Artificial Intelligence-Based Evaluation of Carotid Artery Compressibility via Point-of-Care Ultrasound in Determining the Return of Spontaneous Circulation During Cardiopulmonary Resuscitation.

AIM: This study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation . METHODS: A prospective study based on data from a South Korean emergency department from 2022 to 2023 investigated carotid artery compressibility in adult patients with cardiac arrest using a novel AI model, RealCAC-Net. The data comprised 11,958 training images from 161 cases and 15,080 test images from 134 cases. RealCAC-Net processes images in three steps: TransUNet-based segmentation, the carotid artery compressibility measurement algorithm for improved segmentation and CAC calculation, and CAC-based classification from 0 (indicating a circular shape) to 1 (indicating high compression). The accuracy of the ROSC classification model was tested using metrics such as the dice similarity coefficient, intersection-over-union, precision, recall, and F1 score. RESULTS: RealCAC-Net, which applied the carotid artery compressibility measurement algorithm, performed better than the baseline model in cross-validation, with an average dice similarity coefficient of 0.90, an intersection-over-union of 0.84, and a classification accuracy of 0.96. The test set achieved a classification accuracy of 0.96 and an F1 score of 0.97, demonstrating its efficacy in accurately identifying ROSC in cardiac arrest situations. CONCLUSIONS: RealCAC-Net enabled precise CAC quantification for ROSC determination during cardiopulmonary resuscitation. Future research should integrate this AI-enhanced ultrasound approach to revolutionize emergency care.

Pixel-Wise Interstitial Lung Disease Interval Change Analysis: A Quantitative Evaluation Method for Chest Radiographs Using Weakly Supervised Learning.

Interstitial lung disease (ILD) is characterized by progressive pathological changes that require timely and accurate diagnosis. The early detection and progression assessment of ILD are important for effective management. This study introduces a novel quantitative evaluation method utilizing chest radiographs to analyze pixel-wise changes in ILD. Using a weakly supervised learning framework, the approach incorporates the contrastive unpaired translation model and a newly developed ILD extent scoring algorithm for more precise and objective quantification of disease changes than conventional visual assessments. The ILD extent score calculated through this method demonstrated a classification accuracy of 92.98% between ILD and normal classes. Additionally, using an ILD follow-up dataset for interval change analysis, this method assessed disease progression with an accuracy of 85.29%. These findings validate the reliability of the ILD extent score as a tool for ILD monitoring. The results of this study suggest that the proposed quantitative method may improve the monitoring and management of ILD.

Conditional knockout of Shank3 in the ventral CA1 by quantitative in vivo genome-editing impairs social memory in mice.

Individuals with autism spectrum disorder (ASD) have a higher prevalence of social memory impairment. A series of our previous studies revealed that hippocampal ventral CA1 (vCA1) neurons possess social memory engram and that the neurophysiological representation of social memory in the vCA1 neurons is disrupted in ASD-associated Shank3 knockout mice. However, whether the dysfunction of Shank3 in vCA1 causes the social memory impairment observed in ASD remains unclear. In this study, we found that vCA1-specific Shank3 conditional knockout (cKO) by the adeno-associated virus (AAV)- or specialized extracellular vesicle (EV)- mediated in vivo gene editing was sufficient to recapitulate the social memory impairment in male mice. Furthermore, the utilization of EV-mediated Shank3-cKO allowed us to quantitatively examine the role of Shank3 in social memory. Our results suggested that there is a certain threshold for the proportion of Shank3-cKO neurons required for social memory disruption. Thus, our study provides insight into the population coding of social memory in vCA1, as well as the pathological mechanisms underlying social memory impairment in ASD.

Efficacy of RCI001 as a therapeutic candidate of dry eye disease in a modified mixed dry eye model.

BACKGROUND: To evaluate the therapeutic effects of topical RCI001 (RCI) and compare its efficacy with that of 1% prednisolone acetate (PDE) and 5% Lifitegrast in a modified mixed dry eye disease (DED) model. METHODS: The environmental DED model was induced in BALB/c mice in a dry chamber with scopolamine. The eyes of the mice were treated topically with phosphate buffered saline (PBS), PDE, Lifitegrast or RCI twice daily for 1 week. Ocular surface staining (OSS), tear secretion, inflammatory cytokines in the ocular surface and lacrimal gland, and immunofluorescence staining in the conjunctiva and cornea(CC) were assessed. RESULTS: The RCI group demonstrated better improvement of OSS and tear secretion than the PBS group (OSS, PBS: 13.0 ± 1.6, RCI: 9.4 ± 3.0; tear secretion, PBS: 5.0 ± 0.4 mm, RCI: 7.0 ± 0.3 mm, each P < 0.001) and better clinical efficacy than PDE and Lifitegrast groups on Day 7 (improvement rate of OSS, RCI: 32.45%, Lifitegrast: 13.13%, PDE: 12.25%). The RCI group resulted in significantly lower expression of oxidative stress markers in the CC than the PBS group (4-HNE, NOX2, and NOX4 in the conjunctiva; NOX2 in the cornea, each P < 0.05). However, the PDE and Lifitegrast groups did not show significant differences compared with the PBS group. There were no significant differences of inflammatory cytokines in the ocular surface and lacrimal gland between all groups. CONCLUSION: Topical RCI001 showed excellent therapeutic effects in environmental DED models by stimulating tear secretion, modulating oxidative stress and improving corneal epithelial healing compared to 1% PDE and 5% Lifitegrast.

Quintic refractive index profile-based funnel-shaped silicon antireflective structures for enhanced photodetector performance.

Antireflection, vital in optoelectronics devices such as solar cells and photodetectors, reduces light reflection and increases absorption. Antireflective structures (ARS), a primary method by which to realize this effect, control the refractive index (RI) profile based on their shape. The antireflection efficiency depends on the refractive index profile, with the quintic RI profile being recognized as ideal for superior antireflection. However, fabricating nano-sized structures with a desired shape, particularly in silicon with a quintic RI profile, has been a challenge. In this study, we introduce a funnel-shaped silicon (Si) ARS with a quintic RI profile. Its antireflective properties are demonstrated through reflectance measurements and by an application to a photodetector surface. Compared to the film Si and cone-shaped ARS types, which are common structures to achieve antireflection, the funnel-shaped ARS showed reflectance of 4.24% at 760 nm, whereas those of the film Si and cone-shaped ARS were 32.8% and 10.6%, respectively. Photodetectors with the funnel-shaped ARS showed responsivity of 0.077 A/W at 950 nm, which is 19.54 times higher than that with the film Si and 2.45 times higher than that with the cone-shaped ARS.

Differentiation Between Invasive Adenocarcinoma and Focal Interstitial Fibrosis among Persistent Pulmonary Part-solid Nodules: With Emphasis on the CT Morphologic Analysis.

PURPOSE: Focal interstitial fibrosis (FIF) manifesting as a persistent part-solid nodule (PSN) has been mistakenly treated surgically due to similar imaging features to invasive adenocarcinoma (ADC). The purpose of this study was to observe predictive imaging features correlated with FIF through CT morphologic analysis. MATERIALS AND METHODS: From January 2009 to December 2020, 44 patients with surgically proven FIF in a single institution were enrolled and compared with 88 ADC patients through propensity score matching. Patient characteristics and CT morphologic analysis of persistent PSNs were used to identify predictive imaging features of FIF. Receiver operating characteristic (ROC) curve analysis was used to quantify the performance of imaging features. RESULTS: A total of 132 patients with 132 PSNs (44 FIF, 88 ADC; mean age, 67.7±7.58; 75 females) were involved in our analysis. Multivariable analysis demonstrated that preserved peritumoral vascular margin (preserved vascular margin), preserved secondary pulmonary lobule margin (preserved lobular margin), and lower coronal to axial ratio (C/A ratio; cutoff: 1.005) were significant independent predictors of FIF (P<0.05). ROC curve analysis to evaluate the predictive value of the logistic model based on the imaging features of FIF, and the AUC value was 0.881. CONCLUSION: CT imaging features of preserved vascular margin, preserved lobular margin, and lower C/A ratio (cutoff, <1.005) might be helpful imaging features in discriminating FIF over ADC among persistent PSN in clinical practice.

SMARCA4-deficient non-small cell lung carcinoma: clinicodemographic, computed tomography, and positron emission tomography-computed tomography features.

Background: SMARCA4-deficient non-small cell lung carcinoma (SD-NSCLC) is a relatively rare tumor, which occurs in 5-10% of NSCLC. Based on World Health Organization thoracic tumor classification system, SMARCA4-deficient undifferentiated tumor (SD-UT) is recognized as a separate entity from SD-NSCLC. Differentiation between SD-NSCLC and SD-UT is often difficult due to shared biological continuum, but often required for choosing appropriate treatment regimen. Therefore, the aim of our study was to identify the clinicopathologic, computed tomography (CT), and positron emission tomography (PET)-CT imaging features of SD-NSCLC. Methods: Nine patients of pathologically confirmed SD-NSCLC were included in our analysis. We reviewed electronic medical records for clinical information, demographic features, CT, and PET-CT imaging features were analyzed. Results: Smoking history and male predominance are observed in all patients with SD-NSCLC (n=9). On CT, SD-NSCLC appeared as relatively well-defined masses with lobulated contour (n=8) and peripheral location (n=7). Invasion of adjacent pleura or chest wall (n=7) were frequently observed, regardless of small tumor size. Four cases showed lymph node metastases. Among nine patients, three patients showed multiple bone metastases, and one patient showed lung-to-lung metastases. Conclusions: In patient with SD-NSCLC, there was tendency for male smokers, peripheral location and invasion of adjacent pleural or chest wall invasion regardless of small tumor size, when compared to SD-UT.

Clinical and Imaging Characteristics of SARS-CoV-2 Breakthrough Infection in Hospitalized Immunocompromised Patients.

OBJECTIVE: To evaluate the clinical and imaging characteristics of SARS-CoV-2 breakthrough infection in hospitalized immunocompromised patients in comparison with immunocompetent patients. MATERIALS AND METHODS: This retrospective study analyzed consecutive adult patients hospitalized for COVID-19 who received at least one dose of the SARS-CoV-2 vaccine at two academic medical centers between June 2021 and December 2022. Immunocompromised patients (with active solid organ cancer, active hematologic cancer, active immune-mediated inflammatory disease, status post solid organ transplantation, or acquired immune deficiency syndrome) were compared with immunocompetent patients. Multivariable logistic regression analysis was performed to evaluate the effect of immune status on severe clinical outcomes (in-hospital death, mechanical ventilation, or intensive care unit admission), severe radiologic pneumonia (≥ 25% of lung involvement), and typical CT pneumonia. RESULTS: Of 2218 patients (mean age, 69.5 ± 16.1 years), 274 (12.4%), and 1944 (87.6%) were immunocompromised an immunocompetent, respectively. Patients with active solid organ cancer and patients status post solid organ transplantation had significantly higher risks for severe clinical outcomes (adjusted odds ratio = 1.58 [95% confidence interval {CI}, 1.01-2.47], P = 0.042; and 3.12 [95% CI, 1.47-6.60], P = 0.003, respectively). Patient status post solid organ transplantation and patients with active hematologic cancer were associated with increased risks for severe pneumonia based on chest radiographs (2.96 [95% CI, 1.54-5.67], P = 0.001; and 2.87 [95% CI, 1.50-5.49], P = 0.001, respectively) and for typical CT pneumonia (9.03 [95% CI, 2.49-32.66], P < 0.001; and 4.18 [95% CI, 1.70-10.25], P = 0.002, respectively). CONCLUSION: Immunocompromised patients with COVID-19 breakthrough infection showed an increased risk of severe clinical outcome, severe pneumonia based on chest radiographs, and typical CT pneumonia. In particular, patients status post solid organ transplantation was specifically found to be associated with a higher risk of all three outcomes than hospitalized immunocompetent patients.

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.

We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer's disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer's disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer's disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of [Formula: see text]%, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0± 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer's diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).

Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study.

BACKGROUND: Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability. OBJECTIVE: The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers. METHODS: We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods. RESULTS: Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910). CONCLUSIONS: RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

Pulmonary Foreign Body Granulomatosis after Cosmetic Injection.

INTRODUCTION: We occasionally encounter irregular marginated masses discovered incidentally in young individuals. In most cases, further investigations are conducted to assess the presence of a primary malignancy, as these masses often raise suspicions of malignancy. However, rare exceptional cases leave us perplexed. Granulomas arising from common lung infections and those induced by foreign substances can often pose challenge in distinguishing them from lung cancer. Therefore, we aimed to present a case of multiple pulmonary granulomatosis following cosmetic procedure. CASE PRESENTATION: A 55-year-old woman visited the hospital after an incidental discovery of an abnormal chest radiograph during a routine health check-up. Subsequent computed tomography (CT) scans showed worrisome lung nodules, leading to biopsies and positron emission tomography CT scans. Histological examination of the biopsied specimens revealed a chronic inflammatory reaction surrounded by multinucleated foreign body giant cells. Upon sharing the biopsy results with the patient and conducting additional history-taking, she had undergone various cosmetic procedures (botox injection, dermal filler treatments, and thread lifts) around the face and neck, approximately 5-6 months ago. It was hypothesized that these cosmetic materials might have led to the observed pulmonary granulomatosis. After 3 months of conservative care, a follow-up CT showed no change in the lesions. CONCLUSION: We present this case to underscore the importance of considering pulmonary foreign body granulomatosis as a potential differential diagnosis, especially when it closely resembles lung cancer, particularly following cosmetic injections.

Ultrathin Serpentine Insulation Layer Architecture for Ultralow Power Gas Sensor.

Toxic gases have surreptitiously influenced the health and environment of contemporary society with their odorless/colorless characteristics. As a result, a pressing need for reliable and portable gas-sensing devices has continuously increased. However, with their negligence to efficiently microstructure their bulky supportive layer on which the sensing and heating materials are located, previous semiconductor metal-oxide gas sensors have been unable to fully enhance their power efficiency, a critical factor in power-stringent portable devices. Herein, an ultrathin insulation layer with a unique serpentine architecture is proposed for the development of a power-efficient gas sensor, consuming only 2.3 mW with an operating temperature of 300 °C (≈6% of the leading commercial product). Utilizing a mechanically robust serpentine design, this work presents a fully suspended standalone device with a supportive layer thickness of only ≈50 nm. The developed gas sensor shows excellent mechanical durability, operating over 10 000 on/off cycles and ≈2 years of life expectancy under continuous operation. The gas sensor detected carbon monoxide concentrations from 30 to 1 ppm with an average response time of ≈15 s and distinguishable sensitivity to 1 ppm (ΔR/R0 = 5%). The mass-producible fabrication and heating efficiency presented here provide an exemplary platform for diverse power-efficient-related devices.

CT evaluation of systemic artery to pulmonary artery fistula: an underdiagnosed disease in patients with hemoptysis.

Background: Systemic artery to pulmonary artery fistula (SA-PAF) is an uncommon disease which is often incidentally diagnosed during evaluation of hemoptysis patients. The aim of our study was to describe the cases of SA-PAF in our institution and to report the correlating clinical and radiological findings. Methods: We reviewed 231 chest computed tomography (CT) scans performed in our institution due to hemoptysis from January 2020 to February 2023. In patients diagnosed with SA-PAF had their electronic medical records and CT images analyzed. Results: In 231 patients, 19 (8.2%) of them had SA-PAF findings which was characterized by a peripheral nodular soft tissue opacity in the subpleural lung and traceable vascular structure in continuity with one or more peripheral pulmonary artery branches in CT. Etiology of each patient was categorized as either congenital (7, 36.8%), and acquired (12, 63.2%). The origins of SA-PAFs were 16 intercostal, two anterior mediastinal, and one costocervical artery. Eight of 19 patients did not show any associated intralobar imaging abnormalities, while bronchiectasis, cellular bronchiolitis, centrilobular emphysema, and pleura effusion were observed in 11 patients. Conclusions: SA-PAF is a benign vascular anomaly which is frequently overlooked when evaluating hemoptysis by either clinician or radiologists but is an important factor in the differential diagnosis of patients with hemoptysis.

Ultrafast (∼0.6 s), Robust, and Highly Linear Hydrogen Detection up to 10% Using Fully Suspended Pure Pd Nanowire.

The high explosiveness of hydrogen gas in the air necessitates prompt detection in settings where hydrogen is used. For this reason, hydrogen sensors are required to offer rapid detection and possess superior sensing characteristics in terms of measurement range, linearity, selectivity, lifetime, and environment insensitivity according to the publicized protocol. However, previous approaches have only partially achieved the standardized requirements and have been limited in their capability to develop reliable materials for spatially accessible systems. Here, an electrical hydrogen sensor with an ultrafast response (∼0.6 s) satisfying all demands for hydrogen detection is demonstrated. Tailoring structural engineering based on the reaction kinetics of hydrogen and palladium, an optimized heating architecture that thermally activates fully suspended palladium (Pd) nanowires at a uniform temperature is designed. The developed Pd nanostructure, at a designated temperature distribution, rapidly reacts with hydrogen, enabling a hysteresis-free response from 0.1% to 10% and durable characteristics in mechanical shock and repetitive operation (>10,000 cycles). Moreover, the device selectively detects hydrogen without performance degradation in humid or carbon-based interfering gas circumstances. Finally, to verify spatial accessibility, the wireless hydrogen detection system has been demonstrated, detecting and reporting hydrogen leakage in real-time within just 1 s.

Characterization of the probiotic properties of Lacticaseibacillus rhamnosus LR6 isolated from the vaginas of healthy Korean women against vaginal pathogens.

The human microbiome exhibits intricate populations across the body, with the vaginal tract serving as an ecosystem characterized by the prevalence of the genus Lactobacillus. Disruptions in the vaginal microbiota, which are frequently linked to variables such as sexual activity, hormonal fluctuations, and excessive use of antibiotics, can result in vaginal dysbiosis and the development of diseases such as bacterial vaginosis (BV) and candidiasis. Lactobacillus species, owing to their capacity to create an acidic environment through the production of lactic acid, have a key function within this complex microbial community: they inhibit the growth of harmful microorganisms. This study aimed to investigate the genomic characteristics of L. rhamnosus LR6, a newly discovered strain isolated from the vaginal microbiota of 20 healthy women to assess its potential as a vaginal probiotic. We performed a comparative investigation of the genetic traits of L. rhamnosus using 45 publicly available genomes from various sources. We evaluated the genetic characteristics related to carbohydrate utilization, adhesion to host cells, and the presence of bacteriocin clusters. A comprehensive study was conducted by integrating in silico evaluations with experimental techniques to authenticate the physiological characteristics of strain LR6. We further used a rat model to assess the impact of L. rhamnosus LR6 administration on the changes in the gastrointestinal tract and the vaginal microbiome. The assessments revealed a significantly high inhibitory activity against pathogens, enhanced adherence to host cells, and high lactic acid production. Rat experiments revealed changes in both the fecal and vaginal microbiota; in treated rats, Firmicutes increased in both; Lactobacillaceae increased in the fecal samples; and Enterobacteriaceae decreased but Enterococcaceae, Streptococcaceae, and Morganellaceae increased in the vaginal samples. The study results provide evidence of the genetic characteristics and probiotic properties of LR6, and suggest that oral administration of L. rhamnosus LR6 can alter both gut and vaginal microbiome. Collectively, these findings establish L. rhamnosus LR6 as a highly promising candidate for improving vaginal health.

Clinical characteristics and outcomes of hypersensitivity pneumonitis in South Korea.

BACKGROUND: Hypersensitivity pneumonitis (HP) is an interstitial lung disease (ILD) that results from an immune-mediated reaction involving various antigens in susceptible individuals. However, the clinical characteristics and outcomes of HP in South Korea are not well understood. OBJECTIVES: This study was conducted to identify the clinical characteristics and outcomes of HP in South Korea. DESIGN: This is a retrospective observational study investigating patients with pathologically confirmed HP at our center, along with a comprehensive review of published HP cases in the Republic of Korea. METHODS: This retrospective study analyzed 43 patients with pathologically proven HP at a single tertiary hospital in Korea between 1996 and 2020. In addition, case reports of HP published in Korea were collected. The clinical characteristics, etiologies, treatment, and outcomes of patients from our center, as well as case reports, were reviewed. Patients from our hospital were divided into fibrotic and nonfibrotic subtypes according to the ATS/JRS/ALAT guidelines. RESULTS: Among 43 patients with biopsy-proven HP, 12 (27.9%) and 31 (72.1%) patients were classified into the fibrotic and nonfibrotic subtypes, respectively. The fibrotic HP group was older (64.6 ± 8.5 versus 55.2 ± 8.3, p = 0.002) with less frequent complaints of fever (0% versus 45.2%, p = 0.013) compared to the nonfibrotic HP group. The most common inciting antigen was household mold (21, 48.8%), followed by inorganic substances (6, 14.0%). Inciting antigens were not identified in eight (18.6%) patients. Treatment of corticosteroids was initiated in 34 (79.1%) patients. An analysis of 46 patients from Korea by literature review demonstrated that reported cases were relatively younger and drugs were the most common etiology compared to our cohort. CONCLUSION: The analysis of reported cases, as well as our cohort, showed that exposure history and clinical manifestations are heterogeneous for patients with HP in South Korea.

Deep Learning-Based Early Warning Score for Predicting Clinical Deterioration in General Ward Cancer Patients.

BACKGROUND: Cancer patients who are admitted to hospitals are at high risk of short-term deterioration due to treatment-related or cancer-specific complications. A rapid response system (RRS) is initiated when patients who are deteriorating or at risk of deteriorating are identified. This study was conducted to develop a deep learning-based early warning score (EWS) for cancer patients (Can-EWS) using delta values in vital signs. METHODS: A retrospective cohort study was conducted on all oncology patients who were admitted to the general ward between 2016 and 2020. The data were divided into a training set (January 2016-December 2019) and a held-out test set (January 2020-December 2020). The primary outcome was clinical deterioration, defined as the composite of in-hospital cardiac arrest (IHCA) and unexpected intensive care unit (ICU) transfer. RESULTS: During the study period, 19,739 cancer patients were admitted to the general wards and eligible for this study. Clinical deterioration occurred in 894 cases. IHCA and unexpected ICU transfer prevalence was 1.77 per 1000 admissions and 43.45 per 1000 admissions, respectively. We developed two models: Can-EWS V1, which used input vectors of the original five input variables, and Can-EWS V2, which used input vectors of 10 variables (including an additional five delta variables). The cross-validation performance of the clinical deterioration for Can-EWS V2 (AUROC, 0.946; 95% confidence interval [CI], 0.943-0.948) was higher than that for MEWS of 5 (AUROC, 0.589; 95% CI, 0.587-0.560; p < 0.001) and Can-EWS V1 (AUROC, 0.927; 95% CI, 0.924-0.931). As a virtual prognostic study, additional validation was performed on held-out test data. The AUROC and 95% CI were 0.588 (95% CI, 0.588-0.589), 0.890 (95% CI, 0.888-0.891), and 0.898 (95% CI, 0.897-0.899), for MEWS of 5, Can-EWS V1, and the deployed model Can-EWS V2, respectively. Can-EWS V2 outperformed other approaches for specificities, positive predictive values, negative predictive values, and the number of false alarms per day at the same sensitivity level on the held-out test data. CONCLUSIONS: We have developed and validated a deep learning-based EWS for cancer patients using the original values and differences between consecutive measurements of basic vital signs. The Can-EWS has acceptable discriminatory power and sensitivity, with extremely decreased false alarms compared with MEWS.

Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring.

The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.