Sarin(GB)is a typical representative of nerve agents with high toxicity,and very low amount can cause death.GB can cause water and atmospheric environment poisoning,so the detection of GB in water and air is of great significance.In this work,a colorimetric sensor array(CSA)based on GB inhibition of cholinesterase activity was constructed to detect GB with high selectivity.A 4×4 colorimetric array was constructed using acetylcholinesterase(AChE),butyryl cholinesterase(BuChE)and the corresponding substrate acetylthiocholine iodide(S-ACh),butyryl thiocholine iodide(S-BCh),acetylcholine chloride(ACh),butyryl choline chloride(BCh)and 2,6-dichloroindophenol ethyl ester(DCIE).The linear curve of the sensor was Y=131.3×lgC+271.6(R2=0.997),where Y was the array response Euclidean distance,C was the concentration of GB(mg/L),the linear range was 0.03?0.32 mg/L,and the detection limit was 27.6 μg/L.The method could effectively distinguish chemical warfare agents(CWA)such as VX,Soman(GD),mustard gas(HD),Louie reagent(L),and had high anti-interference ability,sensitivity and good repeatability.It was successfully applied to the detection of GB in simulated water and simulated air samples,and the sample recovery rate was 97.2% ?100.9%.This method would be potentially applied to the field rapid detection of nerve agents.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Purpose::Intramedullary nailing is the preferred internal fixation technique for the treatment of subtrochanteric fractures because of its biomechanical advantages. However, no definitive conclusion has been reached regarding whether combined cable cerclage is required during intramedullary nailing treatment. This study is performed to compare the clinical effects of intramedullary nailing with cerclage and non-cerclage wiring in the treatment of irreducible spiral subtrochanteric fractures.Methods::Patients with subtrochanteric fractures admitted to our center from January 2013 to December 2021 were retrospectively analyzed. The patients were enrolled in the case-control study according to the inclusion and exclusion criteria and divided into the non-cerclage group and the cerclage group. The patients' clinical data, including the operative time, intraoperative blood loss, hospital stay, reoperation rate, fracture union time, and Harris hip score, were compared between these 2 groups. Categorical variables were compared using Chi-square or Fisher's exact test. Continuous variables with normal distribution were presented as mean ± standard deviation and analyzed with Student's t-test. Nonnormally distributed variables were expressed as median (Q 1, Q 3) and assessed using the Mann-Whitney test. A p < 0.05 was considered significant. Results::In total, 69 patients were included in the study (35 patients in the non-cerclage group and 34 patients in the cerclage group). The baseline data of the 2 groups were comparable. There were no significant difference in the length of hospital stay (z = -0.391, p = 0.696), operative time (z = -1.289, p = 0.197), or intraoperative blood loss (z = -1.321, p = 0.186). However, compared with non-cerclage group, the fracture union time was shorter (z = -5.587, p < 0.001), the rate of nonunion was lower (χ 2= 6.030, p = 0.03), the anatomical reduction rate was higher (χ 2= 5.449, p = 0.03), and the Harris hip score was higher (z =-2.99, p = 0.003) in the cerclage group, all with statistically significant differences. Conclusions::Intramedullary nailing combined with cable cerclage wiring is a safe and reliable technique for the treatment of irreducible subtrochanteric fractures. This technique can improve the reduction effect, increase the stability of fracture fixation, shorten the fracture union time, reduce the occurrence of nonunion, and contribute to the recovery of hip joint function.

Objective:We aimed to develop a novel visualized model based on nomogram to predict postoperative overall survival.Methods:This was a multicenter, retrospective, observational cohort study, including participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery from 11 medical centers in China from August 1, 2015 to June 30, 2018. Baseline characteristics, histopathological data and nutritional status, as assessed using Nutrition Risk Screening 2002 (NRS 2002) score and the scored Patient-Generated Subjective Global Assessment, were collected. The least absolute shrinkage and selection operator regression and Cox regression were used to identify variables to be included in the predictive model. Internal and external validations were performed.Results:There were 681 and 127 patients in the training and validation cohorts, respectively. A total of 188 deaths were observed over a median follow-up period of 59 (range: 58 to 60) months. Two independent predictors of NRS 2002 and Tumor-Node-Metastasis (TNM) stage were identified and incorporated into the prediction nomogram model together with the factor of age. The model's concordance index for 1-, 3- and 5-year overall survival was 0.696, 0.724, and 0.738 in the training cohort and 0.801, 0.812, and 0.793 in the validation cohort, respectively.Conclusions:In this study, a new nomogram prediction model based on NRS 2002 score was developed and validated for predicting the overall postoperative survival of patients with gastric colorectal cancer. This model has good differentiation, calibration and clinical practicability in predicting the long-term survival rate of patients with gastrointestinal cancer after radical surgery.

Acute pancreatitis(AP)is one of the most common clinically critical diseases,and pancreatic acinar cell death is a central event in the pathological course of AP and a key factor in determining the degree of local or systemic inflammatory damage as well as overall pancreatitis.Programmed cell death is an active and orderly way of death that is regulated by program and widely exists in the development of organisms.A variety of programmed death modes are widely involved in the damage of AP acinar cells,mainly including autophagy,pyrodeath and iron death,etc.This study systematically reviews the mechanism of programmed death in AP acinar cells.The aim is to provide a new understanding of the specific pathogenesis of AP,and to provide new ideas and strategies for the treatment of the disease.

Acute pancreatitis(AP)is one of the most common clinically critical diseases,and pancreatic acinar cell death is a central event in the pathological course of AP and a key factor in determining the degree of local or systemic inflammatory damage as well as overall pancreatitis.Programmed cell death is an active and orderly way of death that is regulated by program and widely exists in the development of organisms.A variety of programmed death modes are widely involved in the damage of AP acinar cells,mainly including autophagy,pyrodeath and iron death,etc.This study systematically reviews the mechanism of programmed death in AP acinar cells.The aim is to provide a new understanding of the specific pathogenesis of AP,and to provide new ideas and strategies for the treatment of the disease.

PURPOSE: SAM junctional tourniquet (SJT) has been applied to control junctional hemorrhage. However, there is limited information about its safety and efficacy when applied in the axilla. This study aims to investigate the effect of SJT on respiration when used in the axilla in a swine model. METHODS: Eighteen male Yorkshire swines, aged 6-month-old and weighing 55 - 72 kg, were randomized into 3 groups, with 6 in each. An axillary hemorrhage model was established by cutting a 2 mm transverse incision in the axillary artery. Hemorrhagic shock was induced by exsanguinating through the left carotid artery to achieve a controlled volume reduction of 30% of total blood volume. Vascular blocking bands were used to temporarily control axillary hemorrhage before SJT was applied. In Group I, the swine spontaneously breathed, while SJT was applied for 2 h with a pressure of 210 mmHg. In Group II, the swine were mechanically ventilated, and SJT was applied for the same duration and pressure as Group I. In Group III, the swine spontaneously breathed, but the axillary hemorrhage was controlled using vascular blocking bands without SJT compression. The amount of free blood loss was calculated in the axillary wound during the 2 h of hemostasis by SJT application or vascular blocking bands. After then, a temporary vascular shunt was performed in the 3 groups to achieve resuscitation. Pathophysiologic state of each swine was monitored for 1 h with an infusion of 400 mL of autologous whole blood and 500 mL of lactated ringer solution. T_b and T₀ represent the time points before and immediate after the 30% volume-controlled hemorrhagic shock, respectively. T₃₀, T₆₀, T₉₀ and T₁₂₀, denote 30, 60, 90, and 120 min after T₀ (hemostasis period), while T₁₅₀, and T₁₈₀ denote 150 and 180 min after T₀ (resuscitation period). The mean arterial pressure and heart rate were monitored through the right carotid artery catheter. Blood samples were collected at each time point for the analysis of blood gas, complete cell count, serum chemistry, standard coagulation tests, etc., and thromboelastography was conducted subsequently. Movement of the left hemidiaphragm was measured by ultrasonography at T_b and T₀ to assess respiration. Data were presented as mean ± standard deviation and analyzed using repeated measures of two-way analysis of variance with pairwise comparisons adjusted using the Bonferroni method. All statistical analyses were processed using GraphPad Prism software. RESULTS: Compared to T_b, a statistically significant increase in the left hemidiaphragm movement at T₀ was observed in Groups I and II (both p < 0.001). In Group III, the left hemidiaphragm movement remained unchanged (p = 0.660). Compared to Group I, mechanical ventilation in Group II significantly alleviated the effect of SJT application on the left hemidiaphragm movement (p < 0.001). Blood pressure and heart rate rapidly increased at T₀ in all three groups. Respiratory arrest suddenly occurred in Group I after T₁₂₀, which required immediate manual respiratory assistance. PaO₂ in Group I decreased significantly at T₁₂₀, accompanied by an increase in PaCO₂ (both p < 0.001 vs. Groups II and III). Other biochemical metabolic changes were similar among groups. However, in all 3 groups, lactate and potassium increased immediately after 1 min of resuscitation concurrent with a drop in pH. The swine in Group I exhibited the most severe hyperkalemia and metabolic acidosis. The coagulation function test did not show statistically significant differences among three groups at any time point. However, D-dimer levels showed a more than 16-fold increase from T₁₂₀ to T₁₈₀ in all groups. CONCLUSION In the swine model, SJT is effective in controlling axillary hemorrhage during both spontaneous breathing and mechanical ventilation. Mechanical ventilation is found to alleviate the restrictive effect of SJT on thoracic movement without affecting hemostatic efficiency. Therefore, mechanical ventilation could be necessary before SJT removal.
Male ; Animals ; Swine ; Shock, Hemorrhagic/therapy* ; Tourniquets ; Axilla ; Hemorrhage/therapy* ; Vascular Diseases ; Respiration

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.

This study analyzed the main chemical components of Zhuru Decoction via ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS/MS), and then predicted the mechanism of Zhuru Decoction in clearing heat, resolving phlegm, detoxifying, and treating vomiting and alcohol-related vomiting caused by heat in stomach based on network pharmacology. The gradient elution was conducted in Agilent ZORBAX extend-C_(18) column(2.1 mm×100 mm, 1.8 μm) with the mobile phase of 0.1% formic acid aqueous solution(A)-acetonitrile(B) at a flow rate of 0.3 mL·min~(-1) and the column temperature of 35 ℃. The MS adopted the positive and negative ion mode of electrospray ionization(ESI), and the data were collected in the scanning range of m/z 100-1 500. A total of 98 compounds in Zhuru Decoction were identified via BATMAN, SYMMAP, TCMSP, and relevant literature, including 36 flavonoids, 7 triterpenoids, 8 gingerols, 20 organic acids, 5 amino acids, and 22 other compounds. On the basis of the available studies, 9 components were selected as index components, and the protein-protein interaction(PPI) network of the common targets was established with STRING 11.0. Finally, 10 core targets associated with the pharmacodynamic effect were screened out. This study established the UPLC-Q-TOF-MS/MS method for identifying the chemical components in the classic prescription Zhuru Decoction, and employed network pharmacology to explore the core targets of its efficacy, which provided a refe-rence for the quality control and the research of the pharmacodynamic substances of Zhuru Decoction.
Humans ; Tandem Mass Spectrometry ; Chromatography, High Pressure Liquid ; Drugs, Chinese Herbal/chemistry* ; Network Pharmacology ; Vomiting