Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.

BACKGROUND: The continuous monitoring and recording of patients' pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps. OBJECTIVE: The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images. METHODS: The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots. RESULTS: A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns. CONCLUSIONS: This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies. TRIAL REGISTRATION: PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181.

Correction: Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.

[This corrects the article DOI: 10.2196/51250.].

Using ACGME milestones as a formative assessment for the internal medicine clerkship: a consecutive two-year outcome and follow-up after graduation.

BACKGROUND: This study evaluated the utility of using Accreditation Council for Graduate Medical Education (ACGME) Milestones as a formative assessment tool for the fifth- and sixth-grade medical students' performance in their internal medicine (IM) clerkship and the same students' performance in their post-graduate year (PGY) IM training. METHODS: Retrospective data were collected from 65 medical students completing the two-year IM clerkship in the academic years 2019 and 2020 and 26 of the above students completing their PGY-1 training at the same university hospital in the academic year 2021. Data included the assessment results of 7 of the ACGME IM Milestones, information on admitted patients assigned to the students, and surveys of the students' satisfaction. RESULTS: The analysis included 390 assessment results during the IM clerkship and 78 assessment results during the PGY-1 training. Clinical teachers commonly rated level 3 to medical students in the IM clerkship, with PC-2 subcompetency receiving the lowest rating among seven subcompetencies. The levels of most subcompetencies showed stationary in the two-year IM clerkship. Significant improvement was observed in all subcompetencies during the PGY-1 training. The medical students in the second-year IM clerkship expressed higher satisfaction with implementing Milestones than in their first-year IM clerkship and perceived Milestones assessments' usefulness as learning feedback. CONCLUSIONS: Using ACGME Milestones as a formative assessment tool in the IM clerkship yielded promising outcomes. Longitudinal follow-up of subcompetencies facilitated tracking students' development and providing constructive feedback.

Late creation of vascular access increased post-hemodialysis mortality, hospitalization, and health-care expenditure: A population-based cohort study in Taiwan.

BACKGROUND/PURPOSE: The optimal timing of vascular access (VA) creation for hemodialysis (HD) and whether this timing affects mortality and health-care utilization after HD initiation remain unclear. Thus, we conducted a population-based study to explore their association. METHODS: We used Taiwan's National Health Insurance Research Database to analyze health-care outcomes and utilization in a cohort initiating HD during 2003-2013. We stratified patients by the following VA creation time points: >180, 91-180, 31-90, and ≤30 days before and ≤30 days after HD initiation and examined all-cause mortality, ambulatory care utilization/costs, hospital admission/costs, and total expenditure within 2 years after HD. Cox regression, Poisson regression, and general linear regression were used to analyze mortality, health-care utilization, and costs respectively. RESULTS: We identified 77,205 patients who started HD during 2003-2013. Compared with the patients undergoing VA surgery >180 days before HD initiation, those undergoing VA surgery ≤30 days before HD initiation had the highest mortality-15.92 deaths per 100-person-years, crude hazard ratio (HR) 1.56, and adjusted HR 1.28, the highest hospital admissions rates- 2.72 admission per person-year, crude rate ratio (RR) 1.48 and adjusted RR 1.32, and thus the highest health-care costs- US$31,390 per person-year, 7% increase of costs and 6% increase with adjustment within the 2-year follow-up after HD initiation. CONCLUSIONS: Late VA creation for HD can increase all-cause mortality, hospitalization, and health-care costs within 2 years after HD initiation. Early preparation of VA has the potential to reduce post-HD mortality and healthcare expenses for the ESKD patients.

Deep learning based decision tree ensembles for incomplete medical datasets.

BACKGROUND: In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose. OBJECTIVE: To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles. METHOD: Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison. RESULTS: The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method. CONCLUSION: The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates.

Trends and projections of inflammatory bowel disease at the global, regional and national levels, 1990-2050: a bayesian age-period-cohort modeling study.

BACKGROUND: Inflammatory bowel disease (IBD) is a global health concern with varying levels and trends across countries and regions. Understanding these differences is crucial for effective prevention and treatment strategies. METHODS: Using data from the 2019 Global Burden of Disease study, we examine IBD incidence, mortality, and disability-adjusted life years (DALYs) rates in 198 countries from 1990 to 2019. To assess changes in the burden of IBD, estimated annual percentage changes (EAPC) were calculated, and a Bayesian age-period-cohort model was used to predict the future 30-year trends of IBD. RESULTS: In 2019, there were 405,000 new IBD cases globally (95% uncertainty interval (UI) 361,000 to 457,000), with 41,000 deaths (95% UI 35,000 to 45,000) and 1.62million DALYs (95% UI 1.36-1.92million). The global age-standardized incidence rate in 2019 was 4.97 per 100,000 person-years (95% UI 4.43 to 5.59), with a mortality rate of 0.54 (95% UI 0.46 to 0.59) and DALYs rate of 20.15 (95% UI 16.86 to 23.71). From 1990 to 2019, EAPC values for incidence, mortality, and DALYs rates were - 0.60 (95% UI - 0.73 to - 0.48), - 0.69 (95% UI - 0.81 to - 0.57), and - 1.04 (95% UI - 1.06 to - 1.01), respectively. Overall, the burden of IBD has shown a slow decline in recent years. In SDI stratification, regions with higher initial SDI (high-income North America and Central Europe) witnessed decreasing incidence and mortality rates with increasing SDI, while regions with lower initial SDI (South Asia, Oceania, and Latin America) experienced a rapid rise in incidence but a decrease in mortality with increasing SDI. Predictions using a Bayesian model showed lower new cases and deaths from 2020 to 2050 than reference values, while the slope of the predicted incidence-time curve closely paralleled that of the 2019 data. CONCLUSION: Increasing cases, deaths, and DALYs highlight the sustained burden of IBD on public health. Developed countries have stabilized or declining incidence rates but face high prevalence and societal burden. Emerging and developing countries experience rising incidence. Understanding these changes aids policymakers in effectively addressing IBD challenges in different regions and economic contexts.

Electroencephalogram-Driven Machine-Learning Scenario for Assessing Impulse Control Disorder Comorbidity in Parkinson's Disease Using a Low-Cost, Custom LEGO-Like Headset.

Patients with Parkinson's disease (PD) may develop cognitive symptoms of impulse control disorders (ICDs) when chronically treated with dopamine agonist (DA) therapy for motor deficits. Motor and cognitive comorbidities critically increase the disability and mortality of the affected patients. This study proposes an electroencephalogram (EEG)-driven machine-learning scenario to automatically assess ICD comorbidity in PD. We employed a classic Go/NoGo task to appraise the capacity of cognitive and motoric inhibition with a low-cost, custom LEGO-like headset to record task-relevant EEG activity. Further, we optimized a support vector machine (SVM) and support vector regression (SVR) pipeline to learn discriminative EEG spectral signatures for the detection of ICD comorbidity and the estimation of ICD severity, respectively. With a dataset of 21 subjects with typical PD, 9 subjects with PD and ICD comorbidity (ICD), and 25 healthy controls (HC), the study results showed that the SVM pipeline differentiated subjects with ICD from subjects with PD with an accuracy of 66.3% and returned an around-chance accuracy of 53.3% for the classification of PD versus HC subjects without the comorbidity concern. Furthermore, the SVR pipeline yielded significantly higher severity scores for the ICD group than for the PD group and resembled the ICD vs. PD distinction according to the clinical questionnaire scores, which was barely replicated by random guessing. Without a commercial, high-precision EEG product, our demonstration may facilitate deploying a wearable computer-aided diagnosis system to assess the risk of DA-triggered cognitive comorbidity in patients with PD in their daily environment.

Prognostic assessment of lung adenocarcinoma patients with early-staging diseases: a nomogram based on coagulation-related factors.

OBJECTIVES: Early-stage lung adenocarcinoma (ADC) has a great heterogeneity in prognosis that is difficult to evaluate effectively. Thus, we developed and validated an effective nomogram prognostic model based on the clinical and laboratory characteristics of stage I-IIA ADC. METHODS: We included 1585 patients with pathologically diagnosed stage I-IIA ADC who underwent surgery at Shanghai Pulmonary Hospital. The nomogram was constructed based on the peripheral blood test and coagulation test indicators and evaluated using Calibration plots, concordance index, decision curve analysis and the X-tile software. Recurrence-free survival (RFS) and overall survival (OS) were estimated by the Kaplan-Meier method and the Cox proportional hazard regression model. The primary end point of this study was RFS. RESULTS: Thrombin time and 4 clinical indicators for RFS were integrated into nomograms. A favourable agreement between the nomogram prediction and validation was observed in the calibration curves for RFS probabilities. The concordance index of the nomogram to predict RFS was 0.736 (95% confidence interval, 0.717-0.755). Moreover, significant differences were shown between the high-risk and low-risk groups in RFS and OS (P < 0.001) after effective cut-off values of risk points were found based on the nomogram. CONCLUSIONS: We established and validated a prognostic nomogram including thrombin time to predict RFS and OS of stage I-IIA ADC patients. This nomogram provided an effective prediction ability for the prognosis of stage I-IIA ADC patients.

Determining patients with spinal metastases suitable for surgical intervention: A cost-effective analysis.

BACKGROUND: Both nonoperative and operative treatments for spinal metastasis are expensive interventions. Patients' expected 3-month survival is believed to be a key factor to determine the most suitable treatment. However, to the best of our knowledge, no previous study lends support to the hypothesis. We sought to determine the cost-effectiveness of operative and nonoperative interventions, stratified by patients' predicted probability of 3-month survival. METHODS: A Markov model with four defined health states was used to estimate the quality-adjusted life years (QALYs) and costs for operative intervention with postoperative radiotherapy and radiotherapy alone (palliative low-dose external beam radiotherapy) of spine metastases. Transition probabilities for the model, including the risks of mortality and functional deterioration, were obtained from secondary and our institutional data. Willingness to pay thresholds were prespecified at $100,000 and $150,000. The analyses were censored after 5-year simulation from a health system perspective and discounted outcomes at 3% per year. Sensitivity analyses were conducted to test the robustness of the study design. RESULTS: The incremental cost-effectiveness ratios were $140,907 per QALY for patients with a 3-month survival probability >50%, $3,178,510 per QALY for patients with a 3-month survival probability <50%, and $168,385 per QALY for patients with independent ambulatory and 3-month survival probability >50%. CONCLUSIONS: This study emphasizes the need to choose patients carefully and estimate preoperative survival for those with spinal metastases. In addition to reaffirming previous research regarding the influence of ambulatory status on cost-effectiveness, our study goes a step further by highlighting that operative intervention with postoperative radiotherapy could be more cost-effective than radiotherapy alone for patients with a better survival outlook. Accurate survival prediction tools and larger future studies could offer more detailed insights for clinical decisions.

Learning more from the inter-rater reliability of interstitial fibrosis assessment beyond just a statistic.

Interstitial fibrosis assessment by renal pathologists lacks good agreement, and we aimed to investigate its hidden properties and infer possible clinical impact. Fifty kidney biopsies were assessed by 9 renal pathologists and evaluated by intraclass correlation coefficients (ICCs) and kappa statistics. Probabilities of pathologists' assessments that would deviate far from true values were derived from quadratic regression and multilayer perceptron nonlinear regression. Likely causes of variation in interstitial fibrosis assessment were investigated. Possible misclassification rates were inferred on reported large cohorts. We found inter-rater reliabilities ranged from poor to good (ICCs 0.48 to 0.90), and pathologists' assessments had the worst agreements when the extent of interstitial fibrosis was moderate. 33.5% of pathologists' assessments were expected to deviate far from the true values. Variation in interstitial fibrosis assessment was found to be correlated with variation in interstitial inflammation assessment (r2 = 32.1%). Taking IgA nephropathy as an example, the Oxford T scores for interstitial fibrosis were expected to be misclassified in 21.9% of patients. This study demonstrated the complexity of the inter-rater reliability of interstitial fibrosis assessment, and our proposed approaches discovered previously unknown properties in pathologists' practice and inferred a possible clinical impact on patients.

DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography.

Detecting the early signs of stroke using non-contrast computerized tomography (NCCT) is essential for the diagnosis of acute ischemic stroke (AIS). However, the hypoattenuation in NCCT is difficult to precisely identify, and accurate assessments of the Alberta Stroke Program Early CT Score (ASPECTS) are usually time-consuming and require experienced neuroradiologists. To this end, this study proposes DGA3-Net, a convolutional neural network (CNN)-based model for ASPECTS assessment via detecting early ischemic changes in ASPECTS regions. DGA3-Net is based on a novel parameter-efficient dihedral group CNN encoder to exploit the rotation and reflection symmetry of convolution kernels. The bounding volume of each ASPECTS region is extracted from the encoded feature, and an attention-guided slice aggregation module is used to aggregate features from all slices. An asymmetry-aware classifier is then used to predict stroke presence via comparison between ASPECTS regions from the left and right hemispheres. Pre-treatment NCCTs of suspected AIS patients were collected retrospectively, which consists of a primary dataset (n = 170) and an external validation dataset (n = 90), with expert consensus ASPECTS readings as ground truth. DGA3-Net outperformed two expert neuroradiologists in regional stroke identification (F1 = 0.69) and ASPECTS evaluation (Cohen's weighted Kappa = 0.70). Our ablation study also validated the efficacy of the proposed model design. In addition, class-relevant areas highlighted by visualization techniques corresponded highly with various well-established qualitative imaging signs, further validating the learned representation. This study demonstrates the potential of deep learning techniques for timely and accurate AIS diagnosis from NCCT, which could substantially improve the quality of treatment for AIS patients.

Public awareness, specific knowledge, and worry about mpox (monkeypox): A preliminary community-based study in Shenzhen, China.

Background: The mpox (monkeypox) outbreak has been declared to be a public health emergency of international concern by the Director-General of World Health Organization in July 2022. However, evidence regarding the awareness, knowledge, and worry about mpox in the general population remains scant. Methods: A community-based survey targeting community residents was preliminarily conducted in Shenzhen, China in August 2022 by using a convenience sampling method. Information on mpox-related awareness, knowledge, and worry was collected from each participant. Binary logistic regression analyses with the stepwise procedure were applied to explore the factors associated with awareness, knowledge, and worry about mpox. Results: A total of 1028 community residents were included in the analysis (mean age: 34.70 years). Among these participants, 77.9% had ever heard of mpox, and 65.3% were aware of the global outbreak of mpox. However, only about half of them had a high level of knowledge regarding mpox (56.5%) and related symptoms (49.7%). More than one-third of them (37.1%) expressed a high level of worry about mpox. Having high knowledge levels of mpox and related symptoms were positively associated with a high level of worry (OR: 1.79, 95%CI: 1.22~2.63 for a single high knowledge level; OR: 1.98, 95%CI: 1.47~2.66 for both high knowledge levels). Conclusions: This study identified the gaps in public awareness and specific knowledge of mpox in Chinese people, providing scientific evidence for the prevention and control network of mpox at the community level. Targeted health education programs are of urgent need, which should be implemented along with psychological interventions to release public worry if necessary.

Associations between the signing status of family doctor contract services and cervical cancer screening behaviors: a cross-sectional study in Shenzhen, China.

BACKGROUND: As a core part of the primary healthcare system, family doctor contract services (FDCS) may help healthcare providers promote cervical cancer screening to the female population. However, evidence from population-based studies remains scant. This study aimed to investigate the potential associations between the signing status of FDCS and cervical cancer screening practices in Shenzhen, China. METHODS: A cross-sectional survey among female residents was conducted between July to December 2020 in Shenzhen, China. A multistage sampling method was applied to recruit women seeking health services in community health service centers. Binary logistic regression models were established to assess the associations between the signing status of FDCS and cervical cancer screening behaviors. RESULTS: Overall, 4389 women were recruited (mean age: 34.28, standard deviation: 7.61). More than half (54.3%) of the participants had signed up with family doctors. Women who had signed up for FDCS performed better in HPV-related knowledge (high-level rate: 49.0% vs. 35.6%, P<0.001), past screening participation (48.4% vs. 38.8%, P<0.001), and future screening willingness (95.9% vs. 90.8%, P<0.001) than non-signing women. Signing up with family doctors was marginally associated with past screening participation (OR: 1.13, 95%CI: 0.99-1.28), which tended to be robust among women with health insurance, being older than 25 years old at sexual debut, using condom consistently during sexual intercourse, and with a low level of HPV related knowledge. Similarly, signing up with family doctors was positively associated with future screening willingness (OR: 1.68, 95%CI: 1.29-2.20), which was more pronounced among women who got married and had health insurance. CONCLUSIONS: This study suggests that signing up with family doctors has positive associations with cervical cancer screening behaviors among Chinese women. Expanding public awareness of cervical cancer prevention and FDCS may be a feasible way to achieve the goal of cervical cancer screening coverage.

Origami Box Folding Exercise: A laparoscopic box simulation training and assessment method in facilitating laparoscopic psychomotor skills with minimal preparation.

OBJECTIVE: Laparoscopic box simulation training is widely recognized as an assessment tool to facilitate psychomotor skills especially for novice surgeons. However, current commercialized training modules including pegs, gauze, clips, pins etc. are generally costly and relatively inaccessible. We introduce a simple and pioneer surgical training drill, the Origami Box Folding Exercise (OBFE), based on the validated evaluating system of objective structured assessment of technical skills (OSATS) constructed with the scoring system of procedure-specific checklist (PSC) and global rating scale (GRS). MATERIALS AND METHODS: Face and content validation of the OBFE and OSATS are evaluated by five endoscopic experts from two medical centers in Taiwan. This is a prospective observational study analyzing the pre-test/post-test result of OBFE from 37 participants in two individual workshops as training and evaluating method for laparoscopic psychomotor skills. Both the pre and post tests are video recorded with a time limit of 5 min graded by two independent evaluators based on the OSATS scoring system. RESULTS: The reliability of PSC, GRS, and intergroup value between PSC and GRS were 0.923, 0.926 and 0.933, respectively. Inter-rater reliability of PSC, GRS, and both were 0.985, 0.932 and 0.977, respectively. Construct validity of PSC and GRS were statistically significant, with p-value 0.006 and 0.001, respectively. CONCLUSION: OBFE enhances laparoscopic psychomotor skills with requirement of a single piece of paper. The associated OSATS tool for a 5-min OBFE test was validated. OBFE training is an efficient training and assessment system to promote psychomotor skills in laparoscopic box simulation drill which requires simple and economical preparation.

Proposal of an automated tumor-stromal ratio assessment algorithm and a nomogram for prognosis in early-stage invasive breast cancer.

BACKGROUND: The tumor-stromal ratio (TSR) has been verified to be a prognostic factor in many solid tumors. In most studies, it was manually assessed on routinely stained H&E slides. This study aimed to assess the TSR using image analysis algorithms developed by the Qupath software, and integrate the TSR into a nomogram for prediction of the survival in invasive breast cancer (BC) patients. METHODS: A modified TSR assessment algorithm based on the recognition of tumor and stroma tissues was developed using the Qupath software. The TSR of 234 invasive BC specimens in H&E-stained tissue microarrays (TMAs) were assessed with the algorithm and categorized as stroma-low or stroma-high. The consistency of TSR estimation between Qupath prediction and pathologist annotation was analyzed. Univariable and multivariable analyses were applied to select potential risk factors and a nomogram for predicting survival in invasive BC patients was constructed and validated. An extra TMA containing 110 specimens was obtained to validate the conclusion as an independent cohort. RESULTS: In the discovery cohort, stroma-low and stroma-high were identified in 43.6% and 56.4% cases, respectively. Good concordance was observed between the pathologist annotated and Qupath predicted TSR. The Kaplan-Meier curve showed that stroma-high patients were associated with worse 5-DFS compared to stroma-low patients (p = 0.007). Multivariable analysis identified age, T stage, N status, histological grade, ER status, HER-2 gene, and TSR as potential risk predictors, which were included in the nomogram. The nomogram was well calibrated and showed a favorable predictive value for the recurrence of BC. Kaplan-Meier curves showed that the nomogram had a better risk stratification capability than the TNM staging system. In the external validation of the nomogram, the results were further validated. CONCLUSIONS: Based on H&E-stained TMAs, this study successfully developed image analysis algorithms for TSR assessment and constructed a nomogram for predicting survival in invasive BC.

Handling Delayed or Missed Dose of Antiseizure Medications: A Model-Informed Individual Remedial Dosing.

BACKGROUND AND OBJECTIVES: Delayed or missed antiseizure medications (ASMs) doses are common during long-term or lifelong antiepilepsy treatment. This study aims to explore optimal individualized remedial dosing regimens for delayed or missed doses of 11 commonly used ASMs. METHODS: To explore remedial dosing regimens, Monte Carlo simulation was used based on previously identified and published population pharmacokinetic models. Six remedial strategies for delayed or missed doses were investigated. The deviation time outside the individual therapeutic range was used to evaluate each remedial regimen. The influences of patients' demographics, concomitant medication, and scheduled dosing intervals on remedial regimens were assessed. RxODE and Shiny in R were used to perform Monte Carlo simulation and recommend individual remedial regimens. RESULTS: The recommended remedial regimens were highly correlated with delayed time, scheduled dosing interval, and half-life of the ASM. Moreover, the optimal remedial regimens for pediatric and adult patients were different. The renal function, along with concomitant medication that affects the clearance of the ASM, may also influence the remedial regimens. A web-based dashboard was developed to provide individualized remedial regimens for the delayed or missed dose, and a user-defined module with all parameters that could be defined flexibly by the user was also built. DISCUSSION: Monte Carlo simulation based on population pharmacokinetic models may provide a rational approach to propose remedial regimens for delayed or missed doses of ASMs in pediatric and adult patients with epilepsy.

Simulating future land use by coupling ecological security patterns and multiple scenarios.

A land use simulation model with coupling constraints of ecological security patterns (ESPs) and multiple scenarios (MSs) was developed using the PLUS model. The research scale was zoned with environmental functional regions, where land management policies were formulated. A case study in Anji County successfully demonstrated the application of the ESP-MS-PLUS model. First, we constructed three different levels of ESPs as ecological constraints by utilizing ecosystem services evaluation and circuit theory. Second, four scenarios of land use and land cover changes (LUCCs) in 2034 were assumed, namely business as usual (BAU), priority given to urban development (PUD), priority given to ecological protection (PEP), and balanced urban development and ecological protection (BUE). Then, the basic ecological constraints (ecological red line areas and waters) and three types of ESPs were coupled with the four scenarios. The results of the simulation and analysis of landscape metrics under each scenario showed that the PEP and BUE scenarios would effectively reduce the degree and speed of ecological destruction. In addition, there were three environmental functional areas that could be used as priority areas for urban construction to ensure economic development. This study provides a new mechanism for land use optimization in the context of ecological protection at scales conducive to practice.

Computed tomography angiography assessment of the degree of simple coarctation of the aorta and its relationship with surgical outcome: A retrospective analysis.

Objective: To investigate the correlation between the degree of aortic coarctation and surgical prognosis in infants with simple coarctation of the aorta (CoA) using computed tomography angiography (CTA). Methods: This study was a retrospective study. Twenty-seven infants with simple CoA who underwent surgical correction from January 2020 to June 2022 were enrolled. Aortic diameters were measured at five different levels and normalized to Z scores based on the square root of body surface area. The relevant data were collected and analyzed, and the predictors associated with surgical outcome were determined. Results: Patients were divided into the mild CoA group and the severe CoA group according to the severity of coarctation. The mechanical ventilation duration and the length of ICU stay in the mild CoA group were significantly lower than those in the severe CoA group. Multiple linear regression analyses revealed that the degree of aortic coarctation was a significant risk factor for a prolonged postoperative ICU stay. In addition, gestational age and age at operation were risk factors for a prolonged postoperative ICU stay. Correlation analysis showed that the degree of aortic coarctation correlated with the Z scores of the ascending aorta and postcoarctation aorta. Conclusion: The degree of the CoA is an important predictor of surgical outcomes in infants with simple CoA and was significantly correlated with the ascending aorta and postcoarctation aorta Z scores. Therefore, preoperative CTA should be routinely performed to assess the degree of aortic coarctation and better identify risk factors.

Assessment of prostate cancer progression using a translational needle photoacoustic sensing probe: Preliminary study with intact human prostates ex-vivo.

In our previous studies, we demonstrated the ability of an interstitial all-optical needle photoacoustic (PA) sensing probe and PA spectral analysis (PASA) to assess the aggressiveness of prostate cancer. In this clinical translation investigation, we integrated the optical components of the needle PA sensing probe into a 18G steel needle. The translational needle PA sensing probe was evaluated using intact human prostates in a simulated ultrasound-guided transperineal prostate biopsy. PA signals were acquired at 1220 nm, 1370 nm, 800 nm and 266 nm at each interstitial measurement location and quantified by PASA within the frequency range of 8-28 MHz. The measurement locations were stained for establishing spatial correlations between the quantitative measurements and the histological diagnosing. Most of the quantitative PA assessments reveal statistically significant differences between the benign and cancerous regions. Multivariate analysis combining the PASA quantifications shows an accuracy close to 90% in differentiating the benign and cancerous regions in the prostates.

A Novel Detection and Assessment Method for Operational Defects of Pipe Jacking Tunnel Based on 3D Longitudinal Deformation Curve: A Case Study.

Adjacent tunnel construction and environmental disturbances can lead to longitudinal deformation in pipe-jacking tunnels. The longitudinal deformation of the tunnel is closely related to the occurrence of joint dislocation, joint opening, and other defects. In view of the difficulty of obtaining 3D longitudinal deformation curves, a method is proposed to obtain 3D longitudinal deformation curves based on a large number of 3D point cloud data with high spatial resolution and large spatial dimensions. Combined with the mechanism of defects occurrence, a theoretical basis for tunnel defects assessment based on tunnel longitudinal deformation is proposed. Taking one pipe jacking tunnel as an example, the longitudinal settlement curve and the 3D longitudinal deformation curve are compared. The correlation between the 3D longitudinal deformation curve and defects such as mud leakage, cracks, and differential deformation is illustrated from the perspective of three indexes: deformation amount, bending deformation, and shearing deformation. The accuracy and reliability of the 3D longitudinal deformation curve in tunnel defects detection and assessment are verified.