SYNTAXIN OF PLANTS81 regulates root meristem activity and stem cell niche maintenance via ROS signaling.

Root growth and development depend on continuous cell division and differentiation in root tips. In these processes, reactive oxygen species (ROS) play a critical role as signaling molecules. However, few ROS signaling regulators have been identified. In this study, we found knockdown of a syntaxin gene, SYNTAXIN OF PLANTS81 in Arabidopsis thaliana (AtSYP81) resulted in a severe reduction in root meristem activity and disruption of root stem cell niche (SCN) identity. Subsequently, we found AtSYP81 was highly expressed in roots and localized on the endoplasmic reticulum (ER). Interestingly, the reduced expression of AtSYP81 conferred a decreased number of peroxisomes in root meristem cells, raising a possibility that AtSYP81 regulates root development through peroxisome-mediated ROS production. Further transcriptome analysis revealed that class III peroxidases, which are responsible for intracellular ROS homeostasis, showed significantly changed expression in the atsyp81 mutants and AtSYP81 overexpression lines, adding evidence of the regulatory role of AtSYP81 in ROS signaling. Accordingly, rescuing the decreased ROS level via applying ROS donors effectively restored the defects in root meristem activity and SCN identity in the atsyp81 mutants. APETALA2 (AP2) transcription factors PLETHORA1 and 2 (PLT1 and PLT2) were then established as the downstream effectors in this pathway, while potential crosstalk between ROS signaling and auxin signaling was also indicated. Taken together, our findings suggest that AtSYP81 regulates root meristem activity and maintains root SCN identity by controlling peroxisome- and peroxidase-mediated ROS homeostasis, thus both broadening and deepening our understanding of the biological roles of SNARE proteins and ROS signaling.

[Research hotspots and frontiers of Realgar based on CiteSpace knowledge graph analysis].

This study employed knowledge graph technology to analyze the research status and hot spots of Realgar and provide guidance for clinical application and further research of this drug. The research articles both in English and Chinese involving Realgar were retrieved from five databases including CNKI, Wanfang, VIP, SinoMed, and Web of Science. And NoteExpress, a literature management software was used to screen literature. CiteSpace was utilized for visualized analysis and presentations of the authors, institutions, and keywords. 2 879 articles in Chinese and 194 articles in English were included. China Journal of Chinese Materia Medica and Journal of Ethnopharmacology were the top Chinese and English journals in terms of publication volume. Realgar is widely used in the treatment of skin diseases, blood diseases, and cancer. JIANG Hong was the author who have published more articles in Chinese and English working with teams. School of Public Health of China Medical University and China Academy of Chinese Medical Sciences published the most articles in Chinese and English. The research on Realgar mainly focuses on clinical application, mechanism of action, reduction of toxicity, and enhancement of efficacy. The authors and institutions of Realgar research are mainly concentrated in China. The study on the mechanism of treating hematological diseases and cancer with Realgar, as well as the research on its effects of reducing toxicity and enhancing efficacy, are the current research hotspots. The mechanism of "same treatment for different diseases" in Realgar needs to be further explored. It is urgent to carry out interdisciplinary research on Realgar. This study can provide a refe-rence for the clinical application of Realgar and provide ideas for further research on Realgar.

Changes in colostrum ingredients of Hu sheep, as well as the missense mutation genes associated with colostrum yield.

This study focused on the changes in the composition and immune evolution in milk from birth to 144 h postpartum and the genes associated with the colostrum yield of Hu sheep. Twelve Hu sheep, which were bred carefully under animal health standards and have a litter size of two kids and similar gestation length (149 ± 1 days), were used. Lambs were transferred into their own cots to avoid interference. The compositional content (i.e., fat, protein, and lactose) and some other properties, including daily colostrum yield, DM, and SNF, were determined. In addition, immunity molecules (IgG, IgA, and IgM concentrations) received remarkable attention. The DM, SNF, fat, and protein contents were higher in the first days postpartum and then dropped quickly from the time of birth to 144 h postpartum. However, the lactose content displayed an increasing pattern and reached normal milk percentage at 48 h. The highest IgG (103.17 mg/mL), IgA (352.82 µg/mL), and IgM (2.79 mg/mL) colostrum concentrations were observed at partum, decreased quickly, and finally stabilized. The change law of concentration of IgA and IgM in colostrum are the same with IgG. Furthermore, the whole-genome resequencing was performed, and a missense variant locus in the SRC gene and two missense locus variants in the HIF1A gene were significantly associated with the colostrum yield of sheep by using the whole-genome selection signal detection analysis. In conclusions, colostrum contains abundant nutrients especially immunoglobulin, and the HIF1A gene may be used as candidate genes for colostrum yield, which has important information as a basic knowledge for the Hu sheep breeding program.

New method for scaling nonpoint source pollution by integrating the SWAT model and IHA-based indicators.

Nonpoint source (NPS) pollution shows spatial scaling effects because it is affected by topography, river networks, and many other factors. Currently, the lack of an integrated methodology for quantifying the scaling effect has become a crucial barrier in evaluating NPS pollution. In this study, a new method was proposed for scaling NPS pollution by integrating hydrological model and hydrological alteration indicators. Nested catchments were delineated by eight-direction algorithm, and a semidistributed hydrological model was used to simulate the interannual process within the drainage area and to obtain data series of runoff, sediment, and total phosphorus (TP) at different spatial scales. In addition, the average, the extrema, the change rate and feature variables of each type of indicators were proposed to quantitatively describe the pattern of NPS pollution at different spatial scales. The results show the coefficients of variation (CVs) of most runoff and TP indicators are 0.6-0.8, while those of sediment vary greatly from 0.4 to 1.6 with the threshold of those indicators being 0.33. With the increase in drainage area, the NPS load-related indicators show an increasing trend, while load intensity indicators show a decreasing trend and their changing patterns are affected by the heterogeneity of topographic or hydrological information included. Based on logarithmic variance of the change rate, 825 km2 was identified as the turning point for scaling transformation where the slope changes dramatically. The proposed methodology comprehensively describes features of the NPS scaling effect that could be utilized for targeted monitoring and control of NPS pollution in other watersheds.

Serratia marcescens PLR enhances lateral root formation through supplying PLR-derived auxin and enhancing auxin biosynthesis in Arabidopsis.

Plant growth promoting rhizobacteria (PGPR) refer to bacteria that colonize the rhizosphere and contribute to plant growth or stress tolerance. To further understand the molecular mechanism by which PGPR exhibit symbiosis with plants, we performed a high-throughput single colony screening from the rhizosphere, and uncovered a bacterium (named promoting lateral root, PLR) that significantly promotes Arabidopsis lateral root formation. By 16S rDNA sequencing, PLR was identified as a novel sub-species of Serratia marcescens. RNA-seq analysis of Arabidopsis integrated with phenotypic verification of auxin signalling mutants demonstrated that the promoting effect of PLR on lateral root formation is dependent on auxin signalling. Furthermore, PLR enhanced tryptophan-dependent indole-3-acetic acid (IAA) synthesis by inducing multiple auxin biosynthesis genes in Arabidopsis. Genome-wide sequencing of PLR integrated with the identification of IAA and its precursors in PLR exudates showed that tryptophan treatment significantly enhanced the ability of PLR to produce IAA and its precursors. Interestingly, PLR induced the expression of multiple nutrient (N, P, K, S) transporter genes in Arabidopsis in an auxin-independent manner. This study provides evidence of how PLR enhances plant growth through fine-tuning auxin biosynthesis and signalling in Arabidopsis, implying a potential application of PLR in crop yield improvement through accelerating root development.

Multi-Strategy Learning Boosted Colony Predation Algorithm for Photovoltaic Model Parameter Identification.

Modeling solar systems necessitates the effective identification of unknown and variable photovoltaic parameters. To efficiently convert solar energy into electricity, these parameters must be precise. The research introduces the multi-strategy learning boosted colony predation algorithm (MLCPA) for optimizing photovoltaic parameters and boosting the efficiency of solar power conversion. In MLCPA, opposition-based learning can be used to investigate each individual's opposing position, thereby accelerating convergence and preserving population diversity. Level-based learning categorizes individuals according to their fitness levels and treats them differently, allowing for a more optimal balance between variation and intensity during optimization. On a variety of benchmark functions, the MLCPA's performance is compared to the performance of the best algorithms currently in use. On a variety of benchmark functions, the MLCPA's performance is compared to that of existing methods. MLCPA is then used to estimate the parameters of the single, double, and photovoltaic modules. Last but not least, the stability of the proposed MLCPA algorithm is evaluated on the datasheets of many manufacturers at varying temperatures and irradiance levels. Statistics have demonstrated that the MLCPA is more precise and dependable in predicting photovoltaic mode critical parameters, making it a viable tool for solar system parameter identification issues.

Kukoamine A inhibits C-C motif chemokine receptor 5 to attenuate lipopolysaccharide-induced lung injury.

The aim of this study was to elucidate the mechanism underlying the effects of Kukoamine A (KuA) treatment on endotoxin-induced lung injury/inflammation. The study was performed in lipopolysaccharide (LPS)-exposed mouse models of lung injury and LPS-induced alveolar epithelial cell model. Relevant kits were used to detect levels of inflammation-related indicators, oxidative stress indicators, and mitochondrial function. Hematoxylin and eosin staining was to detect lung injury. Then, C-C motif chemokine receptor 5 (CCR5) overexpression plasmid was transfected into alveolar epithelial cells to investigate the mechanism of KuA in lung injury. The results showed that LPS induction increased the expression of inflammatory factors, oxidative stress markers, and mitochondrial dysfunction in both animal and cellular models. In the mouse model, KuA treatment improved lung tissue injury, decreased wet-to-dry ratio and MPO levels, reduced the expression of inflammatory factors, and ameliorated oxidative stress and mitochondrial dysfunction. The protective effect of KuA in the cell model remained whereas was markedly reversed after CCR5 overexpression. Taken together, KuA might improve LPS-induced lung injury by inhibiting CCR5. This might also provide a novel theory for KuA in the treatment of lung injury.

Prognostic value of ASXL1 mutations in patients with primary myelofibrosis and its relationship with clinical features: a meta-analysis.

Additional sex combs like 1 (ASXL1) mutations are one of the most common molecular biological abnormalities in patients with primary myelofibrosis (PMF), and the effect of these mutations on prognosis remains controversial. Hence, we conducted a meta-analysis to assess the prognostic value and clinical characteristics of ASXL1 mutations in PMF patients. Eligible studies were systematically searched from PubMed, Embase, and the Cochrane Library. We extracted the hazard ratios (HRs) and their 95% confidence intervals (CIs) of overall survival (OS) and leukemia-free survival (LFS), the number of patients transformed to acute leukemia, and clinical characteristics to carry out a meta-analysis by fixed effect model or random effect model according to the heterogeneity between studies. A total of 4501 PMF patients from 16 cohorts of 14 studies were included in this meta-analysis. The results revealed that ASXL1 mutations might predict a shorter OS (HR = 2.30, 95% CI: 1.79-2.94, P < 0.00001) and a higher probability of transformation to acute leukemia (LFS: HR = 1.77, 95% CI: 1.30-2.42, P = 0.0003; the rate of acute leukemia transformation: OR = 2.06, 95% CI: 1.50-2.83, P < 0.00001). Furthermore, ASXL1 mutations were correlated with patients older than 65 years old, male, a lower level of platelet counts, and a higher risk of the international prognostic score system. These findings indicate that ASXL1 mutations have a significant adverse impact on the prognosis of PMF patients and may contribute to risk stratification and prognostic assessment for PMF patients.

Comparison of the effects between MPL and JAK2V617F on thrombosis and peripheral blood cell counts in patients with essential thrombocythemia: a meta-analysis.

To assess the effects between MPL and JAK2V617F on the thrombosis risk and peripheral blood cell counts in patients with essential thrombocythemia (ET), we identified eligible studies from PubMed, Embase, and the Cochrane Library. Seven studies were ultimately included in this meta-analysis. All studies reported the peripheral blood cell counts of ET patients, and three of them reported the eligible thrombotic events. In comparing the effect of MPL versus JAK2V617F on thrombosis, 1257 ET patients (73 MPL + and 1184 JAK2V617F +) were included. MPL-positive (MPL +) ET patients had a higher risk of thrombosis than JAK2V617F-positive (JAK2V617F +) ET patients [RR = 1.80 (1.08-3.01), P = 0.025]. And 3453 ET patients (138 MPL + and 3315 JAK2V617F +) were included in the comparison of peripheral blood cell counts. Platelet counts of MPL + ET patients were higher than that of JAK2V617F + ET patients [WMD = 81.18 (31.77-130.60), P = 0.001]. MPL + ET patients had lower hemoglobin [WMD = - 11.66 (- 14.32 to - 9.00), P = 0.000] and white blood cell counts [WMD = - 1.01 (- 1.47 to - 0.56), P = 0.000] than JAK2V617F + ET patients. These findings indicate that the MPL mutation is a high-risk factor for thrombosis in ET patients, and it may be rational to include MPL mutation in the revised IPSET as a criterion for thrombosis prediction scores. And given the differences in peripheral blood, it is necessary to further study whether MPL + ET patients differ from JAK2V617F + ET patients in bleeding and survival.

Source apportionment of water pollutants in the upstream of Yangtze River using APCS-MLR.

As the upper reach of the Yangtze River, the Jinsha River has experienced ecological degradation due to increased anthropogenic activities. The potential pollution sources affecting the Jinsha River watershed from 2016 to 2018 were investigated using an improved method in combination with correlation analysis and the absolute principal component score-multiple linear regression receptor modeling technique. Our results identified 5-7 potential pollution sources in the Jinsha main stream watershed and the Pudu, Niulan, and Yalong River watersheds of the Jinsha River. The water pollutant concentrations of the Jinsha main stream watershed were mainly influenced by environmental, agricultural, and human population factors. In the Pudu River watershed, the primary pollution sources changed to natural and sedimentary pollutant sources. It is necessary to control the sedimentary pollutants. The Niulan River watershed was also influenced by natural environment factors. Among those, mineral, sedimentary pollutant, and meteorological sources contributed the most to water quality. In the case of the Yalong River watershed, the influence of non-point source pollution caused by human activities and sedimentary pollutants was the main reason for the deterioration of the ecological environment. The multivariate statistical techniques presented good adaptability for the analysis of pollution sources in the Jinsha River watershed, and the results may be useful for the protection and management of the watershed eco-environment.

Mesoporous Graphitic Carbon-Encapsulated Fe2 O3 Nanocomposite as High-Rate Anode Material for Sodium-Ion Batteries.

A mesoporous graphitic carbon-encapsulated Fe2 O3 nanocomposite is synthesized as a superior anode material for sodium-ion batteries. A threefold strategy is adopted to achieve a high rate performance. First, the mesoporous structure with high specific surface area and large pore volume facilitates the transfer of electrolyte and accommodates the large volume change. Secondly, graphitic carbon encapsulation further improves the electronic conductivity of the nanocomposite. Finally, ultrafine Fe2 O3 nanocrystals effectively shorten the Na+ diffusion length. Consequently, this nanocomposite exhibits stable and fast Na+ storage, thus leading to excellent rate capability and cyclability. Pseudocapacitive behavior is found to dominate in the redox reactions, accounting for the outstanding rate and cycling performance. In addition, full cells, assembled with O3-Na0.9 [Cu0.22 Fe0.30 Mn0.48 ]O2 as cathodes, present good electrochemical performance.

Enhancing drug-food interaction prediction with precision representations through multilevel self-supervised learning.

Drug-food interactions (DFIs) crucially impact patient safety and drug efficacy by modifying absorption, distribution, metabolism, and excretion. The application of deep learning for predicting DFIs is promising, yet the development of computational models remains in its early stages. This is mainly due to the complexity of food compounds, challenging dataset developers in acquiring comprehensive ingredient data, often resulting in incomplete or vague food component descriptions. DFI-MS tackles this issue by employing an accurate feature representation method alongside a refined computational model. It innovatively achieves a more precise characterization of food features, a previously daunting task in DFI research. This is accomplished through modules designed for perturbation interactions, feature alignment and domain separation, and inference feedback. These modules extract essential information from features, using a perturbation module and a feature interaction encoder to establish robust representations. The feature alignment and domain separation modules are particularly effective in managing data with diverse frequencies and characteristics. DFI-MS stands out as the first in its field to combine data augmentation, feature alignment, domain separation, and contrastive learning. The flexibility of the inference feedback module allows its application in various downstream tasks. Demonstrating exceptional performance across multiple datasets, DFI-MS represents a significant advancement in food presentations technology. Our code and data are available at https://github.com/kkkayle/DFI-MS.

Enhanced differential evolution algorithm for feature selection in tuberculous pleural effusion clinical characteristics analysis.

Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction.

Unsupervised domain adaptation based on feature and edge alignment for femur X-ray image segmentation.

The gold standard for diagnosing osteoporosis is bone mineral density (BMD) measurement by dual-energy X-ray absorptiometry (DXA). However, various factors during the imaging process cause domain shifts in DXA images, which lead to incorrect bone segmentation. Research shows that poor bone segmentation is one of the prime reasons of inaccurate BMD measurement, severely affecting the diagnosis and treatment plans for osteoporosis. In this paper, we propose a Multi-feature Joint Discriminative Domain Adaptation (MDDA) framework to improve segmentation performance and the generalization of the network in domain-shifted images. The proposed method learns domain-invariant features between the source and target domains from the perspectives of multi-scale features and edges, and is evaluated on real data from multi-center datasets. Compared to other state-of-the-art methods, the feature prior from the source domain and edge prior enable the proposed MDDA to achieve the optimal domain adaptation performance and generalization. It also demonstrates superior performance in domain adaptation tasks on small amount datasets, even using only 5 or 10 images. In this study, MDDA provides an accurate bone segmentation tool for BMD measurement based on DXA imaging.

Extracorporeal closed-loop respiratory regulation for patients with respiratory difficulty using a soft bionic robot.

OBJECTIVE: Respiratory regulation is critical for patients with respiratory dysfunction. Clinically used ventilators can lead to long-term dependence and injury. Extracorporeal assistance approaches such as iron-lung devices provide a noninvasive alternative, however, artificial actuator counterparts have not achieved marvelous biomimetic ventilation as human respiratory muscles. Here, we propose a bionic soft exoskeleton robot that can achieve extracorporeal closed-loop respiratory regulation by emulating natural human breath. METHODS: For inspiration, a soft vacuum chamber is actuated to produce negative thoracic pressure and thus expand lung volume by pulling the rib cage up and outward through use of external negative pressure. For expiration, a soft origami array under positive pressure pushes the abdominal muscles inward and the diaphragm upward. To achieve in vitro measurement of respiratory profile, we describe a wireless respiratory monitoring device to measure respiratory profiles with high accuracy, validated by quantitative comparisons with spirometer as gold-standard reference. By constructing a human-robot coupled respiratory mechanical model, a model-based proportional controller is designed for continuous tracking of the target respiratory profile. RESULTS: In experiments with ten healthy participants and ten patients with respiratory difficulty, the robot can adjust its assistive forces in real time and drive human-robot coupling respiratory system to track the target profile. CONCLUSION: The biomimetic robot can achieve extracorporeal closed-loop respiratory regulation for a diverse population. SIGNIFICANCE: The soft robot has important potential to assist respiration for people with respiratory difficulty, whether in a hospital or a home setting.

Activated PRKCD-mediated neutrophil extracellular traps pathway may be the prothrombotic mechanism of neutrophils in polycythemia vera patients based on clinical retrospective analysis and bioinformatics study.

Thrombosis is a major cause of morbimortality in patients with polycythemia vera (PV). Furthermore, neutrophils play a significant role in thrombosis, but their role in the pathogenetic mechanisms of PV is not well characterized. Therefore, we investigated the role and mechanisms by which neutrophils regulate thrombosis in PV patients. Univariate and multivariate logistic regression analysis of clinicopathological factors was performed to determine the independent risk factors of thrombosis in PV. Pearson's correlation analysis was performed to determine the relationship between absolute neutrophil count (ANC) and the hypercoagulable state in PV patients. Bioinformatics analysis of the GSE54644 dataset was used to identify hemostasis-related pathways in neutrophils of PV patients. Weighted gene co-expression network analysis (WGCNA) of the integrated dataset (GSE57793, GSE26049 and GSE61629) was used to identify neutrophils-related genes and pathways associated with thrombosis in PV. Ingenuity pathway analysis (IPA) was performed to identify the differentially activated pathways in PV patients with or without thrombosis using GSE47018 dataset. Our data showed increased ANC in PV patients. Multivariate logistic regression analysis showed that ANC was an independent risk factor for the thrombotic events in PV patients before or at diagnosis. ANC correlated with the hypercoagulable state in PV patients. Neutrophil extracellular traps (NETs) pathway was significantly enriched in the neutrophils of PV patients. IPA results demonstrated that PRKCD-mediated NETs pathway was hyperactivated in PV patients with thrombosis. In summary, ANC was an independent risk factor for the thrombotic events in PV patients before or at diagnosis, and PRKCD-mediated NETs pathway was aberrantly activated in the neutrophils of PV patients and was associated with the thrombotic events.

Nurses' sense of organizational support, Self-esteem and perceived professional benefits: A mediating model.

AIM: To explore the mediating role of self-esteem in the relationship between perceived organizational support and professional benefits among Registered Nurses in China. DESIGN: This was an online, cross-sectional study involving 1850 nurses from six hospitals in China. METHODS: Data were collected using a 4-part questionnaire including a sociodemographic questionnaire, perceived organizational support scale, self-esteem scale and brief nurses' perceived professional benefits questionnaire from September to November 2021. Structural equation modelling (SEM) was used to explore the associations among them. RESULTS: Perceived organizational support was positively correlated with self-esteem and perceived professional benefits among nurses, whereas self-esteem positively predicted nurses' perceived professional benefits . Self-esteem partially mediated the relationship between the two variables. The ratio of the mediating effect to the total effect was 16.7%.

A new population initialization of metaheuristic algorithms based on hybrid fuzzy rough set for high-dimensional gene data feature selection.

In the realm of modern medicine and biology, vast amounts of genetic data with high complexity are available. However, dealing with such high-dimensional data poses challenges due to increased processing complexity and size. Identifying critical genes to reduce data dimensionality is essential. The filter-wrapper hybrid method is a commonly used approach in feature selection. Most of these methods employ filters such as MRMR and ReliefF, but the performance of these simple filters is limited. Rough set methods, on the other hand, are a type of filter method that outperforms traditional filters. Simultaneously, many studies have pointed out the crucial importance of good initialization strategies for the performance of the metaheuristic algorithm (a type of wrapper-based method). Combining these two points, this paper proposes a novel filter-wrapper hybrid method for high-dimensional feature selection. To be specific, we utilize the variant of bWOA (binary Whale Optimization Algorithm) based on Hybrid Fuzzy Rough Set to perform attribute reduction, and the reduced attributes are used as prior knowledge to initialize the population. We then employ metaheuristics for further feature selection based on this initialized population. We conducted experiments using five different algorithms on 14 UCI datasets. The experiment results show that after applying the initialization method proposed in this article, the performance of five enhanced algorithms, has shown significant improvement. Particularly, the improved bMFO using our initialization method: fuzzy_bMFO outperformed six currently advanced algorithms, indicating that our initialization method for metaheuristic algorithms is suitable for high-dimensional feature selection tasks.

Effects of intraoperative fixation of residual hernia sac on postoperative seroma in laparoscopic transabdominal preperitoneal inguinal hernia repair: a prospective randomized controlled trial.

The study was aimed to evaluate a prospective randomized controlled trial (RCT) In laparoscopic transabdominal preperitoneal inguinal hernia repair (TAPP), whether fixation of the residual sac after transecting the hernia sac can reduce the severity of postoperative seroma. A total of 252 male patients with a primary unilateral indirect inguinal hernia who underwent TAPP from September 2018 to November 2022 were recruited. Patients were randomized to the control group (CG)and the experimental group (EG). In the experimental group, after the hernia sac was transected, the residual sac was fixed to the lower edge of the rectus abdominis, while it was left in the preperitoneal space in the control group. Close follow-up was arranged to observe the incidence of seroma and other postoperative complications. All 214 patients were discharged successfully. 106 patients were randomly assigned to the control group, and 108 patients were assigned to the experimental group. There was no significant difference in the incidence of postoperative fluid extraction between the experimental group and the control group (11.1% VS.10.4%, p = 0.862), but the patients with seroma after the operation had fewer repeated extraction (0% VS. 45.5%, P = 0.033). The incidences of other postoperative complications were comparable in the two groups. In the treatment of indirect inguinal hernia with TAPP, after transecting the hernia sac, suturing and fixing the residual sac to the inferior edge of the rectus abdominis can reduce the incidence of repeated aspiration.

Proteomic-Based Platelet Activation-Associated Protein SELP May Be a Novel Biomarker for Coagulation and Prognostic in Essential Thrombocythemia.

Abnormal platelet activation can lead to thrombosis in essential thrombocythemia (ET) and thus impact patient prognosis. Platelet activation-associated proteins are key molecules for platelet activation. However, it is unclear which proteins are most closely associated with the disease's prognosis. To determine which platelet activation-related proteins can be employed as ET patient prognosis predictors, we used label-free quantification (LFQ) and parallel reaction monitoring (PRM) technology and first determined the serum proteomic expression levels and the differential proteins of ET patients. Then, based on the IPSET (International Prognostic Score for ET), the differential protein associated with the prognostic score was found. To investigate potential processes affecting prognosis, the connection of this protein with prognostic markers, such as thrombotic history, age, white blood cell count, coagulation factors, and inflammatory factors, were further examined. The levels of platelet activation-related proteins GPIbα, SELP, PF4, MMP1, and FLNA were significantly higher in ET patients, according to LFQ and PRM analyses (p < 0.01). Based on regression analysis of the IPSET prognostic score, it is suggested that the SELP level was positively correlated with the prognostic score and prognostic risk factor analysis (p < 0.05). Further regression analysis of SELP with coagulation factors showed that antithrombin (AT-III) was negatively correlated with SELP levels (p < 0.05). Further regression analysis of the inflammatory factors with AT-III and SELP revealed that IL-10, IL-12P70, and IL-31 were negatively correlated with AT-III and SELP (p < 0.01). Platelet activation pathway-related proteins are expressed more frequently in ET patients, and serum SELP may be a prognostic marker for these individuals by encouraging leukocyte increase and inflammatory factor expression and causing aberrant coagulation.