Critical Value Research Articles

The diagnostic utility of CT attenuation values in detecting calcification within costal cartilage

BackgroundTo establish and verify diagnostic criteria for the identification of costal cartilage calcification based on CT attenuation value. Methods360 chest CT slices of 120 patients were reviewed and annotated retrospectively and ROC curve was used to evaluate the diagnostic ability of CT attenuation value. Another 20 slices containing calcification were randomly selected and annotated by 4 doctors for further validation. H&E and COLX staining was performed on the residual costal cartilage. ResultsIn total 355129 voxels were detected and 187.5 was confirmed as the optimal CT attenuation value threshold, with a sensitivity of 98.6% and a specificity of 99.7%, for costal cartilage calcification diagnosis. Threshold-based identification of calcification demonstrated a similarity of nearly 80% with specialists' assessments, and exhibited advantages in the identification of subtle calcifications in the further validation. We also observed that CT attenuation values among males demonstrated a centralized distribution, whereas those among females exhibited a bimodal distribution. Threshold-based identified calcification showed a positivity of COLX. ConclusionsCT attenuation value could validly and reliably diagnose calcification within costal cartilage. Further investigations involving larger cohorts of patients are required to elucidate the risk factors and underlying mechanisms of costal cartilage calcification.

Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions. Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).

Non-linear response of plant caloric value to N addition and mowing treatments in a meadow steppe

BackgroundCaloric value is an important indicator of grassland ecosystem function, but the response of caloric value to nitrogen (N) addition and mowing is still unclear. We explored the adaptive changes of plant caloric value and energy standing crop along a N addition gradient after six-year NH4NO3 addition and mowing treatments in an Inner Mongolian temperate meadow steppe in northern China.ResultsWe found that the response of plant caloric value to N addition at different organizational levels was diverse. The caloric value of legumes increased linearly with N addition rates. The caloric value of grasses exhibited a non-linear response trend, initially increasing followed by saturation or decrease, with a N response threshold present. Due to the dominance of grass species, the caloric value at the community level followed a similar pattern to that of the grasses along the N addition gradient. Under mowing, the caloric value of plants at each organizational level increased and usually mowing enhanced the N response threshold. Amongst these, the N response threshold of Leymus chinensis increased from 3.302 to 5.443 g N m−2 yr−1, grasses increased from 4.414 to 5.746 g N m−2 yr−1, and community increased from 5.373 to 9.216 g N m−2 yr−1. Under non-mowing treatment, the N response thresholds of the most dominant species, Leymus chinensis, and community energy standing crop were 10.001 and 15.119 g N m−2 yr−1, respectively. Under mowing, the energy standing crops showed a linear increasing trend.ConclusionsN response thresholds of plant caloric value and energy standing crop vary at different organizational levels (community > functional group > species). The results reveal varying regulatory capabilities of plants on the ecological environment at different organizational levels. These findings enhance our understanding of plant-environment interactions in grassland ecosystems under N deposition from an energy perspective, which is of great significance to clarify the response mechanism of grassland ecosystem structure and function to N deposition.

Racial differences in odds of asthma exacerbations among Aspergillus fumigatus–sensitized adults with asthma

BackgroundAllergic sensitization to mold is a risk factor for poor asthma outcomes, but whether it accounts for disparities in asthma outcomes according to race or socioeconomic status is not well-studied. ObjectiveTo identify factors associated with allergic sensitization to molds and evaluate associations of sensitization to molds with asthma exacerbations after stratifying by race. MethodsWe conducted a retrospective cohort study of adults with asthma who had an outpatient visit to a large health system between January 1, 2017 and June 30, 2023 and received aeroallergen testing to Aspergillus fumigatus, Penicillium, Alternaria, and Cladosporium. We used logistic regression models to evaluate factors associated with mold sensitization and the effect of mold sensitization on asthma exacerbations in the 12 months before testing, overall and then stratified by race. ResultsA total of 2732 patients met the inclusion criteria. Sensitization to each mold was negatively associated with being a woman (odds ratios [ORs] ≤ 0.59, P ≤ .001 in 5 models) and positively associated with the Black race (ORs ≥ 2.16 vs White, P < .0005 in 5 models). In the full cohort, sensitization to molds was not associated with asthma exacerbations (ORs = 0.95-1.40, P ≥ .003 in 5 models and all above the corrected P value threshold). Among 1032 Black patients, sensitization to A fumigatus, but not to other molds, was associated with increased odds of asthma exacerbations (OR = 2.04, P < .0005). ConclusionBeing a man and Black race were associated with allergic sensitization to molds. Sensitization to A fumigatus was associated with asthma exacerbations among Black patients but not the overall cohort, suggesting that A fumigatus allergy is a source of disparities in asthma outcomes according to race.

Multiscale Optical Imaging Fusion for Cervical Precancer Diagnosis: Integrating Widefield Colposcopy and High-Resolution Endomicroscopy.

Early detection and treatment of cervical precancers can prevent disease progression. However, in low-resource communities with a high incidence of cervical cancer, high equipment costs and a shortage of specialists hinder preventative strategies. This manuscript presents a low-cost multiscale in vivo optical imaging system coupled with a computer-aided diagnostic system that could enable accurate, real-time diagnosis of high-grade cervical precancers. The system combines portable colposcopy and high-resolution endomicroscopy (HRME) to acquire spatially registered widefield and microscopy videos. A multiscale imaging fusion network (MSFN) was developed to identify cervical intraepithelial neoplasia grade 2 or more severe (CIN 2+). The MSFN automatically identifies and segments the ectocervix and lesions from colposcopy images, extracts nuclear morphology features from HRME videos, and integrates the colposcopy and HRME information. With a threshold value set to achieve sensitivity equal to clinical impression (0.98 [p = 1.0]), the MSFN achieved a significantly higher specificity than clinical impression (0.75 vs. 0.43, p = 0.000006). Our findings show that multiscale optical imaging of the cervix allows the highly sensitive and specific detection of high-grade precancers. The multiscale imaging system and MSFN could facilitate the accurate, real-time diagnosis of cervical precancers in low-resource settings.

Performance of GPT-4 with Vision on Text- and Image-based ACR Diagnostic Radiology In-Training Examination Questions.

Background Recent advancements, including image processing capabilities, present new potential applications of large language models such as ChatGPT (OpenAI), a generative pretrained transformer, in radiology. However, baseline performance of ChatGPT in radiology-related tasks is understudied. Purpose To evaluate the performance of GPT-4 with vision (GPT-4V) on radiology in-training examination questions, including those with images, to gauge the model's baseline knowledge in radiology. Materials and Methods In this prospective study, conducted between September 2023 and March 2024, the September 2023 release of GPT-4V was assessed using 386 retired questions (189 image-based and 197 text-only questions) from the American College of Radiology Diagnostic Radiology In-Training Examinations. Nine question pairs were identified as duplicates; only the first instance of each duplicate was considered in ChatGPT's assessment. A subanalysis assessed the impact of different zero-shot prompts on performance. Statistical analysis included χ2 tests of independence to ascertain whether the performance of GPT-4V varied between question types or subspecialty. The McNemar test was used to evaluate performance differences between the prompts, with Benjamin-Hochberg adjustment of the P values conducted to control the false discovery rate (FDR). A P value threshold of less than.05 denoted statistical significance. Results GPT-4V correctly answered 246 (65.3%) of the 377 unique questions, with significantly higher accuracy on text-only questions (81.5%, 159 of 195) than on image-based questions (47.8%, 87 of 182) (χ2 test, P < .001). Subanalysis revealed differences between prompts on text-based questions, where chain-of-thought prompting outperformed long instruction by 6.1% (McNemar, P = .02; FDR = 0.063), basic prompting by 6.8% (P = .009, FDR = 0.044), and the original prompting style by 8.9% (P = .001, FDR = 0.014). No differences were observed between prompts on image-based questions with P values of .27 to >.99. Conclusion While GPT-4V demonstrated a level of competence in text-based questions, it showed deficits interpreting radiologic images. © RSNA, 2024 See also the editorial by Deng in this issue.

A Hypothesis-Free Approach to Identifying Potential Effects of Relative Age in School Year: an Instrumental Variable Phenome-Wide Association Study in the UK Biobank.

A child's relative age within their school year ('relative age') is associated with educational attainment and mental health. However, hypothesis driven studies often re-examine the same outcomes and exposure, potentially leading to confirmation and reporting biases, and missing unknown effects. Hypothesis-free outcome-wide analyses can potentially overcome these limitations. We conducted a hypothesis-free investigation of the effects of relative age within school year. We used an instrumental variable (IV)-pheWAS in the UK Biobank (participants aged 40-69 years at baseline), using the PHESANT software package. We created two IVs for relative age: being born in September vs. August (n=64 075) and week of birth (n=383 309). Outcomes passing the Bonferroni-corrected P value threshold for either instrument were plotted to identify a discontinuity at the school year transition. 13 traits associated with at least one of the instruments showed a discontinuity. Previously identified effects included those with a younger relative age being less likely to have educational qualifications and more likely to have started smoking at a younger age. We detected a few associations not explored by previous studies. For example, those with younger relative age had better lung function as adults. Hypothesis-free approaches could help address confirmation and reporting biases in epidemiology.

MRI-based assessment of the mylohyoid muscle in oral squamous cell carcinoma, a 7-point scoring method.

To investigate preoperative MRI evaluation of the features of the mylohyoid muscle (MM) predictive of its infiltration in oral squamous cell carcinoma (OSCC) treatment planning, defining the most appropriate sequences to study its deep extension into the floor of the mouth (FOM). We applied a 7-point score to retrospectively evaluate preoperative imaging of patients who underwent surgery for OSCC over 11 years. The results were compared with histopathological findings using Spearman's rank coefficient. Receiver operating characteristic curves were employed to assess the score's ability to predict MM infiltration, determining optimal thresholds for sensitivity, specificity, and predictive values. The Mann-Whitney U-test confirmed that infiltration judgments did not overlap around this threshold. Cohen's K statistical coefficient was used to evaluate the interobserver agreement. Fifty-two patients (mean age 66.4 ± 11.9 years, 36 men) were evaluated. Histopathological examination found MM infiltration in 21% of cases (n = 11), with 90% classified in the highest Score categories. A score > 4 proved to be the best cut-off for predicting the risk of MM infiltration, with a sensitivity of 91% (CI: 0.57-0.99), specificity 61% (CI: 0.45-0.76), PPV 38% (CI: 0.21-0.59), and NPV 96% (CI: 0.78-0.99). At the subsequent single-sequence assessment, the TSE-T2wi had the highest diagnostic accuracy, with sensitivity 90% (CI: 0.57-0.99), specificity 70% (CI: 0.53-0.82), PPV 45% (CI: 0.25-0.67), and NPV 96% (CI: 0.80-0.99). The 7-point score is a promising predictor of safe surgical margins for MM in OSCC treatment, with the particular benefit of T2-weighted sequences. Our scoring system for tumor infiltration of MM, which is easy to use even for less experienced radiologists, allows for uniformity in radiological language, thereby ensuring crucial preoperative information for the surgeon. The relationship of the MM to an oral lesion may impact surgical planning. As the score increases, there is a greater incidence of infiltration in the MM. Our score system improves radiologists' reporting for MM involvement by tumor.

Mendelian Randomization and Bayesian Colocalization Analysis Implicate Glycoprotein VI as a Potential Drug Target for Cardioembolic Stroke in South Asian Populations.

Circulating plasma proteins are clinically useful biomarkers for stroke risk. We examined the causal links between plasma proteins and stroke risk in individuals of South Asian ancestry. We applied proteome-wide Mendelian randomization and colocalization approaches to understand causality of 2922 plasma proteins on stroke risk in individuals of South Asian ancestry. We obtained genetic instruments (proxies) for plasma proteins from the UK Biobank (N=920). Genome-wide association studies summary data for strokes (N≤11 312) were sourced from GIGASTROKE consortium. Our primary approach involved the Wald ratio or inverse-variance-weighted methods, with statistical significance set at false discovery rate <0.1. Additionally, a Bayesian colocalization approach assessed shared causal variants among proteome, transcriptome, and stroke phenotypes to minimize bias from linkage disequilibrium. We found evidence of a potential causal effect of plasma GP6 (glycoprotein VI) levels on cardioembolic stroke (odds ratio [OR]Wald ratio=2.53 [95% CI, 1.59-4.03]; P=9.2×10-5, false discovery rate=0.059). Generalized Mendelian randomization accounting for correlated single nucleotide polymorphisms (SNPs), with the P value threshold at P<5×10-8 and clumped at r2=0.3, showed consistent direction of effect of GP6 on cardioembolic stroke (ORgeneralized inverse-variance-weighted=2.21 [95% CI, 1.46-3.33]; P=1.6×10-4). Colocalization analysis indicated that plasma GP6 levels colocalize with cardioembolic stroke (posterior probability=91.4%). Multitrait colocalization combining transcriptome, proteome, and cardioembolic stroke showed moderate to strong evidence that these 2 traits colocalize with GP6 expression in the coronary artery and brain tissues (multitrait posterior probability>50%). The potential causal effect of GP6 on cardioembolic stroke was not significant in European populations (ORinverse-variance-weighted=1.08 [95% CI, 0.93-1.26]; P=0.29). Our joint Mendelian randomization and colocalization analyses suggest that genetically predicted GP6 is potentially causally associated with cardioembolic stroke risk in individuals of South Asian ancestry. As genetic data on individuals of South Asian ancestry increase, future Mendelian randomization studies with larger sample size for plasma GP6 levels should be implemented to further validate our findings. Additionally, clinical studies will be necessary to verify GP6 as a therapeutic target for cardioembolic stroke in South Asians.

Classification of Temperature and Humidity in Green Open Spaces by Implementing Internet of Things (IoT) using Mamdani Fuzzy Logic

The condition of green open spaces plays a pivotal role in influencing the comfort and well-being of users engaged in various activities within these environments. Hydrological and microclimate factors, particularly temperature and humidity, hold significant sway over the ecological balance in these areas. This study addresses the need for a tool to assess air temperature and humidity, facilitating public access to critical environmental data that impacts the psychological well-being of living organisms. In pursuit of this objective, a comprehensive monitoring system harnessing the power of the IoT was developed. This system integrates DHT22 and MQ-9 sensors to monitor temperature, humidity, and CO2 gas levels within multiple green open spaces. Data from these sensors is transmitted to a central database with minimal time delays: 2 seconds from the sensors to the server, 1 second from the server to the application, and 3 seconds from the hardware to the application. Impressively, this data transmission achieved a 100% success rate. To assess the comfort levels of these green open spaces, the system employs fuzzy Mamdani logic. It processes data obtained from the DHT22 sensor, using predefined temperature and humidity value thresholds. This innovative monitoring system provides valuable insights into the environmental conditions of green open spaces, contributing to the enhancement of the overall quality and suitability of these areas for various activities.

3D Topology Optimization and Mesh Dependency for Redesigning Locking Compression Plates Aiming to Reduce Stress Shielding.

Current fixation plates for bone fracture treatments are built with biocompatible metallic materials such as stainless steel, titanium, and its alloys (e.g., Ti6Al4V). The stiffness mismatch between the metallic material of the plate and the host bone leads to stress shielding phenomena, bone loss, and healing deficiency. This paper explores the use of three dimensional topology-optimization, based on compliance (i.e., strain energy) minimization, reshaping the design domain of three locking compression plates (four-screw holes, six-screw holes, and eight-screw holes), considering different volume reductions (25, 45, and 75%) and loading conditions (bending, compression, torsion, and combined loads). A finite-element study was also conducted to measure the stiffness of each optimized plate. Thirty-six designs were obtained. Results showed that for a critical value of volume reductions, which depend on the load condition and number of screws, it is possible to obtain designs with lower stiffness, thereby reducing the risk of stress shielding.

Windows: socio-environmental dimensions in Palestinian residential buildings

People spend a significant portion of their lives indoors. Nowadays, windows are the essential architectural element that allows users to have contact with the outside world. This contact has an important impact on our physiology, psychology, well-being, and indoor environment. Understanding the physical and behavioral (social) characteristics of this element in the Palestinian residential building is still insufficiently explored. A cross-sectional survey of 272 respondents was conducted to investigate the openings’ characteristics and user’s behavior to assess their influence on residents’ satisfaction and indoor performance. This assessment was based on the concept that residents’ satisfaction with their homes is a measure of buildings’ performance in meeting users’ needs, expectations, and aspirations. The findings show that the physical properties (i.e. Window wall ratio, Window floor ratio, position, orientation, etc.) of windows do not meet the minimum threshold of sustainable and energy-efficient values (i.e. Daylight, thermal comfort). In addition, the survey analysis determines the driving parameters and their corresponding variations influencing window operations in different periods of the day and season. The culture plays a central role in the Palestinians’ window operation behavior.

HPV Extended Genotyping to Triage Abnormal Cervical Cancer Screens-Balancing the Harms and Benefits of an Additional Triage Test before Direct Colposcopy Referral.

The Netherlands' cervical cancer screening program transitioned to primary human papillomavirus (HPV) screening in 2017. After the introduction of HPV-based screening, the country saw increases in colposcopy referral rates and detections of low-grade lesions. In July 2022, genotyping was introduced, and those with borderline or mild dyskaryotic (BMD) cytologic abnormalities were only referred to colposcopy if positive for HPV type 16 or 18, and repeat screening otherwise. In this article, various strategies using extended genotyping (HPV16/18/31/33/45/52/58) as a triage test after an abnormal screen were explored using data from HPV-positive participants with normal or BMD cytology in the Population-Based Screening Study Amsterdam (POBASCAM) trial. The authors assessed positive and negative predictive values and colposcopy referral rates for each strategy using extended genotyping to triage women to either direct referral to colposcopy or repeat screening. Direct referral did not meet positive and negative predictive value thresholds for efficiency for any strategies. However, the authors note that direct referral may nonetheless be useful among those with BMD due to minimal increases in colposcopy referrals and concerns of loss to follow-up at repeat screening. These findings demonstrate the potential utility of extended genotyping as a triage test in primary HPV screening programs. The results should be considered alongside the fact that referral to repeat screening may result in loss of engagement of women who need treatment to prevent invasive cancer. See related article by Kroon et al., p. 1037.

Incomplete label distribution learning via label correlation decomposition

Label distribution learning (LDL) has garnered increased attention in recent studies on label ambiguity. However, collecting complete annotations for LDL tasks is often time-consuming and labor-intensive compared to traditional learning paradigms. Therefore, designing effective incomplete LDL algorithms is crucial to broaden LDL’s application scope. In this paper, we propose a novel LDL algorithm, called Incomplete Label Distribution Learning via Label Correlation Decomposition (IncomLDL-LCD), which simultaneously learns label distributions and recovers missing description degrees of labels through label correlations. Specifically, we decompose the label correlation into sparse local label correlation and low-rank global label correlation using a soft-thresholding operator and a singular value thresholding operator, respectively. The former is utilized to capture the related label subsets necessary for reconstructing each possible label, while the latter focuses on extracting the coarse-grained semantic concepts from all labels and exploring the groupings of labels. Additionally, we develop an alternating solution with the accelerated proximal gradient descent method for optimization. Extensive experiments on 16 real-world data sets with varying degrees of missing annotations validate that our algorithm effectively handles incomplete LDL tasks and outperforms state-of-the-art algorithms.

O-260 Identifying seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE) using deep learning

Abstract Study question Is it possible to utilize artificial intelligence (AI) in the selection of seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE)? Summary answer AI algorithm was able to segment seminiferous tubules deemed to be good candidates for sperm retrieval with IoU of 0.746 and F1 score of 72.5%. What is known already M-TESE is an advanced surgical procedure reserved for sperm retrieval in men with non-obstructive azoospermia (NOA). Meta-analysis have shown M-TESE might be superior in relation to sperm retrieval rates and postsurgical complications when compared to conventional techniques. Limitations of the technique are the relatively long learning curve for the surgeons and the longer intraoperative time required to identify the tubules with active spermatogenesis. Convolutional neural networks (CNNs) have been shown to successfully identify areas of interest in medical images such as CT and MRI scans. The possibility of utilizing trained CNNs in M-TESE procedures has so far not been studied. Study design, size, duration This is a cross sectional study analyzing image data gathered during M-TESE procedures including the first five men enrolled in a prospective study designed specifically to test the utilization of AI in the treatment of NOA. All participants signed written informed consent. Images were collected intraoperatively with low and high resolution cameras according to predefined protocol. Participants/materials, setting, methods CNN model with down and upsampling architecture was trained using patch-based technique with corresponding labelled truth image pairs based on combination of semi-automated hue saturation value thresholding and manual segmentation of seminiferous tubules. Tubules were allocated into two different classes by the operating surgeon according to the visual perception of their morphology as type 1 (i.e. larger, opaque, good candidates for micro dissection) and type 2 (i.e. sclerotic or atrophic; unlikely to harbor active spermatogenesis). Main results and the role of chance CNN models were trained on 1809 patches with corresponding ground truth segmented images. Two algorithms were tested: the first was trained solely to detect and semantically distinguish type 1 seminiferous tubules. The second was trained to semantically segment the major anatomical structures: tunica albuginea, testicular parenchyma, blood vessels; lobules and the two types of seminiferous tubules describe above. Validation was performed on 988 image patches not previously shown to the algorithm. After grid search for optimal learning rate and augmentation the first algorithm was able to segment type 1 tubules with F1 score of 72.5% and Jaccard Index (IoU) of 0.746. Training took 856 minutes on 48 core high performance computer (HPC) equipped with single graphics processing unit (GPU). Same computational environment was used in the training of the second algorithm. This model was able to segment anatomical structures such as parenchyma (IoU=0.732) and testicular lobules (IoU=0.683) but performed sub optimally on the clinically relevant type 1 tubules (IoU=0.432). The first trained algorithm was able to segment a single high resolution surgical image in 3.4 seconds on an HPC. However, the same procedure took 5.5 minutes on standard desktop computer with 8 core Intel processor without GPU. Limitations, reasons for caution The number of patients is relatively small and therefore more training data would be needed to achieve robust performance. Training data were labelled by single surgeon limiting the generalizability and possibly biasing the selection process. The need of HPC environment for real time segmentation can limit the clinical use. Wider implications of the findings This pilot study shows the potential of AI to help in choosing suitable tubules for micro dissection, shortening the learning curve for young andrological surgeons. The access to cluster based computation is getting easier by the day and can soon allow instant intraoperative AI segmentation, thus shortening the surgical procedure. Trial registration number NA

Opportunistic use of chest low-dose computed tomography (LDCT) imaging for low bone mineral density and osteoporosis screening: cutoff thresholds for the attenuation values of the lower thoracic and upper lumbar vertebrae.

Osteoporosis remains substantially underdiagnosed and undertreated worldwide. Chest low-dose computed tomography (LDCT) may provide a valuable and popular opportunity for osteoporosis screening. This study sought to evaluate the feasibility of the screening of low bone mineral density (BMD) and osteoporosis with mean attenuation values of the lower thoracic compared to upper lumbar vertebrae. The cutoff thresholds of the mean attenuation values in Hounsfield units (HU) were derived to facilitate implementation of opportunistic screening using chest LDCT. The participants aged 30 years or older who underwent chest LDCT and quantitative computed tomography (QCT) examinations from August 2018 to October 2020 in our hospital were consecutively included in this retrospective study. A region of interest (ROI) was placed in the trabecular bone of each vertebral body to measure the HU values. The correlations of mean HU values of lower thoracic (T11-T12) and upper lumbar (L1-L2) vertebrae with age and lumbar BMD obtained with QCT were performed using the Pearson correlation coefficient, respectively. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve was generated to determine the cutoff thresholds for distinguishing low BMD from normal and osteoporosis from non-osteoporosis. A total of 1,112 participants were included in the final study cohort (743 men and 369 women, mean age 58.2±8.9 years; range, 32-88 years). The mean HU values of T11-T12 and L1-L2 were significantly different among 3 QCT-defined BMD categories of osteoporosis, osteopenia, and normal (P<0.001). The differences in HU values between T11-T12 and L1-L2 in each category of bone status were statistically significant (P<0.001). The mean HU values of T11-T12 (r=-0.453, P<0.001) and L1-L2 (r=-0.498, P<0.001) had negative correlations with age. Positive correlations were observed between the mean HU values of T11-T12 (r=0.872, P<0.001) and L1-L2 (r=0.899, P<0.001) with BMD. The optimal cutoff thresholds for distinguishing low BMD from normal were average T11-T12 ≤157 HU [AUC =0.941, 95% confidence interval (CI): 0.925-0.954, P<0.001] and L1-L2 ≤138 HU (AUC =0.950, 95% CI: 0.935-0.962, P<0.001), as well as distinguishing osteoporosis from non-osteoporosis were average T11-T12 ≤125 HU (AUC =0.960, 95% CI: 0.947-0.971, P<0.001) and L1-L2 ≤107 HU (AUC =0.961, 95% CI: 0.948-0.972, P<0.001). There was no significant difference between the AUC values of T11-T12 and L1-L2 for low BMD (P=0.07) and osteoporosis (P=0.92) screening. We have conducted a study on low BMD and osteoporosis screening using mean attenuation values of lower thoracic and upper lumbar vertebrae. Assessment of mean attenuation values of T11-T12 and L1-L2 can be used interchangeably for low BMD and osteoporosis screening using chest LDCT, and their cutoff thresholds were established.

Preoperative Laboratory Values Predicting Periprosthetic Joint Infection in Morbidly Obese Patients Undergoing Total Hip or Knee Arthroplasty.

Morbidly obese patients are an ever-growing high-risk population undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) for end-stage osteoarthritis. This study sought to identify preoperative laboratory values that may serve as predictors of periprosthetic joint infection (PJI) in morbidly obese patients undergoing THA or TKA. All morbidly obese patients with preoperative laboratory data before undergoing primary elective TKA or THA were identified using the Premier Healthcare Database. Patients who developed PJI within 90 days after surgery were compared with patients without PJI. Laboratory value thresholds were defined by clinical guidelines or primary literature. Univariate and multivariable regression analyses were utilized to assess the association between PJI and preoperative laboratory values, including total lymphocyte count, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), albumin level, platelet count, albumin-globulin ratio, hemoglobin level, and hemoglobin A1c. Of the 6,780 patients identified (TKA: 76.67%; THA: 23.33%), 47 (0.69%) developed PJI within 90 days after surgery. The rate of PJI was 1.69% for patients with a hemoglobin level of <12 g/dL (for females) or <13 g/dL (for males), 2.14% for those with a platelet count of <142,000/µL or >417,000/µL, 1.11% for those with an NLR of >3.31, 1.69% for those with a PLR of >182.3, and 1.05% for those with an SII of >776.2. After accounting for potential confounding factors, we observed an association between PJI and an abnormal preoperative NLR (adjusted odds ratio [aOR]: 2.38, 95% confidence interval [CI]: 1.04 to 5.44, p = 0.039), PLR (aOR: 4.86, 95% CI: 2.15 to 10.95, p < 0.001), SII (aOR: 2.44, 95% CI: 1.09 to 5.44, p = 0.029), platelet count (aOR: 3.50, 95% CI: 1.11 to 10.99, p = 0.032), and hemoglobin level (aOR: 2.62, 95% CI: 1.06 to 6.50, p = 0.038). This study identified preoperative anemia, abnormal platelet count, and elevated NLR, PLR, and SII to be associated with an increased risk of PJI among patients with a body mass index of ≥40 kg/m 2 . These findings may help surgeons risk-stratify this high-risk patient population. Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

A Rate of Change and Center of Gravity Approach to Calculating Composite Indicator Thresholds: Moving from an Empirical to a Theoretical Perspective

A composite indicator (CI) is the mathematical aggregation of sub-dimension (local) indicators used to provide an overall score for the multidimensional concept being measured. CIs are widely used to assess the benefits or risks in human endeavors, such as by creating life satisfaction indices or disaster risk indicators. One important aspect of the development of CIs is setting up value thresholds for taking action, such as in determining the minimum acceptable level of life satisfaction in a community or the maximum acceptable flood risk value beyond which people should be ordered to evacuate from the area in danger. The analytic hierarchy/network process (AHNP) is widely used for the development of CIs. In a review of 111 AHP/ANP CI studies, fewer than 10% discussed any threshold. This means that about 90% of the developed CIs were theoretically sound but lacked the actionable thresholds necessary to be of practical use. Furthermore, for the few studies that set thresholds, the values were typically set arbitrarily or using inadequate statistical approaches. To address this important concern, this study first discusses the most commonly used approaches to setting up thresholds, as well as their inadequacies, and proposes the development of AHP/ANP CI thresholds using a mathematical approach based on the rate of change and center of gravity (RCCG) concepts. Using this approach, a virtual reference alternative, i.e., a threshold profile (TP) made up of the local thresholds of each indicator, is calculated. The key advantage of the proposed method is that it not only provides a non-arbitrary way to set up a CI threshold; more importantly, it is independent of the data and/or alternatives to be evaluated; that is, a threshold calculated with the proposed approach constitutes an absolute reference value, outside the dataset.

Nanoscale gold nanoparticle (GNP)-laden tumor cell model and its use for estimation of intracellular dose from GNP-induced secondary electrons.

Gold nanoparticles (GNPs) accumulated within tumor cells have been shown to sensitize tumors to radiotherapy. From a physics point of view, the observed GNP-mediated radiosensitization is due to various downstream effects of the secondary electron (SE) production from internalized GNPs such as GNP-mediated dose enhancement. Over the years, numerous computational investigations on GNP-mediated dose enhancement/radiosensitization have been conducted. However, such investigations have relied mostly on simple cellular geometry models and/or artificial GNP distributions. Thus, it is at least desirable, if not necessary, to conduct further investigations using cellular geometry models that properly reflect realistic cell morphology as well as internalized GNP distributions at the nanoscale. The primary aim of this study was to develop a nanometer-resolution geometry model of a GNP-laden tumor cell for computational investigations of GNP-mediated dose enhancement/radiosensitization. The secondary aim was to demonstrate the utility of this model by quantifying GNP-induced SE tracks/dose distribution at sub-cellular levels for further validation of a nanoscopic dose point kernel (nDPK) method against full-fledged Geant4 Monte Carlo (MC) simulation. A transmission electron microscopy (TEM) image of a single cell showing cytoplasm, cellular nucleus, and internalized GNPs in the cellular endosome was segmented into sub-cellular levels based on pixel value thresholding. A corresponding material density was allocated to each pixel, and, by adding a thickness, each pixel was transformed to a geometric voxel and imported as a Geant4-acceptable input geometry file. In Geant4-Penelope MC simulation, a clinical 6MV photon beam was applied, vertically or horizontally to the cell surface, and energy deposition to the cellular nucleus and cytoplasm, due to SEs emitted by internalized GNPs, was scored. Next, nDPK calculations were performed by generating virtual electron tracks from each GNP voxel to all nucleus and cytoplasm voxels. Subsequently, another set of Geant4 simulation was performed with both Penelope and DNA physics models under the geometry closely mimicking in vitro cell irradiation with a clinical 6MV photon beam, allowing for derivation of nDPK specific to this geometry and further comparison between Gean4 simulation and nDPK method. The Geant4-calculated SE tracks and associated energy depositions showed significant dependence on photon incidence angle. For perpendicular incidence, nDPK results showed good agreement (average percentage pixel-to-pixel difference of 0.4% for cytoplasm and 0.5% for nucleus) with Geant4 results, while, for parallel incidence, the agreement became worse (-1.7%-0.7% for cytoplasm and -5.5%-0.8% for nucleus). Under the 6MV cell irradiation geometry, nDPK results showed reasonable agreement (pixel-to-pixel Pearson's product moment correlation coefficient of 0.91 for cytoplasm and 0.98 for nucleus) with Geant4 results. The currently developed TEM-based model of a GNP-laden cell offers unprecedented details of realistic intracellular GNP distributions for nanoscopic computational investigations of GNP-mediated dose enhancement/radiosensitization. A benchmarking study performed with this model showed reasonable agreement between Geant4- and nDPK-calculated intracellular dose deposition by SEs emitted from internalized GNPs, especially under perpendicular incidence - a popular cell irradiation geometry and when the Geant4-Penelope physics model was used.

An Inventory Control of Wuling Car Spare Parts Using a Risk Analysis Appoarch

PT XWZ is a service company engaged in the automotive sector, which serves the stages of unit sales to service car vehicles (after sales service) for the Wuling car brand. In improving the vehicle service or maintenance process, it is necessary to have an optimal availability of spare parts. From the inventory data in the warehouse, the majority of spare parts stored in the warehouse are classified as slow moving parts. By conducting an ABC and FMEA approach, it is expected to be able to increase the reliability of maintenance needs so as to increase inventory according to the criticality value of spare parts. In this research, 19 spare parts items were found to be classified as critical or high risk items.