A Comparison of Maxillofacial and Head Injuries Following Electric Scooter and Bicycle Accidents.

BACKGROUND: The prevalence of maxillofacial and head injuries associated with electric scooters (e-scooter, ES) has risen in concordance with its popularity. PURPOSE: The purpose of this study was to compare maxillofacial and head injury location, type, and severity related to ES and bicycle accidents and to identify factors contributing to injury severity. STUDY DESIGN, SETTING, SAMPLE: The authors implemented a multicenter retrospective cohort study in Seattle, Washington, and enrolled a sample of ES riders and bicyclists who sustained maxillofacial injuries between September 2020 and September 2022. The exclusion criteria included nonmotorized scooters, motorized bicycles, injuries with other operators, or vehicles, and pre-evaluation deaths. PREDICTOR VARIABLE: The predictor variable was vehicle type, bicycle or ES. OUTCOME VARIABLES: The outcome variables included maxillofacial injury location, distinguished by horizontal facial thirds and injury type, defined as hard or soft tissue. Associated head injury types were also reported as hard (calvaria) or soft (scalp) tissue injuries. The severity of these injuries was quantified using both the injury severity score and the face and head abbreviated injury scale. COVARIATES: Demographic, injury, and treatment-related variables were collected. ANALYSES: Bivariate, multivariate, and regression statistics were computed. Statistical significance was P < .05. RESULTS: The final sample was composed of 205 total subjects, of which 52 (25.4%) were in the ES group and 153 (74.6%) in the bicycle group. Isolated midface injuries were the most common hard tissue location in the ES (15.4%) and bicycle (29.4%) groups. The most common soft tissue injury location included the upper face and midface in the ES group (19.2%) and the midface in the bicycle group (22.9%). Both hard and soft tissue head injuries were more prevalent in the ES group (P < .0002 and P < .0001). Moreover, intracranial injuries were seen in 36.5% of ES subjects compared to 9.8% bicycle subjects (P < .0001). Between the two groups there was no difference in maxillofacial injury severity, but head injuries were more severe in the ES group (P < .0002). Using regression analysis, drug use was found to have a significant impact on the mean injury severity score (P < .002) and helmet use did not have significant impact on face or head injury severity. CONCLUSION: Maxillofacial injury location, type, and severity are comparable among ES and bicycles. However, ES riders are at greater risk of severe head injuries compared to bicycles, and riding while intoxicated has the greatest effect on injury severity.

Enhancing the Electrochemical Performances of Disordered Li1.23Ni0.3Nb0.3Fe0.16O0.85F0.15 Cathode Material for Lithium-Ion Batteries by LiNbOx Coating.

For the next generation of lithium-ion batteries (LIBs), it is primary to seek high capacity and long-lifetime electrode materials. Li-excess disordered rock-salt structure (DRS) cathodes have gained much attention due to their high specific capacity. However, Li-excess can lead to a decrease in the structural stability of an electrode material. A new Li-rich DRS oxyfluorides, Li1.23Ni0.3Nb0.3Fe0.16O0.85F0.15 (F0.15) with a series amounts of LiNbOx (LN) coating (0, 5, 10, and 15 wt % denoted as F0.15-LN0, F0.15-LN5, F0.15-LN10, and F0.15-LN15, respectively), are successfully synthesized and evaluated as cathode materials in LIBs. Among them, F0.15-LN10 exhibits the highest initial discharge specific capacity of 296.1 mAh g-1 (at a current density of 20 mA g-1) with the capacity retention rate of 14% higher than that of the uncoated F0.15 after 80 cycles. Even at 300 mA g-1, F0.15-LN10 still delivers the highest discharge specific capacity of 130 mAh g-1. After 20 cycles, the charge-transfer impedance of F0.15-LN10 remained the smallest. The characterizations indicate that LN coating reduces the surface polarization of the cathode materials, slows the interfacial side reactions between the electrolyte and the electrode, and speeds up the Li+ diffusion. These results demonstrate that LN coating is an effective strategy to improve the electrochemical performance.

Optimal intraventricular hemorrhage volume cutoff for predicting poor outcome in patients with intracerebral hemorrhage.

BACKGROUND AND OBJECTIVES: The prognosis of patients with spontaneous intracerebral hemorrhage (ICH) is often influenced by hematoma volume, a well-established predictor of poor outcome. However, the optimal intraventricular hemorrhage (IVH) volume cutoff for predicting poor outcome remains unknown. METHODS: We analyzed 313 patients with spontaneous ICH not undergoing evacuation, including 7 cases with external ventricular drainage (EVD). These patients underwent a baseline CT scan, followed by a 24-hour CT scan for measurement of both hematoma and IVH volume. We defined hematoma growth as hematoma growth > 33 % or 6 mL at follow-up CT, and poor outcome as modified Rankin Scale score≥3 at three months. Cutoffs with optimal sensitivity and specificity for predicting poor outcome were identified using receiver operating curves. RESULTS: The receiver operating characteristic analysis identified 6 mL as the optimal cutoff for predicting poor outcome. IVH volume> 6 mL was observed in 53 (16.9 %) of 313 patients. Patients with IVH volume>6 mL were more likely to be older and had higher NIHSS score and lower GCS score than those without. IVH volume>6 mL (adjusted OR 2.43, 95 % CI 1.13-5.30; P = 0.026) was found to be an independent predictor of poor clinical outcome at three months in multivariable regression analysis. CONCLUSIONS: Optimal IVH volume cutoff represents a powerful tool for improving the prediction of poor outcome in patients with ICH, particularly in the absence of clot evacuation or common use of EVD. Small amounts of intraventricular blood are not independently associated with poor outcome in patients with intracerebral hemorrhage. The utilization of optimal IVH volume cutoffs may improve the clinical trial design by targeting ICH patients that will obtain maximal benefit from therapies.

Effects of reactive oxygen species and mitochondrial dysfunction on reproductive aging.

Mitochondria, the versatile organelles crucial for cellular and organismal viability, play a pivotal role in meeting the energy requirements of cells through the respiratory chain located in the inner mitochondrial membrane, concomitant with the generation of reactive oxygen species (ROS). A wealth of evidence derived from contemporary investigations on reproductive longevity strongly indicates that the aberrant elevation of ROS level constitutes a fundamental factor in hastening the aging process of reproductive systems which are responsible for transmission of DNA to future generations. Constant changes in redox status, with a pro-oxidant shift mainly through the mitochondrial generation of ROS, are linked to the modulation of physiological and pathological pathways in gametes and reproductive tissues. Furthermore, the quantity and quality of mitochondria essential to capacitation and fertilization are increasingly associated with reproductive aging. The article aims to provide current understanding of the contributions of ROS derived from mitochondrial respiration to the process of reproductive aging. Moreover, understanding the impact of mitochondrial dysfunction on both female and male fertility is conducive to finding therapeutic strategies to slow, prevent or reverse the process of gamete aging, and thereby increase reproductive longevity.

CETSA-MS-based target profiling of anti-aging natural compound quercetin.

BACKGROUND: Quercetin is widely distributed in nature and abundant in the human diet, which exhibits diverse biological activities and potential medical benefits. However, there remains a lack of comprehensive understanding about its cellular targets, impeding its in-depth mechanistic studies and clinical applications. PURPOSE: This study aimed to profile protein targets of quercetin at the proteome level. METHODS: A label-free CETSA-MS proteomics technique was employed for target enrichment and identification. The R package Inflect was used for melting curve fitting and target selection. D3Pocket and LiBiSco tools were used for binding pocket prediction and binding pocket analysis. Western blotting, molecular docking, site-directed mutagenesis and pull-down assays were used for target verification and validation. RESULTS: We curated a library of direct binding targets of quercetin in cells. This library comprises 37 proteins that show increased thermal stability upon quercetin binding and 33 proteins that display decreased thermal stability. Through Western blotting, molecular docking, site-directed mutagenesis and pull-down assays, we validated CBR1 and GSK3A from the stabilized protein group and MAPK1 from the destabilized group as direct binding targets of quercetin. Moreover, we characterized the shared chemical properties of the binding pockets of quercetin with targets. CONCLUSION: Our findings deepen our understanding of the proteins pivotal to the bioactivity of quercetin and lay the groundwork for further exploration into its mechanisms of action and potential clinical applications.

Clinical Application of Automatic Assessment of Scoliosis Cobb Angle Based on Deep Learning.

INTRODUCTION: A recently developed deep-learning-based automatic evaluation model provides reliable and efficient Cobb angle measurements for scoliosis diagnosis. However, few studies have explored its clinical application, and external validation is lacking. Therefore, this study aimed to explore the value of automated assessment models in clinical practice by comparing deep-learning models with manual measurement methods. METHODS: The 481 spine radiographs from an open-source dataset were divided into training and validation sets, and 119 spine radiographs from a private dataset were used as the test set. The mean Cobb angle values assessed by three physicians in the hospital's PACS system served as the reference standard. The results of Seg4Reg, VFLDN, and manual measurement were statistically analyzed. The intra-class correlation coefficients (ICC) and the Pearson correlation coefficient (PCC) were used to compare their reliability and correlation. The Bland-Altman method was used to compare their agreement. The Kappa statistic was used to compare the consistency of Cobb angles at different severity levels. RESULTS: The mean Cobb angle values measured were 35.89° ± 9.33° with Seg4Reg, 31.54° ± 9.78° with VFLDN, and 32.23° ± 9.28° with manual measurement. The ICCs for the reliability of Seg4Reg and VFLDN were 0.809 and 0.974, respectively. The PCC and MAD between Seg4Reg and manual measurements were 0.731 (p<0.001) and 6.51°, while those between VFLDN and manual measurements were 0.952 (p<0.001) and 2.36°. The Kappa statistic indicated VFLDN (k= 0.686, p< 0.001) was superior to Seg4Reg and manual measurements for Cobb angle severity classification. CONCLUSION: The deep-learning-based automatic scoliosis Cobb angle assessment model is feasible in clinical practice. Specifically, the keypoint-based VFLDN is more valuable in actual clinical work with higher accuracy, transparency, and interpretability.

A quality assurance framework for routine monitoring of deep learning cardiac substructure computed tomography segmentation models in radiotherapy.

BACKGROUND: For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor-collected data) and the data on which the model is deployed in the clinic are typically not the same, potentially impacting the performance of these models by a process called domain shift. Tools to routinely monitor and predict segmentation performance are needed for quality assurance. Here, we develop an approach to perform such monitoring and performance prediction for cardiac substructure segmentation. PURPOSE: To develop a quality assurance (QA) framework for routine or continuous monitoring of domain shift and the performance of cardiac substructure autosegmentation algorithms. METHODS: A benchmark dataset consisting of computed tomography (CT) images along with manual cardiac substructure delineations of 241 breast cancer radiotherapy patients were collected, including one "normal" image domain of clean images and five "abnormal" domains containing images with artifact (metal, contrast), pathology, or quality variations due to scanner protocol differences (field of view, noise, reconstruction kernel, and slice thickness). The QA framework consisted of an image domain shift detector which operated on the input CT images and a shape quality detector on the output of an autosegmentation model, and a regression model for predicting autosegmentation model performance. The image domain shift detector was composed of a trained denoising autoencoder (DAE) and two hand-engineered image quality features to detect normal versus abnormal domains in the input CT images. The shape quality detector was a variational autoencoder (VAE) trained to estimate the shape quality of the auto-segmentation results. The output from the image domain shift and shape quality detectors was used to train a regression model to predict the per-patient segmentation accuracy, measured by Dice coefficient similarity (DSC) to physician contours. Different regression techniques were investigated including linear regression, Bagging, Gaussian process regression, random forest, and gradient boost regression. Of the 241 patients, 60 were used to train the autosegmentation models, 120 for training the QA framework, and the remaining 61 for testing the QA framework. A total of 19 autosegmentation models were used to evaluate QA framework performance, including 18 convolutional neural network (CNN)-based and one transformer-based model. RESULTS: When tested on the benchmark dataset, all abnormal domains resulted in a significant DSC decrease relative to the normal domain for CNN models ( p < 0.001 $p < 0.001$ ), but only for some domains for the transformer model. No significant relationship was found between the performance of an autosegmentation model and scanner protocol parameters ( p = 0.42 $p = 0.42$ ) except noise ( p = 0.01 $p = 0.01$ ). CNN-based autosegmentation models demonstrated a decreased DSC ranging from 0.07 to 0.41 with added noise, while the transformer-based model was not significantly affected (ANOVA, p = 0.99 $p=0.99$ ). For the QA framework, linear regression models with bootstrap aggregation resulted in the highest mean absolute error (MAE) of 0.041 ± 0.002 $0.041 \pm 0.002$ , in predicted DSC (relative to true DSC between autosegmentation and physician). MAE was lowest when combining both input (image) detectors and output (shape) detectors compared to output detectors alone. CONCLUSIONS: A QA framework was able to predict cardiac substructure autosegmentation model performance for clinically anticipated "abnormal" domain shifts.

Age-related differences in risk factors, clinical characteristics, and outcomes for intracerebral hemorrhage.

Background and purpose: Intracerebral hemorrhage (ICH) is a severe form of stroke that remains understudied in the young adults. We aimed to investigate the clinical presentation, and risk factors associated with ICH in this age group and compare them to older patients. Methods: Our study included ICH patients admitted between March 2016 and December 2021 in the First Affiliated Hospital of Chongqing Medical University from our ongoing prospective cohort database. Demographic characteristics, etiology, risk factors, and clinical outcomes were compared between elderly and young patients. Furthermore, logistic regression analysis was employed to explore risk factors associated with the functional outcome at 3-months. Results: We selected 1,003 patients (mean age, 59.9 ±13.8 years old), 746 (74.4%) patients were aged >50 years. The logistic regression analysis showed young patients have a higher proportion of secondary ICH, higher white blood cell count and higher body mass index (BMI), but less diabetes mellitus. Of all patients, predictors of 3-month functional independence was first-ever ICH and age ≤50 years. The history of nephropathy and stroke, higher baseline NIHSS score, larger hematoma volume, and the presence of hydrocephalus were associated with poor outcomes. And the white blood cell count could significantly influence the prognosis among young ICH patients. Three-month functional outcome based on modified Rankin scale score was better in young patients than the elderly (OR, 1.232; 95% CI, 1.095-1.388; p < 0.001). Conclusions: The highest incidence of ICH occurs in the age groups of 50-59 and 60-69. ICH in young adults had higher white blood cell and BMI compared to the elderly, and differs in etiological distribution. The young patients also had similar short-term mortality but more favorable functional outcomes than the elderly. Furthermore, NIHSS score and larger hematoma volumes were associated with poor outcome in all patients.

DAUF: A disease-related attentional UNet framework for progressive and stable mild cognitive impairment identification.

Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) plays a significant role in early Alzheimer's disease (AD) diagnosis, which can effectively boost the life quality of patients. Recently, convolutional neural network (CNN)- based methods using structural magnetic resonance imaging (sMRI) images have shown effective for AD identification. However, these CNN-based methods fail to effectively explore the feature extraction of disease-related multi-scale tissues, such as ventricles, hippocampi and cerebral cortex. To address this issue, we propose an end-to-end disease-related attentional UNet framework (DAUF) for identifying pMCI and sMCI, by embedding a devised dual disease-related attention module (D2AM) and a novel tree-structured feature fusion classifier (TFFC). Specifically, D2AM leverages the complementarity between feature maps and attention maps and the complementary features from the encoder and decoder, so as to highlight discriminative semantic and detailed features. Additionally, TFFC is a powerfully joint multi-scale feature fusion and classification head, by employing the homogeneity among multi-scale features, so that the discriminative features of the multi-scale tissues are adequately fused for enhancing classification performance. Finally, extensive experiments demonstrate the superior performance of DAUF, with the effectiveness of D2AM and TFFC on identifying pMCI and sMCI subjects.

Prediction of condylar movement envelope surface based on facial morphology.

The present study aimed to predict the envelope surfaces from facial morphology. Condylar envelope surfaces for 34 healthy adults were formed and simplified as sagittal section curves. Cephalometric and maximum mandibular moving distances measurement were performed on the participants. There was no statistically significant difference (p = 0.763) between the left and right maximum lateral movements. There was a statistically significant difference in the mandibular body length between the sexes. The envelope surfaces were divided into type 1 with Hp2 ≥ 1/3 Hp1 and type 2 with Hp2 < 1/3 × Hp1. SNA and SNB for type 2 were significantly greater than those for type 1 (p < 0.001). Therefore, the participants were divided into four groups based on gender and envelope surface morphology. The curves could be fitted using the second-order Fourier function (R-square ≥0.95). Six facial parameters were selected and a matrix was used to map facial morphology to the envelope surface. Individual sagittal curves were predicted using the matrix and facial parameters, and the envelope surface was predicted using the curve and the condyle model. Deviation analysis for the predicted envelope surface using the actual envelope as a reference was carried out (root mean square = 0.9970 mm ± 0.2918 mm). This method may lay a foundation for the geometric design of artificial fossa components of temporomandibular joint replacement systems. It may improve prosthesis design without flexible tissue repair and guide the movement of the artificial joint head.

Engineering Active-Site-Induced Homogeneous Growth of Polydopamine Nanocontainers on Loading-Enhanced Ultrathin Graphene for Smart Self-Healing Anticorrosion Coatings.

Engineering nanocontainers with encapsulated inhibitors onto graphene has been an emerging technology for developing self-healing anticorrosion coatings. However, the loading contents of inhibitors are commonly limited by inhomogeneous nanostructures of graphene platforms. Here, we propose an activation-induced ultrathin graphene platform (UG-BP) with the homogeneous growth of polydopamine (PDA) nanocontainers encapsulated with benzotriazole (BTA). Ultrathin graphene prepared by catalytic exfoliation and etching activation provides an ideal platform with an ultrahigh specific surface area (1646.8 m2/g) and homogeneous active sites for the growth of PDA nanocontainers, which achieves a high loading content of inhibitors (40 wt %). The obtained UG-BP platform exhibits pH-sensitive corrosion inhibition effects due to its charged groups. The epoxy/UG-BP coating possesses integrated properties of enhanced mechanical properties (>94%), efficient pH-sensitive self-healing behaviors (98.5% healing efficiency over 7 days), and excellent anticorrosion performance (4.21 × 109 Ω·cm2 over 60 days), which stands out from previous related works. Moreover, the interfacial anticorrosion mechanism of UG-BP is revealed in detail, which can inhibit the oxidation of Fe2+ and promote the passivation of corrosion products by a dehydration process. This work provides a universal activation-induced strategy for developing loading-enhanced and tailor-made graphene platforms in extended smart systems and demonstrates a promising smart self-healing coating for advanced anticorrosion applications.

Corrosion Behavior of Nitrided Layer of Ti6Al4V Titanium Alloy by Hollow Cathodic Plasma Source Nitriding.

Ti6Al4V titanium alloys, with high specific strength and good biological compatibility with the human body, are ideal materials for medical surgical implants. However, Ti6Al4V titanium alloys are prone to corrosion in the human environment, which affects the service life of implants and harms human health. In this work, hollow cathode plasm source nitriding (HCPSN) was used to generate nitrided layers on the surfaces of Ti6Al4V titanium alloys to improve their corrosion resistance. Ti6Al4V titanium alloys were nitrided in NH3 at 510 °C for 0, 1, 2, and 4 h. The microstructure and phase composition of the Ti-N nitriding layer was characterized by high-resolution transmission electron microscopy, atomic force microscopy, scanning electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. This modified layer was identified to be composed of TiN, Ti2N, and α-Ti (N) phase. To study the corrosion properties of different phases, the nitriding 4 h samples were mechanically ground and polished to obtain the various surfaces of Ti2N and α-Ti (N) phases. The potentiodynamic polarization and electrochemical impedance measurements were conducted in Hank's solution to characterize the corrosion resistance of Ti-N nitriding layers in the human environment. The relationship between corrosion resistance and the microstructure of the Ti-N nitriding layer was discussed. The new Ti-N nitriding layer that can improve corrosion resistance provides a broader prospect for applying Ti6Al4V titanium alloy in the medical field.

Mercury accumulation potential of aquatic plant species in West Dongting Lake, China.

West Dongting Lake is a protected wetland with the potential for high levels of mercury release via wastewater and deposition from industry and agriculture during the last decade. To find out the ability of various plant species to accumulate mercury pollutants from soil and water, nine sites were studied in the downstream direction of the flow of the Yuan and Li Rivers, which are tributaries of the Yellow River flowing into West Dongting Lake, where mercury levels arere high in soil and plant tissues. The total mercury (THg) concentration in wetland soil was 0.078-1.659 mg/kg, which varied along the gradient of water flow along the river. According to canonical correspondence analysis and correlation analysis, there was a positive correlation between the soil THg concentration and the soil moisture in West Dongting Lake. There is high heterogeneity in the spatial distribution of soil THg concentration in West Dongting Lake, which may be related to the spatial heterogeneity of the soil moisture. Some plant species had higher THg concentrations in aboveground tissues (translocation factor >1), but none of these plant species fit the criteria as hyperaccumulators of mercury. And some species of the same ecological type (e.g., emergent, submergent, floating-leaved) exhibited very different strategies for mercury uptake. The concentrations of mercury in these species were lower than in other studies but these had relatively higher translocation factors. To phytoremediate soil mercury in West Dongting Lake, the regular harvest of plants could help remove mercury from soil and plant tissue.

Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction.

BACKGROUND: Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling. PURPOSE: This study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images. METHODS: A 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC). RESULTS: The trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average (± standard deviation) RMSE in mm-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040 ± 0.0009, 0.0029 ± 0.0002, 0.0024 ± 0.0003 and 0.0021 ± 0.0003. Corresponding NCC were 0.84 ± 0.05, 0.91 ± 0.05, 0.91 ± 0.05 and 0.93 ± 0.04. CONCLUSIONS: CNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.

Clinical and imaging predictors of dysphagia and swallowing ability recovery in acute ischemic stroke.

OBJECTIVE: Dysphagia is one of the most common complications of acute ischemic stroke, and prediction of dysphagia is crucial for post-stroke treatment. We aimed to identify predictors of dysphagia and swallowing function recovery following ischemic stroke and to investigate dysphagia-associated lesion location. METHODS: We prospectively enrolled patients with acute ischemic stroke confirmed on diffusion-weighted imaging. All patients received swallowing evaluation within 48 h after admission. Follow-up oral intake ability was measured on 7 and 30 days after stroke onset. Voxel-based lesion-symptom mapping was performed to determine locations associated with dysphagia. RESULTS: Of 126 patients included in the final analysis, 23 patients (18.3%) were classified as initial dysphagia. The presence of facial palsy (P = 0.008) and larger white matter hyperintensity (WMH) volume (P = 0.003) was associated with initial dysphagia. Initial risk of aspiration assessed by Any2 score (P = 0.001) at baseline was identified as independent predictor for dysphagia at day 7. Patients with higher Any2 score (P < 0.001), aphasia (P = 0.013), and larger WMH volume (P = 0.010) were less likely to have a full swallowing function recovery at 1 month. Acute infarcts in right corona radiata and right superior longitudinal fasciculus were correlated with impaired recovery of swallowing ability at 1 month. CONCLUSIONS: Initial risk of aspiration was identified as risk factor for short-term and long-term dysphagia. Aphasia and larger WMH volume were revealed to be significant predictors for swallowing function recovery at 1 month. Right corona radiata was identified as an essential brain area for dysphagia.

Automatic Microtitrator for Small Volume Samples.

Electroanalytical sensors for point-of-care biomedical or point-of-use environmental sample analysis are gaining popularity due to low limits of detection, ease of miniaturization, convenience, and ability to work with small sample volumes. Since pH must be tightly controlled for optimum electrochemical performance, adjustment of pH in these samples is often a necessity. Yet manual titration is time-consuming and can be especially challenging for small volumes. End point determination can also be difficult. Current commercial automatic pH titrators are generally designed for large volume (>1 mL) batch titrations, while the existing microvolume titrators are semiautomatic at best, still relying on multiple manual steps. To address the gap, we developed an automatic microtitration system suitable for small volume samples. The system was validated using digested whole blood microsamples, successfully demonstrating accurate and rapid pH adjustment for samples as small as 100 µL. The simple modular construction of the system makes it compatible with acid washing for trace metal detection and other cleaning or sample preparation steps. The electrochemical detection of manganese heavy metal in blood at the parts per billion level showed no detectable contamination induced by the system. Ultimately, our simple, accurate, user-friendly automatic microtitration system can be used in the pH adjustment of microvolume samples and can potentially be extended to other pH end point analysis.

Reactive Oxygen Species Contributes to Type 2 Diabetic Neuropathic Pain via the Thioredoxin-Interacting Protein-NOD-Like Receptor Protein 3- N -Methyl-D-Aspartic Acid Receptor 2B Pathway.

BACKGROUND: The number of patients with diabetic neuropathic pain (DNP) continues to increase, but available treatments are limited. This study aimed to examine the influence of reactive oxygen species (ROS)-thioredoxin-interacting protein (TXNIP)-NOD-like receptor protein 3 (NLRP3)- N -methyl-D-aspartic acid receptor 2B (NR2B) pathway on type 2 DNP. METHODS: Male Sprague-Dawley rats were fed with a high-fat and high-sugar diet for 8 weeks. Then, rats were intraperitoneally injected with streptozotocin (STZ, 35 mg/kg) to induce type 2 diabetes mellitus in rats. Diabetic rats with <85% of their basic levels in mechanical withdrawal threshold and thermal withdrawal latency were classified as DNP rats on day 14 after STZ injection. DNP rats were treated with ROS scavenger N-tert-Butyl-α-phenylnitrone (PBN, 100 mg·kg -1 ·d -1 ) or TXNIP small interfering ribonucleic acid (10 µg/d) once daily for 14 days. The level of ROS, protein levels of NLRP3, TXNIP, cysteinyl aspartate-specific proteinase-1 (caspase-1), interleukin-1ß (IL-1ß), NR2B phosphorylation at Tyr1472 (p-NR2B), total NR2B (t-NR2B), and distribution of NLRP3 in the spinal cord were examined. In vitro experiments, BV2 cells and PC12 cells were individually cultured and cocultured in a high-glucose environment (35 mmol/L D-glucose). The level of ROS and protein levels of NLRP3, TXNIP, caspase-1, and IL-1ß in BV2 cells, and p-NR2B, t-NR2B in PC12 cells were detected. The level of ROS was detected by the flow cytometry approach. The protein levels were detected by the Western blot technique. The location of NLRP3 was observed by immunofluorescent staining. The interaction between TXNIP and NLRP3 was detected by coimmunoprecipitation assay. RESULTS: The level of spinal ROS increased in DNP rats. The mechanical allodynia and thermal hyperalgesia of DNP rats were alleviated after systemic administration of PBN. This administration decreased protein levels of NLRP3, TXNIP, caspase-1, IL-1ß, and p-NR2B and the coupling of TXNIP to NLRP3 in spinal cords of DNP rats. Furthermore, knockdown of spinal TXNIP alleviated nociceptive hypersensitivity and decreased protein levels of NLRP3, TXNIP, caspase-1, IL-1ß, and p-NR2B in DNP rats. The level of ROS and protein levels of NLRP3, TXNIP, caspase-1, IL-1ß, the coupling of TXNIP to NLRP3, and the IL-1ß secretion increased in BV2 cells, and the protein expression of p-NR2B increased in cocultured PC12 cells in a high-glucose environment. All of these in vitro effects were significantly blocked after treatment of PBN. CONCLUSIONS: Our findings suggest that spinal ROS can contribute to type 2 DNP through TXNIP-NLRP3-NR2B pathway.

Effect of sodium-glucose cotransporter protein-2 inhibitors on left ventricular hypertrophy in patients with type 2 diabetes: A systematic review and meta-analysis.

Objective: This systematic review and meta-analysis was performed to compare the effect of sodium-glucose cotransporter protein-2 inhibitors (SGLT-2i) and placebo on left ventricular hypertrophy (LVH) in patients with type 2 diabetes. Method: Randomized controlled trials (RCTs) comparing the LVH parameters of SGLT-2i to placebo in patients with type 2 diabetes were included. Our primary outcomes were the changes in left ventricular mass (LVM) and left ventricular mass index (LVMI) from baseline to the study endpoint. Secondary outcomes were the changes in left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), left ventricular ejection fraction (LVEF), and the ratio of early mitral inflow velocity to atrial inflow velocity (E/A). Summary odds ratios were estimated using a fixed-effect or random-effect model. Results: A total of 11 articles were included. Data were extracted from 11 original studies matching our inclusion criteria. In our meta-analysis, there were significant improvement in LVM (SMD -0.23, 95% CI -0.44 to -0.02, I 2 = 22.6%, p = 0.034), LVMI (SMD -0.25, 95% CI -0.38 to -0.12, I 2 = 0.0%, p = 0.000), LVEDV (SMD -0.19, 95% CI -0.36 to -0.01, I 2 = 62.3%, p = 0.035), and LVESV (SMD -0.21, 95% CI -0.39 to -0.04, I 2 = 32.9%, p = 0.017) in the SGLT-2i group compared with the placebo group. Furthermore, no significant differences were found in LVEF (SMD 0.13, 95% CI 0.00 to 0.26, I 2 = 0.0%, p = 0.050) and E/A (SMD -0.01, 95% CI -0.22 to 0.20, I 2 = 0%, p = 0.908) between the two groups. Conclusions: This meta-analysis confirmed the beneficial effects of SGLT-2i on reversal of left ventricular remodeling. The LVH regression was more pronounced in studies of type 2 diabetes patients receiving SGLT-2i than placebo.

THAN: task-driven hierarchical attention network for the diagnosis of mild cognitive impairment and Alzheimer's disease.

BACKGROUND: To assist doctors to diagnose mild cognitive impairment (MCI) and Alzheimer's disease (AD) early and accurately, convolutional neural networks based on structural magnetic resonance imaging (sMRI) images have been developed and shown excellent performance. However, they are still limited in their capacity in extracting discriminative features because of large sMRI image volumes yet small lesion regions and the small number of sMRI images. METHODS: We proposed a task-driven hierarchical attention network (THAN) taking advantage of the merits of patch-based and attention-based convolutional neural networks for MCI and AD diagnosis. THAN consists of an information sub-network and a hierarchical attention sub-network. In the information sub-network, an information map extractor, a patch-assistant module, and a mutual-boosting loss function are designed to generate a task-driven information map, which automatically highlights disease-related regions and their importance for final classification. In the hierarchical attention sub-network, a visual attention module and a semantic attention module are devised based on the information map to extract discriminative features for disease diagnosis. RESULTS: Extensive experiments were conducted for four classification tasks: MCI versus (vs.) normal controls (NC), AD vs. NC, AD vs. MCI, and AD vs. MCI vs. NC. Results demonstrated that THAN attained the accuracy of 81.6% for MCI vs. NC, 93.5% for AD vs. NC, 80.8% for AD vs. MCI, and 62.9% for AD vs. MCI vs. NC. It outperformed advanced attention-based and patch-based methods. Moreover, information maps generated by the information sub-network could highlight the potential biomarkers of MCI and AD, such as the hippocampus and ventricles. Furthermore, when the visual and semantic attention modules were combined, the performance of the four tasks was highly improved. CONCLUSIONS: The information sub-network can automatically highlight the disease-related regions. The hierarchical attention sub-network can extract discriminative visual and semantic features. Through the two sub-networks, THAN fully exploits the visual and semantic features of disease-related regions and meanwhile considers global features of sMRI images, which finally facilitate the diagnosis of MCI and AD.

Identification of a novel SPT inhibitor WXP-003 by docking-based virtual screening and investigation of its anti-fungi effect.

Serine palmitoyltransferase (SPT) plays the key role on catalysing the formation of 3-ketodihydrosphingosine, which is the first step of the de novo biosynthesis of sphingolipids. SPT is linked to many diseases including fungal infection, making it a potential therapeutic target. Thus, a logical docking-based virtual screening method was used to screen selective SPT inhibitor against fungi, not human. We used myriocin-similarity database to identify compounds with good binding with fungal SPT and poor binding with homology human SPT model. Preliminary bio-assay led to the discovery of a promising inhibitor WXP-003, which displayed good inhibitory activity against diversity fungi strains with MIC ranging from 0.78 to 12.5 µg/mL. WXP-003 could significantly reduce sphingolipids content in fungi and no effect on mouse fibroblast cell line L929. Molecular dynamics simulation depicted that SPT/WXP-003 complex formed the favoured interactions. Taken together, discovery of WXP-003 provided valuable guide for the development of novel anti-fungal agents.