The exploration of perioperative hypotension subtypes: a prospective, single cohort, observational pilot study.

Background: Hypotension is a risk factor for postoperative complications, but evidence from randomized trials does not support that a higher blood pressure target always leads to optimized outcomes. The heterogeneity of underlying hemodynamics during hypotension may contribute to these contradictory results. Exploring the subtypes of hypotension can enable optimal management of intraoperative hypotension. Methods: This is a prospective, observational pilot study. Patients who were ≥ 45 years old and scheduled to undergo moderate-to-high-risk noncardiac surgery were enrolled in this study. The primary objective of this pilot study was to investigate the frequency and distribution of perioperative hypotension and its subtypes (hypotension with or without cardiac output reduction). The exposure of hypotension and its subtypes in patients with and without myocardial or acute kidney injury were also explored. Results: Sixty patients were included in the analysis. 83% (50/60) of the patients experienced perioperative hypotension. The median duration of hypotension for each patient was 8.0 [interquartile range, 3.1-23.3] minutes. Reduced cardiac output was present during 77% of the hypotension duration. Patients suffering from postoperative myocardial or acute kidney injury displayed longer duration and more extensive exposure in all hypotension subtypes. However, the percentage of different hypotension subtypes did not differ in patients with or without postoperative myocardial or acute kidney injury. Conclusion: Perioperative hypotension was frequently accompanied by cardiac output reduction in moderate-to-high-risk noncardiac surgical patients. However, due to the pilot nature of this study, the relationship between hypotension subtypes and postoperative myocardial or acute kidney injury still needs further exploration. Clinical trial registration: https://www.chictr.org.cn/showprojEN.html?proj=134260, CTR2200055929.

Serum metabolism characteristics of patients with myocardial injury after noncardiac surgery explored by the untargeted metabolomics approach.

BACKGROUND: Myocardial injury after noncardiac surgery (MINS) is one of the most common complications associated with postoperative adverse cardiovascular outcomes and mortality. However, MINS often fails to be timely diagnosed due to the absence of clinical symptoms and limited diagnostic methods. The metabolomic analysis might be an efficient way to discover new biomarkers of MINS. Characterizing the metabolomic features of MINS patients may provide new insight into the diagnosis of MINS. METHODS: In this study, serum samples from 20 matched patients with or without MINS (n = 10 per group) were subjected to untargeted metabolomics analysis to investigate comprehensive metabolic information. Differential metabolites were identified, and the enriched metabolic pathway was determined based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. RESULTS: A comprehensive analysis revealed 124 distinct metabolites, predominantly encompassing lipids, amino acids and other compounds. The observed modifications in metabolic pathways in patients with or without MINS showed significant clustering in cholesterol metabolism, aldosterone synthesis and secretion, primary bile acid biosynthesis, as well as cysteine and methionine metabolism. Four specific metabolites (taurocholic acid, L-pyroglutamic acid, taurochenodeoxycholic acid, and pyridoxamine) exhibited promising potential as biomarkers for prognosticating MINS. CONCLUSIONS: This study contributes valuable insights into the metabolomic features of MINS and the discovery of potential biomarkers which may help the early diagnosis of MINS. The identified metabolites and altered pathways offer valuable insights into the molecular underpinnings of MINS, paving the way for improved diagnostic approaches and potential intervention strategies.

Evaluation of a modified ultrasound-assisted technique for mid-thoracic epidural placement: a prospective observational study.

BACKGROUND: Although mid-thoracic epidural analgesia benefits patients undergoing major surgery, technical difficulties often discourage its use. Improvements in technology are warranted to improve the success rate on first pass and patient comfort. The previously reported ultrasound-assisted technique using a generic needle insertion site failed to demonstrate superiority over conventional landmark techniques. A stratified needle insertion site based on sonoanatomic features may improve the technique. METHODS: Patients who presented for elective abdominal or thoracic surgery requesting thoracic epidural analgesia for postoperative pain control were included in this observational study. A modified ultrasound-assisted technique using a stratified needle insertion site based on ultrasound images was adopted. The number of needle passes, needle skin punctures, procedure time, overall success rate, and incidence of procedure complications were recorded. RESULTS: One hundred and twenty-eight subjects were included. The first-pass success and overall success rates were 75% (96/128) and 98% (126/128), respectively. In 95% (122/128) of patients, only one needle skin puncture was needed to access the epidural space. The median [IQR] time needed from needle insertion to access the epidural space was 59 [47-122] seconds. No complications were observed during the procedure. CONCLUSIONS: This modified ultrasound-assisted mid-thoracic epidural technique has the potential to improve success rates and reduce the needling time. The data shown in our study may be a feasible basis for a prospective study comparing our ultrasound-assisted epidural placements to conventional landmark-based techniques.

Ultrasound-assisted lumbar puncture with a horizontal and perpendicular paramedian approach based on positioning in patients with abnormal spinal anatomy: a case report and technical description.

BACKGROUND: The use of ultrasound has been reported to be beneficial in challenging neuraxial procedures. The angled probe is responsible for the main limitations of previous ultrasound-assisted techniques. We developed a novel technique for challenging lumbar puncture, aiming to locate the needle entry point which allowed for a horizontal and perpendicular needle trajectory and thereby addressed the drawbacks of earlier ultrasound-assisted techniques. CASE PRESENTATION: Patient 1 was an adult patient with severe scoliosis who underwent a series of intrathecal injections of nusinersen. The preprocedural ultrasound scan revealed a cephalad probe's angulation (relative to the edge of the bed) in the paramedian sagittal oblique view, and then the probe was rotated 90° into a transverse plane and we noted that a rocking maneuver was required to obtain normalized views. Then the shoulders were moved forward to eliminate the need for cephalad angulation of the probe. The degree of rocking was translated to a lateral offset from the midline of the spine through an imaginary lumbar puncture's triangle model, and a needle entry point was marked. The spinal needle was advanced through this marking-point without craniocaudal and lateromedial angulation, and first-pass success was achieved in all eight lumbar punctures. Patient 2 was an elderly patient with ankylosing spondylitis who underwent spinal anesthesia for transurethral resection of the prostate. The patient was positioned anteriorly obliquely to create a vertebral rotation that eliminated medial angulation in the paramedian approach. The procedure succeeded on the first pass. CONCLUSIONS: This ultrasound-assisted paramedian approach with a horizontal and perpendicular needle trajectory may be a promising technique that can help circumvent challenging anatomy. Larger case series and prospective studies are warranted to define its superiority to alternative approaches of lumbar puncture for patients with difficulties.

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion.

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging.

In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000-2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.

Conversion of Polyethylene to High-Yield Fuel Oil at Low Temperatures and Atmospheric Initial Pressure.

The transformation of waste plastics into fuels via energy-efficient and low-cost pyrolysis could incentivize better waste plastic management. Here, we report pressure-induced phase transitions in polyethylene, which continue to heat up without additional heat sources, prompting the thermal cracking of plastics into premium fuel products. When the nitrogen initial pressure is increased from 2 to 21 bar, a monotonically increasing peak temperature is observed (from 428.1 °C to 476.7 °C). At 21 bar pressure under different atmosphere conditions, the temperature change driven by high-pressure helium is lower than that driven by nitrogen or argon, indicating that phase transition is related to the interaction between long-chain hydrocarbons and intercalated high-pressure medium layers. In view of the high cost of high-pressure inert gases, the promotion or inhibition effect of low-boiling hydrocarbons (transitioning into the gaseous state with increasing temperature) on phase transition is explored, and a series of light components are used as phase transition initiators to replace high-pressure inert gases to experiment. The reason that the quantitative conversion of polyethylene to high-quality fuel products is realized through the addition of 1-hexene at a set temperature of 340 °C and the initial atmospheric pressure. This discovery provides a method for recycling plastics by low energy pyrolysis. In addition, we envisage recovering some of the light components after plastic pyrolysis as phase change initiators for the next batch of the process. This method is able to reduce the cost of light hydrocarbons or high-pressure gas insertion, reduce heat input, and improve material and energy utilization.

Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling.

Dried Hami jujube has great commercial and nutritional value. Starch-head and mildewed fruit are defective jujubes that pose a threat to consumer health. A novel method for detecting starch-head and mildewed fruit in dried Hami jujubes with visible/near-infrared spectroscopy was proposed. For this, the diffuse reflectance spectra in the range of 400-1100 nm of dried Hami jujubes were obtained. Borderline synthetic minority oversampling technology (BL-SMOTE) was applied to solve the problem of imbalanced sample distribution, and its effectiveness was demonstrated compared to other methods. Then, the feature variables selected by competitive adaptive reweighted sampling (CARS) were used as the input to establish the support vector machine (SVM) classification model. The parameters of SVM were optimized by the modified reptile search algorithm (MRSA). In MRSA, Tent chaotic mapping and the Gaussian random walk strategy were used to improve the optimization ability of the original reptile search algorithm (RSA). The final results showed that the MRSA-SVM method combined with BL-SMOTE had the best classification performance, and the detection accuracy reached 97.22%. In addition, the recall, precision, F1 and kappa coefficient outperform other models. Furthermore, this study provided a valuable reference for the detection of defective fruit in other fruits.