Development and evaluation of a deep learning-based model for simultaneous detection and localization of rib and clavicle fractures in trauma patients' chest radiographs.

Purpose: To develop a rib and clavicle fracture detection model for chest radiographs in trauma patients using a deep learning (DL) algorithm. Materials and methods: We retrospectively collected 56 145 chest X-rays (CXRs) from trauma patients in a trauma center between August 2008 and December 2016. A rib/clavicle fracture detection DL algorithm was trained using this data set with 991 (1.8%) images labeled by experts with fracture site locations. The algorithm was tested on independently collected 300 CXRs in 2017. An external test set was also collected from hospitalized trauma patients in a regional hospital for evaluation. The receiver operating characteristic curve with area under the curve (AUC), accuracy, sensitivity, specificity, precision, and negative predictive value of the model on each test set was evaluated. The prediction probability on the images was visualized as heatmaps. Results: The trained DL model achieved an AUC of 0.912 (95% CI 87.8 to 94.7) on the independent test set. The accuracy, sensitivity, and specificity on the given cut-off value are 83.7, 86.8, and 80.4, respectively. On the external test set, the model had a sensitivity of 88.0 and an accuracy of 72.5. While the model exhibited a slight decrease in accuracy on the external test set, it maintained its sensitivity in detecting fractures. Conclusion: The algorithm detects rib and clavicle fractures concomitantly in the CXR of trauma patients with high accuracy in locating lesions through heatmap visualization.

Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images.

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.

Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning.

X-ray imaging plays a crucial role in diagnostic medicine. Yet, a significant portion of the global population lacks access to this essential technology due to a shortage of trained radiologists. Eye-tracking data and deep learning models can enhance X-ray analysis by mapping expert focus areas, guiding automated anomaly detection, optimizing workflow efficiency, and bolstering training methods for novice radiologists. However, the literature shows contradictory results regarding the usefulness of eye-tracking data in deep-learning architectures for abnormality detection. We argue that these discrepancies between studies in the literature are due to (a) the way eye-tracking data is (or is not) processed, (b) the types of deep learning architectures chosen, and (c) the type of application that these architectures will have. We conducted a systematic literature review using PRISMA to address these contradicting results. We analyzed 60 studies that incorporated eye-tracking data in a deep-learning approach for different application goals in radiology. We performed a comparative analysis to understand if eye gaze data contains feature maps that can be useful under a deep learning approach and whether they can promote more interpretable predictions. To the best of our knowledge, this is the first survey in the area that performs a thorough investigation of eye gaze data processing techniques and their impacts in different deep learning architectures for applications such as error detection, classification, object detection, expertise level analysis, fatigue estimation and human attention prediction in medical imaging data. Our analysis resulted in two main contributions: (1) taxonomy that first divides the literature by task, enabling us to analyze the value eye movement can bring for each case and build guidelines regarding architectures and gaze processing techniques adequate for each application, and (2) an overall analysis of how eye gaze data can promote explainability in radiology.

JMJD2A mediates transcriptional activation of SFRP4 and regulates oxidative stress and mitochondrial dysfunction in heart failure.

The importance of mitochondrial dysfunction and oxidative stress has been indicated in the progression of heart failure (HF). The molecular mechanisms, however, remain to be fully elucidated. This study aimed to explore the role and underlying mechanism of secreted frizzled-related protein 4 (SFRP4) in these two events in HF. Mice with HF were developed using transverse aortic constriction, and hematoxylin-eosin staining, MASSON staining, and Terminal deoxynucleotidyl transferase (TdT)-mediated 2'-Deoxyuridine 5'- Triphosphate nick end labeling (TUNEL assays) were conducted to detect morphological damage in the myocardial tissues of mice. HL-1 mouse cardiomyocytes were induced with isoproterenol (ISO), and cell viability and apoptosis were examined using cell counting kit-8 and TUNEL assays. SFRP4 and Jumonji domain-containing protein 2A (JMJD2A) were highly expressed in myocardial tissues. Suppression of SFRP4 alleviated apoptosis and fibrosis in myocardial tissues of mice. In addition, the extent of mitochondrial dysfunction and oxidative stress in damaged myocardial tissues and HL-1 cells was mitigated by SFRP4 inhibition as well. JMJD2A catalyzed demethylation modification of the SFRP4 promoter, thus promoting SFRP4 transcription in the development of HF. JMJD2A is responsible for SFRP4 transcription activation in the failing hearts of mice. Blockade of JMJD2A or SFRP4 might be a novel therapy effective in mitigating HF progression.

[Heterocyclic compounds and phenolic glycosides from flowers of Dendrobium officinale].

Eight heterocyclic compounds and twelve phenolic glycosides were separated from the water extract of Dendrobium officinale flowers through chromatographic techniques, such as Diaion HP-20 macroporous adsorption resin column chromatography(CC), silica gel CC, ODS CC, Sephadex LH-20 CC, and preparative high performance liquid chromatography(PHPLC). According to the spectroscopic analyses(MS, ~1H-NMR, and ~(13)C-NMR) and optical rotation data, the compounds were identified as dendrofurfural A(1), 2'-deoxyadenosine(2), 4-[2-formyl-5-(hydroxymethyl)-1H-pyrrol-1-yl] butanoic acid(3), 4-[2-formyl-5-(methoxymethyl)-1H-pyrrol-1-yl] butanoic acid(4), 1-(2-hydroxyethyl)-5-(methoxymethyl)-1H-pyrrole-2-carbaldehyde(5), 5-(methoxymethyl)-1H-pyrrole-2-carbaldehyde(6), methyl 5-(hydroxymethyl)-furan-2-carboxylate(7),(S)-5-hydroxymethyl-5H-furan-2-one(8), 2-methoxyphenyl-1-O-ß-D-glucopyranoside(9), arbutin(10), isotachioside(11), 2,6-dimethoxy-4-hydroxyphenol-1-O-ß-D-glucopyranoside(12), orcinol glucoside(13), tachioside(14), gastrodin(15), 4-O-ß-D-glucopyranosylvanillyl alcohol(16), 2,6-dimethoxy-4-hydroxymethylphenol-1-O-ß-D-glucopyranoside(17), icariside D_2(18), 4-formylphenyl-ß-D-glucopyranoside(19), and vanillin-4-O-ß-D-glucopyranoside(20). Among them, compound 1 is a new furfural benzyl alcohol condensate, with the skeleton first found in Dendrobium. Compounds 2-9, 11, 13, and 19 are reported from Dendrobium for the first time, and compounds 14 and 18 are reported for the first time from D. officinale. Compounds 11 and 14 showed moderate DPPH radical scavenging capacity, and compounds 11-14 demonstrated potent ABTS radical scavenging capacity, possessing antioxidant activity.

MDF-Net for abnormality detection by fusing X-rays with clinical data.

This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray images alone, consultations with practicing radiologists indicate that clinical data is highly informative and essential for interpreting medical images and making proper diagnoses. In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data). Since these data modalities are in different dimensional spaces, we propose a spatial arrangement strategy, spatialization, to facilitate the multimodal learning process in a Mask R-CNN model. We performed an extensive experimental evaluation using MIMIC-Eye, a dataset comprising different modalities: MIMIC-CXR (chest X-ray images), MIMIC IV-ED (patients' clinical data), and REFLACX (annotations of disease locations in chest X-rays). Results show that incorporating patients' clinical data in a DL model together with the proposed fusion methods improves the disease localization in chest X-rays by 12% in terms of Average Precision compared to a standard Mask R-CNN using chest X-rays alone. Further ablation studies also emphasize the importance of multimodal DL architectures and the incorporation of patients' clinical data in disease localization. In the interest of fostering scientific reproducibility, the architecture proposed within this investigation has been made publicly accessible( https://github.com/ChihchengHsieh/multimodal-abnormalities-detection ).

Speeding pelvic fracture fixation: CT scan with simultaneous 3-D pelvic reconstruction in the emergency department.

INTRODUCTION: Contrast-enhanced computed tomography (CT) scans are usually needed in the emergency department (ED) to evaluate intra-abdominal injuries associated with pelvic fractures. Three-dimensional (3-D) images for pelvis reconstruction are also needed for planning surgical fixation after admission. This study investigates the advantages integrating a one-stage computed tomography (CT) scan with these two diagnostic modalities simultaneously to reduce the time to surgery and improve the outcomes of pelvic fracture fixation. METHODS: A retrospective cohort study (2018-2021) of patients with pelvic fractures was performed. Patients were categorized into the one-stage CT group or the two-stage CT group, and propensity score matching was used to address biases. The outcome measures included time to surgical fixation, time to CT scan for 3-D pelvis reconstruction, and overall length of hospital stay. RESULTS: Four hundred forty-four pelvic fracture patients who underwent definite surgical fixation were identified. Of those, 320 underwent a one-stage CT scan, while the remaining 124 underwent a two-stage CT scan. After well-balanced matching, those in the one-stage CT group had a significantly shorter time to surgical fixation than those in the two-stage CT group (4.6 vs. 6.8 days, p < 0.001). Even among critically ill patients necessitating intensive care unit (ICU) admission, the one-stage CT scan group had a shorter time to definitive surgical fixation (5.5 vs. 7.2 days, p = 0.002) and a shorter hospital stay (19.0 vs. 32.7 days, p = 0.006). CONCLUSION: A one-stage contrast-enhanced CT scan combined with simultaneous 3-D pelvis reconstruction is promising for expediting surgical fixation in pelvic fracture patients. This innovative strategy may improve patient outcomes by facilitating timely surgical interventions and minimizing delays associated with additional CT scans.

The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection.

(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use design thinking steps to develop a DL algorithm that accelerates deployment in clinical practice and improves its performance to meet clinical requirements. (2) Methods: We applied the design thinking process to interview clinical doctors and gain insights to develop and modify the DL algorithm to meet clinical scenarios. We also compared the DL performance of the algorithm before and after the integration of design thinking. (3) Results: After empathizing with clinical doctors and defining their needs, we identified the unmet need of five trauma surgeons as "how to reduce the misdiagnosis of femoral fracture by pelvic plain film (PXR) at initial emergency visiting". We collected 4235 PXRs from our hospital, of which 2146 had a hip fracture (51%) from 2008 to 2016. We developed hip fracture DL detection models based on the Xception convolutional neural network by using these images. By incorporating design thinking, we improved the diagnostic accuracy from 0.91 (0.84-0.96) to 0.95 (0.93-0.97), the sensitivity from 0.97 (0.89-1.00) to 0.97 (0.94-0.99), and the specificity from 0.84 (0.71-0.93) to 0.93(0.990-0.97). (4) Conclusions: In summary, this study demonstrates that design thinking can ensure that DL solutions developed for trauma care are user-centered and meet the needs of patients and healthcare providers.

A Mousepad Triboelectric-Piezoelectric Hybrid Nanogenerator (TPHNG) for Self-Powered Computer User Behavior Monitoring Sensors and Biomechanical Energy Harvesting.

Hybrid nanogenerators based on the principle of surface charging of functional films are significant in self-powering sensing and energy conversion devices due to their multiple functions and high conversion efficiency, although applications remain limited due to a lack of suitable materials and structures. Here, we investigate a triboelectric-piezoelectric hybrid nanogenerator (TPHNG) in the form of a mousepad for computer user behavior monitoring and energy harvesting. Triboelectric and piezoelectric nanogenerators with different functional films and structures work independently to detect sliding and pressing movements, and the profitable coupling between the two nanogenerators leads to enhanced device outputs/sensitivity. Different mouse operations such as clicking, scrolling, taking-up/putting-down, sliding, moving rate, and pathing can be detected by the device via distinguishable patterns of voltage ranging from 0.6 to 36 V. Based on operation recognition, human behavior monitoring is realized, with monitoring of tasks such as browsing a document and playing a computer game being successfully demonstrated. Energy harvesting from mouse sliding, patting, and bending of the device is realized with output voltages up to 37 V and power up to 48 µW while exhibiting good durability up to 20,000 cycles. This work presents a TPHNG utilizing surface charging for self-powered human behavior sensing and biomechanical energy harvesting.

Detection of cytotoxins by sandwich-ELISA for discrimination of cobra envenomation and indication of necrotic severity.

Snake envenoming is both a healthcare and socioeconomic problem for developing countries and underserved communities. In Taiwan, clinical management of Naja atra envenomation is a major challenge, since cobra venom-induced symptoms are usually confused with hemorrhagic snakebites and current antivenom treatments do not effectively prevent venom-induced necrosis for which early surgical debridement should be administered. Identification and validation of biomarkers of cobra envenomation is critical for progress in setting a realistic goal for snakebite management in Taiwan. Previously, cytotoxin (CTX) was determined as one of potential biomarker candidates; however, its ability to discriminate cobra envenoming remains to be verified, especially in clinical practice. In this study, we selected a monoclonal single-chain variable fragment (scFv) and a polyclonal antibody to develop a sandwich enzyme-linked immunosorbent assay (ELISA) for CTX detection, which successfully recognized CTX from N. atra venom over that from other snake species. Using this specific assay, the CTX concentration in envenoming mice was shown to remain consistent in about 150 ng/mL during the 2-hour post-injection period. The measured concentration was highly correlated with the size of local necrosis in mouse dorsal skin, which the correlation coefficient is about 0.988. Furthermore, our ELISA method displayed 100 % of specificity and sensitivity in discriminating cobra envenoming among snakebite victims through CTX detection and the level of CTX in victim plasma was ranged from 5.8 to 253.9 ng/mL. Additionally, patients developed tissue necrosis at plasma CTX concentrations higher than 150 ng/mL. Thus, CTX not only serves as a verified biomarker for discrimination of cobra envenoming but also a potential indicator of severity of local necrosis. In this context, detection of CTX may facilitate reliable identification of envenoming species and improve snakebite management in Taiwan.

[A new bibenzyl derivative from stems of Dendrobium officinale].

Eleven compounds were isolated from the 95% ethanol extract of the stems of Dendrobium officinale after water extraction by various modern chromatographic techniques, such as silica gel column chromatography(CC), octadecyl-silica(ODS) CC, Sephadex LH-20 CC, preparative thin layer chromatography(PTLC) and preparative high performance liquid chromatography(PHPLC). According to spectroscopic analyses(MS, 1D-NMR, 2D-NMR) combined with optical rotation data and calculated electronic circular dichroism(ECD), their structures were identified as dendrocandin Y(1), 4,4'-dihydroxybibenzyl(2), 3-hydroxy-4',5-dimethoxybibenzyl(3), 3,3'-dihydroxy-5-methoxybibenzyl(4), 3-hydroxy-3',4',5-trimethoxybibenzyl(5), crepidatin(6), alternariol(7), 4-hydroxy-3-methoxypropiophenone(8), 3-hydroxy-4,5-dimethoxypropiophenone(9), auriculatum A(10) and hyperalcohol(11). Among them, compound 1 was a new bibenzyl derivative; compounds 2 and 7-11 have not been previously reported from Dendrobium plants; compound 6 was reported from D.officinale for the first time. Compounds 3-6 exhibited potent antioxidant activity with IC_(50) values of 3.11-9.05 µmol·L~(-1) in ABTS radical scavenging assay. Compound 4 showed significant inhibitory effect on α-glucosidase, with IC_(50) value of 17.42 µmol·L~(-1), indicating that it boasted hypoglycemic activity.

Immunoprofiling of Equine Plasma against Deinagkistrodon acutus in Taiwan: Key to Understanding Differential Neutralization Potency in Immunized Horses.

Snakebite envenoming is a public health issue linked to high mortality and morbidity rates worldwide. Although antivenom has been the mainstay treatment for envenomed victims receiving medical care, the diverse therapeutic efficacy of the produced antivenom is a major limitation. Deinagkistrodon acutus is a venomous snake that poses significant concern of risks to human life in Taiwan, and successful production of antivenom against D. acutus envenoming remains a considerable challenge. Among groups of horses subjected to immunization schedules, few or none subsequently meet the quality required for further scale-up harvesting. The determinants underlying the variable immune responses of horses to D. acutus venom are currently unknown. In this study, we assessed the immunoprofiles of high-potency and low-potency horse plasma against D. acutus venom and explored the conspicuous differences between these two groups. Based on the results of liquid chromatography with tandem mass spectrometry (LC-MS/MS), acutolysin A was identified as the major component of venom proteins that immunoreacted differentially with the two plasma samples. Our findings indicate underlying differences in antivenoms with variable neutralization efficacies, and may provide valuable insights for improvement of antivenom production in the future.

Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.

AIMS: To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients. METHOD: This was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and precision. RESULTS: In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765-0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%. CONCLUSIONS: The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model.

The Clinical Usefulness of Taiwan Bivalent Freeze-Dried Hemorrhagic Antivenom in Protobothrops mucrosquamatus- and Viridovipera stejnegeri-Envenomed Patients.

Snakebites from Protobothrops mucrosquamatus (Taiwan habus) and Viridovipera stejnegeri (green bamboo vipers) account for the most venomous snakebites in Taiwan. The bivalent freeze-dried hemorrhagic (FH) antivenom is employed to treat these two snakebite patients without a strict clinical trial. We evaluated the clinical usefulness of Taiwan bivalent freeze-dried hemorrhagic (FH) antivenom in Taiwan habu- and green bamboo viper-envenomed patients. We checked ELISA- based serum venom antigen levels before and after FH antivenom to evaluate FH's ability to neutralize patients' serum snake venom and its usefulness in reducing limb swelling after snakebites. Patients who had higher serum venom antigen levels had more severe limb swelling. Of the 33 enrolled patients, most of their snake venom antigen levels were undetected after the appliance of antivenom. Most enrolled patients (25/33) had their limb swelling subside within 12 h after antivenom treatment. The failure to reduce limb swelling was probably due to an inadequate antivenom dose applied in more severely envenomated patients. Our data indicate the feasibility of the FH antivenom in effectively eliminating venom and resolving the affected limb swelling caused by Taiwan habu and green bamboo viper bites.

Surface Plasmon Effect Dominated High-Performance Triboelectric Nanogenerator for Traditional Chinese Medicine Acupuncture.

Available, effectively converting low-frequency vibration into available electricity, triboelectric nanogenerator (TENG) is always research hot nowadays. However, the enhancing effect of the existing methods for the output have all sorts of drawbacks, i.e., low efficiency and unstable, and its practical applications still need to be further explored. Here, leveraging core-shell nanoparticles Ag@SiO2 doping into tribo-materials generates the surface plasmon effect to boost the output performance of the TENG. On one hand, the shell alleviated the seepage effect from conventional nanoparticles; on the other hand, the surface plasmon effect enabled the core-shell nanoparticles to further boost the output performance of TENG. We circumvent the limitations and present a TENG whose output power density can be up to 4.375 mW/cm2. Points is that this article novelty investigate the high-performance TENG applicating for traditional Chinese medicine and develop a pratical self-powered acupuncture system. This technology enables rapid, routine regulation of human health at any age, which has potential applications in nearly any setting across healthcare platforms alike.

The Surgical Timing and Complications of Rib Fixation for Rib Fractures in Geriatric Patients.

Rib fractures (RF) are a common injury that cause significant morbidity and mortality, especially in geriatric patients. RF fixation could shorten hospital stay and improve survival. The aim of this retrospective study was to evaluate the clinical impact and proper surgical timing of RF fixation in geriatric patients. We reviewed all the medical data of patients older than 16 years old with RF from the trauma registry database between January 2017 and December 2019 in Chang Gung Memorial Hospital. A total of 1078 patients with RF were enrolled, and 87 patients received RF fixation. The geriatric patients had a higher chest abbreviated injury scale than the non-geriatric group (p = 0.037). Univariate analysis showed that the RF fixation complication rates were significantly related to the injury severity scores (Odds ratio 1.10, 95% CI 1.03-1.20, p = 0.009) but not associated with age (OR 0.99, 95% CI 0.25-3.33, p = 0.988) or the surgical timing (OR 2.94, 95% CI 0.77-12.68, p = 0.122). Multivariate analysis proved that only bilateral RF was an independent risk factor of complications (OR 6.60, 95% CI 1.38-35.54, p = 0.02). RF fixation can be postponed for geriatric patients after they are stabilized and other lethal traumatic injuries are managed as a priority.

Supporting carers in online role-diverse communities: A case study in Australia.

This study aims to understand the effects that role-diverse online communities have on informal carers, particularly in providing support. Australian Facebook communities used to support those involved in the National Disability Insurance Scheme (NDIS) were explored. Social network analysis of an NDIS-centred community was conducted, based on 909 publicly visible interactions that occurred in May-June and August-September 2019. Two managers of informal NDIS communities were interviewed, the transcripts of which were analysed using NVivo. Results from the two analyses suggest that both an individual carer's attributes and the collective attributes of the network defined the capability of the network to support the carer, often depending on the experiences and expertise of those offering support. Support was unconstrained by role, though differing goals and expectations often impeded collaboration between roles. The outcomes of support provision were shown to affect not only individuals but also the collective network. However, while effective, community spaces currently lack organisational backing and resources available to informal communities are constrained. Findings drawn from this study, which we believe are applicable to a broader, international context, are three-fold. Firstly, it is recommended that informal support communities clearly define purpose and create multiple channels to ensure that all participants can meet their needs. Secondly, the benefits of participation to organisations should be further explored. Finally, the use of social network analysis as a method in this study has provided significant insights into the communication patterns and activities of the community under study. Future use of SNA in similar studies may provide further insight into the effectiveness and interactions of community-based support methods.

The Influence of Serious Extracranial Injury on In-Hospital Mortality in Children with Severe Traumatic Brain Injury.

(1) Background: Severe traumatic brain injury (sTBI) is the leading cause of death in children. Serious extracranial injury (SEI) commonly coexists with sTBI after the high impact of trauma. Limited studies evaluate the influence of SEI on the prognosis of pediatric sTBI. We aimed to analyze SEI's clinical characteristics and initial presentations and evaluate if SEI is predictive of higher in-hospital mortality in these sTBI children. (2) Methods: In this 11-year-observational cohort study, a total of 148 severe sTBI children were enrolled. We collected patients' initial data in the emergency department, including gender, age, mechanism of injury, coexisting SEI, motor components of the Glasgow Coma Scale (mGCS) score, body temperature, blood pressure, blood glucose level, initial prothrombin time, and intracranial Rotterdam computed tomography (CT) score of the first brain CT scan, as potential mortality predictors. (3) Results: Compared to sTBI children without SEI, children with SEI were older and more presented with initial hypotension and hypothermia; the initial lab showed more prolonged prothrombin time and a higher in-hospital mortality rate. Multivariate analysis showed that motor components of mGCS, fixed pupil reaction, prolonged prothrombin time, and higher Rotterdam CT score were independent predictors of in-hospital mortality in sTBI children. SEI was not an independent predictor of mortality. (4) Conclusions: sTBI children with SEI had significantly higher in-hospital mortality than those without. SEI was not an independent predictor of mortality in our study. Brain injury intensity and its presentations, including lower mGCS, fixed pupil reaction, higher Rotterdam CT score, and severe injury-induced systemic response, presented as initial prolonged prothrombin time, were independent predictors of in-hospital mortality in these sTBI children.

HDAC1 Promotes Myocardial Fibrosis in Diabetic Cardiomyopathy by Inhibiting BMP-7 Transcription Through Histone Deacetylation.

OBJECTIVE: Diabetic cardiomyopathy (DCM) constitutes a primary cause of mortality in diabetic patients. Histone deacetylase (HDAC) inhibition can alleviate diabetes-associated myocardial injury. This study investigated the mechanism of HDAC1 on myocardial fibrosis (MF) in DCM. METHODS: A murine model of DCM was established by a high-fat diet and streptozotocin injection. The bodyweight, blood glucose, serum insulin, and cardiac function of mice were analyzed. Lentivirus-packaged sh-HDAC1 was injected into DCM mice and high glucose (HG)-induced cardiac fibroblasts (CFs). The pathological structure of the myocardium and the level of myocardial fibrosis were observed by histological staining. HDAC1 expression in mouse myocardial tissues and CFs was determined. Collagen I, collagen III, alpha-smooth muscle actin (α-SMA), and vimentin levels in CFs were detected, and CF proliferation was tested. HDAC activity and histone acetylation levels in tissues and cells were measured. Bone morphogenetic protein-7 (BMP-7) expression in myocardial tissues and CFs was determined. Functional rescue experiments were conducted to confirm the effects of histone acetylation and BMP-7 on myocardial fibrosis. RESULTS: DCM mice showed decreased bodyweight, elevated blood glucose and serum insulin, and cardiac dysfunction. Elevated HDAC1 and reduced BMP-7 expressions were detected in DCM mice and HG-induced CFs. HDAC1 repressed BMP-7 transcription through deacetylation. HDAC1 silencing alleviated MF, reduced CF proliferation and decreased collagen I, -III, α-SMA, and vimentin levels. However, reducing histone acetylation level or BMP-7 downregulation reversed the effects of HDAC1 silencing on CF fibrosis. CONCLUSION: HDAC1 repressed BMP-7 transcription by enhancing histone deacetylation, thereby promoting MF and aggravating DCM.

Genomic and Molecular Signatures of Successful Patient-Derived Xenografts for Oral Cavity Squamous Cell Carcinoma.

Background: Oral cavity squamous cell carcinoma (OSCC) is an aggressive malignant tumor with high recurrence and poor prognosis in the advanced stage. Patient-derived xenografts (PDXs) serve as powerful preclinical platforms for drug testing and precision medicine for cancer therapy. We assess which molecular signatures affect tumor engraftment ability and tumor growth rate in OSCC PDXs. Methods: Treatment-naïve OSCC primary tumors were collected for PDX models establishment. Comprehensive genomic analysis, including whole-exome sequencing and RNA-seq, was performed on case-matched tumors and PDXs. Regulatory genes/pathways were analyzed to clarify which molecular signatures affect tumor engraftment ability and the tumor growth rate in OSCC PDXs. Results: Perineural invasion was found as an important pathological feature related to engraftment ability. Tumor microenvironment with enriched hypoxia, PI3K-Akt, and epithelial-mesenchymal transition pathways and decreased inflammatory responses had high engraftment ability and tumor growth rates in OSCC PDXs. High matrix metalloproteinase-1 (MMP1) expression was found that have a great graft advantage in xenografts and is associated with pooled disease-free survival in cancer patients. Conclusion: This study provides a panel with detailed genomic characteristics of OSCC PDXs, enabling preclinical studies on personalized therapy options for oral cancer. MMP1 could serve as a biomarker for predicting successful xenografts in OSCC patients.