Thrombomodulin reduces α-synuclein generation and ameliorates neuropathology in a mouse model of Parkinson's disease.

The neurotoxic α-synuclein (α-syn) oligomers play an important role in the occurrence and development of Parkinson's disease (PD), but the factors affecting α-syn generation and neurotoxicity remain unclear. We here first found that thrombomodulin (TM) significantly decreased in the plasma of PD patients and brains of A53T α-syn mice, and the increased TM in primary neurons reduced α-syn generation by inhibiting transcription factor p-c-jun production through Erk1/2 signaling pathway. Moreover, TM decreased α-syn neurotoxicity by reducing the levels of oxidative stress and inhibiting PAR1-p53-Bax signaling pathway. In contrast, TM downregulation increased the expression and neurotoxicity of α-syn in primary neurons. When TM plasmids were specifically delivered to neurons in the brains of A53T α-syn mice by adeno-associated virus (AAV), TM significantly reduced α-syn expression and deposition, and ameliorated the neuronal apoptosis, oxidative stress, gliosis and motor deficits in the mouse models, whereas TM knockdown exacerbated these neuropathology and motor dysfunction. Our present findings demonstrate that TM plays a neuroprotective role in PD pathology and symptoms, and it could be a novel therapeutic target in efforts to combat PD. Schematic representation of signaling pathways of TM involved in the expression and neurotoxicity of α-syn. A TM decreased RAGE, and resulting in the lowered production of p-Erk1/2 and p-c-Jun, and finally reduce α-syn generation. α-syn oligomers which formed from monomers increase the expression of p-p38, p53, C-caspase9, C-caspase3 and Bax, decrease the level of Bcl-2, cause mitochondrial damage and lead to oxidative stress, thus inducing neuronal apoptosis. TM can reduce intracellular oxidative stress and inhibit p53-Bax signaling by activating APC and PAR-1. B The binding of α-syn oligomers to TLR4 may induce the expression of IL-1ß, which is subsequently secreted into the extracellular space. This secreted IL-1ß then binds to its receptor, prompting p65 to translocate from the cytoplasm into the nucleus. This translocation downregulates the expression of KLF2, ultimately leading to the suppression of TM expression. By Figdraw.

Nrf2 expression, mitochondrial fission, and neuronal apoptosis in the prefrontal cortex of methamphetamine abusers and rats.

Methamphetamine (MA), a representative amphetamine-type stimulant, is one of the most abused drugs worldwide. Studies have shown that MA-induced neurotoxicity is strongly associated with oxidative stress and apoptosis. While nuclear factor E2-related factor 2 (Nrf2), an antioxidant transcription factor, is known to exert neuroprotective effects, its role in MA-induced dopaminergic neuronal apoptosis remains incompletely understood. In the present study, we explored the effects of MA on the expression levels of Nrf2, dynamin-related protein 1 (Drp1), mitofusin 1 (Mfn1), cytochrome c oxidase (Cyt-c), and cysteine aspartate-specific protease 3 (Caspase 3), as well as the correlations between Nrf2 and mitochondrial dynamics and apoptosis. Brain tissue from MA abusers was collected during autopsy procedures. An MA-dependent rat model was also established by intraperitoneal administration of MA (10 mg/kg daily) for 28 consecutive days, followed by conditioned place preference (CPP) testing. Based on immunohistochemical staining and western blot analysis, the protein expression levels of Nrf2 and Mfn1 showed a decreasing trend, while levels of Drp1, Cyt-c, and Caspase 3 showed an increasing trend in the cerebral prefrontal cortex of both MA abusers and MA-dependent rats. Notably, the expression of Nrf2 was positively associated with the expression of Mfn1, but negatively associated with the expression levels of Drp1, Cyt-c, and Caspase 3. These findings suggest that oxidative stress and mitochondrial fission contribute to neuronal apoptosis, with Nrf2 potentially playing a critical role in MA-induced neurotoxicity.

Analysis of related factors for neuropsychiatric comorbidities in children with epilepsy.

OBJECTIVE: To analyze the risk factors affecting psychiatric behavior and study the psychobehavioral conditions of children with epilepsy. METHOD: We randomly selected and enrolled 294 children with epilepsy who visited and were hospitalized in the pediatric clinic of Hebei General Hospital between January 2017 and January 2022, as the study participants. We comprehensively assessed their cognitive functions using the Gesell development schedule or Wechsler Intelligence Scales. The participants were divided into the study group (n = 123) with cognitive impairment and the control group (n = 171) with normal cognitive functions, for analysis. RESULTS: There were statistically significant differences between the two groups in disease course, frequency of epilepsy, status epilepticus, and the number of antiseizure medications (ASMs) used (P < 0.05), while there were no statistically significant differences in age, gender, age of onset, form of onset, interictal epileptiform discharge, history of febrile convulsion, and the time from onset to initial visit (P > 0.05). Based on multivariate logistic regression analysis, the course of disease, frequency of onset, status epilepticus and number of ASMs used were identified as high-risk factors for cognitive impairment in children with epilepsy. Similarly, early onset, long course of disease, known etiology, and combination of multiple drugs have a negative impact on behavioral problems, school education, and social adaptability. CONCLUSION: The course of disease, the frequency of onset, status epilepticus, and the number of ASMs used are high-risk factors for cognitive impairment in children with epilepsy, which can be prevented and controlled early. When selecting ASMs, their advantages and disadvantages should be weighed. Moreover, the availability of alternative treatment options must be considered. With the help of genomic technology, the causes of epilepsy should be identified as early as possible, and precision medicine and gene therapy for children with epilepsy should be actively developed.

A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning.

PURPOSE: Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning. MATERIALS AND METHODS: Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI4DCT). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVISyn) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman's correlation (rs) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI4DCT and CTVISyn. Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI4DCT or CTVISyn, aimed at sparing high-functional lungs defined as the top 50% of the percentile ventilation ranges. Dose-volume (DVH) parameters regarding the planning target volume (PTV) and organs at risk (OARs) were recorded. Radiation pneumonitis (RP) risk was estimated using a dose-function (DFH)-based normal tissue complication probability (NTCP) model. RESULTS: CTVISyn showed a mean rs value of 0.65 ± 0.04 compared to CTVI4DCT. Mean DSC values over the top 50% and 60% of ventilation ranges were 0.41 ± 0.07 and 0.52 ± 0.10, respectively. In the test set (n = 7), all patients' RP-risk benefited from CTVI4DCT-guided plans (Riskmean_4DCT_vs_Clinical: 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVISyn-guided plans (Riskmean_Syn_vs_Clinical: 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVISyn and CTVI4DCT-guided plan (P > 0.05). CONCLUSION: Using deep-learning techniques, CTVISyn generated from planning CT exhibited a moderate-to-high correlation with CTVI4DCT. The CTVISyn-guided plans were comparable to the CTVI4DCT-guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings.

Correlating Buffering Agents' Premier pH with Interface Stability Toward Long-Term Zn Metal Anodes.

Aqueous solvents in Zn metal batteries inevitably induces hydrogen evolution reactions (HER) due to fluctuating pH levels in electrolytes, leading to severe side reactions and dendrite growth. To address these challenges, buffering agents have been recently proposed as a solution to maintain constant electrolyte pH values upon cycling. Nonetheless, the critical role of buffering additives' premier pH in determining interface stability is largely overlooked. Herein, two types of buffering agents, single amphoteric and conjugate acid-base pairs, are employed to correlate their initial pHs with the interface stability. Based on the observations, the lifetime of Zn metal anodes initially increases and then decreases as the initial pH level goes up from 2.0 to 5.0, with an optimal lifetime at pH 3.3 for both buffering agent categories. This phenomenon lies in ample H+ in low pH and rich OH- in high pH, leading to either severe HER or by-products passivation layer. The optimized pH allows cells to deliver a high average Coulombic efficiency of 99.61% over 1500 cycles at a large current density of 5 mA cm-2 , which is significantly superior to 345 cycles achieved in the pristine electrolyte. Furthermore, this enhanced interface enables stable Zn/activated carbon full batteries over 15 000 cycles.

Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks.

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.

Developing a Predictive Model for Minimal or Mild Endometriosis as a Clinical Screening Tool in Infertile Women: Uterosacral Tenderness as a Key Predictor.

STUDY OBJECTIVE: To develop a noninvasive predictive model based on patients with infertility for identifying minimal or mild endometriosis. DESIGN: A retrospective cohort study. SETTING: This study was conducted at a tertiary referral center. PATIENTS: A total of consecutive 1365 patients with infertility who underwent laparoscopy between January 2013 and August 2020 were divided into a training set (n = 910) for developing the predictive model and a validation set (n = 455) to confirm the model's prediction efficiency. The patients were randomly assigned in a 2:1 ratio. INTERVENTIONS: Sensitivities, specificities, area under the curve, the Hosmer-Lemeshow goodness of fit test, Net Reclassification Improvement index, and Integrated Discrimination Improvement index were evaluated in the training set to select the optimum model. In the validation set, the model's discriminations, calibrations, and clinical use were tested for validation. MEASUREMENTS AND MAIN RESULTS: In the training set, there were 587 patients with minimal or mild endometriosis and 323 patients without endometriosis. The combination of clinical parameters in the model was evaluated for both statistical and clinical significance. The best-performing model ultimately included body mass index, dysmenorrhea, dyspareunia, uterosacral tenderness, and serum cancer antigen 125 (CA-125). The nomogram based on this model demonstrated sensitivities of 87.7% and 93.3%, specificities of 68.6% and 66.4%, and area under the curve of 0.84 (95% confidence interval 0.81-0.87) and 0.85 (95% confidence interval 0.80-0.89) for the training and validation sets, respectively. Calibration curves and decision curve analyses also indicated that the model had good calibration and clinical value. Uterosacral tenderness emerged as the most valuable predictor. CONCLUSION: This study successfully developed a predictive model with high accuracy in identifying infertile women with minimal or mild endometriosis based on clinical characteristics, signs, and cost-effective blood tests. This model would assist clinicians in screening infertile women for minimal or mild endometriosis, thereby facilitating early diagnosis and treatment.

Structure of native chromatin fibres revealed by Cryo-ET in situ.

The structure of chromatin plays pivotal roles in regulating gene transcription, DNA replication and repair, and chromosome segregation. This structure, however, remains elusive. Here, using cryo-FIB and cryo-ET, we delineate the 3D architecture of native chromatin fibres in intact interphase human T-lymphoblasts and determine the in situ structures of nucleosomes in different conformations. These chromatin fibres are not structured as uniform 30 nm one-start or two-start filaments but are composed of relaxed, variable zigzag organizations of nucleosomes connected by straight linker DNA. Nucleosomes with little H1 and linker DNA density are distributed randomly without any spatial preference. This work will inspire future high-resolution investigations on native chromatin structures in situ at both a single-nucleosome level and a population level under many different cellular conditions in health and disease.

PGC-1α regulates critical period onset/closure, mediating cortical plasticity.

Peroxisome proliferator-activated receptor PPARγ coactivator-α (PGC-1α) is concentrated in inhibitory interneurons and plays a vital role in neuropsychiatric diseases. We previously reported some characteristic features of schizophrenia (SZ) in GABAergic neuron-specific Pgc-1alpha knockout (KO) mice (Dlx5/6-Cre: Pgc-1alphaf/f). However, there is a fundamental gap in the molecular mechanism by which the Pgc-1alpha gene is involved in the neurobehavioral abnormalities of SZ. The loss of critical period (CP) triggers-maturations of parvalbumin interneurons (PVIs) and brakes-and the formation of perineuronal nets (PNNs) implicates mistimed trajectories during adult brain development. In this study, using the Pgc-1alpha KO mouse line, we investigated the association of Pgc-1alpha gene deletion with SZ-like behavioral deficits, PVI maturation, PNN integrity and synaptic ultrastructure. These findings suggest that Pgc-1alpha gene deletion resulted in a failure of CP onset and closure, thereby prolonging cortical plasticity timing. To determine whether the manipulation of the PNN structure is a potential method of altering neuronal plasticity, GM6001, a broad-spectrum matrix metalloproteinase (MMP)-inhibitor was applied. Here we confirmed that the treatment could effectively correct the CP plasticity window and ameliorate the synaptic ultrastructure in the Pgc-1alpha KO brain. Moreover, the intervention effect on neuronal plasticity was followed by the rescue of short-term habituation deficits and the mitigation of aberrant salience, which are some characteristic features of SZ. Taken collectively, these findings suggest that the role of PGC-1α in regulating cortical plasticity is mediated, at least partially, through the regulation of CP onset/closure. Strategically introduced reinforcement of molecular brakes may be a novel preventive therapy for psychiatric disorders associated with PGC-1α dysregulation.

Effectiveness of different exercises in improving postural balance among Parkinson's disease patients: a systematic review and network meta-analysis.

Background: Exercise has been reported as an effective intervention for Parkinson's disease. However, there is still debate on the what kinds of exercises prior to choosing. This study aimed to compare and rank the different exercises that effectively enhance postural balance in Parkinson's disease patients by quantifying the information gleaned from randomized controlled trials (RCTs). Methods: We conducted a comprehensive database search, including PubMed, Cochrane Library, Embase, Web of Science, and PsycINFO. The included studies were evaluated for methodological quality by the Cochrane Risk of Bias tool. Results: The RCTs were collected between the earliest available date and March 2023. Sixty RCTs were included and the total sample size used in the study was 3,537. Thirty-five studies were defined as low risk of bias, twenty-one studies as medium risk of bias, and four studies as high risk of bias. The network meta-analysis results showed that exergaming exercise can significantly improve patients' Timed-Up-and-Go time (SUCRA = 91.5%). Dance can significantly enhance patients' Berg Balance Scale (surface under the cumulative ranking curve, SUCRA = 81.3%), and rhythmical auditory exercise can significantly improve patients' Mini-Balance Evaluation Systems Test score (SUCRA = 95.6%). Conclusion: Compared with other exercises, exergaming exercise, Dance, and rhythmical auditory exercise showed superior efficacy in improving postural balance among Parkinson's disease patients. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42023411918.

Bioinspired rotary flight of light-driven composite films.

Light-driven actuators have great potential in different types of applications. However, it is still challenging to apply them in flying devices owing to their slow response, small deflection and force output and low frequency response. Herein, inspired by the structure of vine maple seeds, we report a helicopter-like rotary flying photoactuator (in response to 0.6 W/cm2 near-infrared (NIR) light) with ultrafast rotation (~7200 revolutions per minute) and rapid response (~650 ms). This photoactuator is operated based on a fundamentally different mechanism that depends on the synergistic interactions between the photothermal graphene and the hygroscopic agar/silk fibroin components, the subsequent aerodynamically favorable airscrew formation, the jet propulsion, and the aerodynamics-based flying. The soft helicopter-like photoactuator exhibits controlled flight and steering behaviors, making it promising for applications in soft robotics and other miniature devices.

Clinical evaluation of deep learning-based automatic clinical target volume segmentation: a single-institution multi-site tumor experience.

PURPOSE: The large variability in tumor appearance and shape makes manual delineation of the clinical target volume (CTV) time-consuming, and the results depend on the oncologists' experience. Whereas deep learning techniques have allowed oncologists to automate the CTV delineation, multi-site tumor analysis is often lacking in the literature. This study aimed to evaluate the deep learning models that automatically contour CTVs of tumors at various sites on computed tomography (CT) images from objective and subjective perspectives. METHODS AND MATERIALS: 577 patients were selected for the present study, including nasopharyngeal (n = 34), esophageal (n = 40), breast-conserving surgery (BCS) (left-sided, n = 71; right-sided, n = 71), breast-radical mastectomy (BRM) (left-sided, n = 43; right-sided, n = 37), cervical (radical radiotherapy, n = 45; postoperative, n = 85), prostate (n = 42), and rectal (n = 109) carcinomas. Manually delineated CTV contours by radiation oncologists are served as ground truth. Four models were evaluated: Flexnet, Unet, Vnet, and Segresnet, which are commercially available in the medical product "AccuLearning AI model training platform". The data were divided into the training, validation, and testing set at a ratio of 5:1:4. The geometric metrics, including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), were calculated for objective evaluation. For subjective assessment, oncologists rated the segmentation contours of the testing set visually. RESULTS: High correlations were observed between automatic and manual contours. Based on the results of the independent test group, most of the patients achieved satisfactory quantitative results (DSC > 0.8), except for patients with esophageal carcinoma (DSC: 0.62-0.64). The subjective review indicated that 82.65% of predicted CTVs scored either as clinically accepting (8.68%) or requiring minor revision (73.97%), and no patients were scored as rejected. CONCLUSION: This experimental work demonstrated that auto-generated contours could serve as an initial template to help oncologists save time in CTV delineation. The deep learning-based auto-segmentations achieve acceptable accuracy and show the potential to improve clinical efficiency for radiotherapy of a variety of cancer.

Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image.

BACKGROUND: The main application of [18F] FDG-PET (18 FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and CT images to mine pulmonary perfusion information for functional lung avoidance radiation therapy (FLART) is desirable but remains challenging. PURPOSE: To develop a deep-learning-based (DL) method to combine 18 FDG-PET and CT images for producing pulmonary perfusion images (PPI). METHODS: Pulmonary technetium-99 m-labeled macroaggregated albumin SPECT (PPISPECT ), 18 FDG-PET, and CT images obtained from 53 patients were enrolled. CT and PPISPECT images were rigidly registered, and registration displacement was subsequently used to align 18 FDG-PET and PPISPECT images. The left/right lung was separated and rigidly registered again to improve the registration accuracy. A DL model based on 3D Unet architecture was constructed to directly combine multi-modality 18 FDG-PET and CT images for producing PPI (PPIDLM ). 3D Unet architecture was used as the basic architecture, and the input was expanded from a single-channel to a dual-channel to combine multi-modality images. For comparative evaluation, 18 FDG-PET images were also used alone to generate PPIDLPET . Sixty-seven samples were randomly selected for training and cross-validation, and 36 were used for testing. The Spearman correlation coefficient (rs ) and multi-scale structural similarity index measure (MS-SSIM) between PPIDLM /PPIDLPET and PPISPECT were computed to assess the statistical and perceptual image similarities. The Dice similarity coefficient (DSC) was calculated to determine the similarity between high-/low- functional lung (HFL/LFL) volumes. RESULTS: The voxel-wise rs and MS-SSIM of PPIDLM /PPIDLPET were 0.78 ± 0.04/0.57 ± 0.03, 0.93 ± 0.01/0.89 ± 0.01 for cross-validation and 0.78 ± 0.11/0.55 ± 0.18, 0.93 ± 0.03/0.90 ± 0.04 for testing. PPIDLM /PPIDLPET achieved averaged DSC values of 0.78 ± 0.03/0.64 ± 0.02 for HFL and 0.83 ± 0.01/0.72 ± 0.03 for LFL in the training dataset and 0.77 ± 0.11/0.64 ± 0.12, 0.82 ± 0.05/0.72 ± 0.06 in the testing dataset. PPIDLM yielded a stronger correlation and higher MS-SSIM with PPISPECT than PPIDLPET (p < 0.001). CONCLUSIONS: The DL-based method integrates lung metabolic and anatomy information for producing PPI and significantly improved the accuracy over methods based on metabolic information alone. The generated PPIDLM can be applied for pulmonary perfusion volume segmentation, which is potentially beneficial for FLART treatment plan optimization.

Chlorogenic Acid Induced Neuroblastoma Cells Differentiation via the ACAT1-TPK1-PDH Pathway.

BACKGROUND: Chlorogenic acid (CHA) has been shown to have substantial biological activities, including anti-inflammatory, antioxidant, and antitumor effects. However, the pharmacological role of CHA in neuroblastoma has not yet been assessed. Neuroblastoma is a type of cancer that develops in undifferentiated sympathetic ganglion cells. This study aims to assess the antitumor activity of CHA against neuroblastoma and reveal its mechanism of action in cell differentiation. METHODS: Be(2)-M17 and SH-SY5Y neuroblastoma cells were used to confirm the differentiation phenotype. Subcutaneous and orthotopic xenograft mouse models were also used to evaluate the antitumor activity of CHA. Seahorse assays and metabolomic analyses were further performed to investigate the roles of CHA and its target ACAT1 in mitochondrial metabolism. RESULTS: CHA induced the differentiation of Be(2)-M17 and SH-SY5Y neuroblastoma cells in vivo and in vitro. The knockdown of mitochondrial ACAT1, which was inhibited by CHA, also resulted in differentiation characteristics in vivo and in vitro. A metabolomic analysis revealed that thiamine metabolism was involved in the differentiation of neuroblastoma cells. CONCLUSIONS: These results provide evidence that CHA shows good antitumor activity against neuroblastoma via the induction of differentiation, by which the ACAT1-TPK1-PDH pathway is involved. CHA is a potential drug candidate for neuroblastoma therapy.

Examining Concentration-Reliant Zn Deposition/Stripping Behavior in Organic Alcohol/Sulfones-Modified Aqueous Electrolytes.

The practical application of Zn metal anodes in electronic devices is hindered by dendrite growth and parasitic reactions. Electrolyte optimization, particularly the introduction of organic co-solvents, is widely used to circumvent these challenges. Various organic solvents in a wide range of concentrations have been reported; however, their influences and corresponding working mechanisms at different concentrations are largely unexplored in the same organic species. Herein, economical, low-flammable ethylene glycol (EG) is used as a model co-solvent in aqueous electrolytes to examine the relationship between its concentration, anode-stabilizing effect, and mechanism. Two maximal values are observed for the lifetime of Zn/Zn symmetric batteries under EG concentrations from 0.05 vol% to 48 vol%. Zn metal anodes can stably run for over 1700 h at a low EG content (0.25 vol%) and high EG content (40 vol%). Based on the complementary experimental and theoretical calculations, the enhancements in low- and high-content EG are ascribed to the specific surface adsorption for suppressed dendrite growth and the regulated solvation structure for inhibited side reactions, respectively. Intriguingly, a similar concentration-reliant bimodal phenomenon is observed in other low-flammable organic solvents (e.g., glycerol and dimethyl sulfoxide), thereby suggesting universality of this study and providing insight into electrolyte optimization.

Point-of-care ultrasound diagnosis of skull fracture in Chinese children 0-6 years old with scalp hematoma from minor head trauma: A preliminary prospective observational study.

Background: Previous studies have suggested that point-of-care ultrasound could help to evaluate and diagnose pediatric skull fracture for the closed scalp hematoma from blunt trauma. However, relevant data in Chinese children are missing, especially in children 0-6 years old. Objectives: Our study aimed to evaluate the efficacy of point-of-care ultrasound to diagnose skull fracture in children 0-6 years old with scalp hematoma in China. Methods: We performed a prospective observational study and screened children 0-6 years old with closed scalp hematoma and a Glasgow coma scale of 14-15 at Hospital in China. Enrolled children (N = 152) were first evaluated for skull fracture with point-of-care ultrasound by the emergency physician and then received a head computed tomography scan. Results: The point-of-care ultrasound examination and computed tomography scan revealed skull fracture in 13 (8.6%) and 12 (7.9%) children, respectively. The kappa test showed a satisfactory agreement between two examinations (P < 0.0001), with kappa = 0.87 (95% confidence interval, i.e., 95% CI, [0.69, 1.00]) and area under the curve = 0.95 (95% CI [0.86, 1], P < 0.0001). The point-of-care ultrasound examination had the sensitivity of 91.7% (95% CI [62.5%, 100%]), specificity of 98.6% (95% CI [94.6%, 100%]), positive predictive value of 84.6% (95% CI [56.5%, 96.9%]), negative predictive value of 99.2% (95% CI [95.6%, 100%]), and accuracy of 98.0% (95% CI [94.1%, 99.6%]). Conclusions: While our study is preliminary in nature, our findings may guide future larger studies in assessing the utility of point-of-care ultrasound examination in diagnosing skull fractures in children with scalp hematoma from minor head trauma.

Prediction of potential drug-microbe associations based on matrix factorization and a three-layer heterogeneous network.

Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly. Using computational methods to predict microbe-drug associations can effectively complement biological experiments. In this experiment, we constructed heterogeneity networks for drugs, microbes, and diseases using multiple biomedical data sources. Then, we developed a model with matrix factorization and a three-layer heterogeneous network (MFTLHNMDA) to predict potential drug-microbe associations. The probability of microbe-drug association was obtained by a global network-based update algorithm. Finally, the performance of MFTLHNMDA was evaluated in the framework of leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The results showed that our model performed better than six state-of-the-art methods that had AUC of 0.9396 and 0.9385 + /- 0.0000, respectively. This case study further confirms the effectiveness of MFTLHNMDA in identifying potential drug-microbe associations and new drug-microbe associations.

Molecular Simulation Study on the Interaction between Porcine CR1-like and C3b.

The molecular basis of porcine red blood cell immune adhesion function stems from the complement receptor type 1-like (CR1-like) on its cell membrane. The ligand for CR1-like is C3b, which is produced by the cleavage of complement C3; however, the molecular mechanism of the immune adhesion of porcine erythrocytes is still unclear. Here, homology modeling was used to construct three-dimensional models of C3b and two fragments of CR1-like. An interaction model of C3b-CR1-like was constructed by molecular docking, and molecular structure optimization was achieved using molecular dynamics simulation. A simulated alanine mutation scan revealed that the amino acids Tyr761, Arg763, Phe765, Thr789, and Val873 of CR1-like SCR 12-14 and the amino acid residues Tyr1210, Asn1244, Val1249, Thr1253, Tyr1267, Val1322, and Val1339 of CR1-like SCR 19-21 are key residues involved in the interaction of porcine C3b with CR1-like. This study investigated the interaction between porcine CR1-like and C3b using molecular simulation to clarify the molecular mechanism of the immune adhesion of porcine erythrocytes.

CT ventilation image-guided helical Tomotherapy at sparing functional lungs for locally advanced lung cancer: analysis of dose-function metrics and the impact on pulmonary toxicity.

PURPOSE: CT ventilation image (CTVI)-guided radiotherapy that selectively avoids irradiating highly-functional lung regions has potential to reduce pulmonary toxicity. Considering Helical TomoTherapy (HT) has higher modulation capabilities, we investigated the capability and characteristic of HT at sparing functional lungs for locally advanced lung cancer. METHODS AND MATERIALS: Pretreatment 4DCT scans were carried out for 17 patients. Local lung volume expansion (or contraction) during inspiration is related to the volume change at a given lung voxel and is used as a surrogate for ventilation. The ventilation maps were generated from two sets of CT images (peak-exhale and peak-inhale) by deformable registration and a Jacobian-based algorithm. Each ventilation map was normalized to percentile images. Six plans were designed for each patient: one anatomical plan without ventilation map and five functional plans incorporating ventilation map which designed to spare varying degrees of high-functional lungs that were defined as the top 10%, 20%, 30%, 40%, and 50% of the percentile ventilation ranges, respectively. The dosimetric and evaluation factors were recorded regarding planning target volume (PTV) and other organs at risk (OARs), with particular attention to the dose delivered to total lung and functional lungs. An established dose-function-based normal tissue complication probability (NTCP) model was used to estimate risk of radiation pneumonitis (RP) for each scenario. RESULTS: Patients were divided into a benefit group (8 patients) and a non-benefit group (9 patients) based on whether the RP-risk of functional plan was lower than that of anatomical plan. The distance between high-ventilated region and PTV, as well as tumor volume had significant differences between the two groups (P < 0.05). For patients in the benefit group, the mean value of fV5, fV10, fV20, and fMLD (functional V5, V10, V20, and mean lung dose, respectively) were significantly lower starting from top 30% functional plan than in anatomical plan (P < 0.05). With expand of avoidance region in functional plans, the dose coverage of PTV is not sacrificed (P > 0.05) but at the cost of increased dose received by OARs. CONCLUSION: Ventilation image-guided HT plans can reduce the dose received by highly-functional lung regions with a range up to top 50% ventilated area. The spatial distribution of ventilation and tumor size were critical factors to better select patients who could benefit from the functional plan.

FOXG1 Contributes Adult Hippocampal Neurogenesis in Mice.

Strategies to enhance hippocampal precursor cells efficiently differentiate into neurons could be crucial for structural repair after neurodegenerative damage. FOXG1 has been shown to play an important role in pattern formation, cell proliferation, and cell specification during embryonic and early postnatal neurogenesis. Thus far, the role of FOXG1 in adult hippocampal neurogenesis is largely unknown. Utilizing CAG-loxp-stop-loxp-Foxg1-IRES-EGFP (Foxg1fl/fl), a specific mouse line combined with CreAAV infusion, we successfully forced FOXG1 overexpressed in the hippocampal dentate gyrus (DG) of the genotype mice. Thereafter, we explored the function of FOXG1 on neuronal lineage progression and hippocampal neurogenesis in adult mice. By inhibiting p21cip1 expression, FOXG1-regulated activities enable the expansion of the precursor cell population. Besides, FOXG1 induced quiescent radial-glia like type I neural progenitor, giving rise to intermediate progenitor cells, neuroblasts in the hippocampal DG. Through increasing the length of G1 phase, FOXG1 promoted lineage-committed cells to exit the cell cycle and differentiate into mature neurons. The present results suggest that FOXG1 likely promotes neuronal lineage progression and thereby contributes to adult hippocampal neurogenesis. Elevating FOXG1 levels either pharmacologically or through other means could present a therapeutic strategy for disease related with neuronal loss.