zero-carbon villages.

Background: The purpose of this study was to externally validate a predictive score for fracture-related infections, establishing generalizability for absolute and relative risk of infection in the setting of orthopedic fracture surgery. Materials and Methods: This was a retrospective, case-control study performed at a level I academic trauma center that included 147 patients with fracture-related infection in the study group and 300 control patients. We analyzed the same 8 independent predictors of fracture-related infection cited by a previous study. We then used the area under the receiver operating characteristics curve (AUC) to compare the derivation and validation cohorts. The validation and derivation cohorts were then compared by grouping patients into 4 strata of Wise score groups. This allowed for comparison of AUC and risk of fracture-related infection in our institution with those in the previously studied institution. Results: The resulting data yielded an AUC (0.74) nearly identical to that of the previously studied institution. It was also found that the relative risk of infection correlated with the Wise score in the same way the initial model did with the absolute risks being similar. Conclusion: The previous predictive model was externally validated and shown to be generalizable to a different patient population. The relative risk of a fracture-related infection can be determined using this scoring model preoperatively with the goal of aiding in patient counseling and surgical decision-making, giving a quantitative value to patient risk factors. [Orthopedics. 2024;47(5):e268–e272.] [ABSTRACT FROM AUTHOR]

Cyanobacterial NdhM, an oxygenic photosynthesis‐specific NDH‐1 subunit, has been found to be essential for the formation of a large complex of NDH‐1 (NDH‐1L). The cryo‐electron microscopic (cryo‐EM) structure of NdhM from Thermosynechococcus elongatus showed that the N‐terminus of NdhM contains three β‐sheets, while two α‐helixes are present in the middle and C‐terminal part of NdhM. Here, we obtained a mutant of the unicellular cyanobacterium Synechocystis 6803 expressing a C‐terminal truncated NdhM subunit designated NdhMΔC. Accumulation and activity of NDH‐1 were not affected in NdhMΔC under normal growth conditions. However, the NDH‐1 complex with truncated NdhM is unstable under stress. Immunoblot analyses showed that the assembly process of the cyanobacterial NDH‐1L hydrophilic arm was not affected in the NdhMΔC mutant even under high temperature. Thus, our results indicate that NdhM can bind to the NDH‐1 complex without its C‐terminal α‐helix, but the interaction is weakened. NDH‐1L with truncated NdhM is more prone to dissociation, and this is particularly evident under stress conditions. [ABSTRACT FROM AUTHOR]

Objective: This manuscript describes strategies for assessment of precision of several diagnostic artificial intelligence (AI) tools in orthodontics, available open-source image analysis platforms, as well as the use of three-dimensional (3D) surface models and superimpositions.Results: The advances described in this manuscript present perspectives on the controversies of whether AI is smarter than clinicians and may replace human clinical decisions. A thorough orthodontic diagnosis requires comprehensive 3D analysis of the interrelationships among the dentition, craniofacial skeleton and soft tissues. Forecasts have indicated that 3D printing technology will provide more than 60% of all dental treatment needs by 2025, and orthodontic companies as well as remote monitoring companies are already using AI technology, being it essential that the clinicians are prepared and knowledgeable with the technology advances now available.Conclusions: The AI applications in orthodontics rely on the implementation into diagnostic image records, data analysis for clinical practice and research applications. Continuous training and validation of the AI orthodontic image tools are essential for improving the performance and generalizability of these methods. [ABSTRACT FROM AUTHOR]