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Neuromuscular scoliosis can be caused by muscular or nervous system dysfunction resulting from genetic variants. Variation in MYH7 may cause hypertrophic or dilated cardiomyopathy, skeletal myopathies, or a combination of both; however, scoliosis has rarely been reported. We analyzed a Chinese pedigree with two members suffering from scoliosis. Whole-exome sequencing identified a variant (NM_000257.4:c.2011C > T) of MYH7 that cosegregated with the scoliosis phenotype. The variant resulted in a change in the evolutionarily conserved amino acid residue 671 from arginine to cystine (p.R671C), which was predicted to disrupt the structure and function of the motor domain of the slow/β-cardiac myosin heavy chain encoded by MYH7. To date, 913 MYH7 variants were associated with cardiomyopathy and/or skeletal myopathies according to the Human Gene Mutation Database. However, only 15 cases of scoliosis have been reported. In our case, the c.2011C > T variant caused scoliosis with 100 % penetrance and hypertrophic cardiomyopathy with partial penetrance.

In recent years, artificial intelligence (AI), especially deep learning models, has increasingly been integrated into diagnosing and treating diabetic retinopathy (DR). From delving into the singular realm of ocular fundus photography to the gradual development of proteomics and other molecular approaches, from machine learning (ML) to deep learning (DL), the journey has seen a transition from a binary diagnosis of “presence or absence” to the capability of discerning the progression and severity of DR based on images from various stages of the disease course. Since the FDA approval of IDx-DR in 2018, a plethora of AI models has mushroomed, gradually gaining recognition through a myriad of clinical trials and validations. AI has greatly improved early DR detection, and we’re nearing the use of AI in telemedicine to tackle medical resource shortages and health inequities in various areas. This comprehensive review meticulously analyzes the literature and clinical trials of recent years, highlighting key AI models for DR diagnosis and treatment, including their theoretical bases, features, applicability, and addressing current challenges like bias, transparency, and ethics. It also presents a prospective outlook on the future development in this domain.

Summary: Background: We aimed to study the clinical characteristics, myocardial injury, and longitudinal outcomes of COVID-19 vaccine-associated myocarditis (C-VAM). Methods: In this longitudinal retrospective observational cohort multicenter study across 38 hospitals in the United States, 333 patients with C-VAM were compared with 100 patients with multisystem inflammatory syndrome in children (MIS-C). We included patients ≤30 years of age with a clinical diagnosis of acute myocarditis after COVID-19 vaccination based on clinical presentation, abnormal biomarkers and/or cardiovascular imaging findings. Demographics, past medical history, hospital course, biochemistry results, cardiovascular imaging, and follow-up information from April 2021 to November 2022 were collected. The primary outcome was presence of myocardial injury as evidenced by late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) imaging. Findings: Patients with C-VAM were predominantly white (67%) adolescent males (91%, 15.7 ± 2.8 years). Their initial clinical course was more likely to be mild (80% vs. 23%, p 15 years, OR 2.74 [95% CI: 1.28, 5.83, p = 0.009]) and when C-VAM occurred after the first or second dose as compared to the third dose of mRNA vaccine. Mid-term clinical outcomes of C-VAM at a median follow-up of 178 days (IQR 114–285 days) were reassuring. No cardiac deaths or heart transplantations were reported until the time of submission of this report. LGE persisted in 60% of the patients at follow up. Interpretation: Myocardial injury at initial presentation and its persistence at follow up, despite a mild initial course and favorable mid-term clinical outcome, warrants continued clinical surveillance and long-term studies in affected patients with C-VAM. Funding: The U.S. Food and Drug Administration.

Mild traumatic brain injury (mTBI) is a known risk factor for neurodegenerative diseases, yet the precise pathophysiological mechanisms remain poorly understand, often obscured by group-level analysis in non-invasive neuroimaging studies. Individual-based method is critical to exploring heterogeneity in mTBI. We recruited 80 mTBI patients and 40 matched healthy controls, obtaining high-resolution structural MRI for constructing Individual Differential Structural Covariance Networks (IDSCN). Comparisons were conducted at both the individual and group levels. Connectome-based Predictive Modeling (CPM) was applied to predict cognitive performance based on whole-brain connectivity. During the acute stage of mTBI, patients exhibited significant heterogeneity in the count and direction of altered edges, obscured by group-level analysis. In the chronic stage, the number of altered edges decreased and became more consistent, aligning with clinical observations of acute cognitive impairment and gradual improvement. Subgroup analysis based on loss of consciousness/post-traumatic amnesia revealed distinct patterns of alterations. The temporal lobe, particularly regions related to the limbic system, significantly predicted cognitive function from acute to chronic stage. The use of IDSCN and CPM has provided valuable individual-level insights, reconciling discrepancies from previous studies. Additionally, the limbic system may be an appropriate target for future intervention efforts.

Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of follow-up data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

The study of the dynamic response characteristics of “W”-type deep canyon terrain to double-span concrete arch bridges under earthquake action holds great practical significance. In this research, a bridge in Sichuan Province is taken as the object of study. The boundary-integral equation method and peak frequency shift method are combined to apply an embedded linear time–history analysis algorithm to the finite element spatial dynamic calculation model of the entire bridge, resulting in an improved model. By comparing these two methods with model test results, the seismic response characteristics of the middle part of a “W” concrete arch bridge under different foundation depths and seismic intensities are examined. The boundary integral equation method was utilized to calculate ground motion response at any point on site, revealing a significant amplifying effect of increased seismic wave intensity on acceleration response at the top of the arch bridge. When input seismic wave intensity increased from 0.1 g to 0.3 g, maximum acceleration at buried depths of 3 m and 8 m in the middle of the arch bridge foundation increased by 102.63% and 79.16%, respectively, indicating that shallow buried depth structures are more sensitive to seismic wave intensity. Furthermore, using peak frequency shift rules for analyzing seismic wave propagation characteristics in “W”-type deep canyon topography confirms the sensitivity of shallow buried depth structures to seismic wave intensity and reveals the mechanism through which topography influences seismic wave propagation. This study provides a helpful method for understanding the propagation law and energy distribution characteristics of seismic waves in complex terrain. It was observed that the displacement at the top of the arch bridge increased significantly with an increase in seismic intensity. When subjected to 0.1 g, 0.2 g, and 0.3 g EI-Centro seismic waves, the maximum displacement at the top of the arch bridge model with a foundation buried depth of 3 m was 8 mm, 32 mm, and 142 mm, respectively. For arch bridge models with an 8-m foundation buried depth, these displacements were measured at 6.2 mm, 21 mm, and 68 mm, respectively. The results from model tests verified that increasing the depth of foundation burial effectively reduces the displacement at the top of the structure. Furthermore, by combining a boundary-integral equation method and peak-frequency shift method, this study accurately predicted significant influences on W-shaped double deep canyon topography from seismic response, and successfully captured stress concentration and seismic wave amplification/focusing effects on arch foot structures. The calculated results from both methods align well with model test data which confirm their effectiveness and complementarity when analyzing seismic responses under complex terrain conditions for bridge structures.

The purpose of this study is to investigate the feasibility of replacing the fly ash in synchronous grouting material by reusing the shield mud produced in the clay stratum during the shield construction of Wuhan Rail Transit Line 11. The test utilizes the shield mud from the clay stratum to replace the fly ash material in synchronous grouting at percentages of 20%, 40%, 60%, 80%, and 100%, and research and analyses are conducted on the fluidity, stability, strength, and resistance to water dispersion of the slurry after the replacement; at the same time, improvements in the undesirable phenomenon produced by the synchronous grouting slurry are also examined after the replacement. The results show that, when the fly ash is replaced by shield mud at 80%, the mortar still has good stability and strength performance, but, at the same time, the initial value of consistency and the phenomenon of flow time loss is too large. Through the adjustment of the water–binder ratio and the addition of an appropriate amount of a polycarboxylate superplasticizer agent, the adverse phenomenon of the slurry is effectively improved, and the compressive strength and ease of the slurry are also improved. At the same time, when adding an appropriate amount of hydroxyethyl methyl cellulose (HEMC), the slurry has good water dispersion resistance, but, with the gradual increase in HEMC, the fluidity of the slurry deteriorates and the compressive strength decreases. The test proves that the shield mud in the clay stratum can be used to replace most of the fly ash in an appropriate proportion, which not only solves the problem of the shield mud being difficult to work with, but also provides more valuable insights for tunneling projects.

The high incidence of mild traumatic brain injury (mTBI) and the associated post-concussion symptoms, such as headache and cognitive deficits, have captured the significant attention from researchers globally. Magnetic resonance spectroscopy (MRS), a non-invasively technique derived from Magnetic Resonance Imaging (MRI), provides a complement approach to investigating brain metabolites as biomarkers for in vivo pathophysiological changes following mTBI, which are not evident in traditional MRI or CT scans. However, the separate review of MRS in mTBI patients has been limited, given the myriad factors involved and wide spectrum of TBI severity. In this review, we first delve into metabolite changes after mTBI, highlighting a reduction in N-acetyl-aspartate (NAA) as a relatively stable marker associated with neuronal loss or disfunction following mTBI. We then discuss the varying results observed for different metabolites and enumerate possible factors contributing to these inconsistent findings. These factors include variations in experimental methods, such as scanner types, acquisition methods, and region of interest. Additionally, we address subjects-specific factors, such as occupation, cause of injury, control group selection, injury stage, severity, the number of traumatic events, and the assessment of clinical features. Finally, we discuss the trend for future research in this field.

Objective: Allergic asthma is driven by an antigen-specific immune response. This study aimed to identify immune-related differentially expressed genes in childhood asthma and establish a classification diagnostic model based on these genes. Methods: GSE65204 and GSE19187 were downloaded and served as training set and validation set. The immune cell composition was evaluated with ssGSEA algorithm based on the immune-related gene set. Modules that significantly related to the asthma were selected by WGCNA algorithm. The immune-related differentially expressed genes (DE-IRGs) were screened, the protein-protein interaction network and diagnostic model of DE-IRGs was constructed. The pathway and immune correlation analysis of hub DE-IRGs was analyzed. Results: Eight immune cell types exhibited varying levels of abundance between the asthma and control groups. A total of 112 differentially expressed immune-related genes (DE-IRGs) was identified. Through the application of four ranking methods (MCC, MNC, DEGREE, and EPC), 17 hub DE-IRGs with overlapping significance were further selected. Subsequently, 8 optimized were identified using univariate logistic regression analysis and the LASSO regression algorithm, based on which a robust diagnostic model was constructed. Notably, TNF and CD40LG emerged as direct participants in asthma-related signaling pathways, displaying a positive correlation with the immune cell types of immature B cells, activated B cells, activated CD8 T cells, activated CD4 T cells, and myeloid-derived suppressor cells. Conclusion: The diagnostic model constructed using the DE-IRGs (CCL5, CCR5, CD40LG, CD8A, IL2RB, PDCD1, TNF, and ZAP70) exhibited high and specific diagnostic value for childhood asthma. The diagnostic model may contribute to the diagnosis of childhood asthma.

Percutaneous interventions are gaining rapid acceptance in cardiology and revolutionizing the treatment of structural heart disease (SHD). As new percutaneous procedures of SHD are being developed, their associated complexity and anatomical variability demand a high-resolution special understanding for intraprocedural image guidance. During the last decade, three-dimensional (3D) transesophageal echocardiography (TEE) has become one of the most accessed imaging methods for structural interventions. Although 3D-TEE can assess cardiac structures and functions in real-time, its limitations (e.g., limited field of view, image quality at a large depth, etc.) must be addressed for its universal adaptation, as well as to improve the quality of its imaging and interventions. This review aims to present the role of TEE in the intraprocedural guidance of percutaneous structural interventions. We also focus on the current and future developments required in a multimodal image integration process when using TEE to enhance the management of congenital and SHD treatments.

Abstract. Hypertrophic cardiomyopathy (HCM), the most common single-gene cardiovascular disease, is associated with increased risk for arrhythmias, heart failure, and sudden cardiac death. The hemodynamic changes known to occur during pregnancy can exacerbate heart failure and arrhythmias in women with HCM. We present a 30-year-old woman with HCM to illustrate the benefits of multidisciplinary team management of severe left ventricular outflow tract obstruction (peak gradient >100 mmHg) for optimal maternal and fetal outcomes.

White matter (WM) disruption is an important determinant of cognitive impairment after mild traumatic brain injury (mTBI), but traditional diffusion tensor imaging (DTI) shows some limitations in assessing WM damage. Diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) show advantages over DTI in this respect. Therefore, we used these three diffusion models to investigate complex WM changes in the acute stage after mTBI. From 32 mTBI patients and 31 age-, sex-, and education-matched healthy controls, we calculated eight diffusion metrics based on DTI (fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity), DKI (mean kurtosis), and NODDI (orientation dispersion index, volume fraction of intracellular water (Vic), and volume fraction of the isotropic diffusion compartment). We used tract-based spatial statistics to identify group differences at the voxel level, and we then assessed the correlation between diffusion metrics and cognitive function. We also performed subgroup comparisons based on loss of consciousness. Patients showed WM abnormalities and cognitive deficit. And these two changes showed positive correlation. The correlation between Vic of the splenium of the corpus callosum and Digit Symbol Substitution Test scores showed the smallest p-value (p = 0.000, r = 0.481). We concluded that WM changes, especially in the splenium of the corpus callosum, correlate to cognitive deficit in this study. Furthermore, the high voxel count of NODDI results and the consistency of mean kurtosis and the volume fraction of intracellular water in previous studies and our study showed the functional complementarity of DKI and NODDI to DTI.

Background: The abuse of methamphetamine (MA) worldwide has gained international attention as the most rapidly growing illicit drug problem. The classification and treatment response prediction of MA addicts are thereby paramount, in order for effective treatments to be more targeted to individuals. However, there has been limited progress.Methods: In the present study, 43 MA-dependent participants and 38 age- and gender-matched healthy controls were enrolled, and their resting-state functional magnetic resonance imaging data were collected. MA-dependent participants who showed 50% reduction in craving were defined as responders to treatment. The present study used the machine learning method, which is a support vector machine (SVM), to detect the most relevant features for discriminating and predicting the treatment response for MA-dependent participants based on the features extracted from the functional graph metrics.Results: A classifier was able to differentiate MA-dependent subjects from normal controls, with a cross-validated prediction accuracy, sensitivity, and specificity of 73.2% [95% confidence interval (CI) = 71.23–74.17%), 66.05% (95% CI = 63.06–69.04%), and 80.35% (95% CI = 77.77–82.93%), respectively, at the individual level. The most accurate combination of classifier features included the nodal efficiency in the right middle temporal gyrus and the community index in the left precentral gyrus and cuneus. Between these two, the community index in the left precentral gyrus had the highest importance. In addition, the classification performance of the other classifier used to predict the treatment response of MA-dependent subjects had an accuracy, sensitivity, and specificity of 71.2% (95% CI = 69.28–73.12%), 86.75% (95% CI = 84.48–88.92%), and 55.65% (95% CI = 52.61–58.79%), respectively, at the individual level. Furthermore, the most accurate combination of classifier features included the nodal clustering coefficient in the right orbital part of the superior frontal gyrus, the nodal local efficiency in the right orbital part of the superior frontal gyrus, and the right triangular part of the inferior frontal gyrus and right temporal pole of middle temporal gyrus. Among these, the nodal local efficiency in the right temporal pole of the middle temporal gyrus had the highest feature importance.Conclusion: The present study identified the most relevant features of MA addiction and treatment based on SVMs and the features extracted from the graph metrics and provided possible biomarkers to differentiate and predict the treatment response for MA-dependent patients. The brain regions involved in the best combinations should be given close attention during the treatment of MA.

BackgroundMethamphetamine (MA) abuse is one of the most rapidly growing illicit drug problems worldwide. Impulsivity has been considered as a core impairment underpinning addictive behavior. Studies have demonstrated that MA addicts have white matter abnormalities based on ROIs. There are few studies on whole brain, and the association between whole brain tracts and impulsivity in MA dependence remain unclear. Tract-based spatial statistics (TBSS) was used to detect four DTI measures, and these were correlated with the Barratt Impulsivity Scale (BIS) to verify and expand the previous results.MethodsA total of 28 MA addicts and 22 healthy controls were recruited. MRI was performed to evaluate the brain structural changes, the BIS was used to evaluate impulsivity behavior, white matter differences were compared between MA addicts and healthy controls, and then determine correlation between diffusion parameters and BIS scores.ResultsMA addicts had significantly lower FA, and higher AD, RD, and MD in a wide range of white matter, which mainly included: corona radiata, internal capsule, superior longitudinal fasciculus, external capsule, inferior fronto-occipital fascicules, posterior thalamic radiation, sagittal stratum, fornix and stria terminalis, cerebral peduncle, superior cerebellar peduncle, corpus callosum, and corticolspinal tract compared with controls. The MA group had significantly higher total score, attention and motor scores compared to healthy controls. Higher MD in the right corticospinal tract was significantly associated with higher total scores.ConclusionMA addicts exhibit a globally diminished white matter integrity. furthermore, they present with high levels of impulsivity, and this dysfunction is associated with MD in corticospinal tracts. Future studies on larger sample sizes, gender effects and longitudinal studies are needed.

Drawing on the practical perspective of empowerment, we investigated how empowering leader behavior influences employee voice via in-role (i.e., task performance) and extrarole (i.e., organizational citizenship behaviors toward individuals) performances, and examined the moderating effect of work stress on the empowering leader behavior–employee voice process. Using multiwave data from 996 supervisor–subordinate dyads, we found that empowering leader behavior promoted employee prosocial voice via organizational citizenship behaviors toward individuals, whereas it prohibited employee acquiescent voice and defensive voice via task performance. Further, work stress attenuated empowering leader behavior’s indirect effect on prosocial voice via organizational citizenship behaviors toward individuals, whereas it augmented empowering leader behavior’s indirect effects on employee acquiescent voice and defensive voice via task performance, respectively. [ABSTRACT FROM AUTHOR]

There is a dearth of evidence-based tools to design the safest Computerized Provider Order Entry (CPOE) system possible. An evidence-based list of usability principles for the design of the CPOE interface was developed following a literature review, and validated with the Chief Medical Information Officer and CPOE team at Island Health. The list includes 11 usability principles that can be used to inform ongoing CPOE interface design and evaluation efforts to improve patient safety. [ABSTRACT FROM AUTHOR]