Calibration Capabilities Research Articles

Developments of interlaboratory comparisons on pressure measurements in the Philippines

The National Metrology Laboratory of the Philippines, Industrial Technology Development Institute under the Department of Science and Technology (ITDI-DOST) offered first of its kind interlaboratory comparison in pressure measurement in the country following a demand survey identifying the gaps in proving the competency of local calibration laboratories. This paper presents the development in implementing the two interlaboratory comparisons in the Philippines with six years interval using the same pressure artefact. With its many objectives, the program also aimed to provide these laboratories access to proficiency testing (PT) to comply with the ISO/IEC 17025 requirements. Comparatively, while both are considered successfully implemented, the improved awareness and commitment to quality of the participants and the enhanced competency of the pilot laboratory in implementing such activity are a few of the enumerated factors instigating the increase in the number of participants as well as those obtaining satisfactory results in the latter PT. The interlaboratory comparison schemes offered aimed at sustaining the demands of metrology stakeholders and continuously develop this service to support further progress in the calibration and measurement capabilities of local laboratories in the country.

A nomogram combining thoracic CT and tumor markers to predict the malignant grade of pulmonary nodules ≤3 cm in diameter.

With the popularity of computed tomography (CT) of the thorax, the rate of diagnosis for patients with early-stage lung cancer has increased. However, distinguishing high-risk pulmonary nodules (HRPNs) from low-risk pulmonary nodules (LRPNs) before surgery remains challenging. A retrospective analysis was performed on 1064 patients with pulmonary nodules (PNs) admitted to the Qilu Hospital of Shandong University from April to December 2021. Randomization of all eligible patients to either the training or validation cohort was performed in a 3:1 ratio. Eighty-three PNs patients who visited Qianfoshan Hospital in the Shandong Province from January through April of 2022 were included as an external validation. Univariable and multivariable logistic regression (forward stepwise regression) were used to identify independent risk factors, and a predictive model and dynamic web nomogram were constructed by integrating these risk factors. A total of 895 patients were included, with an incidence of HRPNs of 47.3% (423/895). Logistic regression analysis identified four independent risk factors: the size, consolidation tumor ratio, CT value of PNs, and carcinoembryonic antigen levels in blood. The area under the ROC curves was 0.895, 0.936, and 0.812 for the training, internal validation, and external validation cohorts, respectively. The Hosmer-Lemeshow test demonstrated excellent calibration capability, and the fit of the calibration curve was good. DCA has shown the nomogram to be clinically useful. The nomogram performed well in predicting the likelihood of HRPNs. In addition, it identified HRPNs in patients with PNs, achieved accurate treatment with HRPNs, and is expected to promote their rapid recovery.

A Controller for Si MZI-Based Spanke-Benes Optical Switch Fabric With Automatic Calibration Capability

A Controller for Si MZI-Based Spanke-Benes Optical Switch Fabric With Automatic Calibration Capability

Development and validation of a random forest model to predict functional outcome in patients with intracerebral hemorrhage.

To develop and validate a machine learning (ML)-based model to predict functional outcome in Chinese patients with intracerebral hemorrhage (ICH). This retrospective cohort study enrolled patients with ICH between November 2017 and November 2020. The follow-up period ended in February 2021. The study population was divided into training and testing sets with a ratio of 7:3. All variables were included in the least absolute shrinkage and selection operator (LASSO) regression for feature selection. The selected variables were incorporated into the random forest algorithm to construct the prediction model. The predictive performance of the model was evaluated via the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and calibration curve. A total of 412 ICH patients were included, with 288 in the training set, and 124 in the testing set. Twelve attributes were selected: neurological deterioration, Glasgow Coma Scale (GCS) score at 24 h, baseline GCS score, time from onset to the emergency room, blood glucose, diastolic blood pressure (DBP) change in 24 h, hematoma volume change in 24 h, systemic immune-inflammatory index (SII), systolic blood pressure (SBP) change in 24 h, serum creatinine, serum sodium, and age. In the testing set, the accuracy, AUC, sensitivity, specificity, PPV, and NPV of the model were 0.895, 0.964, 0.872, 0.906, 0.810, and 0.939, respectively. The calibration curves showed a good calibration capability of the model. This developed random forest model performed well in predicting 3-month poor functional outcome for Chinese ICH patients.

IKBIP might be a potential prognostic biomarker for glioblastoma multiforme.

Due to the negative association between inhibitor of nuclear factor-kB kinase-interacting protein (IKBIP) and survival in gliomas, this study aimed to comprehensively analyze the potential function of IKBIP in glioblastoma multiforme (GBM). GBM samples were retrieved from The Cancer Genome Atlas and Chinese Glioma Genome Atlas as training and validation cohorts, respectively, and survival and Cox regression analyses were conducted. Based on clinical indicators and IKBIP, three prognostic models were established and then verified using the validation dataset. Infiltrating immune cell analysis and single-sample gene set enrichment analysis were also conducted to explore the underlying mechanisms. Finally, the key findings were validated through molecular biology experiments. Patients in the high IKBIP score group had poorer survival. Based on Cox regression and subgroup analyses, IKBIP was identified as an independent prognostic factor. Among the three models constructed, the model combining the IKBIP signature and clinical features displayed good performance in terms of discrimination, calibration, and model improvement capability in the training cohort. This model was also successfully validated in an external cohort from the CGGA. Further analysis revealed that many immune cells and related pathways were involved in the high-risk group. In vitro experiments revealed that the knockdown of IKBIP inhibited cell invasion and proliferation, and promoted their senescence. The prognostic value of IKBIP and its positive impact on the invasiveness of GBM were identified, indicating that IKBIP may serve as an underlying target for the treatment of GBM.

Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients

BackgroundAtrial Fibrillation (AF) is the most common arrhythmia in the intensive care unit (ICU) and is associated with increased morbidity and mortality. Identification of patients at risk for AF is not routinely performed as AF prediction models are almost solely developed for the general population or for particular ICU populations. However, early AF risk identification could help to take targeted preemptive actions and possibly reduce morbidity and mortality. Predictive models need to be validated across hospitals with different standards of care and convey their predictions in a clinically useful manner. Therefore, we designed AF risk models for ICU patients using uncertainty quantification to provide a risk score and evaluated them on multiple ICU datasets. MethodsThree CatBoost models, utilizing feature windows comprising data 1.5-13.5, 6-18, or 12-24 hours before AF occurrence, were built using 2-repeat-10-fold cross-validation on AmsterdamUMCdb, the first freely available European ICU database. Furthermore, AF Patients were matched with no-AF patients for training. Transferability was validated using a direct and a recalibration evaluation on two independent external datasets, MIMIC-IV and GUH. The calibration of the predicted probability, used as an AF risk score, was measured using the Expected Calibration Error (ECE) and the presented Expected Signed Calibration Error (ESCE). Additionally, all models were evaluated across time during the ICU stay. ResultsThe model performance reached Areas Under the Curve (AUCs) of 0.81 at internal validation. Direct external validation showed partial generalizability with AUCs reaching 0.77. However, recalibration resulted in performances matching or exceeding that of the internal validation. All models furthermore showed calibration capabilities demonstrating adequate risk prediction competence. ConclusionUltimately, recalibrating models reduces the challenge of generalization to unseen datasets. Moreover, utilizing the patient-matching methodology together with the assessment of uncertainty calibration can serve as a step toward the development of clinical AF prediction models.

A nomogram to predict the risk of sarcopenia in older people.

The burden of sarcopenia is increasing worldwide. However, most cases of sarcopenia are undiagnosed due to the lack of simple screening tools. This study aimed to develop and validate an individualized and simple nomogram for predicting sarcopenia in older adults. A total of 180 medical examination populations aged ≥60 years were enrolled in this study. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia 2019 consensus. The primary data were randomly divided into training and validation sets. Univariate logistic regression analysis was performed to select the risk factors of sarcopenia, which were subjected to the least absolute shrinkage and selection operator for feature selection. A nomogram was established using multivariate logistic regression analysis by incorporating the features selected in the least absolute shrinkage and selection operator regression model. The discrimination and calibration of the predictive model were verified by the concordance index, receiver operating characteristic curve, and calibration curve. In this study, 55 cases of sarcopenia were available. Risk predictors included age, albumin, blood urea nitrogen, grip strength, and calf circumference. The model had good discrimination and calibration capabilities. concordance index was 0.92 (95% confidence interval: 0.84-1.00), and the area under the receiver operating characteristic curve was 0.92 (95% confidence interval: 0.83-1.00) in the validation set. The Hosmer-Lemeshow test had a P value of .94. The predictive model in this study will be a clinically useful tool for predicting the risk of sarcopenia, and it will facilitate earlier detection and therapeutic intervention for sarcopenia.

Prediction models for overall and cancer-specific survival in patients with metastatic early-onset colorectal cancer: a population-based study

Metastatic early-onset colorectal cancer (EO-CRC) is on the rise, yet there is a dearth of predictive models for this disease. Therefore, it is crucial to develop a nomogram to aid in the early detection and management of metastatic colorectal cancer in young patients. We retrieved data from the SEER database on patients with metastatic colorectal cancer aged 50 or younger between 2010 and 2017. The data were randomly allocated in a 7:3 ratio to training and validation cohorts, and univariate and multivariate Cox regression analyses were used to identify independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) at 1, 3, and 5years. The nomograms were developed based on these factors, and their discriminatory and calibration capabilities were validated. Using the nomogram risk scores, patients were stratified into low-risk and high-risk groups. The study included 2470 patients with metastatic EO-CRC. Univariate and multivariate Cox regression analysis identified 12 independent risk factors that were included in the nomogram. The training cohort had a consistency index (C-index) of 0.71, while the validation cohort had a C-index of 0.70, demonstrating good predictive accuracy. Calibration plots showed a high level of consistency between the observed and predicted values, with overlapping plots along the diagonal. The decision curve analysis (DCA) revealed that the nomogram had a high clinical application value. The novel nomograms were created to predict the prognosis of patients with metastatic EO-CRC, which can aid clinicians in developing more effective treatment strategies and contribute to more accurate prognostic assessments.

Lymph node metastasis in T1-2 colorectal cancer: a population-based study.

We performed this study to identify predictive factors for lymph node metastasis (LNM) and analyze the impact of LNM on the prognosis of patients with T1-2 colorectal cancer (CRC), with the intention of providing guidance for the treatment. The Surveillance Epidemiology and End Result database was used to identify 20,492 patients diagnosed with T1-2 stage CRC between 2010 and 2019, who underwent surgery and lymph node evaluation and had complete prognostic information. Clinicopathological data of patients with T1-2 stage colorectal cancer treated with surgery at Peking University People's Hospital from 2017 to 2021 with complete clinical information were retrieved. We identify and confirm the risk factors for positive lymph node involvement, and the results of follow-up were analyzed. Age, preoperative carcinoembryonic antigen (CEA) level, perineural invasion, and primary tumor site were independent risk factors for LNM in T1-2 CRC based on the analysis of the SEER database, while tumor size and histology of mucinous carcinoma were also independent risk factors in T1 CRC. We then make the nomogram model for predicting LNM risk and showed an acceptable consistency and calibration capability. Survival analysis showed that LNM was an independent prognostic indicator of 5-year disease-specific survival (P = 0.013) and disease-free survival (P < 0.001) in patients with T1 and T2 CRC. Age, CEA level and primary tumor site should be taken into consideration before making the surgical decision in T1-2 CRC patients. The tumor size and histology of mucinous carcinoma also need to be thought about in T1 CRC. Conventional imaging tests do not appear to provide a precise assessment for this issue.

External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.

A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile.

One of the main lessons of the COVID-19 pandemic is that we must prepare to face another pandemic like it. Consequently, this article aims to develop a general framework consisting of epidemiological modeling and a practical identifiability approach to assess combined vaccination and non-pharmaceutical intervention (NPI) strategies for the dynamics of any transmissible disease. Epidemiological modeling of the present work relies on delay differential equations describing time variation and transitions between suitable compartments. The practical identifiability approach relies on parameter optimization, a parametric bootstrap technique, and data processing. We implemented a careful parameter optimization algorithm by searching for suitable initialization according to each processed dataset. In addition, we implemented a parametric bootstrap technique to accurately predict the ICU curve trend in the medium term and assess vaccination. We show the framework's calibration capabilities for several processed COVID-19 datasets of different regions of Chile. We found a unique range of parameters that works well for every dataset and provides overall numerical stability and convergence for parameter optimization. Consequently, the framework produces outstanding results concerning quantitative tracking of COVID-19 dynamics. In addition, it allows us to accurately predict the ICU curve trend in the medium term and assess vaccination. Finally, it is reproducible since we provide open-source codes that consider parameter initialization standardized for every dataset. This work attempts to implement a holistic and general modeling framework for quantitative tracking of the dynamics of any transmissible disease, focusing on accurately predicting the ICU curve trend in the medium term and assessing vaccination. The scientific community could adapt it to evaluate the impact of combined vaccination and NPIs strategies for COVID-19 or any transmissible disease in any country and help visualize the potential effects of implemented plans by policymakers. In future work, we want to improve the computational cost of the parametric bootstrap technique or use another more efficient technique. The aim would be to reconstruct epidemiological curves to predict the combined NPIs and vaccination policies' impact on the ICU curve trend in real-time, providing scientific evidence to help anticipate policymakers' decisions.

Predicting Malignancy of Thyroid Micronodules: Radiomics Analysis Based on Two Types of Ultrasound Elastography Images

To develop a multimodal ultrasound radiomics nomogram for accurate classification of thyroid micronodules. A retrospective study including 181 thyroid micronodules within 179 patients was conducted. Radiomics features were extracted from strain elastography (SE), shear wave elastography (SWE) and B-mode ultrasound (BMUS) images. Minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select malignancy-related features. BMUS, SE, and SWE radiomics scores (Rad-scores) were then constructed. Multivariable logistic regression was conducted using radiomics signatures along with clinical data, and a nomogram was ultimately established. The calibration, discriminative, and clinical usefulness were considered to evaluate its performance. A clinical prediction model was also built using independent clinical risk factors for comparison. An aspect ratio ≥ 1, mean elasticity index, BMUS Rad-score, SE Rad-score, and SWE Rad-score were identified as the independent predictors for predicting malignancy of thyroid micronodules by multivariable logistic regression. The radiomics nomogram based on these characteristics showed favorable calibration and discriminative capabilities (AUCs: 0.903 and 0.881 for training and validation cohorts, respectively), all outperforming clinical prediction model (AUCs: 0.791 and 0.626, respectively). The decision curve analysis also confirmed clinical usefulness of the nomogram. The significant improvement of net reclassification index and integrated discriminatory improvement indicated that multimodal ultrasound radiomics signatures might work as new imaging markers for classifying thyroid micronodules. The nomogram combining multimodal ultrasound radiomics features and clinical factors has the potential to be used for accurate diagnosis of thyroid micronodules in the clinic.

Classification and prediction of cognitive trajectories of cognitively unimpaired individuals.

Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts. A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model. Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model). Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.

Nomogram for Predicting Recurrence-Free Survival of Primary Localized Gastrointestinal Stromal Tumor.

This study aimed to establish a new nomogram that predicts recurrence-free survival (RFS) after a complete surgical resection of primary localized gastrointestinal stromal tumors (GISTs); it also aimed to evaluate the discrimination, calibration, and clinical utility of the decision-making nomogram. The clinicopathological data of patients with primary localized GISTs at the First Affiliated Hospital of Chongqing Medical University from January 2000 to June 2022 were retrospectively analyzed. The clinicopathological data were randomly split into two sets (7:3 ratio) for training and validation. Suitable variables for the construction of a nomogram for the 1-, 3-, and 5-year RFS were selected using univariate and multivariate Cox regression analyses. Receiver operating characteristic (ROC) analysis and a concordance index (C-index) were used to quantify the discrimination of the nomogram and were compared with four commonly used prognostic scoring systems: Memorial Sloan Kettering Cancer Center prognostic nomogram, National Institutes of Health-Fletcher staging system, Chen's prognostic nomogram, and Air Forces Institute of Pathology risk criteria-Miettinen staging system. The calibration and clinical utility for the decision-making nomogram were validated using calibration curves and decision curves, respectively. In total, 641 patients were screened and analyzed in this retrospective, observational study. RFS was significantly related to tumor size, mitotic count, gender, DOG-1, and adjuvant therapy with imatinib according to the results of the multivariate and univariate Cox analyses. The nomogram was constructed using the above variables (all p < 0.05) for the 1-, 3-, and 5-year RFS. In the training set, the 1-, 3-, and 5-year ROC and C-index values of the nomogram were 0.868, 0.838, 0.816, and 0.830, respectively. For internal validation, we performed model fitting on the validation set, and the 1-, 3-, and 5-year ROC and C-indices were 0.977, 0.845, 0.869, and 0.849, respectively. Among the five GIST prognostic scoring systems, our nomogram had almost all the largest area under these decision curves and had a good calibration capability. The newly constructed nomogram based on tumor size, gender, mitotic count, DOG-1, and adjuvant treatment with imatinib exhibited an excellent performance and may serve as a prognostic scoring system to support therapeutic decision-making and individualized treatment for GISTs in China.

Miniaturized NIR spectroscopy and chemometrics: A smart combination to solve food authentication challenges

Over the years, technology has allowed more accurate, more effective, and prompt food integrity assessments to assure the quality and authenticity of food material. Thanks to the development of portable and hand-held near infrared (NIR) as a rapid, reliable, non-destructive, and user-friendly instrument, on-site food analysis is provided with more feasibility. However, miniaturized NIR devices have some significant challenges due to the presence of varying noise resources which can lead to misinterpretation. In this context, chemometric methods with the capability of resolution, identification, classification, and calibration play a pivotal role in achieving precise and in-depth comprehension of the data. In the present mini-review, we will discuss miniaturized NIR instrumentation, some chemometric concepts, and introduce the most popular algorithm in food authentication problem. The main feature of this review is avoiding mathematical details as much as possible to make the material accessible to a broad audience but highlighting the key features of chemometric methods with some simple illustrative examples in the scope of food authenticity.

A robust computational strategy for failure prediction of masonry structures using an improved multi‐surface damage‐plastic based interface model

AbstractIn this study, a new traction‐separation based constitutive model for use in finite element simulation of masonry joints under complex loading conditions is developed for cohesive elements. The proposed model is formulated using damage parameters and plastic deformation with mutual couplings, and can accurately simulate the complex nonlinear behaviors of masonry joints considering hardening or softening of strength and stiffness degradation. To enhance the numerical stability of the model, plasticity and damage are separated algorithmically and implemented in two phases. In the first phase, the plastic deformations are treated using a multi‐surface plasticity model composed of a smooth hyperbolic yield surface for tension‐shear mixed‐mode failure and an elliptical cap primarily for the compressive failure. This is implemented in effective stress space and helps restrict the evolution of yield surfaces with no softening, significantly enhancing the efficiency of stress return mapping by the closed point projection method. In addition, an adaptive sub‐stepping scheme is adopted to further improve the robustness of the numerical implementation. In the second phase, nominal stresses are computed from the effective stresses using damage parameters. The evolution of these damage parameters is defined in terms of plastic work which is defined by a polynomial form, and is recommended in this study for a better calibration capability. Improvements are made in the formulation of compressive cap including incorporation of hardening of strength and stiffness degradations as these are ignored in existing interface models. This approach helped improve simulation of masonry under cyclic loads with tension‐compression transitions. For the structural level applications, the interface model is implemented within a finite element program, which is utilized to simulate failure of a number of masonry specimens under in‐plane/out‐of‐plane monotonic/cyclic loading. The simulated results are rigorously validated with existing experimental data that shows a good potential in modeling masonry structures.

MRI-based radiomics nomogram for differentiation of solitary metastasis and solitary primary tumor in the spine

BackgroundDifferentiating between solitary spinal metastasis (SSM) and solitary primary spinal tumor (SPST) is essential for treatment decisions and prognosis. The aim of this study was to develop and validate an MRI-based radiomics nomogram for discriminating SSM from SPST.MethodsOne hundred and thirty-five patients with solitary spinal tumors were retrospectively studied and the data set was divided into two groups: a training set (n = 98) and a validation set (n = 37). Demographics and MRI characteristic features were evaluated to build a clinical factors model. Radiomics features were extracted from sagittal T1-weighted and fat-saturated T2-weighted images, and a radiomics signature model was constructed. A radiomics nomogram was established by combining radiomics features and significant clinical factors. The diagnostic performance of the three models was evaluated using receiver operator characteristic (ROC) curves on the training and validation sets. The Hosmer–Lemeshow test was performed to assess the calibration capability of radiomics nomogram, and we used decision curve analysis (DCA) to estimate the clinical usefulness.ResultsThe age, signal, and boundaries were used to construct the clinical factors model. Twenty-six features from MR images were used to build the radiomics signature. The radiomics nomogram achieved good performance for differentiating SSM from SPST with an area under the curve (AUC) of 0.980 in the training set and an AUC of 0.924 in the validation set. The Hosmer–Lemeshow test and decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model.ConclusionsA radiomics nomogram as a noninvasive diagnostic method, which combines radiomics features and clinical factors, is helpful in distinguishing between SSM and SPST.

Transfer-Learning-Assisted Multielement Calibration for Active Phased Antenna Arrays

A transfer-learning-based method for accelerating power-only calibration of phased array antennas by combining the conventional array theory with deep learning is presented in this communication. The existing power-only calibration methods either require a significant number of measurement cycles or have restrictive phase shifter resolution requirements. The proposed array calibration method uses a surrogate model to calibrate all the array elements in one pass without restricting phase resolution requirements. We developed a novel feature extraction scheme (FES) that picks out the most important power features resulting in reduced measurement cycles. The burden of data acquisition for model training is further reduced by relational knowledge transfer learning. The surrogate model acquires its general calibration capability from massive theoretical data, which is easily collected by the radiation multiplication theorem, and captures the detailed nonideal response from a small number of simulations. The proposed methodology has been demonstrated and tested on several arrays for validation. The effectiveness and performance of the method have been verified, and hence, it can serve as a complementary tool to accelerate the calibration process of phased antenna arrays.

Semi-automated 3D fluorescence speckle analyzer (3D-Speckler) for microscope calibration and nanoscale measurement.

The widespread use of fluorescence microscopy has prompted the ongoing development of tools aiming to improve resolution and quantification accuracy for study of biological questions. Current calibration and quantification tools for fluorescence images face issues with usability/user experience, lack of automation, and comprehensive multidimensional measurement/correction capabilities. Here, we developed 3D-Speckler, a versatile, and high-throughput image analysis software that can provide fluorescent puncta quantification measurements such as 2D/3D particle size, spatial location/orientation, and intensities through semi-automation in a single, user-friendly interface. Integrated analysis options such as 2D/3D local background correction, chromatic aberration correction, and particle matching/filtering are also encompassed for improved precision and accuracy. We demonstrate 3D-Speckler microscope calibration capabilities by determining the chromatic aberrations, field illumination uniformity, and response to nanometer-scale emitters above and below the diffraction limit of our imaging system using multispectral beads. Furthermore, we demonstrated 3D-Speckler quantitative capabilities for offering insight into protein architectures and composition in cells.

Development and validation of a diagnostic model for differentiating tuberculous spondylitis from brucellar spondylitis using machine learning: A retrospective cohort study.

Tuberculous spondylitis (TS) and brucellar spondylitis (BS) are commonly observed in spinal infectious diseases, which are initially caused by bacteremia. BS is easily misdiagnosed as TS, especially in underdeveloped regions of northwestern China with less sensitive medical equipment. Nevertheless, a rapid and reliable diagnostic tool remains to be developed and a clinical diagnostic model to differentiate TS and BS using machine learning algorithms is of great significance. A total of 410 patients were included in this study. Independent factors to predict TS were selected by using the least absolute shrinkage and selection operator (LASSO) regression model, permutation feature importance, and multivariate logistic regression analysis. A TS risk prediction model was developed with six different machine learning algorithms. We used several metrics to evaluate the accuracy, calibration capability, and predictability of these models. The performance of the model with the best predictability was further verified with the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the calibration curve. The clinical performance of the final model was evaluated by decision curve analysis. Six variables were incorporated in the final model, namely, pain severity, CRP, x-ray intervertebral disc height loss, x-ray endplate sclerosis, CT vertebral destruction, and MRI paravertebral abscess. The analysis of appraising six models revealed that the logistic regression model developed in the current study outperformed other methods in terms of sensitivity (0.88 ± 0.07) and accuracy (0.79 ± 0.07). The AUC of the logistic regression model predicting TS was 0.86 (95% CI, 0.81-0.90) in the training set and 0.86 (95% CI, 0.78-0.92) in the validation set. The decision curve analysis indicated that the logistic regression model displayed a higher clinical efficiency in the differential diagnosis. The logistic regression model developed in this study outperformed other methods. The logistic regression model demonstrated by a calculator exerts good discrimination and calibration capability and could be applicable in differentiating TS from BS in primary health care diagnosis.