Fusarium amaranthuense sp. nov. from amaranth is an emergent species closely related to Fusarium circinatum.

Amaranth (Amaranthus spp. L) is not native to South Korea but cultivated in small scales for ornamental purposes as well as leafy vegetables and pseudo cereals. In this study, a new species within the genus Fusarium was isolated from amaranth, showing stem rot symptoms from a farmer field in Hwaseong, South Korea. The disease is characterized by dark-brown spots with black borders, leading to withering. Phylogenetic analysis based on concatenated sequences of translation elongation factor 1-alpha (TEF1), beta-tubulin (tub2), calmodulin (cmdA), RNA polymerase largest subunit (RPB1) and RNA polymerase II second largest subunit (RPB2) genes, revealed that the obtained isolates were formed a distinct clade within the Fusarium fujikuroi species complex and closely related to F. circinatum. Cultural and morphological characteristics and pathogenicity on healthy amaranth plants (stem and leaves) were examined. The isolates readily differentiated from F. circinatum based on 1-5 septate macroconidia and the absence of sterile hyphae. Based on molecular and morphological characteristics, this fungus is demonstrated to be a new species and is described here as Fusarium amaranthuense, the causal agent of stem rot of amaranth in South Korea.

Effectiveness and Usefulness of Bone Turnover Marker in Osteoporosis Patients: A Multicenter Study in Korea.

BACKGROUND: This study aimed to investigate real-world data of C-terminal telopeptide (CTX), propeptide of type I collagen (P1NP), and osteocalcin through present multicenter clinical study, and retrospectively analyze the usefulness of bone turnover markers (BTMs) in Koreans. METHODS: The study focused on pre- and post-menopausal patients diagnosed with osteoporosis and excluded patients without certain test results or with test intervals of over 1 year. The demographic data and 3 BTMs (CTX, P1NP, and osteocalcin) were collected. The patients were classified by demographic characteristics and the BTM concentrations were analyzed by the group. RESULTS: Among women with no history of fractures, the levels of P1NP (N=2,100) were 43.544±36.902, CTX (N=1,855) were 0.373 ±0.927, and osteocalcin (N=219) were 10.81 ±20.631. Among men with no history of fractures, the levels of P1NP (N=221) were 48.498±52.892, CTX (N=201) were 0.370±0.351, and osteocalcin (N=15) were 7.868 ±10.674. Treatment with teriparatide increased the P1NP levels after 3 months in both men and women, with a 50% increase observed in women. Similarly, treatment with denosumab decreased the CTX levels after 3 months in both men and women, with a reduction of 50% observed in women. CONCLUSIONS: The results of this study can contribute to the accurate assessment of bone replacement status in Koreans. We also provide the P1NP level in the Korean population for future comparative studies with other populations.

Analysis of Changes in Sleep Quality and Patterns after Hip Fracture Using Real Evidence of Artificial Intelligence Linked (REAL) Hip Cohort Data.

Background and Objectives: Hip fractures are commonly found in elderly patients, and often result in chronic pain and decreased physical function, as well as worsening of overall health. It is known that early surgical intervention during the acute phase and rehabilitation are important for improving clinical outcomes for these patients. However, the importance of management for improving the quality of life of these patients is becoming more emphasized. Studies on changes in sleep patterns after hip fractures are rare overseas. Therefore, the aim of this study is to investigate the prevalence of sleep disturbance in patients with hip fractures and to analyze the changes in sleep disturbance after surgery by comparing the preoperative and postoperative results. Materials and Methods: During the period from August 2022 to January 2023, patients who underwent surgical treatment for hip fractures and were recruited into the REAL Hip Cohort were selected as research subjects. The sleep survey was conducted using the Pittsburgh Sleep Quality Index (PSQI). The PSQI is composed of 18 questions, each divided into areas of sleep quality, sleep latency, duration, efficiency, disturbance, use of medication, and daytime dysfunction. Each area is scored 0-3 points and the total is 0-21. A score greater than five indicates sleep disorder. The PSQI was surveyed during hospitalization and three months after surgery for post-fracture sleep status. To analyze changes before and after the fracture, paired T-tests and chi-square tests were performed. Results: From August 2022 to January 2023, a total of 40 patients who were recruited into the REAL Hip Cohort responded to the PSQI survey. The average age was 77.4 years and 36 were female. Sleep quality worsened from 0.75 ± 1.0 before surgery to 1.4 ± 1.0 three months after surgery (p = 0.019), and sleep efficiency also worsened from 0.4 ± 0.6 to 1.4 ± 1.0 (p < 0.001). The PSQI increased from an average of 5.2 ± 2.8 before surgery to 8.2 ± 4.2 three months after surgery (p = 0.007), and the number of patients who could be diagnosed with sleep disorders also increased from 12 (40%) to 24 (60%) (p = 0.030). Conclusions: A decline in overall sleep status was observed in patients in a survey on sleep patterns three months after hip fracture. Additional management is needed to improve their sleep patterns.

Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review.

BACKGROUND: Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology. METHODS: The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including "hip fractures" AND "artificial intelligence". RESULTS: A total of 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%. CONCLUSIONS: We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.

Osteoporosis Feature Selection and Risk Prediction Model by Machine Learning Using a Cross-Sectional Database.

BACKGROUND: The purpose of this study was to verify the accuracy and validity of using machine learning (ML) to select risk factors, to discriminate differences in feature selection by ML between men and women, and to develop predictive models for patients with osteoporosis in a big database. METHODS: The data on 968 observed features from a total of 3,484 the Korea National Health and Nutrition Examination Survey participants were collected. To find preliminary features that were well-related to osteoporosis, logistic regression, random forest, gradient boosting, adaptive boosting, and support vector machine were used. RESULTS: In osteoporosis feature selection by 5 ML models in this study, the most selected variables as risk factors in men and women were body mass index, monthly alcohol consumption, and dietary surveys. However, differences between men and women in osteoporosis feature selection by ML models were age, smoking, and blood glucose level. The receiver operating characteristic (ROC) analysis revealed that the area under the ROC curve for each ML model was not significantly different for either gender. CONCLUSIONS: ML performed a feature selection of osteoporosis, considering hidden differences between men and women. The present study considers the preprocessing of input data and the feature selection process as well as the ML technique to be important factors for the accuracy of the osteoporosis prediction model.

Effectiveness of Weekly Teriparatide Injection in Postmenopausal Patients with Hip Fractures.

Background: Teriparatide is an effective anabolic agent used in the treatment of severe osteoporosis. In addition, it is also used to promote fracture healing. The purpose of this double-blind randomized controlled trial was to evaluate the influence of weekly teriparatide administration on bone formation in hip fracture patients. Methods: The control group (n = 41) was composed of patients treated with normal saline other than teriparatide, and the teriparatide group (n = 51) consisted of patients who received weekly teriparatide. Bone turnover markers, C-terminal telopeptide (CTx) and osteocalcin (OC), were assessed through blood tests at the initial hospital visit and 3-month, 6-month, and 1-year follow-ups. Dual-energy X-ray absorptiometry was performed 5 days postoperatively and at 1-year postoperative follow-up. The degree of fracture union was evaluated by comparing the radiographic union scoring system for hips using Radiographic Union Score for Hip (RUSH) scores between the two groups at 3 months, 6 months, and 1 year after surgery. Results: Evaluation of the rate of change in bone mineral density over 1 year showed that the lumber bone mineral density increased by more than 7% in the experimental group. The control group did not show a difference between the CTx and OC at 6 months, but the difference between the CTx and OC values was large at 6 months in the experimental group. The mean RUSH score was significantly different between the control group and the experimental group: 12.105 and 15.476, respectively (p = 0.004), at 3 months and 18.571 and 22.389, respectively, at 6 months (p = 0.006). Conclusions: Weekly use of teriparatide improved fracture healing, bone formation, and clinical outcomes at 1 year after hip fracture surgery by the anabolic window effect.

High Platelet Reactivity Combined with CYP2C19 Genotype in Predicting Outcomes in East Asian Patients Undergoing Percutaneous Coronary Intervention.

Loss-of-function (LoF) alleles of cytochrome P450 2C19 (CYP2C19), which are prevalent in East Asians, are linked to high platelet reactivity (HPR) phenotype and poor prognosis. We aimed to investigate the incremental predictive value of HPR combined with CYP2C19 genotype in predicting outcomes after drug-eluting stent (DES) implantation. The patients treated with platelet function and genotype-related long-term prognosis in drug-eluting stent (PTRG-DES) consortium enrolled a total of 13,160 Korean patients treated with DES who had platelet function test (PFT) or CYP2C19 genotype, of which, 6,717 patients with PFT and genotype together were categorized. HPR was defined as VerifyNow ≥ 252 P2Y12 reaction unit. The primary outcome was the incidence of major adverse cardiac and cerebrovascular event (MACCE) 5 years after treatment. The patients with both HPR and CYP2C19 LoF/LoF had the highest MACCE rates (6.2%) and increased MACCE risk (adjusted hazard ratio: 1.89, 95% confidence interval: 1.20-2.91, P = 0.006) compared with those without both HPR and CYP2C19 LoF/LoF. There was no effect of interaction between HPR and CYP2C19 genotype on the primary outcome (P = 0.424). Adding combined HPR and CYP2C19 genotype to the conventional model had an incremental influence in predicting MACCE and stent thrombosis. Compared to the model including HPR or CYP2C19 genotype alone, a combination model significantly improved the risk stratification for stent thrombosis but not MACCE. In DES-treated East Asian patients, the combined evaluation of PFT results and CYP2C19 genotyping might improve risk prediction of ischemic events during clopidogrel treatment.

Relationship between DNA methylation changes and skeletal muscle mass.

BACKGROUND: Sarcopenia is a disease diagnosed in the elderly. In patients with sarcopenia, the muscle mass decreases every year. The occurrence of sarcopenia is greatly affected by extrinsic factors such as eating habits, exercise, and lifestyle. The present study aimed to determine the relationship between muscle mass traits and genes affected by epigenetic factors with three different adjustment methods using Korean Genome and Epidemiology Study (KOGES) data. RESULTS: We conducted a demographic study and DNA methylation profiling by three studies according to the muscle mass index (MMI) adjustment methods: appendicular skeletal muscle mass divided by body weight (MMI1); appendicular skeletal muscle mass divided by square of height (MMI2); appendicular skeletal muscle mass divided by BMI (MMI3). We analyzed differentially methylated regions (DMRs) for each group. We then restricted our subjects to be top 30% (T30) and bottom 30% (B30) based on each MMI adjustment method. Additionally, we performed enrichment analysis using PathfindR to evaluate the relationship between identified DMRs and sarcopenia. A total of 895 subjects were included in the demographic study. The values of BMI, waist, and hip showed a significant difference in all three groups. Among 446 participants, 44 subjects whose DNA methylation profiles were investigated were included for DNA methylation analysis. The results of enrichment analysis showed differences between groups. In the women group through MMI1 method, only the glutamatergic synapse pathway showed a significant result. In the men group through MMI2 method, the adherens junction pathway was the most significant. Women group through MMI2 method showed similar results, having an enriched Rap1 signaling pathway. In men group through MMI3 method, the Fc epsilon RI signaling pathway was the most enriched. Particularly, the notch signaling pathway was significantly enriched in women group through MMI3 method. CONCLUSION: This study presents results about which factor should be concerned first in muscle mass index (MMI) adjustment. The present study suggested that GAB2 and JPH3 in MMI1 method, HLA-DQB1 and TBCD in MMI2 method, GAB2, NDUFB4 and ISPD in MMI3 method are potential genes that can have an impact on muscle mass. It could enable future epigenetic studies of genes based on annotation results. The present study is a nationwide study in Korea with the largest size up to date that compares adjustment indices for MMI in epigenetic research.

Prevalence of Sarcopenia in Africa: A Systematic Review.

Objective: The world population gradually getting older, age-related sarcopenia is becoming more frequent. Known to be highly prevalent in high income countries, relative data in Africa are still scarce. This review aims to estimate the prevalence of sarcopenia in Africa and its characteristics. Study Design and Setting: A literature search in PubMed, Web of Science, Google Scholar, and Scopus was conducted in October 2022. All studies reporting the prevalence of sarcopenia in Africa within 15 years were included, and we did an assessment of bias with Hoy et al's risk bias assessment tool. The estimated prevalence of sarcopenia was the outcome and we performed secondary analyses by age, gender, and diagnostic criteria. The random effect model was used for the prevalence estimation. The prevalence of sarcopenia and 95% confidence interval (95% CI) were calculated using the inverse-variance method. Results: A total of 17 studies met our eligibility criteria, for a study population of 12,690 participants with 44.3% males and 55.7% females. The overall prevalence of sarcopenia was 25% (95% CI: 19-30%). The prevalence of sarcopenia among 50 years old and older was 23% (95% CI: 17-29%). We had a higher prevalence of sarcopenia among males (30%, %95 IC: 20-39%) than females (29%, %95 IC: 21-36%). The prevalence of sarcopenia was different depending on the diagnosis criteria used. Conclusion: The prevalence of sarcopenia in Africa was relatively high. However, the fact that the majority of included studies were hospital-based studies shows the necessity of further community-based studies in order to have a more accurate representation of the situation in the general population.

Correlation between Harris hip score and gait analysis through artificial intelligence pose estimation in patients after total hip arthroplasty.

BACKGROUND: Recently, open pose estimation using artificial intelligence (AI) has enabled the analysis of time series of human movements through digital video inputs. Analyzing a person's actual movement as a digitized image would give objectivity in evaluating a person's physical function. In the present study, we investigated the relationship of AI camera-based open pose estimation with Harris Hip Score (HHS) developed for patient-reported outcome (PRO) of hip joint function. METHOD: HHS evaluation and pose estimation using AI camera were performed for a total of 56 patients after total hip arthroplasty in Gyeongsang National University Hospital. Joint angles and gait parameters were analyzed by extracting joint points from time-series data of the patient's movements. A total of 65 parameters were from raw data of the lower extremity. Principal component analysis (PCA) was used to find main parameters. K-means cluster, X-squared test, Random forest, and mean decrease Gini (MDG) graph were also applied. RESULTS: The train model showed 75% prediction accuracy and the test model showed 81.8% reality prediction accuracy in Random forest. "Anklerang_max", "kneeankle_diff", and "anklerang_rl" showed the top 3 Gini importance score in the Mean Decrease Gini (MDG) graph. CONCLUSION: The present study shows that pose estimation data using AI camera is related to HHS by presenting associated gait parameters. In addition, our results suggest that ankle angle associated parameters could be key factors of gait analysis in patients who undergo total hip arthroplasty.

Accelerometer-derived physical activity analysis of elderly osteoarthritis patients.

BACKGROUND: Because disability in Osteoarthritis (OA) may change physical activity (PA), which might affect the disease progression, it is important to measure a patient's daily PA to study the relationship between a patient's PA and disease progression. OBJECTIVE: The objective of the present study was to investigate the relationship between PA and patients with OA and people without OA using data from the Korea National Health and Nutrition Examination Survey (KNHANES). METHODS: Demographic study was conducted to obtain data of comorbidities of participants. PA was compared between the group with OA (OA group) and the group without OA (non-OA group). In addition, PAs of OA patients with comorbidities and those without comorbidities were compared. The cut-off of moderate to vigorous physical activity (MVPA) was obtained through a receiver operating characteristic (ROC) curve. RESULTS: In the demographic study, there were significantly more educated participants in the OA group (p < .001). Actigraph data showed a significant decrease in MVPA (p < .001) but a significant increase in light activity (p = .002) in the OA group. In addition, the OA group showed significantly lower light PA but significantly higher MVPA in ≥10 min bout length. OA patients with comorbidities showed higher MVPA than OA patients without comorbidities (p = .044). The cut-off point of MVPA was 7.071 min/day when ROC curve was conducted. CONCLUSIONS: The present study suggests that patients with OA and low activity need a certain level of physical activity and a cut-off point for MVPA is presented which accounts for comorbidities in OA patients.

A Meta-Analysis of Polymerase Chain Reaction for Prosthetic Joint Infection Diagnosis.

BACKGROUND: One of the most dangerous side effects of joint replacement for the hip, knee, shoulder, and elbow is prosthesis joint infection (PJI). Polymerase chain reaction (PCR) has been considered a promising method for PJI diagnosis due to its short diagnostic time and high sensitivity. Although several PCR methods such as multiplex PCR and broad-range PCR are useful diagnostic methods for detecting microorganisms causing PJI, values of different PCR methods for the diagnosis of PJI remain unclear. Thus, the objective of this study was to perform a meta-analysis of different PCR methods in the diagnosis of PJI to determine their diagnostic characteristics including sensitivity and specificity. METHODS: The following data were extracted: PCR method, number of patients, sample site and type, diagnosis standard, true positive, false positive, false negative, and true negative. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Meta-regression analysis was conducted to assess heterogeneity. Subgroup analysis was also performed to assess effects of several variables on meta-analysis results. RESULTS: The current study showed that pooled sensitivity and pooled specificity were 0.70 (95% CI: 0.67 - 0.73) and 0.94 (95% CI: 0.92 - 0.95), respectively. Results of subgroup analysis indicated that sequencing method showed the lowest sensitivity (0.63, 95% CI: 0.59 - 0.67). However, after excluding studies using tissue samples directly, sequencing method showed higher sensitivity (0.83, 95% CI: 0.73 - 0.90) than other PCR methods (0.74, 95% CI: 0.69 - 0.78). CONCLUSIONS: The main significance of this study was that we attempted to classify accuracies of several PCR methods and found that sequencing with a reliable sampling method could be used as an early screening strategy for PJI. Further comparisons for PCR technologies are needed to evaluate their cost effectiveness and diagnostic procedures, not just diagnostic values, to discover the optimal one for PJI diagnosis.

The Effect of Light and Dark Treatment on the Production of Rosmarinic Acid and Biological Activities in Perilla frutescens Microgreens.

This study aimed to investigate the effect of light [a long-day photoperiod (16 h light/8 h dark cycle)] and dark treatment on the production of rosmarinic acid in P. frutescens microgreens and to determine its antioxidant and antibacterial activities. Microgreens of P. frutescens were grown under light and dark conditions and harvested after 10, 15, 20, and 25 days of each treatment. Although dry weight values of microgreens gradually increased from 10 to 25 days of both treatments, the microgreens grown under light treatment possessed slightly higher levels of dry weight than those grown in the dark. Rosmarinic acid and total phenolic content (TPC) were also analyzed using high-performance liquid chromatography (HPLC) and Folin-Ciocalteu assay. The accumulation patterns of rosmarinic acid and TPC gradually increased and decreased, respectively, in P. frutescens microgreens grown in continuous darkness. The highest accumulation was observed in microgreens grown for 20 days. However, rosmarinic acid and TPC values were not significantly different in microgreens grown under light conditions. According to the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical inhibition assay, the extracts of P. frutescens microgreens were confirmed to be strong antioxidants, and their ability to scavenge DPPH radicals was positively correlated with the total phenolic content in the microgreens after 10, 15, 20, and 25 days of both treatments. Considering the relatively higher values of dry weight, rosmarinic acid, TPC, and DPPH assay, P. frutescens microgreens after 20 days of darkness and 20 days of light treatment, respectively, were selected for screening antibacterial activity using nine pathogens. Both microgreen extracts showed strong antibacterial activity against pathogens. In particular, the extracts of microgreens grown for 20 days under light treatment showed higher antimicrobial effects. Therefore, the light treatments for 20 days, as well as the darkness treatment for 20 days, were the best conditions for P. frutescens microgreen production because of their high levels of dry weight, phenolics, and biological activities.

Rutin Prevents Dexamethasone-Induced Muscle Loss in C2C12 Myotube and Mouse Model by Controlling FOXO3-Dependent Signaling.

One of the causes of sarcopenia is that homeostasis between anabolism and catabolism breaks down due to muscle metabolism changes. Rutin has shown antioxidant and anti-inflammatory effects in various diseases, but there are few studies on the effect on muscle loss with aging. The effect of rutin on muscle loss was evaluated using dexamethasone-induced muscle loss C2C12 myoblast and mouse model. In the group treated with dexamethasone, the muscle weight of gastrocnemius (GA), tibialis anterior (TA), and extensor digitorum longus (EDL) in the mouse model were significantly decreased (p < 0.0001 in GA, p < 0.0001 in TA, and p < 0.001 in EDL) but recovered (p < 0.01 in GA, p < 0.0001 in TA, and p < 0.01 in EDL) when treated with rutin. MAFbx, MuRF1, and FOXO3 protein expression of C2C12 myoblast were significantly increased (p < 0.01 in MAFbx, p < 0.01 in MuRF1, and p < 0.01 in FOXO3) when treated with dexamethasone, but it was recovered (p < 0.01 in MAFbx, p < 0.01 in MuRF1, and p < 0.01 in FOXO3) when rutin was treated. In addition, MAFbx and FOXO3 protein expression in GA of mouse model was significantly increased (p < 0.0001 in MAFbx and p < 0.001 in FOXO3) when treated with dexamethasone, but it was also recovered (p < 0.01 in MAFbx and p < 0.001 in FOXO3) when rutin was treated. The present study shows that rutin blocks the FOXO3/MAFbx and FOXO3/MuRf1 pathways to prevent protein catabolism. Therefore, rutin could be a potential agent for muscle loss such as sarcopenia through the blocking ubiquitin-proteasome pathway associated with catabolic protein degradation.

Genome-wide analyses of early-onset acute myocardial infarction identify 29 novel loci by whole genome sequencing.

Early-onset acute myocardial infarction (AMI) may have a higher genetic predisposition than late-onset AMI. The present study aimed to identify and characterize germline variants that affect early-onset AMI using whole-genome sequencing (WGS). We performed a genome-wide association study based on the WGS of 1239 Koreans, including 596 early-onset AMI patients and 643 healthy individuals. Patients with AMI who underwent percutaneous coronary intervention (PCI) caused by atherothrombotic occlusive lesions were included in the study. A total of 29 novel loci were found to be associated with early-onset AMI. These loci are involved in thrombosis, fibrinolysis, inflammation, and lipid metabolism. One of the associated single nucleotide variants (SNVs), rs1614576, located upstream of PRKCB, is known to be associated with thrombus formation. Additionally, the results revealed a novel locus, rs78631167, located upstream of PLAUR which plays a critical role in regulating plasminogen activation and is related to fibrinolysis. The association between early-onset AMI and rs9357455, which is located upstream of PHACTR1 and regulates inflammation in AMI, was found. Moreover, we identified a lipid metabolism related genetic risk locus, rs5072, in the APOA1-AS gene. This study provides new evidence supporting the genetic association between early-onset AMI and thrombosis and fibrinolysis, as well as inflammation and lipid metabolism, by analyzing the whole-genome of 596 patients with early-onset AMI who have been treated with PCI. Our findings highlight potential genetic markers for the prediction and management of AMI, as well as for understanding the etiology of AMI.

Wearable Fabric Loop Sensor Based on Magnetic-Field-Induced Conductivity for Simultaneous Detection of Cardiac Activity and Respiration Signals.

In this study, a noncontact fabric loop sensor based on magnetic-field-induced conductivity, which can simultaneously detect cardiac activity and respiration signals, was developed and the effects of the sensor's shape and measurement position on the sensing performance were analyzed. Fifteen male subjects in their twenties wore sleeveless shirts equipped with various types of fabric loop sensors (spiky, extrusion, and spiral), and the cardiac activity and respiratory signals were measured twice at positions P2, P4, and P6. The measurements were verified by comparing them against the reference electrocardiogram (ECG) and respiratory signals measured using BIOPAC® (MP150, ECG100B, RSP100C). The waveforms of the raw signal measured by the fabric loop sensor were filtered with a bandpass filter (1-20 Hz) and qualitatively compared with the ECG signal obtained from the Ag/AgCI electrode. Notwithstanding a slight difference in performance, the three fabric sensors could simultaneously detect cardiac activity and respiration signals at all measurement positions. In addition, it was verified through statistical analysis that the highest-quality signal was obtained at the measurement position of P4 or P6 using the spiral loop sensor.

Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review.

BACKGROUND: In the emergency room, clinicians spend a lot of time and are exposed to mental stress. In addition, fracture classification is important for determining the surgical method and restoring the patient's mobility. Recently, with the help of computers using artificial intelligence (AI) or machine learning (ML), diagnosis and classification of hip fractures can be performed easily and quickly. The purpose of this systematic review is to search for studies that diagnose and classify for hip fracture using AI or ML, organize the results of each study, analyze the usefulness of this technology and its future use value. METHODS: PubMed Central, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched to identify relevant studies published up to June 2022 with English language restriction. The following search terms were used [All Fields] AND (", "[MeSH Terms] OR (""[All Fields] AND "bone"[All Fields]) OR "bone fractures"[All Fields] OR "fracture"[All Fields]). The following information was extracted from the included articles: authors, publication year, study period, type of image, type of fracture, number of patient or used images, fracture classification, reference diagnosis of fracture diagnosis and classification, and augments of each studies. In addition, AI name, CNN architecture type, ROI or important region labeling, data input proportion in training/validation/test, and diagnosis accuracy/AUC, classification accuracy/AUC of each studies were also extracted. RESULTS: In 14 finally included studies, the accuracy of diagnosis for hip fracture by AI was 79.3-98%, and the accuracy of fracture diagnosis in AI aided humans was 90.5-97.1. The accuracy of human fracture diagnosis was 77.5-93.5. AUC of fracture diagnosis by AI was 0.905-0.99. The accuracy of fracture classification by AI was 86-98.5 and AUC was 0.873-1.0. The forest plot represented that the mean AI diagnosis accuracy was 0.92, the mean AI diagnosis AUC was 0.969, the mean AI classification accuracy was 0.914, and the mean AI classification AUC was 0.933. Among the included studies, the architecture based on the GoogLeNet architectural model or the DenseNet architectural model was the most common with three each. Among the data input proportions, the study with the lowest training rate was 57%, and the study with the highest training rate was 95%. In 14 studies, 5 studies used Grad-CAM for highlight important regions. CONCLUSION: We expected that our study may be helpful in making judgments about the use of AI in the diagnosis and classification of hip fractures. It is clear that AI is a tool that can help medical staff reduce the time and effort required for hip fracture diagnosis with high accuracy. Further studies are needed to determine what effect this causes in actual clinical situations.

Exogenous gibberellin can effectively and rapidly break intermediate physiological dormancy of Amsonia elliptica seeds.

Accelerated global warming is leading to the loss of plant species diversity, and ex situ preservation of seeds is becoming an increasingly important aspect of species conservation. However, information on dormancy and germination is lacking in many endangered species. Amsonia elliptica (Apocynaceae) is the only Amsonia species native to Korea, and the South Korean Ministry of Environment has designated it Class II endangered wildlife. Nevertheless, the dormancy class and the dormancy breaking method for seeds of this species for germination are not precisely known. We identified the structure of A. elliptica seeds and the causes of dormancy, which inhibits germination. In addition, we tried to develop an effective germination promotion method by testing the wet stratified condition, which breaks dormancy, and the form of gibberellin that can replace it. Fresh seeds of A. elliptica imbibe water, but the covering layers (endosperm and seed coat) inhibit germination by mechanically restricting the embryo. Initial germination tests confirmed low embryo growth potential and physiological dormancy (PD). Restriction due to the covering layer was eliminated by seed scarification, and abnormal germination was observed. After 12 weeks of cold moist stratification at 4°C, only 12% of seeds germinated. However, 68.8% of seeds subjected to 8 weeks of warm moist stratification followed by 12 weeks of cold stratification germinated, indicating that warm stratification pretreatment before cold stratification is effective in breaking dormancy. A. elliptica seeds exhibited intermediate PD. Furthermore, 61.3% of seeds soaked in 500 mg/L GA4+7 for 14 days and incubated at 25/15°C germinated. Therefore, GA4+7 rapidly broke the dormancy of A. elliptica seeds compared with warm plus cold stratification treatment, thus providing an efficient method for seedling production.

A review of osteoarthritis signaling intervention using small-molecule inhibitors.

Numerous small-molecule inhibitors (SMIs) have been approved as adjuvant or first-line therapies for malignancies. Based on cancer treatment using SMIs, next-generation SMIs that can be used to optimize the therapeutic index, overcome drug resistance, and establish combination therapies are in development. Osteoarthritis (OA) is the most common chronic joint disease with senescence, and there are various approaches to OA treatment; however, the gold standard treatment is controversial. Therefore, in this manuscript, we demonstrated the potential of using SMIs in OA treatment and described the general strategies for using SMIs in OA treatment.

Screening of Particulate Matter Reduction Ability of 21 Indigenous Korean Evergreen Species for Indoor Use.

The formation and pollution of particulate matter (PM), a side effect of rapid industrialization and urbanization, is considered a global issue. However, various plant species are able to effectively capture and reduce atmospheric PM concentrations. We investigated the indoor growth and morphology of 21 indigenous Korean evergreen species at low light intensities to ascertain their ability to reduce PM of aerosol particles in a closed acrylic chamber. The decrease in PM mass concentration differed significantly across species, with a significant correlation (8 h; p < 0.001). The reduction in the mass concentration of PM differed with particle size and across species. The highest reduction of PM2.5 occurred after 8 h with Dryopteris lacera (86.8%), Ilex × wandoensis (84.9%), Machilus thunbergii (84.3%), and Rhododendron brachycarpum (84.0%). Reduction of PM10 after 8 h was highest with Cephalotaxus harringtonii (98.3%), I. × wandoensis (98.5%), M. thunbergii (98.5%), and R. brachycarpum (98.3%). Plant morphological characteristics (category, plant height, leaf shape, leaf area) and relative humidity were closely related to the decrease in PM mass concentration. In conclusion, our findings can be used to identify Korean plant species that can reduce PM concentration and are suitable for indoor use.