P2Y12 inhibitor use predicts hematoma expansion in patients with intracerebral hemorrhage.

OBJECTIVE: Hematoma expansion (HE) predicts disability and death after acute intracerebral hemorrhage (ICH). Aspirin and anticoagulants have been associated with HE. We tested the hypothesis that P2Y12 inhibitors predict subsequent HE in patients. We explored laboratory measures of P2Y12 inhibition and dual antiplatelet therapy with aspirin (DAPT). METHODS: We prospectively identified patients with ICH. Platelet activity was measured with the VerifyNow-P2Y12 assay. Hematoma volumes for initial and follow-up CTs were calculated using a validated semi-automated technique. HE was defined as the difference between hematoma volumes on the initial and follow-up CT scans. Nonparametric statistics were performed with Kruskal-Wallis H, and correction for multiple comparisons performed with Dunn's test. RESULTS: In 194 patients, 15 (7.7%) were known to take a P2Y12 inhibitor (clopidogrel in all but one). Patients taking a P2Y12 inhibitor had more HE compared to patients not taking a P2Y12 inhibitor (3.5 [1.2-11.9] vs. 0.1 [-0.8-1.4] mL, p = 0.004). Patients taking DAPT experienced the most HE (7.2 [2.6-13.8] vs. 0.0 [-1.0-1.1] mL, p = 0.04). The use of P2Y12 inhibitors was associated with less P2Y12 activity (178 [149-203] vs. 288 [246-319] P2Y12 reaction units, p = 0.005). INTERPRETATION: Patients taking a P2Y12 inhibitor had more HE and less P2Y12 activity. The effect was most pronounced in patients on DAPT, suggesting a synergistic effect of P2Y12 inhibitors and aspirin with respect to HE. Acute reversal of P2Y12 inhibitors in acute ICH requires further study.

AI-based denoising of head impact kinematics measurements with convolutional neural network for traumatic brain injury prediction.

OBJECTIVE: Wearable devices are developed to measure head impact kinematics but are intrinsically noisy because of the imperfect interface with human bodies. This study aimed to improve the head impact kinematics measurements obtained from instrumented mouthguards using deep learning to enhance traumatic brain injury (TBI) risk monitoring. METHODS: We developed one-dimensional convolutional neural network (1D-CNN) models to denoise mouthguard kinematics measurements for tri-axial linear acceleration and tri-axial angular velocity from 163 laboratory dummy head impacts. The performance of the denoising models was evaluated on three levels: kinematics, brain injury criteria, and tissue-level strain and strain rate. Additionally, we performed a blind test on an on-field dataset of 118 college football impacts and a test on 413 post-mortem human subject (PMHS) impacts. RESULTS: On the dummy head impacts, the denoised kinematics showed better correlation with reference kinematics, with relative reductions of 36% for pointwise root mean squared error and 56% for peak absolute error. Absolute errors in six brain injury criteria were reduced by a mean of 82%. For maximum principal strain and maximum principal strain rate, the mean error reduction was 35% and 69%, respectively. On the PMHS impacts, similar denoising effects were observed and the peak kinematics after denoising were more accurate (relative error reduction for 10% noisiest impacts was 75.6%). CONCLUSION: The 1D-CNN denoising models effectively reduced errors in mouthguard-derived kinematics measurements on dummy and PMHS impacts. SIGNIFICANCE: This study provides a novel approach for denoising head kinematics measurements in dummy and PMHS impacts, which can be further validated on more real-human kinematics data before real-world applications.

The Effects of Ambient Temperature and Atmospheric Humidity on the Diffusion Dynamics of Hydrogen Fluoride Gas Leakage Based on the Computational Fluid Dynamics Method.

In order to investigate the impact of environmental temperature and atmospheric humidity on the leakage and diffusion of hydrogen fluoride (HF) gas, this study focused on the real scenario of an HF chemical industrial park. Based on the actual dispersion scenario of HF gas, a proportionally scaled-down experimental platform for HF gas leakage was established to validate the accuracy and feasibility of numerical simulations under complex conditions. Using the validated model, the study calculated the complex scenarios of HF leakage and diffusion within the temperature range of 293 K to 313 K and the humidity range of 0% to 100%. The simulation results indicated that different environmental temperatures had a relatively small impact on the hazardous areas (the lethal area, severe injury area, light injury area, and maximum allowable concentration (MAC) area) formed by HF gas leakage. At 600 s of dispersion, the fluctuation range of hazardous area sizes under different temperature conditions was between 3.11% and 13.07%. In contrast to environmental temperature, atmospheric relative humidity had a more significant impact on the dispersion trend of HF leakage. Different relative humidity levels mainly affected the areas of the lethal zone, light injury zone, and MAC zone. When HF continued to leak and disperse for 600 s, compared to 0% relative humidity, 100% relative humidity reduced the lethal area by 35.7%, while increasing the light injury area and MAC area by 27.26% and 111.6%, respectively. The impact on the severe injury area was relatively small, decreasing by 1.68%. The results of this study are crucial for understanding the dispersion patterns of HF gas under different temperature and humidity conditions.

Enhancing the photodynamic effect of curcumin through modification with TiO2 nanoparticles and cationic polymers.

Curcumin (CUR), a natural compound extracted from turmeric, has shown potential as a photosensitizer in photodynamic therapy (PDT). The aim of this work was to enhance the efficacy of CUR by modifying it using titanium dioxide (TiO2) nanoparticles and a cationic polymer called Sofast to create a nanocomposite TiO2-CUR-Sofast (TCS). Compared to unmodified CUR, TCS exhibited a broadening toward longer wavelength in the absorption wavelength within the 400-550 nm range, leading to improved CUR absorption. Cellular uptake efficiency of TCS was also enhanced, and it demonstrated nearly 4.7-fold higher reactive oxygen species (ROS) generation than CUR. Furthermore, TCS displayed the ability to attach to the cell membrane and enter cells within a 30-min incubation period. Upon irradiation, TCS exhibited remarkable cytotoxicity, resulting in a significant reduction in the viability of various cancer cells. Autofluorescence lifetime imaging of intracellular reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) enzymes indicated that cancer cells treated with TCS and irradiation undergo a metabolic pathway shift from oxidative phosphorylation to glycolysis. These findings highlight the potential of TCS as an effective PDT agent for cancer treatment.

Topical rhubarb charcoal-crosslinked chitosan/silk fibroin sponge scaffold for the repair of diabetic ulcers improves hepatic lipid deposition in db/db mice via the AMPK signalling pathway.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is closely linked to metabolic syndrome, characterised by insulin resistance, hyperglycaemia, abnormal lipid metabolism, and chronic inflammation. Diabetic ulcers (DUs) comprise consequential complications that arise as a result of T2DM. To investigate, db/db mice were used for the disease model. The findings demonstrated that a scaffold made from a combination of rhubarb charcoal-crosslinked chitosan and silk fibroin, designated as RCS/SF, was able to improve the healing process of diabetic wounds in db/db mice. However, previous studies have primarily concentrated on investigating the impacts of the RSC/SF scaffold on wound healing only, while its influence on the entire body has not been fully elucidated. MATERIAL AND METHODS: The silk fibroin/chitosan sponge scaffold containing rhubarb charcoal was fabricated in the present study using a freeze-drying approach. Subsequently, an incision with a diameter of 8 mm was made on the dorsal skin of the mice, and the RCS/SF scaffold was applied directly to the wound for 14 days. Subsequently, the impact of RCS/SF scaffold therapy on hepatic lipid metabolism was assessed through analysis of serum and liver biochemistry, histopathology, quantitative real-time PCR (qRT-PCR), immunohistochemistry, and Western blotting. RESULTS: The use of the RCS/SF scaffold led to an enhancement in the conditions associated with serum glucolipid metabolism in db/db mice. An assessment of hepatic histopathology further confirmed this enhancement. Additionally, the qRT-PCR analysis revealed that treatment with RCS/SF scaffold resulted in the downregulation of genes associated with fatty acid synthesis, fatty acid uptake, triglyceride (TG) synthesis, gluconeogenesis, and inflammatory factors. Moreover, the beneficial effect of the RCS/SF scaffold on oxidative stress was shown by assessing antioxidant enzymes and lipid peroxidation. Additionally, the network pharmacology analysis verified that the adenosine monophosphate-activated protein kinase (AMPK) signalling pathway had a vital function in mitigating non-alcoholic fatty liver disease (NAFLD) by utilizing R. officinale. The measurement of AMPK, sterol regulatory element binding protein 1 (SREBP1), fatty acid synthase (FASN), and acetyl CoA carboxylase (ACC) gene and protein expression provided support for this discovery. Furthermore, the molecular docking investigations revealed a robust affinity between the active components of rhubarb and the downstream targets of AMPK (SREBP1 and FASN). CONCLUSION: By regulating the AMPK signalling pathway, the RCS/SF scaffold applied topically effectively mitigated hepatic lipid accumulation, decreased inflammation, and attenuated oxidative stress. The present study, therefore, emphasises the crucial role of the topical RCS/SF scaffold in regulating hepatic lipid metabolism, thereby confirming the concept of "external and internal reshaping".

Brain deformation estimation with transfer learning for head impact datasets across impact types.

OBJECTIVE: Brain strain and strain rate are effective biomechanics predictors of traumatic brain injury (TBI) caused by head impacts. However, state-of-the-art finite element modeling (FEM) demands considerable computational time, limiting its application in real-time TBI risk monitoring. To accelerate, machine learning head models (MLHMs) were developed to predict brain strain based on head kinematics measurements, but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM. METHODS: To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). RESULTS: The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than 7 s-1 in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models. CONCLUSION: The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. SIGNIFICANCE: This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

Robust point light source calibration method for near-field photometric stereo using feature points selection.

In this paper, we present a robust method for nonisotropic point light source calibration through feature points selection. By analyzing the relationship between the observed surface and its image intensity under near-field lighting, the feature points selection method is first developed to effectively address the noisy observations and improve calibration robustness. Afterward, to enhance efficiency and accuracy of the calibration, a cost function of l p-norm is established based on the above relationship, and an improved Newton method-based iteration process is applied to calculate the light source parameters. The simulations demonstrate that the proposed method is capable of achieving robust calibration results with the estimation error less than 2.7 mm and 0.8°, even though the image intensities are corrupted by Gaussian white noise with standard deviation up to 0.4. The experimental validation is performed using a self-designed photometric stereo system, where the calibration of point light sources is conducted, and measurements are taken on a standard sphere and compressor blade based on the obtained calibration results, which demonstrates the effectiveness of what we believe to be a new method.

Achieving health-oriented air pollution control requires integrating unequal toxicities of industrial particles.

Protecting human health from fine particulate matter (PM) pollution is the ambitious goal of clean air actions, but current control strategies  largely ignore the role of source-specific PM toxicity. Here, we proposed health-oriented control strategies by integrating the unequal toxic potencies of the most polluting industrial PMs. Iron and steel industry (ISI)-emitted PM2.5 exhibit about one order of magnitude higher toxic potency than those of cement and power industries. Compared with the current mass-based control strategy (prioritizing implementation of ultralow emission standards in the power sector), the proposed health-oriented control strategy (priority control of the ISI sector) could generate 5.4 times higher reduction in population-weighted toxic potency-adjusted PM2.5 exposure among polluting industries in China. Furthermore, the marginal abatement cost per unit of toxic potency-adjusted mass of ISI-emitted PM2.5 is only a quarter of that of the other two sectors under ultralow emission scenarios. We highlight that a health-oriented air pollution control strategy is urgently required to achieve cost-effective reductions in particulate exposure risks.

Higher Toxicity of Gaseous Organics Relative to Particulate Matters Emitted from Typical Cooking Processes.

Cooking emission is known to be a significant anthropogenic source of air pollution in urban areas, but its toxicities are still unclear. This study addressed the toxicities of fine particulate matter (PM2.5) and gaseous organics by combining chemical fingerprinting analysis with cellular assessments. The cytotoxicity and reactive oxygen species activity of gaseous organics were ∼1.9 and ∼8.3 times higher than those of PM2.5, respectively. Moreover, these values of per unit mass PM2.5 were ∼7.1 and ∼15.7 times higher than those collected from ambient air in Shanghai. The total oleic acid equivalent quantities for carcinogenic and toxic respiratory effects of gaseous organics, as estimated using predictive models based on quantitative structure-property relationships, were 1686 ± 803 and 430 ± 176 µg/mg PM2.5, respectively. Both predicted toxicities were higher than those of particulate organics, consistent with cellular assessment. These health risks are primarily attributed to the high relative content and toxic equivalency factor of the organic compounds present in the gas phase, including 7,9-di-tert-butyl-1-oxaspiro(4,5)deca-6,9-diene-2,8-dione, 2-ethylhexanoic acid, and 2-phenoxyethoxybenzene. Furthermore, these compounds and fatty acids were identified as prominent chemical markers of cooking-related emissions. The obtained results highlight the importance of control measures for cooking-emitted gaseous organics to reduce the personal exposure risks.

Recent advances in the metabolic engineering and physiological opportunities for microbial synthesis of L-aspartic acid family amino acids: A review.

L-aspartic acid, L-threonine, L-isoleucine, l-lysine, and L-methionine constitute the l-aspartate amino acids (AFAAs). Except for L-aspartic acid, these are essential amino acids that cannot be synthesized by humans or animals themselves. E. coli and C. glutamicum are the main model organisms for AFAA production. It is necessary to reconstitute microbial cell factories and the physiological state of industrial fermentation cells for in-depth research into strains with higher AFAA production levels and optimal growth states. Considering that the anabolic pathways of the AFAAs and engineering modifications have rarely been reviewed in the latest progress, this work reviews the central metabolic pathways of two strains and strategies for the metabolic engineering of AFAA synthetic pathways. The challenges posed by microbial physiology in AFAA production and possible strategies to address them, as well as future research directions for constructing strains with high AFAA production levels, are discussed in this review article.

Rhubarb charcoal-crosslinked chitosan/silk fibroin sponge scaffold with efficient hemostasis, inflammation, and angiogenesis for promoting diabetic wound healing.

Diabetic patients often experience long-term risks due to chronic inflammation and delayed re-epithelialization during impaired wound healing. Although the severity of this condition is well known, the treatment options for diabetic wounds are limited. Rhubarb charcoal, a well-known traditional Chinese medicine, has been used to treat skin wounds for thousands of years. We produced a chitosan/silk fibroin sponge scaffold loaded with natural carbonized rhubarb and crosslinked it by freeze-drying to create a highly efficient RCS/SF scaffold. Rhubarb carbon and carboxymethyl chitosan exhibit antibacterial activity and promote wound healing. Owing to its 3D porous structure, this scaffold is antibacterial and pro-angiogenic. It also possesses remarkable properties, such as excellent swelling and biocompatibility. The supportive effect of carbonized rhubarb on mouse fibroblast migration is mediated at the cellular/tissue level by increased skin neovascularization and re-epithelization. Compared to the control group, RCS/SF scaffolds promoted faster healing, increased neovascularization, enhanced collagen deposition, and re-epithelialization within two weeks. The scaffold's pro-healing properties and efficient release of carbonized rhubarb, with rapid hemostatic and good sterilization effects, make it an outstanding candidate for treating diabetic wounds and novel therapeutic interventions for diabetic ulcers.

Astragaloside IV Blunts Epithelial-Mesenchymal Transition and G2/M Arrest to Alleviate Renal Fibrosis via Regulating ALDH2-Mediated Autophagy.

Previous studies show that astragaloside IV (ASIV) has anti-renal fibrosis effects. However, its mechanism remains elusive. In this study, we investigated the anti-fibrosis mechanisms of ASIV on chronic kidney disease (CKD) in vivo and in vitro. A CKD model was induced in rats with adenine (200 mg/kg/d, i.g.), and an in vitro renal fibrosis model was induced in human kidney-2 (HK-2) cells treated with TGF-ß1. We revealed that ASIV significantly alleviated renal fibrosis by suppressing the expressions of epithelial-mesenchymal transition (EMT)-related proteins, including fibronectin, vimentin, and alpha-smooth muscle actin (α-SMA), and G2/M arrest-related proteins, including phosphorylated p53 (p-p53), p21, phosphorylated histone H3 (p-H3), and Ki67 in both of the in vivo and in vitro models. Transcriptomic analysis and subsequent validation showed that ASIV rescued ALDH2 expression and inhibited AKT/mTOR-mediated autophagy. Furthermore, in ALDH2-knockdown HK-2 cells, ASIV failed to inhibit AKT/mTOR-mediated autophagy and could not blunt EMT and G2/M arrest. In addition, we further demonstrated that rapamycin, an autophagy inducer, reversed the treatment of ASIV by promoting autophagy in TGF-ß1-treated HK-2 cells. A dual-luciferase report assay indicated that ASIV enhanced the transcriptional activity of the ALDH2 promoter. In addition, a further molecular docking analysis showed the potential interaction of ALDH2 and ASIV. Collectively, our data indicate that ALDH2-mediated autophagy may be a novel target in treating renal fibrosis in CKD models, and ASIV may be an effective targeted drug for ALDH2, which illuminate a new insight into the treatment of renal fibrosis and provide new evidence of pharmacology to elucidate the anti-fibrosis mechanism of ASIV in treating renal fibrosis.

Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation.

Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose brain deformation estimators that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on on-field head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation accuracy, with the DRCA method significantly outperforming other domain adaptation methods in prediction accuracy (p<0.001): MPS RMSE: 0.027 (CF) and 0.037 (MMA); MPSR RMSE: 7.159 (CF) and 13.022 (MMA). On another two hold-out testsets with 195 college football impacts and 260 boxing impacts, the DRCA model significantly outperformed the baseline model without domain adaptation in MPS and MPSR estimation accuracy (p<0.001). The DRCA domain adaptation reduces the MPS/MPSR estimation error to be well below TBI thresholds, enabling accurate brain deformation estimation to detect TBI in future clinical applications.

Short tandem repeats of human genome are intrinsically unstable in cultured cells in vivo.

Short tandem repeats (STRs) are a class of abundant structural or functional elements in the human genome and exhibit a polymorphic nature of repeat length and genetic variation within human populations. Interestingly, STR expansions underlie about 60 neurological disorders. However, "stutter" artifacts or noises render it difficult to investigate the pathogenesis of STR expansions. Here, we systematically investigated STR instability in cultured human cells using GC-rich CAG and AT-rich ATTCT tandem repeats as examples. We found that triplicate bidirectional Sanger sequencing with PCR amplification under proper conditions can reliably assess STR length. In addition, we found that next-generation sequencing with paired-end reads bidirectionally covering STR regions can accurately and reliably assay STR length. Finally, we found that STRs are intrinsically unstable in cultured human cell populations and during single-cell cloning. Our data suggest a general method for accurately and reliably assessing STR length and have important implications in investigating pathogenesis of STR expansion diseases.

Finite element evaluation of an American football helmet featuring liquid shock absorbers for protecting against concussive and subconcussive head impacts.

Introduction: Concern has grown over the potential long-term effects of repeated head impacts and concussions in American football. Recent advances in impact engineering have yielded the development of soft, collapsible, liquid shock absorbers, which have demonstrated the ability to dramatically attenuate impact forces relative to existing helmet shock absorbers. Methods: To further explore how liquid shock absorbers can improve the efficacy of an American football helmet, we developed and optimized a finite element (FE) helmet model including 21 liquid shock absorbers spread out throughout the helmet. Using FE models of an anthropomorphic test headform and linear impactor, a previously published impact test protocol representative of concussive National Football League impacts (six impact locations, three velocities) was performed on the liquid FE helmet model and four existing FE helmet models. We also evaluated the helmets at three lower impact velocities representative of subconcussive football impacts. Head kinematics were recorded for each impact and used to compute the Head Acceleration Response Metric (HARM), a metric factoring in both linear and angular head kinematics and used to evaluate helmet performance. The head kinematics were also input to a FE model of the head and brain to calculate the resulting brain strain from each impact. Results: The liquid helmet model yielded the lowest value of HARM at 33 of the 36 impact conditions, offering an average 33.0% (range: -37.5% to 56.0%) and 32.0% (range: -2.2% to 50.5%) reduction over the existing helmet models at each impact condition in the subconcussive and concussive tests, respectively. The liquid helmet had a Helmet Performance Score (calculated using a summation of HARM values weighted based on injury incidence data) of 0.71, compared to scores ranging from 1.07 - 1.21 from the other four FE helmet models. Resulting brain strains were also lower in the liquid helmet. Discussion: The results of this study demonstrate the promising ability of liquid shock absorbers to improve helmet safety performance and encourage the development of physical prototypes of helmets featuring this technology. The implications of the observed reductions on brain injury risk are discussed.

6-Gingerol Ameliorates Hepatic Steatosis, Inflammation and Oxidative Stress in High-Fat Diet-Fed Mice through Activating LKB1/AMPK Signaling.

6-Gingerol, one of the major pharmacologically active ingredients extracted from ginger, has been reported experimentally to exert hepatic protection in non-alcoholic fatty liver disease (NAFLD). However, the molecular mechanism remains largely elusive. RNA sequencing indicated the significant involvement of the AMPK signaling pathway in 6-gingerol-induced alleviation of NAFLD in vivo. Given the significance of the LKB1/AMPK pathway in metabolic homeostasis, this study aims to investigate its role in 6-gingerol-induced mitigation on NAFLD. Our study showed that 6-gingerol ameliorated hepatic steatosis, inflammation and oxidative stress in vivo and in vitro. Further experiment validation suggested that 6-gingerol activated an LKB1/AMPK pathway cascade in vivo and in vitro. Co-immunoprecipitation analysis demonstrated that the 6-gingerol-elicited activation of an LKB1/AMPK pathway cascade was related to the enhanced stability of the LKB1/STRAD/MO25 complex. Furthermore, radicicol, an LKB1 destabilizer, inhibited the activating effect of 6-gingerol on an LKB1/AMPK pathway cascade via destabilizing LKB1/STRAD/MO25 complex stability in vitro, thus reversing the 6-gingerol-elicited ameliorative effect. In addition, molecular docking analysis further predicated the binding pockets of LKB1 necessary for binding with 6-gingerol. In conclusion, our results indicate that 6-gingerol plays an important role in regulating the stability of the LKB1/STRAD/MO25 complex and the activation of LKB1, which might weigh heavily in the 6-gingerol alleviation of NAFLD.

Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.

The last decade has seen significant advances in the accumulation of medical data, the computational techniques to analyze that data, and corresponding improvements in management. Interventions such as thrombolytics and mechanical thrombectomy improve patient outcomes after stroke in selected patients; however, significant gaps remain in our ability to select patients, predict complications, and understand outcomes. Big data and the computational methods needed to analyze it can address these gaps. For example, automated analysis of neuroimaging to estimate the volume of brain tissue that is ischemic and salvageable can help triage patients for acute interventions. Data-intensive computational techniques can perform complex risk calculations that are too cumbersome to be completed by humans, resulting in more accurate and timely prediction of which patients require increased vigilance for adverse events such as treatment complications. To handle the accumulation of complex medical data, a variety of advanced computational techniques referred to as machine learning and artificial intelligence now routinely complement traditional statistical inference. In this narrative review, we explore data-intensive techniques in stroke research, how it has informed the management of stroke patients, and how current work could shape clinical practice in the future.

Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.

BACKGROUND: Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification. METHODS: Data were analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain. RESULTS: The classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low- and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2 value than baseline models without classification. CONCLUSION: The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.

Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings.

Protective headgear effects measured in the laboratory may not always translate to the field. In this study, we evaluated the impact attenuation capabilities of a commercially available padded helmet shell cover in the laboratory and on the field. In the laboratory, we evaluated the padded helmet shell cover's efficacy in attenuating impact magnitude across six impact locations and three impact velocities when equipped to three different helmet models. In a preliminary on-field investigation, we used instrumented mouthguards to monitor head impact magnitude in collegiate linebackers during practice sessions while not wearing the padded helmet shell covers (i.e., bare helmets) for one season and whilst wearing the padded helmet shell covers for another season. The addition of the padded helmet shell cover was effective in attenuating the magnitude of angular head accelerations and two brain injury risk metrics (DAMAGE, HARM) across most laboratory impact conditions, but did not significantly attenuate linear head accelerations for all helmets. Overall, HARM values were reduced in laboratory impact tests by an average of 25% at 3.5 m/s (range: 9.7 to 39.6%), 18% at 5.5 m/s (range: - 5.5 to 40.5%), and 10% at 7.4 m/s (range: - 6.0 to 31.0%). However, on the field, no significant differences in any measure of head impact magnitude were observed between the bare helmet impacts and padded helmet impacts. Further laboratory tests were conducted to evaluate the ability of the padded helmet shell cover to maintain its performance after exposure to repeated, successive impacts and across a range of temperatures. This research provides a detailed assessment of padded helmet shell covers and supports the continuation of in vivo helmet research to validate laboratory testing results.

Ribosome biogenesis in disease: new players and therapeutic targets.

The ribosome is a multi-unit complex that translates mRNA into protein. Ribosome biogenesis is the process that generates ribosomes and plays an essential role in cell proliferation, differentiation, apoptosis, development, and transformation. The mTORC1, Myc, and noncoding RNA signaling pathways are the primary mediators that work jointly with RNA polymerases and ribosome proteins to control ribosome biogenesis and protein synthesis. Activation of mTORC1 is required for normal fetal growth and development and tissue regeneration after birth. Myc is implicated in cancer development by enhancing RNA Pol II activity, leading to uncontrolled cancer cell growth. The deregulation of noncoding RNAs such as microRNAs, long noncoding RNAs, and circular RNAs is involved in developing blood, neurodegenerative diseases, and atherosclerosis. We review the similarities and differences between eukaryotic and bacterial ribosomes and the molecular mechanism of ribosome-targeting antibiotics and bacterial resistance. We also review the most recent findings of ribosome dysfunction in COVID-19 and other conditions and discuss the consequences of ribosome frameshifting, ribosome-stalling, and ribosome-collision. We summarize the role of ribosome biogenesis in the development of various diseases. Furthermore, we review the current clinical trials, prospective vaccines for COVID-19, and therapies targeting ribosome biogenesis in cancer, cardiovascular disease, aging, and neurodegenerative disease.