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The average postoperative length of stay in barded suture group was shorter than the compared group (2.10 ± 0.57 vs. 2.33 ± 0.80 days, p = 0.004), and with a significantly higher proportion of patients discharging within 2 days post procedure (88.0% vs. 70.7%, p < 0.001). The operation time for patients with barded suture closure was shorter compared to interrupted closure technique (100.90 ± 16.59 vs. 105.52 ± 18.47 min, p = 0.004). Subgroup analysis of patients treated under different levels ERAS protocol showed comparable results.
The use of barded suture for capsular closure was associated with shorter length of stay after TKA compared to traditional suture, suggesting that barded suturing technique could be one effective intervention for ERAS.
The use of barded suture for capsular closure was associated with shorter length of stay after TKA compared to traditional suture, suggesting that barded suturing technique could be one effective intervention for ERAS.The human microbiome has been linked to several diseases. Gastrointestinal diseases are still one of the most prominent area of study in host-microbiome interactions however the underlying microbial mechanisms in these disorders are not fully established. Irritable bowel syndrome (IBS) remains as one of the prominent disorders with significant changes in the gut microbiome composition and without definitive treatment. IBS has a severe impact on socio-economic and patient's lifestyle. The association studies between the IBS and microbiome have shed a light on relevance of microbial composition, and hence microbiome-based trials were designed. However, there are no clear evidence of potential treatment for IBS. This review summarizes the epidemiology and socioeconomic impact of IBS and then focus on microbiome observational and clinical trials. At the end, we propose a new perspective on using data-driven approach and applying computational modelling and machine learning to design microbiome-aware personalized treatment for IBS.
We aimed to compare the intraoperative and early postoperative clinical outcomes of using an acromioclavicular joint hook plate (AJHP) versus a locking plate (LP) in the treatment of anterior sternoclavicular joint dislocation.
Seventeen patients with anterior sternoclavicular joint dislocation were retrospectively analyzed from May 2014 to September 2019. Six patients were surgically treated with an AJHP, and 11 were surgically treated with an LP. Five male and one female patients composed the AJHP group, and nine male and two female patients composed the LP group. The mean age of all patients was 49.5 years.
Reduction and fixation were performed with AJHP or LP in all 17 patients. The mean operative blood loss, operative time, and length of incision in the AJHP group were significantly better than those in the LP group. Shoulder girdle movement of the AJHP group was significantly better than that of the LP group.
This study revealed that AJHP facilitated glenohumeral joint motion, reduced the risk of rupture of mediastinal structures, required a shorter incision, and had lesser blood loss and a shorter duration of operation compared with LP. However, some deficiencies require further improvement.
This study revealed that AJHP facilitated glenohumeral joint motion, reduced the risk of rupture of mediastinal structures, required a shorter incision, and had lesser blood loss and a shorter duration of operation compared with LP. However, some deficiencies require further improvement.The response of the blood-brain barrier (BBB) following a stroke, including subarachnoid hemorrhage (SAH), has been studied extensively. The main components of this reaction are endothelial cells, pericytes, and astrocytes that affect microglia, neurons, and vascular smooth muscle cells. SAH induces alterations in individual BBB cells, leading to brain homeostasis disruption. Recent experiments have uncovered many pathophysiological cascades affecting the BBB following SAH. Targeting some of these pathways is important for restoring brain function following SAH. BBB injury occurs immediately after SAH and has long-lasting consequences, but most changes in the pathophysiological cascades occur in the first few days following SAH. These changes determine the development of early brain injury as well as delayed cerebral ischemia. SAH-induced neuroprotection also plays an important role and weakens the negative impact of SAH. Supporting some of these beneficial cascades while attenuating the major pathophysiological pathways might be decisive in inhibiting the negative impact of bleeding in the subarachnoid space. In this review, we attempt a comprehensive overview of the current knowledge on the molecular and cellular changes in the BBB following SAH and their possible modulation by various drugs and substances.
Due to increased travel from endemic countries, malaria occurs more frequently in non-endemic regions. It is a challenge for diagnostic laboratories in non-endemic countries to provide reliable results, as experience of staff is often limited to only a few cases per year. This study evaluated the diagnostic accuracy of the fully automated Sysmex XN-31 malaria analyzer in a routine diagnostic setting in a non-endemic region was evaluated.
Samples from 112 patients suspected for malaria were examined by the Sysmex XN-31 analyzer to determine the absolute count of malaria-infected red blood cells count (MI-RBC/µL). Microscopic examination of both Quantitative Buffy Coat capillary tubes and thick and thin blood films were used as reference methods. Limits of blank (LoB), detection (LoD) and quantification (LoQ) were investigated using an in vitro Plasmodium falciparum culture. Nine hundred twenty samples of patients with RBC abnormalities were included to determine which RBC abnormalities trigger indeterminat31 should be interpreted with caution as false positive results can be caused by interfering abnormal erythrocytes.
Based on the results, the XN-31 is a fast and reliable screening method in the detection and quantification of Plasmodium species in patients However, if an abnormal red blood cell morphology is present, the results of the XN-31 should be interpreted with caution as false positive results can be caused by interfering abnormal erythrocytes.
Optimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN), k-nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut (Juglans regia L.). The accuracy of developed models was evaluated using coefficient of determination (R
), root mean square error (RMSE) and mean absolute error (MAE). With the aim of opction of the modeling technique to study depends on the researcher's desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.
Here, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in optimizing plant tissue culture media composition studies. Therefore, selection of the modeling technique to study depends on the researcher's desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.
Global distributions and trends of the risk-attributable burdens of chronic obstructive pulmonary disease (COPD) have rarely been systematically explored. Lysipressin To guide the formulation of targeted and accurate strategies for the management of COPD, we analyzed COPD burdens attributable to known risk factors.
Using detailed COPD data from the Global Burden of Disease study 2019, we analyzed disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), and deaths attributable to each risk factor from 1990 to 2019. Additionally, we calculated estimated annual percentage changes (EAPCs) during the study period. The population attributable fraction (PAF) and summary exposure value (SEV) of each risk factor are also presented.
From 1990 to 2019, the age-standardized DALY and death rates of COPD attributable to smoking and household air pollution, occupational particles, secondhand smoke, and low temperature presented consistently declining trends in almost all socio-demogracall for an urgent need to implement specific and effective measures. Moreover, consideringthe genderdifferencesinCOPD burdens attributable to somerisk factorssuchas ambient particulatematterand ozone with similar SEV, furtherresearchon biological differences between sexes in COPDand relevant policy-makingofdisease preventionarerequired.
Increasing trends of COPD burden attributable to ambient particulate matter, ozone, and high temperature exposure in the low-middle- and low-SDI regions call for an urgent need to implement specific and effective measures. Moreover, considering the gender differences in COPD burdens attributable to some risk factors such as ambient particulate matter and ozone with similar SEV, further research on biological differences between sexes in COPD and relevant policy-making of disease prevention are required.
Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development.
We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple doms were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.
The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.
