Tuberculous pleural effusion-induced Arg-1+ macrophage polarization contributes to lung cancer progression via autophagy signaling.

BACKGROUND: The association between tuberculous fibrosis and lung cancer development has been reported by some epidemiological and experimental studies; however, its underlying mechanisms remain unclear, and the role of macrophage (MФ) polarization in cancer progression is unknown. The aim of the present study was to investigate the role of M2 Arg-1+ MФ in tuberculous pleurisy-assisted tumorigenicity in vitro and in vivo. METHODS: The interactions between tuberculous pleural effusion (TPE)-induced M2 Arg-1+ MФ and A549 lung cancer cells were evaluated. A murine model injected with cancer cells 2 weeks after Mycobacterium bovis bacillus Calmette-Guérin pleural infection was used to validate the involvement of tuberculous fibrosis to tumor invasion. RESULTS: Increased CXCL9 and CXCL10 levels of TPE induced M2 Arg-1+ MФ polarization of murine bone marrow-derived MФ. TPE-induced M2 Arg-1+ MФ polarization facilitated lung cancer proliferation via autophagy signaling and E-cadherin signaling in vitro. An inhibitor of arginase-1 targeting M2 Arg-1+ MФ both in vitro and in vivo significantly reduced tuberculous fibrosis-induced metastatic potential of lung cancer and decreased autophagy signaling and E-cadherin expression. CONCLUSION: Tuberculous pleural fibrosis induces M2 Arg-1+ polarization, and M2 Arg-1+ MФ contribute to lung cancer metastasis via autophagy and E-cadherin signaling. Therefore, M2 Arg-1+ tumor associated MФ may be a novel therapeutic target for tuberculous fibrosis-induced lung cancer progression.

Fast and easy detection of hypochlorite by a smartphone-based fluorescent turn-on probe: Applications to water samples, zebrafish and plant imaging.

We have developed a fluorescent probe DBT-Cl ((E)-2-(2-(4-(diphenylamino)benzylidene) hydrazinyl)-N,N,N-trimethyl-2-oxoethan-1-aminium chloride) for ClO- with an aggregation-induced emission (AIE) strategy depending on solvent polarity. DBT-Cl possessed a prominent solvatochromic emission property with intramolecular charge transfer (ICT) from the TPA (triphenylamine) to the amide group, which was studied by spectroscopic analysis and DFT calculations. These unique AIE properties of DBT-Cl led to the recognition of ClO- with high fluorescent selectivity. DBT-Cl quickly detected ClO- in less than 1 sec with a fluorescent color change from green to cyan. DBT-Cl had a low detection limit of 9.67 µM to ClO-. Detection mechanism of DBT-Cl toward ClO- was illustrated to be oxidative cleavage of DBT-Cl by 1H NMR titrations, ESI-mass, and DFT calculations. We established the viability for dependable detection of ClO- in actual water samples, as well as zebrafish and plant imaging. In particular, DBT-Cl was capable of easily monitoring ClO- through a smartphone application. Therefore, DBT-Cl assured a promising approach for a fast-responsive and multi-applicable ClO- probe in environmental and living organism systems.

A water-soluble colorimetric chemosensor for sequential probing of Cu2+ and S2- and its practical applications to test strips, reversible test, and water samples.

A water-soluble colorimetric chemosensor NHOP ((E)-1-(2-(2-(2-hydroxy-5-nitrobenzylidene)hydrazineyl)-2-oxoethyl)pyridin-1-ium) chloride) was developed for the sequential probing of Cu2+ and S2-. NHOP underwent a color change from pale yellow to colorless in the presence of Cu2+ in pure water. The binding ratio between NHOP and Cu2+ was confirmed to be 1:1 by the Job plot and ESI-MS (electrospray ionization mass spectrometry). The detection limit of NHOP for Cu2+ was calculated as 0.15 µM, which was far below the EPA (Environmental Protection Agency) standard (20 µM). The NHOP-coated test strip was able to easily monitor Cu2+ in real-time. Meanwhile, the NHOP-Cu2+ complex reverted from colorless to pale yellow in the presence of S2- through the demetallation. The stoichiometric ratio between NHOP-Cu2+ and S2- was determined to be 1:1 by analyzing the Job plot and ESI-MS. The detection limit of NHOP-Cu2+ for S2- was calculated as 0.29 µM, which was very below the WHO (World Health Organization) guideline (14.7 µM). NHOP successfully achieved the quantification for Cu2+ and S2- in water samples. NHOP could work as a sequential probe for Cu2+ and S2- at the biological pH range (7.0-8.4). Moreover, NHOP could successively probe Cu2+ and S2- at least three cycles because of its reversible property. The detection mechanisms of NHOP for Cu2+ and NHOP-Cu2+ for S2- were demonstrated with Job plot, ESI-MS, and DFT (density functional theory) calculations. Therefore, NHOP could work as an efficient sequential probe for Cu2+ and S2- in environmental systems.

Selective visual staining of polyurethane microplastics by novel colorimetric and near-infrared (NIR) fluorescent dye: Application to environmental water and natural soil samples.

Microplastics can cause environmental pollution and ecosystem destruction as well as human health problems. Among the types of microplastics, polyurethane (PU) is particularly resistant to heat and difficult to decompose, causing disposal problems, and is evaluated as one of the most hazardous polymers. We present a novel colorimetric and near-infrared (NIR) fluorescence dye, (E)-N-(2-((4-(diphenylamino)benzylidene)amino)phenyl)- 7-nitrobenzo[c][1,2,5]oxadiazol-4-amine (DPNA), designed for selective visual PU microplastic staining. The intramolecular charge transfer (ICT) properties of DPNA are demonstrated through density functional theory (DFT) calculations along with solvatochromic shift. DPNA exhibits red color and red fluorescence emission, showing promising potential as a staining dye. To achieve selective PU microplastic staining, we establish an optimized experimental procedure with the staining dye DPNA by evaluating the staining efficiency under different staining solvent compositions and staining times. DPNA can distinguish PU by both red fluorescence signal and red coloration among different types of microplastics. In addition, DPNA well stain fresh PUs with diverse sizes and at various pH range of 5-9, and the aged PUs can also be dyed as effectively as the fresh PU. Most importantly, DPNA selectively stains PU among 11 types of microplastics and 5 types of natural particles in environmental water and soil with and without any pre-treatments. The adsorption mechanism of DPNA on PU microplastic is demonstrated through field emission scanning electron microscopes (FE-SEM), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), and non-covalent interaction (NCI)-reduced density gradient (RDG) analyses, and proposed that intermolecular hydrogen bonding has a significant effect.

Effects of Prior Metformin Use on Stroke Outcomes in Diabetes Patients with Acute Ischemic Stroke Receiving Endovascular Treatment.

Diabetes mellitus (DM) predisposes individuals to vascular injury, leading to poor outcomes after ischemic stroke and symptomatic hemorrhagic transformation (SHT) after thrombolytic and endovascular treatment (EVT). Metformin (MET), an oral antidiabetic drug, has shown potential neuroprotective effects, but its impact on stroke prognosis in DM patients undergoing EVT remains unclear. In a multicenter study, 231 patients with DM undergoing EVT for acute ischemic stroke were enrolled. Prior MET use was identified, and patients were stratified into MET+ and MET- groups. Demographics, clinical data, and outcomes were compared between groups. Multivariate analysis was used to assess the effect of MET on stroke prognosis. Of the enrolled patients, 59.3% were previously on MET. MET+ patients had lower initial infarct volumes and NIHSS scores compared to MET-taking patients. Multivariate analysis showed that MET+ was associated with a lower risk of stroke progression and SHT (with stroke progression as follows: odd ratio [OR] 0.24, 95% confidence interval [CI] [0.12-0.48], p < 0.001; SHT: OR 0.33, 95% CI [0.14-0.75], p = 0.01) and was also associated with better 3-month functional outcomes (mRS 0-2) after EVT. Prestroke MET use in DM patients undergoing EVT is associated with improved stroke prognosis, including reduced risk of stroke progression and SHT and better functional outcomes. These findings suggest the potential neuroprotective role of MET in this population and highlight its clinical utility as an adjunctive therapy in the management of ischemic stroke. Further research is warranted to elucidate the underlying mechanisms and to optimize MET therapy in this setting.

Computer-aided diagnosis in real-time endoscopy for all stages of gastric carcinogenesis: Development and validation study.

OBJECTIVE: Using endoscopic images, we have previously developed computer-aided diagnosis models to predict the histopathology of gastric neoplasms. However, no model that categorizes every stage of gastric carcinogenesis has been published. In this study, a deep-learning-based diagnosis model was developed and validated to automatically classify all stages of gastric carcinogenesis, including atrophy and intestinal metaplasia, in endoscopy images. DESIGN: A total of 18,701 endoscopic images were collected retrospectively and randomly divided into train, validation, and internal-test datasets in an 8:1:1 ratio. The primary outcome was lesion-classification accuracy in six categories: normal/atrophy/intestinal metaplasia/dysplasia/early /advanced gastric cancer. External-validation of performance in the established model used 1427 novel images from other institutions that were not used in training, validation, or internal-tests. RESULTS: The internal-test lesion-classification accuracy was 91.2% (95% confidence interval: 89.9%-92.5%). For performance validation, the established model achieved an accuracy of 82.3% (80.3%-84.3%). The external-test per-class receiver operating characteristic in the diagnosis of atrophy and intestinal metaplasia was 93.4 ± 0% and 91.3 ± 0%, respectively. CONCLUSIONS: The established model demonstrated high performance in the diagnosis of preneoplastic lesions (atrophy and intestinal metaplasia) as well as gastric neoplasms.

Influence of Curved Video Laryngoscope Blade Sizes and Patient Heights on Video Laryngoscopic Views: A Randomized Controlled Trial.

This study aimed to compare the video laryngoscope views facilitated by curved blades 3 and 4 with an exploration of the relationship between these views and patient height. Conducted as a randomized controlled trial, this study enrolled adults scheduled for surgery under general anesthesia. Intubation procedures were recorded, and the percentage of glottic opening was measured before tube insertion. Multivariate analysis validated the impact of various factors, including blade size and patient height, on the percentage of glottic opening scores. A total of 192 patients were included. The median percentage of glottic opening scores for curved blades 3 and 4 were 100 and 83, respectively (p < 0.001). The unstandardized coefficient indicated a significant negative impact of blade 4 on the percentage of glottic opening scores (-13, p < 0.001). In the locally estimated scatterplot smoothing analysis, blade 3 exhibited a steady rise in glottic opening scores with increasing height, whereas blade 4 showed a peak followed by a decline around 185 cm. The unstandardized coefficient of height showed no significant association (0, p = 0.819). The study observed superior laryngoscopic views with blade 3 compared to blade 4. However, no significant association was found between laryngoscopic views and patient height.

A low-cost TICT-based staining agent for identification of microplastics: Theoretical studies and simple, cost-effective smartphone-based fluorescence microscope application.

A novel staining agent, (5-(4-(diethylamino)benzylidene)- 1,3-dimethylpyrimidine-2,4,6(1 H,3 H,5 H)-trione) (DDB) was developed for the effective detection of environmentally harmful microplastics. DDB has competitive cost advantages, namely its facile synthesis and high yield, over Nile Red (NR), which is commonly used for microplastic staining. The unique photophysical properties of DDB, including emissive twisted intramolecular charge transfer (TICT) and aggregation-induced emission (AIE), were corroborated via spectroscopic investigations and density functional theory (DFT) calculations. Notably, DDB demonstrated superior selectivity for staining microplastics (polyethylene (PE), polyurethane (PU), polypropylene (PP), polyvinyl chloride (PVC), polystyrene (PS), and polyethylene terephthalate (PET)) over non-plastic materials in water. Furthermore, modulation of the solvent environment during the staining process yielded distinct fluorescence in both the green and red channels for specific types of plastic with the interplay between locally excited (LE) and TICT states. Treatment with 5% ethanol results in the selective staining of PE and PET with the emission of red fluorescence, whereas treatment with 30% ethanol facilitates the selective staining of PU, PVC, and PET with the emission of green fluorescence. Additionally, DDB could selectively stain microplastics in spiked soil and river water samples. Furthermore, a smartphone-based fluorescence microscope was developed at a cost below $100, validating the effective detection of microplastics stained with the newly synthesized DDB. The outcomes of this research demonstrate the potential of DDB as an economical and efficient agent for selective microplastic detection.

Dual-channel fluorescence dye: Fluorescent color-dependent visual detection of microplastics and selective polyurethane.

In this study, we developed a dual-channel fluorescent dye ((E)-N'-(4-(diphenylamino)benzylidene)pyrazine-2-carbohydrazide) DPC for visual detection of 8 types of microplastics (MPs; HDPE, MDPE, LDPE, PET, PU, PVC, PS, and PP) and selective PU. The intramolecular charge transfer (ICT) and aggregation-induced emission (AIE) properties of DPC were demonstrated by the spectroscopic analysis, DFT calculations, and Tyndall effect. MPs and nonplastics (cellulose, chitin, sand, shell, and wood) were stained with DPC in water and their respective fluorescence signals in the blue and green channels were analyzed. The staining procedure using DPC was optimized with the concentration of DPC and staining time as parameters. DPC was able to effectively stain 8 types of MPs and only PU in blue and green fluorescence signals, respectively. Furthermore, false positive detections of DPC were minimized through additional ethanol treatment after staining. Moreover, the effects of temperature, pH, and salinity on the staining ability of DPC were investigated. Surprisingly, DPC was able to selectively detect PU through the green fluorescence signal even in a single environment where various MPs existed. Most importantly, DPC is the first fluorescent dye capable of selectively monitoring PU in the green channel as well as staining 8 types of MPs in the blue channel. DPC showed promising potential to be used for MP monitoring on real environmental samples.

Waste-Wood-Isolated Cellulose-Based Activated Carbon Paper Electrodes with Graphene Nanoplatelets for Flexible Supercapacitors.

Waste wood, which has a large amount of cellulose fibers, should be transformed into useful materials for addressing environmental and resource problems. Thus, this study analyzed the application of waste wood as supercapacitor electrode material. First, cellulose fibers were extracted from waste wood and mixed with different contents of graphene nanoplatelets (GnPs) in water. Using a facile filtration method, cellulose papers with GnPs were prepared and converted into carbon papers through carbonization and then to porous activated carbon papers containing GnPs (ACP-GnP) through chemical activation processes. For the morphology of ACP-GnP, activated carbon fibers with abundant pores were formed. The increase in the amount of GnPs attached to the fiber surfaces decreased the number of pores. The Brunauer-Emmett-Teller surface areas and specific capacitance of the ACP-GnP electrodes decreased with an increase in the GnP content. However, the galvanostatic charge-discharge curves of ACPs with higher GnP contents gradually changed into triangular and linear shapes, which are associated with the capacitive performance. For example, ACP with 15 wt% GnP had a low mass transfer resistance and high charge delivery of ions, resulting in the specific capacitance value of 267 Fg-1 owing to micropore and mesopore formation during the activation of carbon paper.

Deep Learning-Based Knee MRI Classification for Common Peroneal Nerve Palsy with Foot Drop.

Foot drop can have a variety of causes, including the common peroneal nerve (CPN) injuries, and is often difficult to diagnose. We aimed to develop a deep learning-based algorithm that can classify foot drop with CPN injury in patients with knee MRI axial images only. In this retrospective study, we included 945 MR image data from foot drop patients confirmed with CPN injury in electrophysiologic tests (n = 42), and 1341 MR image data with non-traumatic knee pain (n = 107). Data were split into training, validation, and test datasets using a 8:1:1 ratio. We used a convolution neural network-based algorithm (EfficientNet-B5, ResNet152, VGG19) for the classification between the CPN injury group and the others. Performance of each classification algorithm used the area under the receiver operating characteristic curve (AUC). In classifying CPN MR images and non-CPN MR images, EfficientNet-B5 had the highest performance (AUC = 0.946), followed by the ResNet152 and the VGG19 algorithms. On comparison of other performance metrics including precision, recall, accuracy, and F1 score, EfficientNet-B5 had the best performance of the three algorithms. In a saliency map, the EfficientNet-B5 algorithm focused on the nerve area to detect CPN injury. In conclusion, deep learning-based analysis of knee MR images can successfully differentiate CPN injury from other etiologies in patients with foot drop.

Factors Associated with Spontaneous Preterm Birth after Ultrasound-Indicated Cerclage.

Ultrasound-indicated cerclage (UIC) is recommended to prevent spontaneous preterm birth (sPTB) in women with a short cervix at mid-trimester and a history of PTB. We assessed the factors related to sPTB after UIC and determined the corresponding risks. This retrospective cohort study was conducted at a university hospital. UIC was performed between 15 and 26 weeks of gestation in women with a cervical length of <2.5 cm. Univariate and multivariate analyses were used to examine factors associated with sPTB after UIC. An earlier gestational age and shorter cervical length at UIC were associated with sPTB after UIC. While PTB history was not associated with an increased risk of sPTB, it did increase the risk of repeat cerclage after UIC. Higher levels of preoperative serum inflammatory markers and obesity significantly increased the risk of sPTB after UIC. These findings provide helpful guidance for patient counseling and management in predicting the delivery timing after UIC in women with a short cervix in the mid-trimester.

High ApoB/ApoA-I Ratio Predicts Post-Stroke Cognitive Impairment in Acute Ischemic Stroke Patients with Large Artery Atherosclerosis.

BACKGROUND: We aimed to investigate the association between the ApoB/ApoA-I ratio and post-stroke cognitive impairment (PSCI) in patients with acute stroke of large artery atherosclerosis etiology. METHODS: Prospective stroke registry data were used to consecutively enroll patients with acute ischemic stroke due to large artery atherosclerosis. Cognitive function assessments were conducted 3 to 6 months after stroke. PSCI was defined as a z-score of less than -2 standard deviations from age, sex, and education-adjusted means in at least one cognitive domain. The ApoB/ApoA-I ratio was calculated, and patients were categorized into five groups according to quintiles of the ratio. Logistic regression analyses were performed to assess the association between quintiles of the ApoB/ApoA-I ratio and PSCI. RESULTS: A total of 263 patients were included, with a mean age of 65.9 ± 11.6 years. The median NIHSS score and ApoB/ApoA-I ratio upon admission were 2 (IQR, 1-5) and 0.81 (IQR, 0.76-0.88), respectively. PSCI was observed in 91 (34.6%) patients. The highest quintile (Q5) of the ApoB/ApoA-I ratio was a significant predictor of PSCI compared to the lowest quintile (Q1) (adjusted OR, 3.16; 95% CI, 1.19-8.41; p-value = 0.021) after adjusting for relevant confounders. Patients in the Q5 group exhibited significantly worse performance in the frontal domain. CONCLUSIONS: The ApoB/ApoA-I ratio in the acute stage of stroke independently predicted the development of PSCI at 3-6 months after stroke due to large artery atherosclerosis. Further, a high ApoB/ApoA-I ratio was specifically associated with frontal domain dysfunction.

Predictive Modeling and Integrated Risk Assessment of Postoperative Mortality and Pneumonia in Traumatic Brain Injury Patients through Clustering and Machine Learning: Retrospective Study.

This study harnessed machine learning to forecast postoperative mortality (POM) and postoperative pneumonia (PPN) among surgical traumatic brain injury (TBI) patients. Our analysis centered on the following key variables: Glasgow Coma Scale (GCS), midline brain shift (MSB), and time from injury to emergency room arrival (TIE). Additionally, we introduced innovative clustered variables to enhance predictive accuracy and risk assessment. Exploring data from 617 patients spanning 2012 to 2022, we observed that 22.9% encountered postoperative mortality, while 30.0% faced postoperative pneumonia (PPN). Sensitivity for POM and PPN prediction, before incorporating clustering, was in the ranges of 0.43-0.82 (POM) and 0.54-0.76 (PPN). Following clustering, sensitivity values were 0.47-0.76 (POM) and 0.61-0.77 (PPN). Accuracy was in the ranges of 0.67-0.76 (POM) and 0.70-0.81 (PPN) prior to clustering and 0.42-0.73 (POM) and 0.55-0.73 (PPN) after clustering. Clusters characterized by low GCS, small MSB, and short TIE exhibited a 3.2-fold higher POM risk compared to clusters with high GCS, small MSB, and short TIE. In summary, leveraging clustered variables offers a novel avenue for predicting POM and PPN in TBI patients. Assessing the amalgamated impact of GCS, MSB, and TIE characteristics provides valuable insights for clinical decision making.

Effect of Cerebral Small Vessel Disease Burden on Infarct Growth Rate and Stroke Outcomes in Large Vessel Occlusion Stroke Receiving Endovascular Treatment.

This study aimed to investigate the association between cerebral small vessel disease (CSVD) burden and infarct growth rate (IGR) in patients with large vessel occlusion (LVO) stroke who underwent endovascular treatment (EVT). A retrospective analysis was conducted on a cohort of 495 patients with anterior circulation stroke who received EVT. CSVD burden was assessed using a CSVD score based on neuroimaging features. IGR was calculated from diffusion-weighted imaging (DWI) lesion volumes divided by the time from stroke onset to imaging. Clinical outcomes included stroke progression and functional outcomes at 3 months. Multivariate analyses were performed to assess the relationship between CSVD burden, IGR, and clinical outcomes. The fast IGR group had a higher proportion of high CSVD scores than the slow IGR group (24.4% vs. 0.8%, p < 0.001). High CSVD burden was significantly associated with a faster IGR (odds ratio [95% confidence interval], 26.26 [6.26-110.14], p < 0.001) after adjusting for confounding factors. High CSVD burden also independently predicted stroke progression and poor functional outcomes. This study highlights a significant relationship between CSVD burden and IGR in LVO stroke patients undergoing EVT. High CSVD burden was associated with faster infarct growth and worse clinical outcomes.

Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study.

BACKGROUND: Our research group previously established a deep-learning-based clinical decision support system (CDSS) for real-time endoscopy-based detection and classification of gastric neoplasms. However, preneoplastic conditions, such as atrophy and intestinal metaplasia (IM) were not taken into account, and there is no established model that classifies all stages of gastric carcinogenesis. OBJECTIVE: This study aims to build and validate a CDSS for real-time endoscopy for all stages of gastric carcinogenesis, including atrophy and IM. METHODS: A total of 11,868 endoscopic images were used for training and internal testing. The primary outcomes were lesion classification accuracy (6 classes: advanced gastric cancer, early gastric cancer, dysplasia, atrophy, IM, and normal) and atrophy and IM lesion segmentation rates for the segmentation model. The following tests were carried out to validate the performance of lesion classification accuracy: (1) external testing using 1282 images from another institution and (2) evaluation of the classification accuracy of atrophy and IM in real-world procedures in a prospective manner. To estimate the clinical utility, 2 experienced endoscopists were invited to perform a blind test with the same data set. A CDSS was constructed by combining the established 6-class lesion classification model and the preneoplastic lesion segmentation model with the previously established lesion detection model. RESULTS: The overall lesion classification accuracy (95% CI) was 90.3% (89%-91.6%) in the internal test. For the performance validation, the CDSS achieved 85.3% (83.4%-97.2%) overall accuracy. The per-class external test accuracies for atrophy and IM were 95.3% (92.6%-98%) and 89.3% (85.4%-93.2%), respectively. CDSS-assisted endoscopy showed an accuracy of 92.1% (88.8%-95.4%) for atrophy and 95.5% (92%-99%) for IM in the real-world application of 522 consecutive screening endoscopies. There was no significant difference in the overall accuracy between the invited endoscopists and established CDSS in the prospective real-clinic evaluation (P=.23). The CDSS demonstrated a segmentation rate of 93.4% (95% CI 92.4%-94.4%) for atrophy or IM lesion segmentation in the internal testing. CONCLUSIONS: The CDSS achieved high performance in terms of computer-aided diagnosis of all stages of gastric carcinogenesis and demonstrated real-world application potential.

Postoperative Nausea and Vomiting Prediction: Machine Learning Insights from a Comprehensive Analysis of Perioperative Data.

Postoperative nausea and vomiting (PONV) are common complications after surgery. This study aimed to present the utilization of machine learning for predicting PONV and provide insights based on a large amount of data. This retrospective study included data on perioperative features of patients, such as patient characteristics and perioperative factors, from two hospitals. Logistic regression algorithms, random forest, light-gradient boosting machines, and multilayer perceptrons were used as machine learning algorithms to develop the models. The dataset of this study included 106,860 adult patients, with an overall incidence rate of 14.4% for PONV. The area under the receiver operating characteristic curve (AUROC) of the models was 0.60-0.67. In the prediction models that included only the known risk and mitigating factors of PONV, the AUROC of the models was 0.54-0.69. Some features were found to be associated with patient-controlled analgesia, with opioids being the most important feature in almost all models. In conclusion, machine learning provides valuable insights into PONV prediction, the selection of significant features for prediction, and feature engineering.

Harnessing Machine Learning for Prediction of Postoperative Pulmonary Complications: Retrospective Cohort Design.

Postoperative pulmonary complications (PPCs) are significant causes of postoperative morbidity and mortality. This study presents the utilization of machine learning for predicting PPCs and aims to identify the important features of the prediction models. This study used a retrospective cohort design and collected data from two hospitals. The dataset included perioperative variables such as patient characteristics, preexisting diseases, and intraoperative factors. Various algorithms, including logistic regression, random forest, light-gradient boosting machines, extreme-gradient boosting machines, and multilayer perceptrons, have been employed for model development and evaluation. This study enrolled 111,212 adult patients, with an overall incidence rate of 8.6% for developing PPCs. The area under the receiver-operating characteristic curve (AUROC) of the models was 0.699-0.767, and the f1 score was 0.446-0.526. In the prediction models, except for multilayer perceptron, the 10 most important features were obtained. In feature-reduced models, including 10 important features, the AUROC was 0.627-0.749, and the f1 score was 0.365-0.485. The number of packed red cells, urine, and rocuronium doses were similar in the three models. In conclusion, machine learning provides valuable insights into PPC prediction, significant features for prediction, and the feasibility of models that reduce the number of features.

Association between Malnutrition and Migraine Risk Assessed Using Objective Nutritional Indices.

Dietary triggers are frequently linked to migraines. Although some evidence suggests that dietary interventions might offer a new avenue for migraine treatment, the connection between migraine and nutrition remains unclear. In this study, we explored the association between nutritional status and migraines. Clinical data spanning 11 years were sourced from the Smart Clinical Data Warehouse. The nutritional statuses of 6603 migraine patients and 90,509 controls were evaluated using the Controlling Nutrition Status (CONUT) score and the Prognostic Nutrition Index (PNI). The results showed that individuals with mild, moderate, and severe malnutrition were at a substantially higher risk of migraines than those with optimal nutrition, as determined by the CONUT score (adjusted odds ratio [aOR]: 1.72, 95% confidence interval [CI]: 1.63-1.82; aOR: 5.09, 95% CI: 4.44-5.84; aOR: 3.24, 95% CI: 2.29-4.59, p < 0.001). Similarly, moderate (PNI: 35-38) and severe (PNI < 35) malnutrition were associated with heightened migraine prevalence (aOR: 4.80, 95% CI: 3.85-5.99; aOR: 3.92, 95% CI: 3.14-4.89, p < 0.001) compared to those with a healthy nutritional status. These findings indicate that both the CONUT and PNI may be used as predictors of migraine risk and underscore the potential of nutrition-oriented approaches in migraine treatment.

Age-Related Variations in Postoperative Pain Intensity across 10 Surgical Procedures: A Retrospective Study of Five Hospitals in South Korea.

Age-related differences in pain perception have been reported in various contexts; however, their impact on postoperative pain intensity remains poorly understood, especially across different surgical procedures. Data from five hospitals were retrospectively analyzed, encompassing patients who underwent 10 distinct surgical procedures. Numeric rating scale scores were used to assess the worst postoperative pain intensity during the 24 h after surgery. The multivariate linear regression model analyzed the relationship between age and pain intensity. Subgroup analyses were performed according to sex and patient-controlled analgesia (PCA). This study included 41,187 patients. Among the surgeries studied, lumbar spine fusion (ß = -0.155, p < 0.001) consistently and significantly exhibited a decrease in worst postoperative pain with increasing age. Similar trends were observed in cholecystectomy (ß = -0.029, p < 0.001) and several other surgeries; however, the results were inconsistent across all analyses. Surgeries with higher percentages of PCA administration had lower median worst-pain scores. In conclusion, age may affect postoperative pain intensity after specific surgeries; however, a comprehensive understanding of the complex interplay between age, surgical intervention, and pain intensity is required. Pain management strategies should consider various factors, including age-related variations.