Establishment and validation of a predictive nomogram for polyuria during general anesthesia in thoracic surgery.

BACKGROUND: To develop and evaluate a predictive nomogram for polyuria during general anesthesia in thoracic surgery. METHODS: A retrospective study was designed and performed. The whole dataset was used to develop the predictive nomogram and used a stepwise algorithm to screen variables. The stepwise algorithm was based on Akaike's information criterion (AIC). Multivariable logistic regression analysis was used to develop the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the model's discrimination ability. The Hosmer-Lemeshow (HL) test was performed to check if the model was well calibrated. Decision curve analysis (DCA) was performed to measure the nomogram's clinical usefulness and net benefits. P < 0.05 was considered to indicate statistical significance. RESULTS: The sample included 529 subjects who had undergone thoracic surgery. Fentanyl use, gender, the difference between mean arterial pressure at admission and before the operation, operation type, total amount of fluids and blood products transfused, blood loss, vasopressor, and cisatracurium use were identified as predictors and incorporated into the nomogram. The nomogram showed good discrimination ability on the receiver operating characteristic curve (0.6937) and is well calibrated using the Hosmer-Lemeshow test. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: Individualized and precise prediction of intraoperative polyuria allows for better anesthesia management and early prevention optimization.

[Predictive Value of the Huaxi Emotional Index in Assessing and Identifying High Suicide Risk Among Inpatients With Depression]. / åŽè¥¿å¿ƒæ™´æŒ‡æ•°åœ¨æŠ‘éƒç—‡ä½é™¢æ‚£è€ é«˜è‡ªæ€é£Žé™©è¯„ä¼°ä¸­çš„é¢„æµ‹ä½œç”¨.

Objective: This study aims to investigate the agreement between the Huaxi Emotional Index (HEI) and the Nurses' Global Assessment of Suicide Risk (NGASR) in assessing high suicide risk and to explore the predictive value of HEI in identifying high suicide risk among patients with depression. Methods: Convenience sampling was used and 386 inpatients with depression were included in this cross-sectional study. All patients were admitted to the Mental Health Center, West China Hospital between June and December 2023. The inclusion criteria were as follows, a diagnosis of depression according to the International Classification of Diseases, Tenth Revision (ICD-10), age over 18, and completion of both NGASR and HEI assessments. According to the exclusion criteria, depression patients who had other comorbid mental disorders or those who had severe cognitive impairments and were unable to communicate effectively were excluded. The study was approved by the Biomedical Ethics Review Committee of West China Hospital (Approval No. 647, 2021). Demographic data such as age, sex, ethnicity, marital status, and educational attainment were collected using a self-designed questionnaire. Both the HEI and NGASR were applied to evaluate the patients. We conducted statistical analyses with SPSS 27, employing Spearman's rank correlation for correlation analysis, Kappa tests for consistency between the two instruments, and receiver operating characteristic (ROC) curves for evaluating the predictive performance of HEI scores for high suicide risk, with the optimal HEI cutoff value determined on the basis of the Youden Index. Results: The study included 386 depression inpatients with an average age of 32 years and an average length-of-stay of 14 days. Of these participants, 252 were female (65.3%) and 134 were male (34.7%). Regarding ethnicity, most of the participants were Han Chinese (89.4%), Tibetans accounted for 7.3%, and other minorities, 3.3%. Regarding marital status, 51.3% of the participants were married, 41.2% single, 6.5% divorced, and 1.0% widowed. Regarding educational attainment, 26.2% had an undergraduate or graduate education, 20.7% had junior college education, 24.8% had high school or secondary technical school education, and 28.2% had middle school education or less. The NGASR identified 57.3% of the participants as being at high suicide risk, while the HEI identified 53.6% as having severe emotional distress. There was a moderate agreement between the HEI and the NGASR scores, with a Kappa value of 0.518 ( P<0.001), indicating statistically significant differences. At an HEI score of 17, the Youden Index peaked at 0.52, predicting high suicide risk with a specificity of 76.36%, a sensitivity of 76.02%, and an area under the ROC curve of 0.829 (95% CI: 0.787-0.871), demonstrating statistically significant differences. Conclusion: HEI and NGASR demonstrate moderate agreement in assessing high suicide risk among depression patients. The HEI questionnaire effectively predicts high suicide risk in patients with depression, with 17 being the optimal cutoff value for assessing high suicide risk.

Identifying the risk factors for pancreatic fistula after laparoscopic pancreaticoduodenectomy in patients with pancreatic cancer.

BACKGROUND: Laparoscopic pancreaticoduodenectomy (LPD) is a surgical procedure for treating pancreatic cancer; however, the risk of complications remains high owing to the wide range of organs involved during the surgery and the difficulty of anastomosis. Pancreatic fistula (PF) is a major complication that not only increases the risk of postoperative infection and abdominal hemorrhage but may also cause multi-organ failure, which is a serious threat to the patient's life. This study hypothesized the risk factors for PF after LPD. AIM: To identify the risk factors for PF after laparoscopic pancreatoduodenectomy in patients with pancreatic cancer. METHODS: We retrospectively analyzed the data of 201 patients admitted to the Fudan University Shanghai Cancer Center between August 2022 and August 2023 who underwent LPD for pancreatic cancer. On the basis of the PF's incidence (grades B and C), patients were categorized into the PF (n = 15) and non-PF groups (n = 186). Differences in general data, preoperative laboratory indicators, and surgery-related factors between the two groups were compared and analyzed using multifactorial logistic regression and receiver-operating characteristic (ROC) curve analyses. RESULTS: The proportions of males, combined hypertension, soft pancreatic texture, and pancreatic duct diameter ≤ 3 mm; surgery time; body mass index (BMI); and amylase (Am) level in the drainage fluid on the first postoperative day (Am > 1069 U/L) were greater in the PF group than in the non-PF group (P < 0.05), whereas the preoperative monocyte count in the PF group was lower than that in the non-PF group (all P < 0.05). The logistic regression analysis revealed that BMI > 24.91 kg/m² [odds ratio (OR) =13.978, 95% confidence interval (CI): 1.886-103.581], hypertension (OR = 8.484, 95%CI: 1.22-58.994), soft pancreatic texture (OR = 42.015, 95%CI: 5.698-309.782), and operation time > 414 min (OR = 15.41, 95%CI: 1.63-145.674) were risk factors for the development of PF after LPD for pancreatic cancer (all P < 0.05). The areas under the ROC curve for BMI, hypertension, soft pancreatic texture, and time prediction of PF surgery were 0.655, 0.661, 0.873, and 0.758, respectively. CONCLUSION: BMI (> 24.91 kg/m²), hypertension, soft pancreatic texture, and operation time (> 414 min) are considered to be the risk factors for postoperative PF.

Evaluating prognostic biomarkers for survival outcomes subject to informative censoring.

Prognostic biomarkers for survival outcomes are widely used in clinical research and practice. Such biomarkers are often evaluated using a C-index as well as quantities based on time-dependent receiver operating characteristic curves. Existing methods for their evaluation generally assume that censoring is uninformative in the sense that the censoring time is independent of the failure time with or without conditioning on the biomarker under evaluation. With focus on the C-index and the area under a particular receiver operating characteristic curve, we describe and compare three estimation methods that account for informative censoring based on observed baseline covariates. Two of them are straightforward extensions of existing plug-in and inverse probability weighting methods for uninformative censoring. By appealing to semiparametric theory, we also develop a doubly robust, locally efficient method that is more robust than the plug-in and inverse probability weighting methods and typically more efficient than the inverse probability weighting method. The methods are evaluated and compared in a simulation study, and applied to real data from studies of breast cancer and heart failure.

Analysis of factors associated with positive surgical margins and the five-year survival rate after prostate cancer resection and predictive modeling.

Background: This study analyzed the risk factors associated with positive surgical margins (PSM) and five-year survival after prostate cancer resection to construct a positive margin prediction model. Methods: We retrospectively analyzed the clinical data of 148 patients treated with prostatectomy. The patients were divided into PSM group and Negative surgical margins (NSM) group. Several parameters were compared between the groups. All patients were followed up for 60 months. The risk factors for PSM and five-year survival were evaluated by univariate analysis, followed by multifactorial dichotomous logistic regression analysis. Finally, ROC curves were plotted for the risk factors to establish a predictive model for PSM after prostate cancer resection. Results: (1) Serum PSA, percentage of positive puncture stitches, clinical stage, surgical approach, Gleason score on puncture biopsy, and perineural invasion were significantly associated with the risk of PSM (P < 0.05). Serum PSA, perineural invasion, Gleason score on puncture biopsy, and percentage of positive puncture stitches were independent risk factors for PSM. (2) Total prostate-specific antigen (tPSA) by puncture, nutritional status, lymph node metastasis, bone metastasis, and seminal vesicle invasion may be risk factors for five-year survival. Lymph node metastasis and nutritional status were the main risk factors for the five-year survival of patients with prostate cancer. (3) After plotting the ROC curve, the area under the curve (AUC) [AUC: 0.776, 95%, confidence interval (CI): 0.725 to 0.854] was found to be a valid predictor of PSM; the AUC [AUC: 0.664, 95%, confidence interval (CI): 0.576 to 0.753] was also a valid predictor of five-year survival (P < 0.05). (4) The scoring system had a standard error of 0.02 and a cut-off value of 6. It predicted PSM after prostate cancer resection with moderate efficacy. Conclusions: Serum PSA, perineural invasion, puncture biopsy Gleason score, and percentage of positive puncture stitches were independent risk factors for positive surgical margins (PSM). Also, lymph node metastasis and nutritional status were the main risk factors for the five-year survival of patients with prostate cancer. Overall, the prediction efficacy of this scoring system concerning the risk of PSM after prostate cancer resection was moderate.

Effect of nanosilica on the hydrological properties of loess and the microscopic mechanism.

Loess areas, such as the Loess Plateau, are characterized by a fragile ecological environment, high soil erosion, and frequent geological disasters due to the unique hydrological properties of loess (e.g., collapsibility and permeability). Therefore, the loess must be stabilized for use in engineering construction. Traditional stabilizers (lime, cement, and fly ash) cause environmental problems, such as soil salinization and greenhouse gas emissions. Therefore, this study investigated the effect of nanosilica on the hydrological properties of loess and the microscopic mechanism. Different nanosilica contents (0.2%, 0.4%, 0.8%, 1%, and 3%) were added to loess sample, and the particle size distribution, Atterberg limits, collapsibility, and soil water characteristics were analyzed. The results revealed the following. The addition of nanosilica changed the particle size distribution, liquid limit, plastic limit, and plasticity index of loess. After the addition of nanosilica with different contents, the loess collapsibility coefficient curve shifted downward, the soil water retention curve shifted upward, and the unsaturated permeability coefficient curve shifted downward. The pores between particles were filled, and the number of large and medium pores and the pore connectivity were lower after the nanosilica addition. The surface of the coarse particles adsorbed more fine particles, and a large number of micro-aggregates or clay aggregates were present in the pores between particles. In conclusion, the environmentally friendly material nanosilica can be used to improve the hydrological properties of loess, which is applicable to alleviating soil erosion and preventing geological disasters on the Loess Plateau.

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS.

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

Diagnostic value of antibody concentration ratio for treatment-refractory myasthenia gravis.

OBJECTIVE: This study aimed to assess the diagnostic potential of the Antibody concentration ratio in identifying treatment-refractory myasthenia gravis (MG). METHODS: A retrospective analysis was conducted on 116 MG patients who underwent antibody detection at least twice between June 1, 2015, and June 1, 2023. Demographic and clinical characteristics were collated to ascertain their association with refractory MG. The Antibody Concentration Ratio was applied to determine treatment response, using the International Consensus Guidance criteria as the reference standard. The area under nonparametric receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the diagnostic efficacy of the Antibody concentration ratio following consecutive immunotherapy relative to initial antibody concentrations for refractory MG. RESULTS: 19 out of 116 patients were unequivocally diagnosed with refractory MG. A significant correlation was found between the Antibody Concentration Ratio and refractory MG status in treatment-refractory and treatment-responsive patients. Subsequently, the AUC demonstrated the robust diagnostic capability of the Antibody concentration ratio for refractory MG, with an AUC of 0.8709 (95% CI: 0.7995-0.9422, p < 0.0001). The optimal cut-off value stood at 0.8903, exhibiting a sensitivity of 94.74% (95% CI: 75.36%-99.73%), a specificity of 68.04% (95% CI: 58.23%-76.48%), and accuracy of 72.41% (95% CI: 64.28%-80.54%). CONCLUSION: Elevated Antibody Concentration Ratio is intrinsically linked with refractory MG and exhibits potential as an diagnostic biomarker for the condition.

Hydraulic property variations with depth in a loess mudstone landslide.

In areas where loess is distributed, landslides represent a significant geohazard with severe implications. Among these events, loess-mudstone landslides are particularly prevalent, posing substantial risks to the safety and property of local residents, and moisture plays a pivotal role as a key factor in causing these disasters. In this study, the hydraulic properties of the soils along the longitudinal section of an ongoing loess-mudstone landslide are investigated through the variation of soil water characteristic curves, which are subsequently fitted by utilizing van Genuchten model. Moreover, a comprehensive experimental investigation was conducted on the loess, mudstone, and loess-mudstone mixtures to facilitate analysis, including X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM) observation, particle size distribution (PSD) analysis, along with fundamental geotechnical tests for parameter determination. It is found that mudstone and loess have distinct SWCC distribution. The SWCC of loess at various depths exhibits a similar distribution pattern due to the occurrence of landslide. The SWCC distribution of loess-mudstone mixture displays a transitional trend between the SWCC of mudstone and that of loess, and the water retention capacity increases as the mudstone content increases. The experimental findings have demonstrated notable agreement between each other and exhibited a satisfactory level of concurrence with the observed phenomena in geological surveys.

Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images.

Objective: This study aimed to develop an automated detection schema for otosclerosis with interpretable deep learning using temporal bone computed tomography images. Methods: With approval from the institutional review board, we retrospectively analyzed high-resolution computed tomography scans of the temporal bone of 182 participants with otosclerosis (67 male subjects and 115 female subjects; average age, 36.42 years) and 157 participants without otosclerosis (52 male subjects and 102 female subjects; average age, 30.61 years) using deep learning. Transfer learning with the pretrained VGG19, Mask RCNN, and EfficientNet models was used. In addition, 3 clinical experts compared the system's performance by reading the same computed tomography images for a subset of 35 unseen subjects. An area under the receiver operating characteristic curve and a saliency map were used to further evaluate the diagnostic performance. Results: In prospective unseen test data, the diagnostic performance of the automatically interpretable otosclerosis detection system at the optimal threshold was 0.97 and 0.98 for sensitivity and specificity, respectively. In comparison with the clinical acumen of otolaryngologists at P < 0.05, the proposed system was not significantly different. Moreover, the area under the receiver operating characteristic curve for the proposed system was 0.99, indicating satisfactory diagnostic accuracy. Conclusion: Our research develops and evaluates a deep learning system that detects otosclerosis at a level comparable with clinical otolaryngologists. Our system is an effective schema for the differential diagnosis of otosclerosis in computed tomography examinations.

Study on Risk Factors and Receiver Operator Characteristic Curve of Iatrogenic Withdrawal Syndrome in Pediatric Intensive Care Units: A Retrospective Study.

INTRODUCTION: The aims of this study were to investigate the independent risk factors associated with iatrogenic withdrawal syndrome in pediatric intensive care units (PICUs) and to establish receiver operator characteristic (ROC) curve to facilitate the diagnosis of iatrogenic withdrawal syndrome in clinical settings. METHODS: Pediatric patients who received analgesic and sedative medication at a tertiary hospital in the southern Zhejiang region of China between January 2016 and December 2022 were selected for the study. Clinical case data were retrospectively analyzed to gather information including age, gender, weight, total dose of analgesic and sedative medication, total treatment duration, average maintenance dose, and other relevant parameters. Medically induced withdrawal symptom scores were assessed using the Sophia Observation Scale for Withdrawal Symptoms (SOS). Univariate and multivariate logistic regression analyses were conducted on the above indicators to identify the risk factors for iatrogenic withdrawal, and an ROC curve was constructed. RESULTS: The study encompassed a total of 104 pediatric patients, comprising 47 patients in the SOS score ≥4 group and 57 patients in the SOS score ≤3 group. The incidence of iatrogenic withdrawal was 45.19%. Univariate analysis identified cumulative total dose of fentanyl, average daily dose of fentanyl, average daily dose of midazolam, and patient weight (p < 0.05) as factors associated with iatrogenic withdrawal syndrome. The logistic multiple regression analysis revealed that the average daily dose of fentanyl was an independent risk factor for the occurrence of iatrogenic withdrawal syndrome in critically ill children (p < 0.05). ROC curve analysis indicated an area under the curve of 0.711 (95% CI: 0.610-0.811) with sensitivity and specificity of 73.7% and 61.7%, respectively. CONCLUSION: The average daily maintenance dose of fentanyl holds significant clinical value in diagnosing and evaluating the prognosis of iatrogenic withdrawal syndrome and can provide a scientific foundation for enhancing sedative and analgesic management in clinical practice.

Exploring Salivary Iodine Concentration as a Biomarker for Iodine Status and Thyroid Nodules in Females From Different Water Iodine Areas: a Cross-sectional Study.

BACKGROUND: It is unclear whether salivary iodine concentration (SIC) can assess iodine status in females from different water iodine regions. OBJECTIVES: Through a cross-sectional study, we explored the feasibility of SIC as a biomarker to assess iodine status in females and develop optimal cutoff values. METHODS: A total of 1991 females were analyzed in this cross-sectional study from the coastal iodine-deficient areas (CIDAs), inland iodine-deficient areas (IIDAs), iodine-adequate areas (IAAs), iodine-excess areas (IEAs), and iodine extra-high areas (IEHAs). SIC, spot urine iodine concentration (SUIC), and daily total iodine intake (TII) were assessed, and ultrasonography was performed in all subjects. RESULTS: There was a positive correlation between SIC and SUIC (r = 0.67; 95% CI: 0.64, 0.69; P < 0.001), and TII (r = 0.47; 95% CI: 0.43, 0.50; P < 0.001). The prevalence of thyroid nodules (TN) showed an upward trend with SIC increasing (Z = -2.83; P-trend = 0.005). The area under the receiver-operating characteristic (ROC) curve for SIC to assess iodine deficiency was 0.62 (95% CI: 0.60, 0.65; P < 0.001) and 0.75 (95% CI: 0.73, 0.77; P < 0.001) for iodine excess. The cutoff values were as follows: SIC < 93.32 µg/L, iodine deficiency; 93.32-224.60 µg/L, iodine adequacy; and >224.60 µg/L, iodine excess. When SIC > 224.60 µg/L, the odds ratio (OR) for UIC > 300 µg/L, excessive TII, and the prevalence of TN were 6.44, 3.68, and 1.27 (95% CI: 4.98, 8.31; 2.83, 4.79; and 1.02, 1.56, respectively; P < 0.05); when SIC < 93.32 µg/L, the OR for UIC < 100 µg/L and insufficient TII were 2.34 and 1.94 (95% CI: 1.73, 3.14 and 1.33, 2.83, respectively; P < 0.05). CONCLUSIONS: Using SIC as a biomarker, females in CIDA exhibited mild iodine deficiency, those in IIDA and IAA demonstrated moderate iodine deficiency, and those in IEA and IEHA exhibited an excess of iodine, consistent with SUIC to assess iodine status. SIC can be used as a good biomarker to evaluate the iodine status in population.

[Relationship between the percentage of energy intake from macronutrients and obesity among adult residents in 15 provinces of China in 1991-2018].

OBJECTIVE: To explore the relationship between the percentage of energy intake from macronutrients and obesity in Chinese adult residents, and analyze the cut-off values of macronutrients for predicting obesity. METHODS: Data was collected in China Health and Nutrition Survey(CHNS)in 1991-2018. Adults who participated in at least two waves of the surveys and were not obese at baseline were selected as the study subjects. Obesity was defined as body mass index(BMI)≥28.0 kg/m~2. Generalized estimating equation was used to analyze the relationship between the percentage of energy intake from macronutrients and BMI and obesity, and receiver operating characteristic curve(ROC) was used to analyze the cut-off values of percentage of energy intake from macronutrients to predict obesity. RESULTS: The percentage of energy intake from protein and fat of adult residents in 15 provinces(autonomous regions and municipalities) in China showed an increasing trend(P<0.01), and the percentage of energy intake from carbohydrate showed a decreasing trend(P<0.01) between 1991 and 2018. After adjusting for covariates, the group of percentage of energy intake from fat in 20%～30%(ß=0.05, 95%CI 0.01-0.08)and ≥30%(ß=0.15, 95%CI 0.11-0.18)were positively correlated with BMI compared with the group of percentage of energy intake from fat <20%, and the risk of obesity in 20%-30% and ≥ 30% was increased by 17%(OR=1.17, 95%CI 1.04-1.31)and 6%(OR=1.06, 95%CI 1.24-1.56), respectively. Compared with the group of the percentage of energy intake from carbohydrate < 50%, the group of 50% to 65%(ß=-0.08, 95% CI-0.11--0.05) and ≥ 65%(ß=-0.17, 95%CI-0.20--0.13) was negatively correlated with BMI, and the percentage of energy intake from carbohydrate ≥ 65% reduced the risk of obesity(OR=0.71, 95%CI 0.63-0.80). CONCLUSION: Carbohydrate intake was inversely correlated with the risk of obesity, and fat intake was positively correlated with the risk of obesity. Moderate intake of carbohydrates and reduced fat intake can prevent obesity.

Variation and correlation between water retention capacity and gas permeability of compacted loess overburden during wetting-drying cycles.

Landfill gases can have numerous detrimental effects on the global climate and urban ecological environment. The protective efficacy of the final cover layer against landfill gases, following exposure to periodic natural meteorological changes during long-term service, remains unclear. This study conducted centrifuge tests and gas permeability tests on compacted loess. The experiments examined the impact and relationship of wetting-drying cycles and dry density on the soil water characteristic curve (SWCC) and gas permeability of compacted loess. Research findings reveal that during the dehumidification process of compacted loess, the gas permeability increases non-linearly, varying the gas permeability of soil with different densities to different extents under wetting-drying cycles. Two models were introduced to describe the impact of wetting-drying cycles on gas permeability of loess with various dry densities, where fitting parameters increased with the number of wetting-drying cycles. Sensitivity analysis of the parameters in the Parker-Van Genuchten-Mualem (P-VG-M) model suggests that parameter γ's accuracy should be ensured in practical applications. Finally, from a microstructural perspective, wetting-drying cycles cause dispersed clay and other binding materials coalesce to fill minuscule pores, leading to an increase in the effective pores responsible for the gas permeability of the soil. These research results offer valuable guidance for designing water retention and gas permeability in compacted loess cover layers under wetting-drying cycles.

Neutrophil CD64 index as a potential blood biomarker for the diagnosis of neurosyphilis in secondary and tertiary syphilis: A retrospective study.

Objective: To examine the correlation of neutrophil CD64 (nCD64) index with neurosyphilis (NS) across different stages of syphilis. Methods: A total of 1243 syphilis patients at different stages (344 of primary, 385 of secondary, and 514 of tertiary) included in this study were divided into NS and non-NS (NNS). Correlations of nCD64 index with currently used syphilis biomarkers were explored using Spearman correlation test. Relationships between nCD64 index and NS at different stages were investigated by stratified analysis and restricted cubic spline model. The diagnostic performance of nCD64 index for NS was assessed by receiver operating characteristic (ROC) curve. Results: Significant statistical correlations of nCD64 index with cerebrospinal fluid (CSF) NS indicators were found in secondary and tertiary syphilis. Increased nCD64 index was associated with increased risk of NS in secondary and tertiary syphilis. ROC analysis values further confirmed the diagnostic potential of nCD64 index for NS. Marked decrease of nCD64 index was observed in NS patients after effective antisyphilitic treatments. Conclusions: The nCD64 index may help to the diagnosis of NS in secondary and tertiary syphilis.

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques.

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers•A novel method for pneumothorax segmentation from chest X-rays.•A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation.•A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays. The proposed method is tested against other pneumothorax segmentation techniques using a dataset of 'chest X-rays' with 'pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning.

Semisupervised transfer learning for evaluation of model classification performance.

In many modern machine learning applications, changes in covariate distributions and difficulty in acquiring outcome information have posed challenges to robust model training and evaluation. Numerous transfer learning methods have been developed to robustly adapt the model itself to some unlabeled target populations using existing labeled data in a source population. However, there is a paucity of literature on transferring performance metrics, especially receiver operating characteristic (ROC) parameters, of a trained model. In this paper, we aim to evaluate the performance of a trained binary classifier on unlabeled target population based on ROC analysis. We proposed Semisupervised Transfer lEarning of Accuracy Measures (STEAM), an efficient three-step estimation procedure that employs (1) double-index modeling to construct calibrated density ratio weights and (2) robust imputation to leverage the large amount of unlabeled data to improve estimation efficiency. We establish the consistency and asymptotic normality of the proposed estimator under the correct specification of either the density ratio model or the outcome model. We also correct for potential overfitting bias in the estimators in finite samples with cross-validation. We compare our proposed estimators to existing methods and show reductions in bias and gains in efficiency through simulations. We illustrate the practical utility of the proposed method on evaluating prediction performance of a phenotyping model for rheumatoid arthritis (RA) on a temporally evolving EHR cohort.

ROC curve analysis: a useful statistic multi-tool in the research of nephrology.

In the past decade, scientific research in the area of Nephrology has focused on evaluating the clinical utility and performance of various biomarkers for diagnosis, risk stratification and prognosis. Before implementing a biomarker in everyday clinical practice for screening a specific disease context, specific statistic measures are necessary to evaluate the diagnostic accuracy and performance of this biomarker. Receiver Operating Characteristic (ROC) Curve analysis is an important statistical method used to estimate the discriminatory performance of a novel diagnostic test, identify the optimal cut-off value for a test that maximizes sensitivity and specificity, and evaluate the predictive value of a certain biomarker or risk, prediction score. Herein, through practical examples, we aim to present a simple methodological approach to explain in detail the principles and applications of ROC curve analysis in the field of nephrology pertaining diagnosis and prognosis.

A framework for estimating soil water characteristic curve and hydraulic conductivity function of permeable reactive media.

The unsaturated behavior of permeable reactive barriers (PRB) is a critical component in predicting the removal efficiency through the adsorption of contaminants. This study investigates the framework to estimate the soil water characteristic curve (SWCC) and hydraulic conductivity function (HCF) for iron oxide-coated sand (IOCS) and zeolite, which are common materials used in PRBs. A multistep outflow (MSO) experiment was performed and the results of the MSO experiment were used to optimize associated parameters in Kosugi's SWCC and HCF. In addition, three scenarios of optimization analysis were investigated to evaluate the best-fitting model for estimating SWCC and HCF. The low root mean square error (RMSE) of fitted parameters indicates the Kosugi model well described the observed suction profiles in MSO experiments. In addition, the lowest RMSE and coefficient of variation suggested the inclusion of the additional parameter ß provided the best estimation of the three materials (clean sand, IOCS, and zeolite). The physically reasonable estimation of SWCC and HCF of the three materials from the optimized parameters suggests the proposed framework is a reasonable model for the unsaturated behavior of PRBs.

Diagnostic performance of shear wave measurement in the detection of hepatic fibrosis: A multicenter prospective study.

AIM: This study aimed to establish the shear wave measurement (SWM) cut-off value for each fibrosis stage using magnetic resonance (MR) elastography values as a reference standard. METHODS: We prospectively analyzed 594 patients with chronic liver disease who underwent SWM and MR elastography. Correlation coefficients (were analyzed, and the diagnostic value was evaluated by the area under the receiver operating characteristic curve. Liver stiffness was categorized by MR elastography as F0 (<2.61 kPa), F1 (≥2.61 kPa, <2.97 kPa, any fibrosis), F2 (≥2.97 kPa, <3.62 kPa, significant fibrosis), F3 (≥3.62 kPa, <4.62 kPa, advanced fibrosis), or F4 (≥4.62 kPa, cirrhosis). RESULTS: The median SWM values increased significantly with increasing fibrosis stage (p < 0.001). The correlation coefficient between SWM and MR elastography values was 0.793 (95% confidence interval 0.761-0.821). The correlation coefficients between SWM and MR elastography values significantly decreased with increasing body mass index and skin-capsular distance; skin-capsular distance values were associated with significant differences in sensitivity, specificity, accuracy, or positive predictive value, whereas body mass index values were not. The best cut-off values for any fibrosis, significant fibrosis, advanced fibrosis, and cirrhosis were 6.18, 7.09, 8.05, and 10.89 kPa, respectively. CONCLUSIONS: This multicenter study in a large number of patients established SWM cut-off values for different degrees of fibrosis in chronic liver diseases using MR elastography as a reference standard. It is expected that these cut-off values will be applied to liver diseases in the future.