Patients beyond the optimal range of rATG-AUC still benefit from the targeted dosing strategy in unmanipulated haplo-PBSCT.

Rabbit anti-thymocyte globulin (rATG) is widely used in allogeneic hematopoietic stem cell transplantation to prevent graft failure and severe graft-versus-host disease (GVHD). We developed a rATG-targeted dosing strategy based on the optimal areas under the concentration-time curve (AUC) of active rATG. This study compared the outcomes of the optimal AUC arm with non-optimal AUC arm to assess the effect of the rATG-targeted dosing strategy. Eighty patients (median age: 32 years) with hematological malignancies who received their first haplo-PBSCT were enrolled successively. With rATG-targeted dosing, the AUC values of 60 patients (75%, optimal AUC arm) fell within the optimal range (100-148.5 UE/mL/day) and 20 fell beyond this range (non-optimal AUC arm). In the historical control group of 102 haplo-PBSCT patients who received a fixed dose of rATG (10 mg/kg), less patients fell within the optimal range (57.8%, P = .016). Looking at the non-optimal AUC arms in both groups, lower cumulative incidence of CMV was noted in the targeted dosing group compared with the historical control group(50.0%, 95% CI, 30.8%-72.9% versus 81.4%, 95% CI, 68.6%-91.3%; P = .004). The cumulative incidences of EBV, relapse, overall survival and disease-free survival tended to be superior in the non-optimal AUC arm in the targeted dosing group compared with the historical control. In the targeted dosing group, the cumulative incidence of cytomegalovirus (CMV) reactivation on day +180 tended to be lower in the optimal AUC arm (30.0%, 95% CI, 20.1%-43.3%) compared with the non-optimal AUC arm (50.0%, 95% CI, 30.8%-72.9%, P = .199) without statistical difference. There were no significant differences of acute or chronic GVHD, relapse, non-relapse mortality, overall survival, disease-free survival or lymphocyte reconstitution between the two arms. In conclusion, the rATG-targeted dosing strategy made the exposure of active rATG in more patients with the optimal AUC range. Even patients who fell beyond this range would still benefit from the strategy.

Diagnostic Accuracy of Mid-Upper Arm Circumference for the Detection of Acute Malnutrition Among Children Aged 6-60 Months: A Diagnostic Accuracy Study.

BACKGROUND: Timely and accurate screening of malnutrition at the community level is essential to identifying malnourished children. The World Health Organization (WHO) guidelines classify non-oedematous acute malnutrition among children using mid-upper arm circumference (MUAC) or weight-for-height Z-score (WHZ). Study Design: A cross-sectional study. METHODS: This study was conducted among children aged 6‒60 months. After necessary exclusions, 433 participants were selected using a multi-stage simple random sampling method. Using WHO guidelines for global acute malnutrition (GAM) [WHZ<-2, MUAC<12.5 cm], the sensitivity (Se), specificity (Sp), predictive values, likelihood ratios, Youden index, and receiver operating characteristic (ROC) curve were calculated for MUAC using WHZ as the criterion. RESULTS: Out of 433 participants, 30% were diagnosed with GAM using WHZ, while 17.6% were found malnourished using MUAC measurements. As per WHO cut-offs, the Se, Sp, positive predictive value (PPV), negative predictive value (NPV), Youden index, positive likelihood ratio (LR+), and negative likelihood ratio (LR-) of MUAC were 48%, 96%, 83%, 81%, 0.44, 12, and 0.54, respectively. The ROC curve displayed an area under the curve of 0.86 (95% confidence interval=0.83, 0.90) for MUAC<12.5 cm. Bivariate Pearson correlation also demonstrated a positive linear relationship (R2=0.302) between the WHZ and MUAC variables. CONCLUSION: Based on the findings, 48% of the children were correctly identified by the MUAC with an 83% probability of GAM (PPV=0.83). Moreover, there was 96% Sp in non-malnourished children, with only 4% false positives. Therefore, personnel at the grassroots level can use MUAC for timely and accurate screening of children in Anganwadi centers (AWCs) due to its ease of use and simplicity.

Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal-external validation.

BACKGROUND: Binary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration. METHODS: We developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal-external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches. RESULTS: Our data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks. CONCLUSION: Time-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable.

Diagnostic accuracy, reliability, and construct validity of the German quick mild cognitive impairment screen.

BACKGROUND: Early detection of cognitive impairment is among the top research priorities aimed at reducing the global burden of dementia. Currently used screening tools have high sensitivity but lack specificity at their original cut-off, while decreasing the cut-off was repeatedly shown to improve specificity, but at the cost of lower sensitivity. In 2012, a new screening tool was introduced that aims to overcome these limitations - the Quick mild cognitive impairment screen (Qmci). The original English Qmci has been rigorously validated and demonstrated high diagnostic accuracy with both good sensitivity and specificity. We aimed to determine the optimal cut-off value for the German Qmci, and evaluate its diagnostic accuracy, reliability (internal consistency) and construct validity. METHODS: We retrospectively analyzed data from healthy older adults (HOA; n = 43) and individuals who have a clinical diagnosis of 'mild neurocognitive disorder' (mNCD; n = 37) with a biomarker supported characterization of the etiology of mNCD of three studies of the 'Brain-IT' project. Using Youden's Index, we calculated the optimal cut-off score to distinguish between HOA and mNCD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic accuracy based on the area under the curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Reliability (internal consistency) was analyzed by calculating Cronbach's α. Construct validity was assessed by analyzing convergent validity between Qmci-G subdomain scores and reference assessments measuring the same neurocognitive domain. RESULTS: The optimal cut-off score for the Qmci-G was ≤ 67 (AUC = 0.96). This provided a sensitivity of 91.9% and a specificity of 90.7%. The PPV and NPV were 89.5% and 92.9%, respectively. Cronbach's α of the Qmci-G was 0.71 (CI95% [0.65 to 0.78]). The Qmci-G demonstrated good construct validity for subtests measuring learning and memory. Subtests that measure executive functioning and/or visuo-spatial skills showed mixed findings and/or did not correlate as strongly as expected with reference assessments. CONCLUSION: Our findings corroborate the existing evidence of the Qmci's good diagnostic accuracy, reliability, and construct validity. Additionally, the Qmci shows potential in resolving the limitations of commonly used screening tools, such as the Montreal Cognitive Assessment. To verify these findings for the Qmci-G, testing in clinical environments and/or primary health care and direct comparisons with standard screening tools utilized in these settings are warranted.

Diagnostic performance of ultrasonography in pre-operative assessment of lymph nodes in patients with cervical cancer.

OBJECTIVES: To assess the diagnostic performance of ultrasonography in pre-operative assessment of lymph nodes in patients with cervical cancer, to compare the outcomes for pelvic and para-aortic regions, and to detect macrometastases and micrometastases separately. METHODS: Patients were retrospectively included if they met the following inclusion criteria: pathologically verified cervical cancer; ultrasonography performed by one of four experienced sonographers; surgical lymph node staging, at least in the pelvic region-sentinel lymph node biopsy or systematic pelvic lymphadenectomy or debulking. The final pathological examination was the reference standard. RESULTS: 390 patients met the inclusion criteria between 2009 and 2019. Pelvic node macrometastases (≥2 mm) were confirmed in 54 patients (13.8%), and micrometastases (≥0.2 mm and <2 mm) in another 21 patients (5.4%). Ultrasonography had sensitivity 72.2%, specificity 94.0%, and area under the curve (AUC) 0.831 to detect pelvic macrometastases, while sensitivity 53.3%, specificity 94.0%, and AUC 0.737 to detect both pelvic macrometastases and micrometastases (pN1). Ultrasonography failed to detect pelvic micrometastases, with sensitivity 19.2%, specificity 85.2%, and AUC 0.522. There was no significant impact of body mass index on diagnostic accuracy. Metastases in para-aortic nodes (macrometastases only) were confirmed in 16 of 71 patients who underwent para-aortic lymphadenectomy. Ultrasonography yielded sensitivity 56.3%, specificity 98.2%, and AUC 0.772 to identify para-aortic node macrometastases. CONCLUSION: Ultrasonography performed by an experienced sonographer can be considered a sufficient diagnostic tool for pre-operative assessment of lymph nodes in patients with cervical cancer, showing similar diagnostic accuracy in detection of pelvic macrometastases as reported for other imaging methods (18F-fluorodeoxyglucose positron emission tomography/CT or diffusion-weighted imaging/MRI). It had low sensitivity for detection of small-volume macrometastases (largest diameter <5 mm) and micrometastases. The accuracy of para-aortic assessment was comparable to that for pelvic lymph nodes, and assessment of the para-aortic region should be an inseparable part of the examination protocol.

BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning.

BACKGROUND: Chest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep learning offer promising solutions, there is still a critical issue of domain inconsistency in cross-domain transfer learning, which hampers the efficiency and accuracy of diagnostic processes. This study aims to address the domain inconsistency problem and improve autonomic abnormality localization performance of heterogeneous chest X-ray image analysis, particularly in detecting abnormalities, by developing a self-supervised learning strategy called "BarlwoTwins-CXR". METHODS: We utilized two publicly available datasets: the NIH Chest X-ray Dataset and the VinDr-CXR. The BarlowTwins-CXR approach was conducted in a two-stage training process. Initially, self-supervised pre-training was performed using an adjusted Barlow Twins algorithm on the NIH dataset with a Resnet50 backbone pre-trained on ImageNet. This was followed by supervised fine-tuning on the VinDr-CXR dataset using Faster R-CNN with Feature Pyramid Network (FPN). The study employed mean Average Precision (mAP) at an Intersection over Union (IoU) of 50% and Area Under the Curve (AUC) for performance evaluation. RESULTS: Our experiments showed a significant improvement in model performance with BarlowTwins-CXR. The approach achieved a 3% increase in mAP50 accuracy compared to traditional ImageNet pre-trained models. In addition, the Ablation CAM method revealed enhanced precision in localizing chest abnormalities. The study involved 112,120 images from the NIH dataset and 18,000 images from the VinDr-CXR dataset, indicating robust training and testing samples. CONCLUSION: BarlowTwins-CXR significantly enhances the efficiency and accuracy of chest X-ray image-based abnormality localization, outperforming traditional transfer learning methods and effectively overcoming domain inconsistency in cross-domain scenarios. Our experiment results demonstrate the potential of using self-supervised learning to improve the generalizability of models in medical settings with limited amounts of heterogeneous data. This approach can be instrumental in aiding radiologists, particularly in high-workload environments, offering a promising direction for future AI-driven healthcare solutions.

Aspartate Aminotransferase-to-Platelet Ratio Index (APRI) as a Novel Score in Early Detection of Complicated Dengue Fever.

Introduction: The occurrence of dengue fever presents a considerable burden for public health care in developing countries. This study aims to validate APRI as predictor score for severity of dengue fever so that catastrophic events could be prevented, and early triage can save lives. Methods: The retrospective cross-sectional study was done on dengue positive patients from August to November 2023. APRI score was calculated for every patient at the time of admission. The primary end-point was non-complicated disease (Simple dengue fever) vs complicated disease (dengue hemorrhagic fever and dengue shock syndrome). ROC curve was used to identify the role of APRI in prediction of dengue complication. Youden index was used to find the cut-off value of APRI along with sensitivity, specificity, positive and negative likelihood ratios. To further evaluate the role of APRI score, patients were divided into two groups, patients with APRI score greater and lesser than cut-off value. The qualitative variables among two groups were compared by chi-square testing. The predictors of complicated dengue were first determined by univariate regression analysis and then confirmed by multivariate regression analysis. Results: The mean APRI score of 135 patients was 20.06 ± 6.31. AUC for APRI score was 0.93 (p < 0.0001) indicating that APRI score calculated at the time of admission is an excellent marker in determining the complicated dengue. The cut-off value for APRI score was 9.04 (sensitivity 84.91%, specificity 89.02%, p < 0.0001). The patients with APRI <9.04 mostly developed simple dengue fever (54.1%) vs DHF (4.4%) and DSS (1.5%), while patients with APRI >9.04 had more DHF (20.7%) and DSS (12.6%) vs simple dengue fever (6.7%). None of the patient died with APRI <9.04 while the mortality rate was 3.7% in patients with APRI >9.04. Conclusion: The APRI score, calculated at the time of admission, is an excellent marker in determining the severe dengue.

Optimization of news dissemination push mode by intelligent edge computing technology for deep learning.

The Internet era is an era of information explosion. By 2022, the global Internet users have reached more than 4 billion, and the social media users have exceeded 3 billion. People face a lot of news content every day, and it is almost impossible to get interesting information by browsing all the news content. Under this background, personalized news recommendation technology has been widely used, but it still needs to be further optimized and improved. In order to better push the news content of interest to different readers, users' satisfaction with major news websites should be further improved. This study proposes a new recommendation algorithm based on deep learning and reinforcement learning. Firstly, the RL algorithm is introduced based on deep learning. Deep learning is excellent in processing large-scale data and complex pattern recognition, but it often faces the challenge of low sample efficiency when it comes to complex decision-making and sequential tasks. While reinforcement learning (RL) emphasizes learning optimization strategies through continuous trial and error through interactive learning with the environment. Compared with deep learning, RL is more suitable for scenes that need long-term decision-making and trial-and-error learning. By feeding back the reward signal of the action, the system can better adapt to the unknown environment and complex tasks, which makes up for the relative shortcomings of deep learning in these aspects. A scenario is applied to an action to solve the sequential decision problem in the news dissemination process. In order to enable the news recommendation system to consider the dynamic changes in users' interest in news content, the Deep Deterministic Policy Gradient algorithm is applied to the news recommendation scenario. Opposing learning complements and combines Deep Q-network with the strategic network. On the basis of fully summarizing and thinking, this paper puts forward the mode of intelligent news dissemination and push. The push process of news communication information based on edge computing technology is proposed. Finally, based on Area Under Curve a Q-Leaning Area Under Curve for RL models is proposed. This indicator can measure the strengths and weaknesses of RL models efficiently and facilitates comparing models and evaluating offline experiments. The results show that the DDPG algorithm improves the click-through rate by 2.586% compared with the conventional recommendation algorithm. It shows that the algorithm designed in this paper has more obvious advantages in accurate recommendation by users. This paper effectively improves the efficiency of news dissemination by optimizing the push mode of intelligent news dissemination. In addition, the paper also deeply studies the innovative application of intelligent edge technology in news communication, which brings new ideas and practices to promote the development of news communication methods. Optimizing the push mode of intelligent news dissemination not only improves the user experience, but also provides strong support for the application of intelligent edge technology in this field, which has important practical application prospects.

Prediction of the benign and malignant nature of masses in COPD background based on Habitat-based enhanced CT radiomics modeling: A preliminary study.

BACKGROUND: It is difficult to differentiate between chronic obstructive pulmonary disease (COPD)-peripheral bronchogenic carcinoma (COPD-PBC) and inflammatory masses. OBJECTIVE: This study aims to predict COPD-PBC based on clinical data and preoperative Habitat-based enhanced CT radiomics (HECT radiomics) modeling. METHODS: A retrospective analysis was conducted on clinical imaging data of 232 cases of postoperative pathological confirmed PBC or inflammatory masses. The PBC group consisted of 82 cases, while the non-PBC group consisted of 150 cases. A training set and a testing set were established using a 7:3 ratio and a time cutoff point. In the training set, multiple models were established using clinical data and radiomics texture changes within different enhanced areas of the CT mass (HECT radiomics). The AUC values of each model were compared using Delong's test, and the clinical net benefit of the models was tested using decision curve analysis (DCA). The models were then externally validated in the testing set, and a nomogram of predicting COPD-PBC was created. RESULTS: Univariate analysis confirmed that female gender, tumor morphology, CEA, Cyfra21-1, CT enhancement pattern, and Habitat-Radscore B/C were predictive factors for COPD-PBC (P< 0.05). The combination model based on these factors had significantly higher predictive performance [AUC: 0.894, 95% CI (0.836-0.936)] than the clinical data model [AUC: 0.758, 95% CI (0.685-0.822)] and radiomics model [AUC: 0.828, 95% CI (0.761-0.882)]. DCA also confirmed the higher clinical net benefit of the combination model, which was validated in the testing set. The nomogram developed based on the combination model helped predict COPD-PBC. CONCLUSION: The combination model based on clinical data and Habitat-based enhanced CT radiomics can help differentiate COPD-PBC, providing a new non-invasive and efficient method for its diagnosis, treatment, and clinical decision-making.

Vancomycin AUC0-24 estimation using first-order pharmacokinetic methods in pediatric patients.

INTRODUCTION: The optimal dosing and monitoring of vancomycin in pediatrics is still unknown but has evolved to emphasize area under the curve over 24 h (AUC0-24) over minimum concentration (Cmin) monitoring. Real-world data supporting the feasibility of two-concentration kinetics with first-order equations for the estimation of vancomycin AUC0-24 in pediatric patients are lacking. OBJECTIVES: To describe the interplay of vancomycin dose, AUC0-24, and Cmin using first-order equations within four pediatric age groups. METHODS: This is a single-center, retrospective cohort study analyzing pediatric patients (<18 years) receiving intravenous vancomycin between 2020 and 2022. Included patients received at least 24 h of intravenous vancomycin with two concentrations obtained within 96 h of therapy initiation. Patients with baseline renal dysfunction were excluded. Patients were divided into four age categories: neonates (≤28 days), infants (29 days to <1 year), children (1-12 years), and adolescents (13-17 years). First-order equations were utilized to estimate pharmacokinetic parameters and AUC0-24. RESULTS: Overall, 219 patients (median age of 6 years [IQR 1-12]) met inclusion criteria. The median vancomycin daily dose was 30 mg/kg in neonates, 70 mg/kg in infants and children, and 52 mg/kg in adolescents. Median Cmin and AUC0-24 values among all age groups were 8.68 mg/L and 505 mg * h/L, respectively. For AUC0-24 values outside of the therapeutic range (400-600 mg * h/L), more values were SUPRAtherapeutic (>600 mg * h/L) than SUBtherapeutic (<400 mg * h/L). The overall trend within our data showed suboptimal correlation between Cmin and AUC0-24. However, 71% of patients with Cmin values of 5-10 mg/L had an AUC0-24 within the therapeutic range of 400-600 mg * h/L, whereas 23 patients (92%) with a SUPRAtherapeutic AUC0-24 had a Cmin value ≥15 mg/L. Approximately 10% of patients experienced acute kidney injury. CONCLUSIONS: Our data describe the relationship between vancomycin dose, Cmin, and AUC0-24 in pediatric patients. We demonstrated the feasibility of using first-order equations to estimate AUC0-24, using two concentrations obtained at steady state to monitor efficacy and safety in pediatric patients receiving intravenous vancomycin. Our data showed suboptimal correlation between AUC0-24 and Cmin, which indicates that Cmin should not be used as a surrogate marker for a therapeutic AUC0-24 in pediatric patients. In alignment with the 2020 vancomycin consensus guidelines, we suggest utilizing AUC0-24 for efficacy and safety monitoring.

Long-Term Risk of Arterial Thrombosis After Intracerebral Hemorrhage: MUCH-Italy.

BACKGROUND: The identification of patients surviving an acute intracerebral hemorrhage who are at a long-term risk of arterial thrombosis is a poorly defined, crucial issue for clinicians. METHODS: In the setting of the MUCH-Italy (Multicenter Study on Cerebral Haemorrhage in Italy) prospective observational cohort, we enrolled and followed up consecutive 30-day intracerebral hemorrhage survivors to assess the long-term incidence of arterial thrombotic events, to assess the impact of clinical and radiological variables on the risk of these events, and to develop a tool for estimating such a risk at the individual level. Primary end point was a composite of ischemic stroke, myocardial infarction, or other arterial thrombotic events. A point-scoring system was generated by the ß-coefficients of the variables independently associated with the long-term risk of arterial thrombosis, and the predictive MUCH score was calculated as the sum of the weighted scores. RESULTS: Overall, 1729 patients (median follow-up time, 43 months [25th to 75th percentile, 69.0]) qualified for inclusion. Arterial thrombotic events occurred in 169 (9.7%) patients. Male sex, diabetes, hypercholesterolemia, atrial fibrillation, and personal history of coronary artery disease were associated with increased long-term risk of arterial thrombosis, whereas the use of statins and antithrombotic medications after the acute intracerebral hemorrhage was associated with a reduced risk. The area under the receiver operating characteristic curve of the MUCH score predictive validity was 0.716 (95% CI, 0.56-0.81) for the 0- to 1-year score, 0.672 (95% CI, 0.58-0.73) for the 0- to 5-year score, and 0.744 (95% CI, 0.65-0.81) for the 0- to 10-year score. C statistic for the prediction of events that occur from 0 to 10 years was 0.69 (95% CI, 0.64-0.74). CONCLUSIONS: Intracerebral hemorrhage survivors are at high long-term risk of arterial thrombosis. The MUCH score may serve as a simple tool for risk estimation.

Evaluating the effectiveness of pre-operative diagnosis of ovarian cancer using minimally invasive liquid biopsies by combining serum human epididymis protein 4 and cell-free DNA in patients with an ovarian mass.

OBJECTIVE: To assess the feasibility of scalable, objective, and minimally invasive liquid biopsy-derived biomarkers such as cell-free DNA copy number profiles, human epididymis protein 4 (HE4), and cancer antigen 125 (CA125) for pre-operative risk assessment of early-stage ovarian cancer in a clinically representative and diagnostically challenging population and to compare the performance of these biomarkers with the Risk of Malignancy Index (RMI). METHODS: In this case-control study, we included 100 patients with an ovarian mass clinically suspected to be early-stage ovarian cancer. Of these 100 patients, 50 were confirmed to have a malignant mass (cases) and 50 had a benign mass (controls). Using WisecondorX, an algorithm used extensively in non-invasive prenatal testing, we calculated the benign-calibrated copy number profile abnormality score. This score represents how different a sample is from benign controls based on copy number profiles. We combined this score with HE4 serum concentration to separate cases and controls. RESULTS: Combining the benign-calibrated copy number profile abnormality score with HE4, we obtained a model with a significantly higher sensitivity (42% vs 0%; p<0.002) at 99% specificity as compared with the RMI that is currently employed in clinical practice. Investigating performance in subgroups, we observed especially large differences in the advanced stage and non-high-grade serous ovarian cancer groups. CONCLUSION: This study demonstrates that cell-free DNA can be successfully employed to perform pre-operative risk of malignancy assessment for ovarian masses; however, results warrant validation in a more extensive clinical study.

Investigating the diagnostic potential of IL-1ß, IL-10, and IL-36γ in gingival crevicular fluid in patients with different periodontal conditions.

BACKGROUND: The study aimed to analyze cytokine levels, including interleukin (IL)-1ß, IL-10, and IL-36γ, to investigate the link between pro- and anti-inflammatory responses in periodontal conditions and assess their potential as diagnostic biomarkers for distinguishing between different types of periodontal conditions. METHODS: 80 systemically healthy non-smokers (25 periodontally healthy, 25 with gingivitis, 30 with periodontitis) were included. Clinical periodontal parameters were recorded, and gingival crevicular fluid (GCF) samples were obtained. Receiver operating characteristic (ROC) curve analysis was applied to determine the diagnostic value of cytokines. RESULTS: IL-36γ had the highest sensitivity for diagnosing periodontitis, although its specificity for identifying those without periodontitis was relatively low. The combination of IL-1ß and IL-36γ was the most effective in differentiating periodontitis from periodontal health. IL-10 was found to be an acceptable discriminator for distinguishing gingivitis from healthy conditions. However, its sensitivity and specificity for identifying gingivitis were lower. The combination of the three cytokines showed the highest ability to distinguish between periodontitis and gingivitis. CONCLUSION: The levels of IL-1ß, IL-10, and IL-36γ in GCF may provide insights into periodontal health and disease status. Further studies are needed to validate these results and explore the potential of these cytokines in periodontal disease management.

All three of these cytokines exhibit exceptional diagnostic accuracy, particularly in distinguishing between chronic periodontitis and periodontal health.Moreover, the combination of IL-1ß and IL-36γ stands out as the most accurate diagnostic indicator for periodontitis. This combination could serve as a robust biomarker panel for the early detection and monitoring of periodontal disease, potentially allowing for timely interventions to prevent disease progression.

Plasma protein signatures of adult asthma.

BACKGROUND: Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma. METHODS: Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)). RESULTS: Meta-analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75-0.79 in training set; AUC = 0.72, 95% CI = 0.69-0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma-atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis. CONCLUSION: This first large-scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management.

Implementation of vancomycin AUC/MIC dosing vs traditional trough dosing and incidence of acute kidney injury at a rural community hospital.

PURPOSE: Vancomycin treats methicillin-resistant Staphylococcus aureus infections in hospitalized patients, yet nephrotoxicity is a major risk. Dosing based on the ratio of vancomycin 24-hour area under the curve to minimum inhibitory concentration (AUC/MIC) is preferred over a trough-only vancomycin dosing approach to minimize the risk of acute kidney injury (AKI). This study compares the safety of AUC/MIC-guided and trough-only vancomycin dosing at a 255-bed hospital. METHODS: A retrospective cohort study of adult patients with stable renal function who received at least 3 days of intravenous vancomycin via either AUC/MIC or trough-only dosing was conducted. The primary outcome was AKI occurrence during treatment. Secondary outcomes included the frequencies of therapeutic, subtherapeutic, and supratherapeutic vancomycin troughs. Relative risk calculations were performed for all outcomes. RESULTS: 600 patients from the trough-only group and 561 patients from the AUC/MIC group were included. 121 patients from the trough-only group and 87 patients from the AUC/MIC group experienced AKI during treatment (relative risk [RR], 0.769; 95% CI, 0.599-0.988; P = 0.0397). Compared with the trough-only group, the AUC/MIC group was significantly less likely to have supratherapeutic troughs (RR, 0.703; 95% CI, 0.611-0.809; P < 0.0001) and significantly more likely to have therapeutic troughs (RR, 1.14; 95% CI, 1.069-1.211; P < 0.0001), with no significant between-group difference in subtherapeutic troughs (RR, 1.03; 95% CI, 0.854-1.25; P = 0.74). CONCLUSION: AUC/MIC dosing was associated with significantly lower risk of AKI, a lower risk of supratherapeutic trough levels, and a higher risk of therapeutic trough levels, with no significant difference in subtherapeutic troughs when compared to trough-only dosing. Limitations of this study included its retrospective nature and reliance on manual chart review.

Racial Differences in the Diagnostic Accuracy of OCT Angiography Macular Vessel Density for Glaucoma.

PURPOSE: To evaluate and compare the diagnostic accuracy of macular vessel density (VD) measured by OCT angiography (OCTA) in individuals of African descent (AD) and European descent (ED) with open-angle glaucoma. DESIGN: Observational, cross sectional study. PARTICIPANTS: A total of 176 eyes of 123 patients with glaucoma and 140 eyes of 88 healthy participants from the Diagnostic Innovations in Glaucoma Study. METHODS: Whole-image ganglion cell complex (wiGCC) thickness and macular VD (parafoveal VD and perifoveal VD) were obtained from 6 × 6 macula scans. Area under the receiver operating characteristic (AUROC) curves were used to evaluate the diagnostic accuracy of macular VD and ganglion cell complex (GCC) thickness in AD and ED participants after adjusting for confounders such as age, visual field mean deviation (VF MD), signal strength index, axial length, self-reported hypertension and diabetes. MAIN OUTCOME MEASURES: Macular VD and wiGCC measurements. RESULTS: Parafoveal and perifoveal VD were significantly lower in ED than AD patients with glaucoma. Parafoveal and perifoveal VD performed significantly worse in AD participants compared with ED participants for detection of glaucoma (adjusted AUROC, 0.75 [95% confidence interval (CI), 0.62, 0.87], 0.85 [95% CI, 0.79, 0.90], P = 0.035; and 0.82 [95% CI, 0.70, 0.92], 0.91 [95% CI, 0.87, 0.94], respectively; P = 0.020). In contrast to VD, diagnostic accuracy of GCC thickness was similar in AD and ED individuals (adjusted AUROC, 0.89 [95% CI, 0.79, 0.96], 0.92 [95% CI, 0.86, 0.96], respectively; P = 0.313). The diagnostic accuracies of both macular VD and GCC thickness for differentiating between glaucoma and healthy eyes increased with increasing VF MD in both AD and ED participants. CONCLUSIONS: Diagnostic performance of OCTA macular VD, but not GCC thickness, for glaucoma detection varies by race. Moreover, macular VD parameters had lower accuracy for detecting glaucoma in AD individuals than in ED individuals. The diagnostic performance of macular VD is race-dependent, and, therefore, race should be taken into consideration when interpreting macular OCTA results. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Multitask deep learning on mammography to predict extensive intraductal component in invasive breast cancer.

OBJECTIVES: To develop a multitask deep learning (DL) algorithm to automatically classify mammography imaging findings and predict the existence of extensive intraductal component (EIC) in invasive breast cancer. METHODS: Mammograms with invasive breast cancers from 2010 to 2019 were downloaded for two radiologists performing image segmentation and imaging findings annotation. Images were randomly split into training, validation, and test datasets. A multitask approach was performed on the EfficientNet-B0 neural network mainly to predict EIC and classify imaging findings. Three more models were trained for comparison, including a single-task model (predicting EIC), a two-task model (predicting EIC and cell receptor status), and a three-task model (combining the abovementioned tasks). Additionally, these models were trained in a subgroup of invasive ductal carcinoma. The DeLong test was used to examine the difference in model performance. RESULTS: This study enrolled 1459 breast cancers on 3076 images. The EIC-positive rate was 29.0%. The three-task model was the best DL model with an area under the curve (AUC) of EIC prediction of 0.758 and 0.775 at the image and breast (patient) levels, respectively. Mass was the most accurately classified imaging finding (AUC = 0.915), followed by calcifications and mass with calcifications (AUC = 0.878 and 0.824, respectively). Cell receptor status prediction was less accurate (AUC = 0.625-0.653). The multitask approach improves the model training compared to the single-task model, but without significant effects. CONCLUSIONS: A mammography-based multitask DL model can perform simultaneous imaging finding classification and EIC prediction. CLINICAL RELEVANCE STATEMENT: The study results demonstrated the potential of deep learning to extract more information from mammography for clinical decision-making. KEY POINTS: • Extensive intraductal component (EIC) is an independent risk factor of local tumor recurrence after breast-conserving surgery. • A mammography-based deep learning model was trained to predict extensive intraductal component close to radiologists' reading. • The developed multitask deep learning model could perform simultaneous imaging finding classification and extensive intraductal component prediction.

Posterior capsule edema in adhesive capsulitis: comparison with established non-contrast MRI findings and multivariable analysis.

OBJECTIVES: To evaluate posterior glenohumeral capsule edema compared to other MRI findings in adhesive capsulitis (AC). METHODS: This study was approved by the local Institutional Review Board and it is HIPAA compliant. A retrospective search identified subjects who received fluoroscopically guided intra-articular corticosteroid injections for AC and had an MRI within 6 months prior to injection. The study group was compared with an age-, sex-, and side-matched control group who underwent the same procedures but did not have AC. MRIs were evaluated for edema of posterior capsule, anterior capsule, axillary pouch, coracohumeral ligament (CHL) and rotator interval (RI), thickness of axillary pouch and CHL, thickness of anterior capsule, RI and subcoracoid fat replacement, and teres minor atrophy and edema. Multivariable analysis was performed. RESULTS: A total of 57 subjects with AC and 57 matched controls were studied: mean age 52 ± 7 (range 31-71) years, 37 female and 20 male, 22 right and 35 left. Posterior capsule edema was more common in the AC group vs. control group (66.7 vs 17.5%, p < 0.001). Multivariable analysis showed posterior capsule edema, CHL edema, and axillary pouch (glenoid) thickness (optimum cutoff = 4 mm) were significant independent predictors of AC. Simplified analysis using these three variables had an area under the curve of 0.860 (95%CI: 0.792-0.928). With all three variables present, the sensitivity and specificity for AC were 32% and 98%, respectively. CONCLUSIONS: Posterior joint capsule edema may be helpful to confirm AC. Posterior capsule edema, CHL edema, and axillary pouch (glenoid) thickness produce a strong model for distinguishing AC from controls. CLINICAL RELEVANCE STATEMENT: Edema involving the posterior shoulder joint capsule is an imaging marker of capsulitis and is useful in differentiating patients with adhesive capsulitis from those without in conjunction with other proven MRI findings. KEY POINTS: • Posterior capsule edema has a sensitivity of 66.7% and a specificity of 82.5% for the detection of adhesive capsulitis. • Posterior capsule edema, coracohumeral ligament (CHL) edema, and axillary pouch (glenoid) thickness were significant independent predictors of adhesive capsulitis, and combining these variables together produces a very strong model for distinguishing cases from controls (AUC = 0.860). • Optimal cutoff values for CHL, axillary pouch (humeral), axillary pouch (glenoid), and axillary pouch (total) thickness were 2.5, 2.6, 4, and 6.3 mm, respectively.

Prediction Model for Unfavorable Outcome in Spontaneous Intracerebral Hemorrhage Based on Machine Learning.

OBJECTIVE: The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). METHODS: Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). RESULTS: We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. CONCLUSION: The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

Cut-off points of neck and waist circumference as predictors of obstructive sleep apnea in the Colombian population: a comparison with polysomnography

ABSTRACT BACKGROUND: Neck circumference (NC) is a useful anthropometric measure for predicting obstructive sleep apnea (OSA). Ethnicity and sex also influence obesity phenotypes. NC cut-offs for defining OSA have not been established for the Latin American population. OBJECTIVES: To evaluate NC, waist circumference (WC), and body mass index (BMI) as predictors of OSA in the Colombian population and to determine optimal cut-off points. DESIGN AND SETTING: Diagnostic tests were conducted at the Javeriana University, Bogota. METHODS: Adults from three cities in Colombia were included. NC, WC, and BMI were measured, and a polysomnogram provided the reference standard. The discrimination capacity and best cut-off points for diagnosing OSA were calculated. RESULTS: 964 patients were included (57.7% men; median age, 58 years) and 43.4% had OSA. The discrimination capacity of NC was similar for men and women (area under curve, AUC 0.63 versus 0.66, P = 0.39) but better for women under 60 years old (AUC 0.69 versus 0.57, P < 0.05). WC had better discrimination capacity for women (AUC 0.69 versus 0.57, P < 0.001). There were no significant differences in BMI. Optimal NC cut-off points were 36.5 cm for women (sensitivity [S]: 71.7%, specificity [E]: 55.3%) and 41 cm for men (S: 56%, E: 62%); and for WC, 97 cm for women (S: 65%, E: 69%) and 99 cm for men (S: 53%, E: 58%). CONCLUSIONS: NC and WC have moderate discrimination capacities for diagnosing OSA. The cut-off values suggest differences between Latin- and North American as well as Asian populations.