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Abstract This study aimed to analyze the complications and long-term survival outcomes in patients who underwent radical gastrectomy for gastric cancer, as well as to identify the risk factors associated with postoperative complications. After conducting a comprehensive search within the medical records system, a total of 2508 patients who underwent radical gastrectomy and met the inclusion criteria were enrolled. Of the 2508 patients, 301 were diagnosed with postoperative complications. The pathological data, postoperative recovery, and survival outcome were compared between complication and control group. Subsequently, univariate and multivariate logistic regression analyses were conducted to identified the risk factors. According to the Clavien-Dindo grading criteria for postoperative complications, the proportions of grade I, II, III, IV, and V complications following radical gastrectomy were observed to be 28.2%, 42.9%, 19.6%, 8.0%, and 1.3%, respectively. The presence of postoperative complications significantly prolonged the duration of gastrointestinal decompression (P 65years [odds ratio (OR) = 1.378, P = 0.020], presence of diabetes (OR = 2.042, P 215 min (OR = 1.450, P = 0.006), intraoperative blood loss > 275 ml (OR = 1.474, P = 0.004), and Roux-en-Y anastomosis for both whole stomach (OR = 1.567, P = 0.021) and distal gastric cancer (OR = 2.083, P = 0.003) were identified as independent risk factors for postoperative complications. This study analyzed the complications and survival outcomes following radical gastrectomy, and investigated the predictors for postoperative complications, thereby providing valuable guidance on the prevention and management of surgical complications in gastric cancer.

Abstract Infection after anterior cruciate ligament reconstruction (ACLR) is a rare and catastrophic postoperative complication. The aims of this study were to investigate the diagnostic, treatment and rehabilitation measures for postoperative infection following after ACLR. A retrospective study was conducted on 1500 patients who underwent ACLR between January 2011 and January 2022. Twenty patients who met the criteria for summarizing the incidence patterns and treatment experiences were selected for a complete investigation of their diagnostic, therapeutic, and rehabilitation processes, as well as outpatient follow-up results. Among the 20 patients who developed postoperative infections, Staphylococcus aureus was the main pathogen (80%). The clinical manifestations mainly included fever (80%) and knee joint pain (100%). Laboratory tests demonstrated that C-reactive protein (CRP) levels were greater than 50 mmol/L in fifteen patients (75%). All of the patients received intravenous antibiotic therapy. Five patients (25%) of tendon socket infection were treated with continuous negative pressure suction irrigation, whereas the other fifteen patients with intra-articular infection were treated with arthroscopic debridement and continuous flushing. The Lysholm score of the affected knee was compared before treatment and 6 months after treatment, and the difference was statistically significant (t = 20.78, P

Abstract Anaplastic thyroid carcinoma (ATC) is a highly aggressive human malignancy without effective treatment. Yes-associated protein (YAP) is a critical effector of the Hippo pathway, which is essential in thyroid carcinogenesis. However, the underlying mechanisms of aberrant YAP expression in ATC are not completely understood. Ubiquitylation-related enzyme siRNA screening identified the ubiquitin protein ligase E3 component n-recognin 1 (UBR1) as a stabilizer of YAP in ATC cells. UBR1 deficiency reduced YAP protein levels and its target gene expression. UBR1 directly interacted with YAP and promoted its monoubiquitylation, competitively suppressing its polyubiquitylation and resulting in extended protein half-life. UBR1 depletion reduced ATC cell proliferation and migration in vitro. Xenograft tumor studies also suggested that UBR1 knockdown suppressed ATC cell growth in vivo. Furthermore, exogenous YAP expression partially reversed the inhibitive effects of UBR1 depletion on ATC cell proliferation and migration. Our studies demonstrated that UBR1 directly interacts with YAP and stabilized it in a monoubiquitylation-dependent manner, consequently promoting ATC tumorigenesis, suggesting that UBR1 might be a potentially therapeutic target for ATC treatment.

Abstract Our research endeavors are directed towards unraveling the stem cell characteristics of lower-grade glioma patients, with the ultimate goal of formulating personalized treatment strategies. We computed enrichment stemness scores and performed consensus clustering to categorize phenotypes. Subsequently, we constructed a prognostic risk model using weighted gene correlation network analysis (WGCNA), random survival forest regression analysis as well as full subset regression analysis. To validate the expression differences of key genes, we employed experimental methods such as quantitative Polymerase Chain Reaction (qPCR) and assessed cell line proliferation, migration, and invasion. Three subtypes were assigned to patients diagnosed with LGG. Notably, Cluster 2 (C2), exhibiting the poorest survival outcomes, manifested characteristics indicative of the subtype characterized by immunosuppression. This was marked by elevated levels of M1 macrophages, activated mast cells, along with higher immune and stromal scores. Four hub genes—CDCA8, ORC1, DLGAP5, and SMC4—were identified and validated through cell experiments and qPCR. Subsequently, these validated genes were utilized to construct a stemness risk signature. Which revealed that Lower-Grade Glioma (LGG) patients with lower scores were more inclined to demonstrate favorable responses to immune therapy. Our study illuminates the stemness characteristics of gliomas, which lays the foundation for developing therapeutic approaches targeting CSCs and enhancing the efficacy of current immunotherapies. By identifying the stemness subtype and its correlation with prognosis and TME patterns in glioma patients, we aim to advance the development of personalized treatments, enhancing the ability to predict and improve overall patient prognosis.

Abstract Aspirin is widely used for both primary and secondary prevention of panvascular diseases, such as stroke and coronary heart disease (CHD). The optimal balance between reducing panvascular disease events and the potential increase in bleeding risk remains unclear. This study aimed to develop a predictive model specifically designed to assess bleeding risk in individuals using aspirin. A total of 58,415 individuals treated with aspirin were included in this study. Detailed data regarding patient demographics, clinical characteristics, comorbidities, medical history, and laboratory test results were collected from the Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups at a ratio of 7:3. The larger group was used for model development, while the smaller group was used for internal validation. To develop the prediction model, we employed least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression. The performance of the model was assessed through metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The LASSO-derived model employed in this study incorporated six variables, namely, sex, operation, previous bleeding, hemoglobin, platelet count, and cerebral infarction. It demonstrated excellent performance at predicting bleeding risk among aspirin users, with a high AUC of 0.866 (95% CI 0.857–0.874) in the training dataset and 0.861 (95% CI 0.848–0.875) in the test dataset. At a cutoff value of 0.047, the model achieved moderate sensitivity (83.0%) and specificity (73.9%). The calibration curve analysis revealed that the nomogram closely approximated the ideal curve, indicating good calibration. The DCA curve demonstrated a favorable clinical net benefit associated with the nomogram model. Our developed LASSO-derived predictive model has potential as an alternative tool for predicting bleeding in clinical settings.

Abstract This study aims to discuss the load intensity monitoring in the training process of sitting volleyball, to help coaches understand the training status of athletes, and to provide a scientific basis for the follow-up training plan. Through big data technology, the physiological changes of athletes can be more accurately grasped. This includes classification and summary of exercise load intensity and experimental study of the relationship between heart rate and rating perceived exertion (RPE). Through monitoring the training process of a provincial women’s sitting volleyball team, it is found that there is a significant positive correlation between athletes’ RPE and average heart rate. This result shows that by monitoring the change in heart rate and RPE of athletes, athletes' training state and physical condition can be more accurately understood. The results reveal that through the use of big data technology and monitoring experiments, it is found that heart rate and RPE are effective monitoring indicators, which can scientifically reflect the load intensity during sitting volleyball training. The conclusions provide coaches with a more scientific basis for making training plans and useful references for sports involving people with disabilities.

Abstract This study explored the risk factors and established a prediction model for intraoperative hypothermia (IOH) in patients undergoing robotic surgery. We conducted a retrospective survey of patients undergoing elective robotic surgery at the China-Japan Union Hospital of Jilin University during June 2020–October 2021 using institutional medical records. Intraoperative core temperatures and potential influencing factors were collected, and regression analyses were used to assess the risk factors for IOH and establish a prediction model for the incidence of IOH. Overall, 833 patients who underwent robotic surgery were included in the final analysis; IOH was observed in 344 patients (incidence, 0.41; 95% confidence interval [CI] 0.38–0.45). A higher body mass index (BMI) and baseline core temperature were protective factors for IOH. A final prediction model for IOH was developed based on the determining factors with an area under the receiver operating characteristic curve of 0.85 under fivefold cross validation (95% CI 0.83–0.88). Accordingly, a lower BMI and baseline core temperature, thoracic surgeries, morning surgeries, and surgeries with longer durations were risk factors for IOH during robotic surgeries. Our prediction model has an excellent discrimination ability for predicting IOH in robotic surgeries.

Abstract It is widely known that mold is one of important indices in assessing the quality of stored wheat. First, mold will decrease the quality of wheat kernels; the wheat kernels infected by mold can produce secondary metabolites, such as aflatoxins, ochratoxin A, zearalenone, fumonisins and so on. Second, the mycotoxins metabolized by mycetes are extremely harmful to humans; once the food or feed is made of by those wheat kernels infected by mold, it will cause serious health problems on human beings as well as animals. Therefore, the effective and accurate detection of wheat mold is vitally important to evaluate the storage and subsequent processing quality of wheat kernels. However, traditional methods for detecting wheat mold mainly rely on biochemical methods, which always involve complex and long pretreatment processes, and waste part of wheat samples for each detection. In view of this, this paper proposes a type of eco-friendly and nondestructive wheat mold detection method based on ultra weak luminescence. The specific implementation process is as follows: firstly, ultra weak luminescence signals of the healthy and the moldy wheat subsamples are measured by a photon analyzer; secondly, the approximate entropy and multiscale approximate entropy are introduced as the main classification features separately; finally, the detection model has been established based on the support vector machine in order to classify two types of wheat subsamples. The receiver operating characteristic curve of the newly established detection model shows that the highest classification accuracy rate can reach 93.1%, which illustrates that our proposed detection model is feasible and promising for detecting wheat mold.

Abstract Neuronal activity in the brain reflects an excitation–inhibition balance that is regulated predominantly by glutamatergic and GABAergic neurotransmission, and often disturbed in neuropsychiatric disorders. Here, we tested the effects of a single oral dose of two anti-glutamatergic drugs (dextromethorphan, an NMDA receptor antagonist; perampanel, an AMPA receptor antagonist) and an L-type voltage-gated calcium channel blocker (nimodipine) on transcranial magnetic stimulation (TMS)-evoked electroencephalographic (EEG) potentials (TEPs) and TMS-induced oscillations (TIOs) in 16 healthy adults in a pseudorandomized, double-blinded, placebo-controlled crossover design. Single-pulse TMS was delivered to the hand area of left primary motor cortex. Dextromethorphan increased the amplitude of the N45 TEP, while it had no effect on TIOs. Perampanel reduced the amplitude of the P60 TEP in the non-stimulated hemisphere, and increased TIOs in the beta-frequency band in the stimulated sensorimotor cortex, and in the alpha-frequency band in midline parietal channels. Nimodipine and placebo had no effect on TEPs and TIOs. The TEP results extend previous pharmaco-TMS-EEG studies by demonstrating that the N45 is regulated by a balance of GABAAergic inhibition and NMDA receptor-mediated glutamatergic excitation. In contrast, AMPA receptor-mediated glutamatergic neurotransmission contributes to propagated activity reflected in the P60 potential and midline parietal induced oscillations. This pharmacological characterization of TMS-EEG responses will be informative for interpreting TMS-EEG abnormalities in neuropsychiatric disorders with pathological excitation–inhibition balance.

To enhance enterprises' interactive exploration capabilities for unstructured chart data, this paper proposes a multimodal chart question-answering method. Facing the challenge of recognizing curved and irregular text in charts, we introduce Gaussian heatmap encoding technology to achieve character-level precise text annotation. Additionally, we combine a key point detection algorithm to extract numerical information from the charts and convert it into structured table data. Finally, by employing a multimodal cross-fusion model, we deeply integrate the queried charts, user questions, and generated table data to ensure that the model can comprehensively capture chart information and accurately answer user questions. Experimental validation has demonstrated that our method achieves a precision of 91.58% in chart information extraction and a chart question-answering accuracy of 82.24%, fully proving the significant advantages of our proposed method in enhancing chart text recognition and question-answering capabilities. Through practical enterprise application cases, our method has shown its ability to answer four types of chart questions, exhibiting mathematical reasoning capabilities and providing robust support for enterprise data analysis and decision-making. [ABSTRACT FROM AUTHOR]

A correlation between UA levels and the development of hypertension has been demonstrated. However, the relationship between UA and all-cause mortality in patients with hypertension remains underexplored. A nonlinear association between UA and all-cause mortality across sexes was observed through smoothed curve fitting. The correlation between UA and all-cause mortality was calculated by threshold and saturation effect analysis, along with Cox regression models. The stability of the results in the presence of different comorbidities was verified through stratified analysis for interaction testing. Smoothed curve fitting was also used to examine the association between UA and various diseases. The association between UA and all-cause mortality in patients with hypertension exhibited a U-shaped curve, with inconsistent inflection points between the sexes. In male patients with hypertension, all-cause mortality gradually decreased with increasing UA levels when UA levels were ≤ 7.2 mg/dL (HR 0.975; 95% CI 0.929–1.024) and gradually increased with increasing UA levels when UA levels were > 7.2 mg/dL (HR 1.204; 95% CI 1.120–1.294). Similar findings were observed in female patients with hypertension, with UA as a protective factor when UA levels were ≤ 5.1 mg/dL (HR 0.902; 95% CI 0.820–0.991) and a risk factor when UA levels were > 5.1 mg/dL (HR 1.120; 95% CI 1.072–1.169). The association between UA and all-cause mortality in patients with hypertension exhibits a U-shaped curve. All-cause mortality tends to decrease and then increase with increasing UA levels, with the inflection point varying between sexes. [ABSTRACT FROM AUTHOR]