Machine learning model for osteoporosis diagnosis based on bone turnover markers.

To assess the diagnostic utility of bone turnover markers (BTMs) and demographic variables for identifying individuals with osteoporosis. A cross-sectional study involving 280 participants was conducted. Serum BTM values were obtained from 88 patients with osteoporosis and 192 controls without osteoporosis. Six machine learning models, including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), CatBoost, random forest, support vector machine, and k-nearest neighbors, were employed to evaluate osteoporosis diagnosis. The performance measures included the area under the receiver operating characteristic curve (AUROC), F1-score, and accuracy. After AUROC optimization, LGBM exhibited the highest AUROC of 0.706. Post F1-score optimization, LGBM's F1-score was improved from 0.50 to 0.65. Combining the top three optimized models (LGBM, XGBoost, and CatBoost) resulted in an AUROC of 0.706, an F1-score of 0.65, and an accuracy of 0.73. BTMs, along with age and sex, were found to contribute significantly to osteoporosis diagnosis. This study demonstrates the potential of machine learning models utilizing BTMs and demographic variables for diagnosing preexisting osteoporosis. The findings highlight the clinical relevance of accessible clinical data in osteoporosis assessment, providing a promising tool for early diagnosis and management.

The relationship of race, ethnicity, gender identity, sex assigned at birth, sexual orientation, parental education, financial hardship and comorbid mental disorders with quality of life in college students with anxiety, depression or eating disorders.

BACKGROUND: Previous studies showed that comorbidity and demographic factors added to burden on health-related quality of life (HRQoL). Only one study explored the relationship between HRQoL and comorbidity in college students with mental disorders, leaving generalizability of findings uncertain. Less is known about the association of demographics on HRQoL. This study investigated HRQoL based on demographics and comorbidity among college students with mental disorders. METHODS: Participants were students (N = 5535) across 26 U.S. colleges and universities who met criteria for depression, generalized anxiety, panic, social anxiety, post-traumatic stress, or eating disorders based on self-report measures. ANOVA and linear regressions were conducted. RESULTS: Overall, female, minoritized (gender, sexual orientation, race, or ethnicity), and lower socioeconomic status students reported lower HRQoL than male, heterosexual, White, non-Hispanic, and higher socioeconomic status peers. After accounting for comorbidity, differences in physical HRQoL based on sex assigned at birth and gender were no longer significant. For mental HRQoL, only gender and sexual orientation remained significant. A greater number of comorbidities was associated with lower HRQoL regardless of demographic group. LIMITATIONS: The non-experimental design limits causal inference. The study focused on univariable associations without examining potential interactions between demographic factors. Future research should explore structural factors like discrimination. CONCLUSION: Results suggested that increased comorbidities placed an additional burden on HRQoL and that certain demographic groups were more vulnerable to HRQoL impairment among students with mental disorders. Findings suggest the need for prevention of disorders and their comorbidity and implementing tailored interventions for specific student subgroups with increased vulnerability.

Integrating ensemble and machine learning models for early prediction of pneumonia mortality using laboratory tests.

Background: The recent use of artificial intelligence (AI) in medical research is noteworthy. However, most research has focused on medical imaging. Although the importance of laboratory tests in the clinical field is acknowledged by clinicians, they are undervalued in medical AI research. Our study aims to develop an early prediction AI model for pneumonia mortality, primarily using laboratory test results. Materials and methods: We developed a mortality prediction model using initial laboratory results and basic clinical information of patients with pneumonia. Several machine learning (ML) models and a deep learning method-multilayer perceptron (MLP)-were selected for model development. The area under the receiver operating characteristic curve (AUROC) and F1-score were optimized to improve model performance. In addition, an ensemble model was developed by blending several models to improve the prediction performance. We used 80,940 data instances for model development. Results: Among the ML models, XGBoost exhibited the best performance (AUROC = 0.8989, accuracy = 0.88, F1-score = 0.80). MLP achieved an AUROC of 0.8498, accuracy of 0.86, and F1-score of 0.75. The performance of the ensemble model was the best among the developed models, with an AUROC of 0.9006, accuracy of 0.90, and F1-score of 0.81. Several laboratory tests were conducted to identify risk factors that affect pneumonia mortality using the "Feature importance" technique and SHapley Additive exPlanations. We identified several laboratory results, including systolic blood pressure, serum glucose level, age, aspartate aminotransferase-to-alanine aminotransferase ratio, and monocyte-to-lymphocyte ratio, as significant predictors of mortality in patients with pneumonia. Conclusions: Our study demonstrates that the ensemble model, incorporating XGBoost, CatBoost, and LGBM techniques, outperforms individual ML and deep learning models in predicting pneumonia mortality. Our findings emphasize the importance of integrating AI techniques to leverage laboratory test data effectively, offering a promising direction for advancing AI applications in medical research and clinical decision-making.

Severe Fever with Thrombocytopenia Syndrome in South Korea, 2016-2021: Clinical Features of Severe Progression and Complications.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infection with a high case fatality rate. The serious clinical features need to be further defined. We performed a retrospective analysis among SFTS patients in South Korea during 2016-2021 to update the current status. The basic epidemiology of all reported cases was analyzed, and the detailed clinical data of the subjects were further collected from study hospitals selected in terms of their geographic location and capability of SFTS care. Cases of SFTS were reported across the country and were greatly increased since the initial endemic phase, even under the passive surveillance system. The case fatality rate remained at approximately 16.8%. Coinfections at admission were present in 7.8% of the patients. Major complications included bleeding (15.2%), hemophagocytic lymphohistiocytosis (6.7%), bacteremia or candidemia (4.0%), and invasive pulmonary aspergillosis (1.7%). It took a median 4 days from the onset of illness to hospital admission. Rapid clinical deterioration was observed with a median 1 day for intensive care unit admission, 3 days for mechanical ventilation, 4 days for renal replacement therapy, and 5 days for death, all after the hospitalization. Multivariate analysis showed that the fatality was associated with older age, bacteremia, or candidemia during hospitalization, and the presence of several variables at admission such as fever, altered mentality, aspartate aminotransferase >200 IU/L, serum creatinine level >1.2 mg/dL, and prolonged prothrombin time and activated partial thromboplastin time. Treatment options to improve clinical outcomes are limited, despite best supportive care. Specific treatment is urgently needed to change the fatal course.

No Additional Effects of Sequential Facilitatory Cerebral and Cerebellar rTMS in Subacute Stroke Patients.

The aim of this study was to investigate the additional effects of cerebellar rTMS on the motor recovery of facilitatory rTMS over affected primary motor cortex (M1) in subacute stroke patients. Twenty-eight subacute stroke patients were recruited in this single-blind, randomized, controlled trial. The Cr-Cbll group received Cr-Cbll rTMS stimulation consisting of high-frequency rTMS over affected M1 (10 min), motor training (10 min), and high-frequency rTMS over contralesional Cbll (10 min). The Cr-sham group received sham rTMS instead of high-frequency rTMS over the cerebellum. Ten daily sessions were performed for 2 weeks. A Fugl-Meyer Assessment (FMA) was measured before (T0), immediately after (T1), and 2 months after the intervention (T2). A total of 20 participants (10 in the Cr-Cbll group and 10 in the Cr-sham group) completed the intervention. There was no significant difference in clinical characteristics between the two groups at T0. FMA was significantly improved after the intervention in both Cr-Cbll and Cr-sham groups (p < 0.05). However, there was no significant interaction in FMA between time and group. In conclusion, these results could not demonstrate that rTMS over the contralesional cerebellum has additional effects to facilitatory rTMS over the affected M1 for improving motor function in subacute stroke patients.

Deep learning model for differentiating acute myeloid and lymphoblastic leukemia in peripheral blood cell images via myeloblast and lymphoblast classification.

Objective: Acute leukemia (AL) is a life-threatening malignant disease that occurs in the bone marrow and blood, and is classified as either acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL). Diagnosing AL warrants testing methods, such as flow cytometry, which require trained professionals, time, and money. We aimed to develop a model that can classify peripheral blood images of 12 cell types, including pathological cells associated with AL, using artificial intelligence. Methods: We acquired 42,386 single-cell images of peripheral blood slides from 282 patients (82 with AML, 40 with ALL, and 160 with immature granulocytes). Results: The performance of EfficientNet-V2 (B2) using the original image size exhibited the greatest accuracy (accuracy, 0.8779; precision, 0.7221; recall, 0.7225; and F1 score, 0.7210). The next-best accuracy was achieved by EfficientNet-V1 (B1), with a 256 × 256 pixels image. F1 score was the greatest for EfficientNet-V1 (B1) with the original image size. EfficientNet-V1 (B1) and EfficientNet-V2 (B2) were used to develop an ensemble model, and the accuracy (0.8858) and F1 score (0.7361) were improved. The classification performance of the developed ensemble model for the 12 cell types was good, with an area under the receiver operating characteristic curve above 0.9, and F1 scores for myeloblasts and lymphoblasts of 0.8873 and 0.8006, respectively. Conclusions: The performance of the developed ensemble model for the 12 cell classifications was satisfactory, particularly for myeloblasts and lymphoblasts. We believe that the application of our model will benefit healthcare settings where the rapid and accurate diagnosis of AL is difficult.

Functional evaluation of Septal L-strut extension graft in East Asians.

BACKGROUND: The septal L-strut extension graft (SLEG) consists of anterior extended spreader graft and caudal septal extension graft. SLEG is used to increase the anterocaudal projection from a low-profile nose by creating the de-novo septum. This retrospective study verified the effectiveness of SLEG in improving the nasal function in East Asians. MATERIALS: Medical records of patients who underwent septorhinoplasty with SLEG were retrospectively reviewed. The clinical features showed under-projected lower two-thirds of the nose with septal deviation, saddle nose, and short nose. We analyzed the post-operative changes in the NOSE score and variables of nasal geometry measured using acoustic rhinometry through long-term follow-up. RESULTS: Patients were divided into two groups, those who underwent SLEG with turbinoplasty (Group A) and SLEG alone (group B). The NOSE scores decreased significantly in groups A and B, and the improvement was statistically more significant in Group A (p < 0.05). Acoustic rhinometry showed an increase in nasal cavity volume (VOL1) on the deviated side in Group A, and an increase in minimal cross-sectional area 1 (MCA1) on the deviated side in Group B (p < 0.05). The non-deviated side did not show significant reduction in MCA1 and VOL1 after SLEG with or without turbinoplasty. Thus, SLEG, by itself, improved airway function in East Asians. CONCLUSIONS: SLEG has proven to be valuable in improving nasal function.

Prominent transcriptomic changes in Mycobacterium intracellulare under acidic and oxidative stress.

BACKGROUND: Mycobacterium avium complex (MAC), including Mycobacterium intracellulare is a member of slow-growing mycobacteria and contributes to a substantial proportion of nontuberculous mycobacterial lung disease in humans affecting immunocompromised and elderly populations. Adaptation of pathogens in hostile environments is crucial in establishing infection and persistence within the host. However, the sophisticated cellular and molecular mechanisms of stress response in M. intracellulare still need to be fully explored. We aimed to elucidate the transcriptional response of M. intracellulare under acidic and oxidative stress conditions. RESULTS: At the transcriptome level, 80 genes were shown [FC] ≥ 2.0 and p < 0.05 under oxidative stress with 10 mM hydrogen peroxide. Specifically, 77 genes were upregulated, while 3 genes were downregulated. In functional analysis, oxidative stress conditions activate DNA replication, nucleotide excision repair, mismatch repair, homologous recombination, and tuberculosis pathways. Additionally, our results demonstrate that DNA replication and repair system genes, such as dnaB, dinG, urvB, uvrD2, and recA, are indispensable for resistance to oxidative stress. On the contrary, 878 genes were shown [FC] ≥ 2.0 and p < 0.05 under acidic stress with pH 4.5. Among these genes, 339 were upregulated, while 539 were downregulated. Functional analysis highlighted nitrogen and sulfur metabolism pathways as the primary responses to acidic stress. Our findings provide evidence of the critical role played by nitrogen and sulfur metabolism genes in the response to acidic stress, including narGHIJ, nirBD, narU, narK3, cysND, cysC, cysH, ferredoxin 1 and 2, and formate dehydrogenase. CONCLUSION: Our results suggest the activation of several pathways potentially critical for the survival of M. intracellulare under a hostile microenvironment within the host. This study indicates the importance of stress responses in M. intracellulare infection and identifies promising therapeutic targets.

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students.

Background: A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated. Methods: The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks. Results: Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia. Conclusion: These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder. Trial registration: Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

Dilated perivascular spaces and steno-occlusive changes in children and adults with moyamoya disease.

BACKGROUND: Dilated perivascular spaces (DPVS), known as one of imaging markers in cerebral small vessel disease, may be found in patients with moyamoya disease (MMD). However, little is known about DPVS in MMD. The purpose of this study was to investigate the distribution pattern of dPVS in children and adults with MMD and determine whether it is related to steno-occlusive changes of MMD. METHODS: DPVS was scored in basal ganglia (BG) and white matter (WM) on T2-weighted imaging, using a validated 4-point semi-quantitative score. The degree of dPVS was classified as high (score > 2) or low (score ≤ 2) grade. The steno-occlusive changes on MR angiography (MRA) was scored using a validated MRA grading. Asymmetry of DPVS and MRA grading was defined as a difference of 1 grade or higher between hemispheres. RESULTS: Fifty-one patients with MMD (mean age 24.9 ± 21.1 years) were included. Forty-five (88.2%) patients had high WM-DPVS grade (degree 3 or 4). BG-DPVS was found in 72.5% of all patients and all were low grade (degree 1 or 2). The distribution patterns of DPVS degree in BG (P = 1.000) and WM (P = 0.767) were not different between child and adult groups. The asymmetry of WM-DPVS (26%) and MRA grade (42%) were significantly correlated to each other (Kendall's tau-b = 0.604, P < 0.001). CONCLUSIONS: DPVS of high grade in MMD is predominantly found in WM, which was not different between children and adults. The correlation between asymmetry of WM-DPVS degree and MRA grade suggests that weak cerebral artery pulsation due to steno-occlusive changes may affect WM-DPVS in MMD.

Identification of Autoantigens in Pediatric Gastric Juices.

Purpose: This study aimed to investigate the presence of autoantigens in the gastric juices of children. Methods: Gastric juice and serum samples were obtained from 53 children <15 years of age who underwent gastric endoscopy. Among these, 8, 22, and 23 participants were in the age groups 0-5, 6-10, and 11-15 years, respectively. These samples were analyzed using two-dimensional electrophoresis (2-DE), immunoblot analysis, and matrix-assisted laser desorption ionization-time of-flight mass spectrometry. Furthermore, we reviewed the histopathological findings and urease test results and compared them with the results of 2-DE and immunoblot analysis. Results: There were no statistically significant differences in urease test positivity, grades of chronic gastritis, active gastritis, or Helicobacter pylori infiltration of the antrum and body among the three age groups. Three distinct patterns of gastric juice were observed on 2-DE. Pattern I was the most common, and pattern III was not observed below the age of 5 years. Histopathological findings were significantly different among active gastritis (p=0.037) and H. pylori infiltration (p=0.060) in the gastric body. The immunoblots showed large spots at an approximate pH of 3-4 and molecular weights of 31-45 kDa. These distinct, large positive spots were identified as gastric lipase and pepsin A and C. Conclusion: Three enzymes, which are normally secreted under acidic conditions were identified as autoantigens. Further investigation of the pathophysiology and function of autoantigens in the stomach is required.

Development of blood demand prediction model using artificial intelligence based on national public big data.

Objective: Modern healthcare systems face challenges related to the stable and sufficient blood supply of blood due to shortages. This study aimed to predict the monthly blood transfusion requirements in medical institutions using an artificial intelligence model based on national open big data related to transfusion. Methods: Data regarding blood types and components in Korea from January 2010 to December 2021 were obtained from the Health Insurance Review and Assessment Service and Statistics Korea. The data were collected from a single medical institution. Using the obtained information, predictive models were developed, including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and category boosting (CatBoost). An ensemble model was created using these three models. Results: The prediction performance of XGBoost, LGBM, and CatBoost demonstrated a mean absolute error ranging from 14.6657 for AB+ red blood cells (RBCs) to 84.0433 for A+ platelet concentrate (PC) and a root mean squared error ranging from 18.5374 for AB+ RBCs to 118.6245 for B+ PC. The error range was further improved by creating ensemble models, wherein the department requesting blood was the most influential parameter affecting transfusion prediction performance for different blood products and types. Except for the department, the features that affected the prediction performance varied for each product and blood type, including the number of RBC antibody screens, crossmatch, nationwide blood donations, and surgeries. Conclusion: Based on blood-related open big data, the developed blood-demand prediction algorithm can efficiently provide medical facilities with an appropriate volume of blood ahead of time.

Cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy versus R0 resection for resectable colorectal cancer with peritoneal metastases and low peritoneal cancer index scores: a collaborative observational study from Korea and Japan.

BACKGROUND: The benefits of hyperthermic intraperitoneal chemotherapy (HIPEC) after cytoreductive surgery (CRS) for colorectal cancer with peritoneal metastasis (CPM) remain controversial. R0 resection without peritoneal stripping might be as effective as CRS plus HIPEC. The authors aimed to compare the long-term oncological outcomes of patients with CPM and peritoneal cancer index (PCI) scores less than or equal to 6 who underwent R0 resection in Japan with those who underwent CRS plus HIPEC in Korea. MATERIALS AND METHODS: This international, retrospective cohort study was conducted in Korea and Japan using a prospectively collected clinical database. Patients who underwent surgery from July 2014 to December 2021 for CPM with a PCI score of less than or equal to 6 and completeness of the cytoreduction score-0 were included. The primary outcome was relapse-free survival (RFS), and the secondary outcomes were overall survival, peritoneal RFS (PRFS), and postoperative outcomes. RESULTS: The 3-year RFS was significantly longer in the CRS+HIPEC group than in the R0 resection group: 35.9% versus 6.9% ( P <0.001); 31.0% versus 6.7% ( P =0.040) after propensity score matching. The median PRFS was significantly longer in the CRS+HIPEC group than in the R0 resection group: 24.5 months versus 17.2 months ( P =0.017). The 3-year overall survival and postoperative complications did not significantly differ between the two groups. CONCLUSIONS: RFS and PRFS rates were significantly prolonged after CRS plus HIPEC, whereas postoperative complications and length of hospital stay were not increased. Therefore, curative CRS plus HIPEC may be considered a treatment strategy for selected patients with resectable CPM and low PCI scores.

National cancer screening program for colorectal cancer in Korea.

Colorectal cancer is the 3rd leading cause of cancer-related deaths in Korea, ranking 4th and 3rd among men and women, respectively. It is also the most common cause of cancer-related deaths in women older than 64 years. This study assessed the National Cancer Screening Program for colorectal cancer and examined its efficacy in enhancing public health. The fecal occult blood test (FOBT), a traditional noninvasive colorectal cancer screening test that can be performed on an outpatient basis was replaced with the fecal immunochemical test (FIT) because of the latter's better predictive value. Since 2004, the Government of South Korea has recommended an annual FIT for people aged 50 years and older as the first step in colorectal cancer screening. Individuals who test positive on the FIT are scheduled for follow-up screening procedures, such as colonoscopy or double-contrast barium enema, whereas those who have a negative FOBT are not recommended for colonoscopy. Colonoscopy, as a screening tool in Korea, has definite merits because it is highly accessible to patients and is performed by qualified specialists. Although the domestic colorectal cancer screening rate is relatively stable, there is scope for improvement. Owing to the low cost of colonoscopy and the wealth of skilled endoscopy specialists, the number of intention-to-screen procedures for colonoscopy has increased. As Korea is rapidly becoming an ultra-elderly society, it is time to reconsider the revision of the classical screening program and recommend region-specific, cost-effective guidelines.

An ultrasmall organic synapse for neuromorphic computing.

High-performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain-inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high-density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as-fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle-to-cycle uniformity of 98.89% and device-to-device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking-plasticity-related algorithm for decision-making tasks.

Weighing the benefits of lymphadenectomy in early-stage colorectal cancer.

Recent advancements in endoscopic procedures have resulted in a growing diagnosis of early colorectal cancer (CRC) cases, where classical en bloc lymph node (LN) dissection is not performed and treatment is terminated with the removal of the main cancer lesion by endoscopy without pathologic LN staging. Although many studies report noninferior outcomes of endoscopic resection in comparison to surgical resection, a cautious approach to completing treatment with endoscopic resection alone is recommended because LN metastases may be present even in early-stage CRC. In most countries, including the United States, Europe, and South Korea, the guidelines for additional surgery after endoscopic resection are very similar. If LN metastasis is suspected, even in T1 stage or lower lesions, further surgery is an essential treatment modality, but confirmation of the presence of LN metastasis is perhaps the most difficult part of this process. Another paradoxical recent trend is the expansion of more extensive and complete surgical lymphadenectomy for CRC. The success rate of surgery has improved dramatically over the past decade with the introduction of surgical devices and minimally invasive surgery, and the associated risks have been significantly reduced. While the burden of surgery on patients is understandable, the indications for surgery in early colon cancer need to be carefully reviewed to improve cure rates. In this process, we believe that an integrated decision-making process with surgeons, radiologists, and pathologists, in addition to the opinions of endoscopists, will be an important process to improve the cure rate.

A scoping review of the different types of exercise programs proposed for the improvement of postural balance in adolescents with idiopathic scoliosis.

BACKGROUND: Adolescent idiopathic scoliosis (AIS), which is the most common type of scoliosis, is a progressive disease that occurs in children aged 10-16 years. Abnormal curvature in AIS provokes spinal asymmetry of the upper body alignment and might deteriorate postural balancing and control ability. OBJECTIVE: To evaluate the effect of exercise interventions on balance and postural stability in patients with adolescent idiopathic scoliosis. METHODS: Embase, Scopus, Pubmed (Medline) and Web of Science databases were searched using the terms idiopathic scoliosis, physiotherapy, and balance. The articles selected were published in English in peer-reviewed journals from 2012 to July 2022. RESULTS: Ten studies met the inclusion criteria. The PEDro scale values ranged from 2 to 6 (mean, 3.6), indicating a low level of scientific rigor. In the sample studies, spinal stabilization exercises were most often trialed (n= 3), followed by Schroth's exercise (n= 2), stretching and self-elongation exercise (n= 2), the exercise protocol of Blount and Moe, physiotherapeutic scoliosis-specific exercise, and proprioceptive neuromuscular facilitation exercise (all n= 1). CONCLUSIONS: Physical therapists will be able to apply hippotherapy, Schroth exercise, physiotherapy scoliosis-specific exercise, trunk stabilization, proprioceptive neuromuscular facilitation exercise, spinal stabilization exercise, core stabilization exercise, and body awareness therapy to manage balance impairments in patients with adolescent idiopathic scoliosis, and further studies are needed to provide stronger evidence.

Direct numerical simulation of an unsteady wall-bounded turbulent flow configuration for the assessment of large-eddy simulation models.

A new benchmark case for the evaluation of direct numerical simulation (DNS) and large-eddy simulation (LES) models and methods is presented in this study. The known Taylor-Green vortex is modified by replacing the periodic boundary conditions in one direction with a no-slip boundary. A passive scalar is added and transported from the wall into the fluid. The addition of walls allows for the study of transient-instationary flows in a simple geometry with clean boundary and initial conditions, which is a key requirement for the assessment of LES modeling strategies. The added scalar mimics heat transfer through the wall. The case features reasonable computational cost for highly-resolved LES and DNS calculations. Simulations of the wall-bounded Taylor-Green vortex are easy to setup and do not require additional modeling. The proposed modification of the case is compared to the default Taylor-Green vortex and the difference in flow-physics is discussed. A detailed convergence study with four meshes, each of them refined by a factor of 2, has been conducted. The results reveal that converged second-order statistics can be obtained up to a dimensionless time of [Formula: see text]. Beyond that, due to the unsteady chaotic nature of the flow, some uncertainties remain. The results show that the case features challenging (near-wall) flow dynamics, which cannot be covered using the default Taylor-Green vortex and hence, justify the proposed case as a useful benchmark.

Obscured Segments of Ruptured Brain Arteriovenous Malformations: Insights from Their Visualization during Emergency Transarterial Embolization.

A ruptured brain arteriovenous malformation (bAVM) presenting with a hematoma may have unseen parts of the shunts in diagnostic angiography in the acute phase, which may lead to innate incomplete evaluation for the whole angioarchitecture of the bAVM. Even though it is generally accepted that the nidus of a ruptured bAVM may be underestimated in angiography during the acute phase due to hematoma compression, documentation of the underestimated parts has not been described in the literature. The authors report 2 cases of ruptured bAVMs in which the obscured segments were cast with liquid embolic material, which suggests a potential presence of obscured segments in bAVMs.

Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records.

BACKGROUND: An artificial-intelligence (AI) model for predicting the prognosis or mortality of coronavirus disease 2019 (COVID-19) patients will allow efficient allocation of limited medical resources. We developed an early mortality prediction ensemble model for COVID-19 using AI models with initial chest X-ray and electronic health record (EHR) data. RESULTS: We used convolutional neural network (CNN) models (Inception-ResNet-V2 and EfficientNet) for chest X-ray analysis and multilayer perceptron (MLP), Extreme Gradient Boosting (XGBoost), and random forest (RF) models for EHR data analysis. The Gradient-weighted Class Activation Mapping and Shapley Additive Explanations (SHAP) methods were used to determine the effects of these features on COVID-19. We developed an ensemble model (Area under the receiver operating characteristic curve of 0.8698) using a soft voting method with weight differences for CNN, XGBoost, MLP, and RF models. To resolve the data imbalance, we conducted F1-score optimization by adjusting the cutoff values to optimize the model performance (F1 score of 0.77). CONCLUSIONS: Our study is meaningful in that we developed an early mortality prediction model using only the initial chest X-ray and EHR data of COVID-19 patients. Early prediction of the clinical courses of patients is helpful for not only treatment but also bed management. Our results confirmed the performance improvement of the ensemble model achieved by combining AI models. Through the SHAP method, laboratory tests that indicate the factors affecting COVID-19 mortality were discovered, highlighting the importance of these tests in managing COVID-19 patients.