Agreement and predictive value of the clinical frailty scale in hospitalized older patients.

PURPOSE: Our objective was to perform an external validity study of the clinical frailty scale (CFS) classification tree by determining the agreement of the CFS when attributed by a senior geriatrician, a junior geriatrician, or using the classification tree. Additionally, we evaluated the predictive value of the CFS for 6-month mortality after admission to an acute geriatric unit. METHODS: This prospective study was conducted in two acute geriatric units in Belgium. The premorbid CFS was determined by a senior and a junior geriatrician based on clinical judgment within the first 72 h of admission. Another junior geriatrician, who did not have a treatment relationship with the patient, scored the CFS using the classification tree. Intra-class correlation coefficient (ICC) was calculated to assess agreement. A ROC curve and Cox regression model determined prognostic value. RESULTS: In total, 97 patients were included (mean age 86 ± 5.2; 66% female). Agreement of the CFS, when determined by the senior geriatrician and the classification tree, was moderate (ICC 0.526, 95% CI [0.366-0.656]). This is similar to the agreement between the senior and junior geriatricians' CFS (ICC 0.643, 95% CI [0.510-0.746]). The AUC for 6-month mortality based on the CFS by respectively the classification tree, the senior and junior geriatrician was 0.719, 95% CI [0.592-0.846]; 0.774, 95% CI [0.673-0.875]; 0.774, 95% CI [0.665-0.882]. Cox regression analysis indicated that severe or very severe frailty was associated with a higher risk of mortality compared to mild or moderate frailty (hazard ratio respectively 6.274, 95% CI [2.613-15.062] by the classification tree; 3.476, 95% CI [1.531-7.888] by the senior geriatrician; 4.851, 95% CI [1.891-12.442] by the junior geriatrician). CONCLUSION: Interrater agreement in CFS scoring on clinical judgment without Comprehensive Geriatric Assessment is moderate. The CFS classification tree can help standardize CFS scoring.

Predicting informal dementia caregivers' desire to institutionalize through mining data from an eHealth platform.

BACKGROUND: Dementia is a leading factor in the institutionalization of older adults. Informal caregivers' desire to institutionalize (DI) their care recipient with dementia (PwD) is a primary predictor of institutionalization. This study aims to develop a prediction model for caregivers' DI by mining data from an eHealth platform in a high-prevalence dementia country. METHODS: Cross-sectional data were collected from caregivers registering on isupport-portugal.pt. One hundred and four caregivers completed the Desire to Institutionalize Scale (DIS) and were grouped into DI (DIS score ≥ 1) and no DI (DIS score = 0). Participants completed a comprehensive set of sociodemographic, clinical, and psychosocial measures, pertaining to the caregiver and the PwD, which were accounted as model predictors. The selected model was a classification tree, enabling the visualization of rules for predictions. RESULTS: Caregivers, mostly female (82.5%), offspring of the PwD (70.2), employed (65.4%), and highly educated (M 15 years of schooling), provided intensive care (Mdn 24 h. week) over a median course of 2.8 years. Two-thirds (66.3%) endorsed at least one item on the DIS (DI group). The model, with caregivers' perceived stress as the root of the classification tree (split at 28.5 points on the Zarit Burden Interview) and including the ages of caregivers and PwD (split at 46 and 88 years, respectively), as well as cohabitation, employed five rules to predict DI. Caregivers scoring 28.5 and above on burden and caring for PwD under 88 are more prone to DI than those caring for older PwD (rules 1-2), suggesting the influence of expectations on caregiving duration. The model demonstrated high accuracy (0.83, 95%CI 0.75, 0.89), sensitivity (0.88, 95%CI 0.81, 0.95), and good specificity (0.71, 95%CI 0.56, 0.86). CONCLUSIONS: This study distilled a comprehensive range of modifiable and non-modifiable variables into a simplified, interpretable, and accurate model, particularly useful at identifying caregivers with actual DI. Considering the nature of variables within the prediction rules, this model holds promise for application to other existing datasets and as a proxy for actual institutionalization. Predicting the institutional placement of PwD is crucial for intervening on modifiable factors as caregiver burden, and for care planning and financing.

Biomarker and proteome analysis of milk from dairy cows with clinical mastitis: Determining the effect of different bacterial pathogens on the response to infection.

Antimicrobial usage (AMU) could be reduced by differentiating the causative bacteria in cases of clinical mastitis (CM) as either Gram-positive or Gram-negative bacteria or identifying whether the case is culture-negative (no growth, NG) mastitis. Immunoassays for biomarker analysis and a Tandem Mass Tag (TMT) proteomic investigation were employed to identify differences between samples of milk from cows with CM caused by different bacteria. A total of 94 milk samples were collected from cows diagnosed with CM across seven farms in Scotland, categorized by severity as mild (score 1), moderate (score 2), or severe (score 3). Bovine haptoglobin (Hp), milk amyloid A (MAA), C-reactive protein (CRP), lactoferrin (LF), α-lactalbumin (LA) and cathelicidin (CATHL) were significantly higher in milk from cows with CM, regardless of culture results, than in milk from healthy cows (all P-values <0.001). Milk cathelicidin (CATHL) was evaluated using a novel ELISA technique that utilises an antibody to a peptide sequence of SSEANLYRLLELD (aa49-61) common to CATHL 1-7 isoforms. A classification tree was fitted on the six biomarkers to predict Gram-positive bacteria within mastitis severity scores 1 or 2, revealing that compared to the rest of the samples, Gram-positive samples were associated with CRP < 9.5 µg/ml and LF ≥ 325 µg/ml and MAA < 16 µg/ml. Sensitivity of the tree model was 64%, the specificity was 91%, and the overall misclassification rate was 18%. The area under the ROC curve for this tree model was 0.836 (95% bootstrap confidence interval: 0.742; 0.917). TMT proteomic analysis revealed little difference between the groups in protein abundance when the three groups (Gram-positive, Gram-negative and no growth) were compared, however when each group was compared against the entirety of the remaining samples, 28 differentially abundant protein were identified including ß-lactoglobulin and ribonuclease. Whilst further research is required to draw together and refine a suitable biomarker panel and diagnostic algorithm for differentiating Gram- positive/negative and NG CM, these results have highlighted a potential panel and diagnostic decision tree. Host-derived milk biomarkers offer significant potential to refine and reduce AMU and circumvent the many challenges associated with microbiological culture, both within the lab and on the farm, while providing the added benefit of reducing turnaround time from 14 to 16 h of microbiological culture to just 15 min with a lateral flow device (LFD).

Latent Profile Analysis of Canadian Military Veterans With Chronic Pain Identifies 5 Meaningful Classes Through Self-Report Measures.

The purpose of this study was to identify meaningful response patterns in self-report survey data collected from Canadian military veterans with chronic pain and to create an algorithm intended to facilitate triage and prioritization of veterans to the most appropriate interventions. An online survey was presented to former members of the Canadian military who self-identified as having chronic pain. Variables collected were related to pain, physical and mental interference, prior traumatic experiences, and indicators from each of the 7 potential drivers of the pain experience. Maximum likelihood estimation-based latent profile analysis was used to identify clinically and statistically meaningful profiles using the 7-axis variables, and classification and regression tree (CRT) analysis was then conducted to identify the most parsimonious set of indicators that could be used to accurately classify respondents into the most relevant profile group. Data from N = 322 veterans were available for analysis. The results of maximum likelihood estimation-based latent profile analysis indicated a 5-profile structure was optimal for explaining the patterns of responses within the data. These were: Mood-Dominant (13%), Localized Physical (24%), Neurosensory-Dominant (33%), Central-Dominant with complex mood and neurosensory symptoms (16%), and Trauma- and mood-dominant (14%). From CRT analysis, an algorithm requiring only 3 self-report tools (central symptoms, mood screening, bodily coherence) achieved 83% classification accuracy across the 5 profiles. The new classification algorithm requiring 16 total items may be helpful for clinicians and veterans in pain to identify the most dominant drivers of their pain experience that may be useful for prioritizing intervention strategies, targets, and relevant health care disciplines. PERSPECTIVE: This article presents the results of latent profile (cluster) analysis of responses to standardized self-report questionnaires by Canadian military veterans with chronic pain. It identified 5 clusters that appear to represent different drivers of the pain experience. The results could be useful for triaging veterans to the most appropriate pain care providers.

Application of machine learning algorithms to predict dead on arrival of broiler chickens raised without antibiotic program.

Understanding the factors of dead-on-arrival (DOA) incidents during pre-slaughter handling is crucial for informed decision-making, improving broiler welfare, and optimizing farm profitability. In this study, 3 different machine learning (ML) algorithms - least absolute shrinkage and selection operator (LASSO), classification tree (CT), and random forest (RF) - were used together with 4 sampling techniques to optimize imbalanced data. The dataset comes from 22,115 broiler truckloads from a large producer in Thailand (2021-2022) and includes 14 independent variables covering the rearing, catching, and transportation stages. The study focuses on DOA% in the range of 0.10 to 1.20%, with a threshold for high DOA% above 0.3%, and records DOA% per truckload during pre-slaughter ante-mortem inspection. With a high DOA rate of 25.2%, the imbalanced dataset prompts the implementation of 4 methods to tune the imbalance parameters: random over sampling (ROS), random under sampling (RUS), both sampling (BOTH), and synthetic sampling or random over sampling example (ROSE). The aim is to improve the performance of the prediction model in classifying and predicting high DOA%. The comparative analysis of the different error metrics shows that RF outperforms the other models in a balanced dataset. In particular, RUS shows a significant improvement in prediction performance across all models compared to the original unbalanced dataset. The identification of the 4 most important variables for predicting high DOA percentages - mortality and culling rate, rearing stocking density, season, and mean body weight - emphasizes their importance for broiler production. This study provides valuable insights into the prediction of DOA status using an ML approach and contributes to the development of more effective strategies to mitigate high DOA percentages in commercial broiler production.

A Precision Mental Health Model for Predicting Stability of 4-year-olds' Attention Deficit/Hyperactivity Disorder Symptoms to Age 6 Diagnostic Status.

OBJECTIVE: Estimates of the stability of a preschooler's diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) into early elementary school vary greatly. Identified factors associated with diagnostic instability provide little guidance about the likelihood a particular child will have ADHD in elementary school. This study examined an approach to predicting age 6 ADHD-any subtype (ADHD-any) from preschoolers' demographics and ADHD symptoms. METHOD: Participants were 796 preschool children (Mage = 4.44; 51% boys; 54% White, non-Hispanic) recruited from primary pediatric care and school settings. Parents completed ADHD Rating Scales at child ages 4 and 5 years, and a structured diagnostic interview (DISC-YC) at ages 4 and 6. Classification tree analyses (CTAs) examined the predictive utility of demographic and symptom variables at ages 4 and 5 years for age 6 ADHD. RESULTS: Over half (52.05%) of preschoolers meeting diagnostic criteria for ADHD-any at age 4 did not meet those criteria at age 6; more than half (52.05%) meeting criteria for ADHD-any at age 6 had not met those criteria at age 4. A CTA conducted at age 4 predicted age 6 ADHD-any diagnosis 65.82% better than chance; an age 5 CTA predicted age 6 ADHD-any 70.60% better than chance. At age 4, likelihood of age 6 ADHD-any diagnosis varied from <5% to >40% across CTA tree branches and from <5% to >78% at age 5. CONCLUSIONS: Parent-reported patterns of preschool-age symptoms may differentially predict ADHD-any at age 6. Psychoeducation regarding these patterns may aid in decision about pursuing multidisciplinary evaluations or initiating treatment.

Content of clinker and other materials in personal thoracic aerosol samples from cement plants estimated by scanning electron microscopy and energy-dispersive X-ray microanalysis.

OBJECTIVES: To estimate the composition and exposure to clinker and other specific components in personal thoracic dust samples of cement production workers. METHODS: A procedure for the classification of airborne particles in cement production plants was developed based on classification trees. For this purpose, the chemical compositions of 27,217 particles in 29 material samples (clinker, limestone, gypsum, clay, quartz, bauxite, iron source, coal fly ash, and coal) were determined automatically by scanning electron microscopy (SEM) and energy-dispersive X-ray microanalysis (EDX). The concentrations of the major elements in cement (calcium, aluminium, silicon, iron, and sulphur) were used for the classifications. The split criteria of the classification trees obtained in the material samples were used to classify 44,176 particles in 34 personal thoracic aerosol samples. The contents of clinker and other materials were estimated, and the clinker contents were analysed statistically for differences between job types and job tasks. RESULTS: Between 64% and 88% of the particles from material samples were classified as actual materials. The material types with variable composition (clay, coal fly ash, and coal) were classified with the lowest consistency (64% to 67%), while materials with a more limited compositional variation (clinker, gypsum, and quartz) were classified more consistently (76% to 85%). The arithmetic mean (AM) of the clinker content in personal samples was 62.1%, the median was 55.3%, and 95% confidence interval (CI) was 42.6% to 68.1%. No significant differences were observed between job types. However, the clinker content in samples when workers handled materials with high clinker content was significantly higher than when materials with lower clinker content were handled, 85% versus 65% (P = 0.02). The limestone content was AM 14.8%, median 13.2% (95% CI 5.5 to 20.9), whereas the other materials were present with relative abundances of median ≤ 6.4%. DISCUSSION: Automated particle analysis by SEM-EDX followed by classification tree analysis quantified clinker with fairly high consistency when evaluated together with raw materials that are expected to be airborne in cement production plants. The clinker proportions for job types were similar. Tasks a priori ranked by assumed clinker content were significantly different and according to expectations, which supports the validity of the chosen methodology. CONCLUSIONS: The composition of personal samples of mineral aerosols in the cement production industry could be estimated by automated single particle analysis with SEM-EDX and classification by a classification tree procedure. Clinker was the major component in the thoracic aerosol that cement production workers were exposed to. Differences between job types were relatively small and not significant. The clinker content from tasks was in agreement with assumptions.

Assessing the potential impact of applying a higher sensitivity test to selected cattle populations for the control of bovine tuberculosis in England.

Bovine tuberculosis (bTB) continues to be the costliest, most complex animal health problem in England. The effectiveness of the test-and-slaughter policy is hampered by the imperfect sensitivity of the surveillance tests. Up to half of recurrent incidents within 24 months of a previous one could have been due to undetected infected cattle not being removed. Improving diagnostic testing with more sensitive tests, like the interferon (IFN)-gamma test, is one of the government's top priorities. However, blanket deployment of such tests could result in more false positive results (due to imperfect specificity), together with logistical and cost-efficiency challenges. A targeted application of such tests in higher prevalence scenarios, such as a subpopulation of high-risk herds, could mitigate against these challenges. We developed classification machine learning algorithms (using 80% of 2012-2019 bTB surveillance data as the training set) to evaluate the deployment of IFN-gamma testing in high-risk herds (i.e. those at risk of an incident in England) in two testing data sets: i) the remaining 20% of 2012-19 data, and ii) 2020 bTB surveillance data. The resulting model, classification tree analysis, with an area under a receiver operating characteristic (ROC) curve (AUC) > 95, showed a 73% sensitivity and a 97% specificity in the 2012-2019 test dataset. Used on 2020 data, it predicted eight percent (3 510 of 41 493) of eligible active herds as at-risk of a bTB incident, the majority of them (66% or 2 328 herds) experiencing at least one. Whilst all predicted at-risk herds could have preventive measures applied, the additional application of IFN-gamma test in parallel interpretation to the statutory skin test, if the risk materialises, would have resulted in 8 585 additional IFN-gamma reactors detected (a 217% increase over the 2 710 IFN-gamma reactors already detected by tests carried out). Only 18% (330 of 1 819) of incidents in predicted high-risk herds had the IFN-gamma test applied in 2020. We therefore conclude that this methodology provides a better way of directing the application of the IFN-gamma test towards the high-risk subgroup of herds. Classification tree analysis ensured the systematic identification of high-risk herds to consistently apply additional measures in a targeted way. This could increase the detection of infected cattle more efficiently, preventing recurrence and accelerating efforts to achieve eradication by 2038. This methodology has wider application, like targeting improved biosecurity measures in avian influenza at-risk farms to limit damage to the industry in future outbreaks.

Migration timing and tributary use of spawning flannelmouth sucker (Catostomus latipinnis).

Spawning phenology and associated migrations of fishes are often regulated by factors such as temperature and stream discharge, but flow regulation of mainstem rivers coupled with climate change might disrupt these cues and affect fitness. Flannelmouth sucker (Catostomus latipinnis) persisting in heavily modified river networks are known to spawn in tributaries that might provide better spawning habitat than neighboring mainstem rivers subject to habitat degradation (e.g., embedded sediments, altered thermal regimes, and disconnected floodplains). PIT tag data and radio telemetry were used to quantify the timing and duration of flannelmouth sucker tributary spawning migrations in relation to environmental cues in McElmo Creek, a tributary of the San Juan River in the American Southwest. We also tested the extent of the tributary migration and assessed mainstem movements prior to and after tributary migrations. Additionally, multiyear data sets of PIT detections from other tributaries in the Colorado River basin were used to quantify interannual and cross-site variation in the timing of flannelmouth sucker spawning migrations in relation to environmental cues. The arrival and residence times of fish spawning in McElmo Creek varied among years, with earlier migration and a 3-week increase in residence time in relatively wet years compared to drier years. Classification tree analysis suggested a combination of discharge- and temperature-determined arrival timing. Of fish PIT tagged in the fall, 56% tagged within 10 km of McElmo Creek spawned in the tributary the following spring, as did 60% of radio-tagged fish, with a decline in its use corresponding to increased distance of tagging location. A broader analysis of four tributaries in the Colorado River basin, including McElmo Creek, found photoperiod and temperature of tributary and mainstem rivers were the most important variables in determining migration timing, but tributary and mainstem discharge also aided in classification success. The largest tributary, the Little Colorado River, had more residential fish or fish that stayed for longer periods (median = 30 days), whereas McElmo Creek fish stayed an average of just 10 days in 2022. Our results generally suggest that higher discharge, across years or across sites, results in extended use of tributaries by flannelmouth suckers. Conservation actions that limit water extraction and maintain natural flow regimes in tributaries, while maintaining open connection with mainstem rivers, may benefit migratory species, including flannelmouth suckers.

Predicting the Behavioral Health Needs of Asian Americans in Public Mental Health Treatment: A Classification Tree Approach.

Given the fact that experiencing pandemic-related hardship and racial discrimination worsen Asian Americans' mental health, this study aimed to identify unique characteristics of behavioral health needs among Asian Americans (N = 544) compared to White Americans (N = 78,704) and Black Americans (N = 11,252) who received publicly funded behavioral health services in Indiana before and during the COVID-19 pandemic. We used 2019-2020 Adults Needs and Strengths Assessment (ANSA) data for adults eligible for Medicaid or funding from the state behavioral health agency. Chi-squared automatic interaction detection (CHAID) was used to detect race-specific differences among demographic variables, the pandemic status, and ANSA items. Results indicated that, regardless of age, gender, or pandemic status, Asian Americans who received behavioral health services, struggled more with cultural-related factors compared to White and Black individuals. Within this context, intersections among behavioral/emotional needs (psychosis), life functioning needs (involvement in recovery, residential stability, decision making, medical/physical health), and strengths (job history, interpersonal, and spiritual) further differentiated the mental health functioning of Asian from White and Black Americans. Classification tree algorithms offer a promising approach to detecting complex behavioral health challenges and strengths of populations based on race, ethnicity, or other characteristics.

Classification tree analysis to evaluate the most useful magnetic resonance image type in the differentiation between early and progressed hepatocellular carcinoma.

AIM: Using classification tree analysis, we evaluated the most useful magnetic resonance (MR) image type in the differentiation between early and progressed hepatocellular carcinoma (eHCC and pHCC). METHODS: We included pathologically proven 214 HCCs (28 eHCCs and 186 pHCCs) in 144 patients. The signal intensity of HCCs was assessed on in-phase (T1in) and opposed-phase T1-weighted images (T1op), ultrafast T2-weighted images (ufT2WI), fat-saturated T2-weighted images (fsT2WI), diffusion-weighted images (DWI), contrast enhanced T1-weighted images in the arterial phase (AP), portal venous phase (PVP), and the hepatobiliary phase. Fat content and washout were also evaluated. Fisher's exact test was performed to evaluate usefulness for the differentiation. Then, we chose MR images using binary logistic regression analysis and performed classification and regression tree analysis with them. Diagnostic performances of the classification tree were evaluated using a stratified 10-fold cross-validation method. RESULTS: T1in, ufT2WI, fsT2WI, DWI, AP, PVP, fat content, and washout were all useful for the differentiation (p < 0.05), and AP and T1in were finally chosen for creating classification trees (p < 0.05). AP appeared in the first node in the tree. The area under the curve, sensitivity and specificity for eHCC, and balanced accuracy of the classification tree were 0.83 (95% CI 0.74-0.91), 0.64 (18/28, 95% CI 0.46-0.82), 0.94 (174/186, 95% CI 0.90-0.97), and 0.79 (95% CI 0.70-0.87), respectively. CONCLUSIONS: AP is the most useful MR image type and T1in the second in the differentiation between eHCC and pHCC.

A survival tree based on stabilized score tests for high-dimensional covariates.

A survival tree can classify subjects into different survival prognostic groups. However, when data contains high-dimensional covariates, the two popular classification trees exhibit fatal drawbacks. The logrank tree is unstable and tends to have false nodes; the conditional inference tree is difficult to interpret the adjusted P-value for high-dimensional tests. Motivated by these problems, we propose a new survival tree based on the stabilized score tests. We propose a novel matrix-based algorithm in order to tests a number of nodes simultaneously via stabilized score tests. We propose a recursive partitioning algorithm to construct a survival tree and develop our original R package uni.survival.tree (https://cran.r-project.org/package=uni.survival.tree) for implementation. Simulations are performed to demonstrate the superiority of the proposed method over the existing methods. The lung cancer data analysis demonstrates the usefulness of the proposed method.

Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows.

Lameness is one of the culling factors such as mastitis, low milk yield, and infertility that cause economic losses in herd management as they threaten animal health and welfare. The purpose of this study was to evaluate the early detection of lameness in Brown Swiss cattle by using a data mining algorithm by both integrating lameness scores and some image parameters such as Lab (CIE L*, a*, b*), HSB (hue, saturation, brightness), RGB (red, green, blue) by processing thermal images with ImageJ program. In the study, the variables obtained as a result of processing the skin surface temperatures and thermal images taken at the fetlock joint of 33 Brown Swiss cattle were used as independent variables. Also, healthy cows (lameness scores 1 and 2) and unhealthy cows (lameness scores 3, 4, and 5) used in the diagnosis of lameness were used as a binary response variable. Classification and regression tree (CART) was used as a data mining algorithm in the diagnosis of lameness. As a result, the CART algorithm correctly classified 12 of the 13 heads unhealthy cows according to locomotion scores. According to locomotion scores by using CART analysis in this study, independent variables that are used to classify healthy and unhealthy (lame) animals were determined as maximum temperature (Tmax), green (mean), L (max), and age (P<0.05). The cut-off values of these independent variables were predicted as 32.40, 149.14, 97.11, and 5.50 for Tmax, green (mean), L (max), and age, respectively. Also, the sensitivity, specificity, and area under the ROC curve (AUC) of the CART algorithm for locomotion scoring were found as 92.31%, 95%, and 93.7% respectively. The area under ROC curve (AUC) was found to be significant in the diagnosis of lameness (P<0.01). Results showed that the use of CART classification algorithm together with thermal camera and image processing methods is a usefull tool in the detection of lameness in the herds. It is recommended that more comprehensive studies by increasing the number of animals in the future would be more beneficial.

Water mobility and microstructure of gluten network during dough mixing using TD NMR.

This work aimed to establish the relationships between flour components, dough behaviour and changes in water distribution at mixing. TD NMR was used to track water distribution in dough during mixing for different mixing times and hydration levels. Four commercial wheat flours with distinct characteristics were expressly selected to exhibit various dough behaviours at mixing. TD NMR measurements of mixed dough samples revealed four to five water mobility domains depending on the flour type and the mixing modality. A classification tree procedure was used to identify characteristic patterns of water mobility in dough, called hydration states (HS). The HS changes with experimental conditions are highly dependent on flour characteristics, and HS were assigned to physical/chemical changes in the gluten network during dough formation. This study proposes an interpretation of the water distribution in dough based on gluten network development. This will help to adapt the mixing process to the flour characteristics.

Analysis of influencing factors of dental fluorosis in children based on logistic regression and classification tree model / 中华地方病学杂志

Objective:To analyze the influencing factors of dental fluorosis of children in the drinking-water-borne endemic fluorosis (referred to as drinking-water-borne fluorosis) areas with qualified drinking water.Methods:In 2020 and 2021, the cluster sampling method was used to select the children aged 8 to 12 years old from the drinking-water-borne fluorisis areas with qualified drinking water in Tianjin City for water and urine fluoride detection, dental fluorosis examination and questionnaire survey, and logistic regression and classification tree model were used to analyze the influencing factors of dental fluorosis in children.Results:A total of 3 795 cases children aged 8 to 12 years old were investigated, and 1 001 cases of dental fluorosis were detected, and the detection rate of dental fluorosis was 26.38% (1 001/3 795). The results of logistic analysis showed that age [odds ratio ( OR) = 1.193, 95% confidence interval ( CI): 1.115 - 1.277], high urinary fluoride (1.84 - 19.40 mg/L, OR = 1.510, 95% CI: 1.169 - 1.952) and the number of permanent residents at home ≥6 ( OR = 1.377, 95% CI: 1.090 - 1.739) were risk factors of dental fluorosis in children; and the mother's with higher education level (college degree or above, OR = 0.664, 95% CI: 0.441 - 0.999), the years of water improvement ≥5 years (5 - < 10 years, OR = 0.193, 95% CI: 0.157 - 0.238; ≥10 years, OR = 0.254, 95% CI: 0.193 - 0.333) were protective factors of dental fluorosis in children. The results of classification tree model analysis showed that the years of water improvement contributed the most to the prevalence of dental fluorosis among children in the drinking-water-borne fluorisis areas with qualified drinking water, followed by age, number of permanent residents at home and urinary fluoride. The area under the receiver operating characteristic curve (AUC) of logistic regression model and classification tree model were 0.730 (95% CI: 0.711 - 0.748) and 0.721 (95% CI: 0.702 - 0.739), respectively, with good fitting effect. Conclusion:The detection rate of children's dental fluorosis in the drinking-water-borne fluorosis areas with qualified drinking water is mainly related to the years of water improvement, age, the number of permanent residents at home and urinary fluoride.

Risk Factors Analysis and Prevention Strategy of Renke Space Edema Complicated with Vocal Cord Adhesion After CO2 Laser Resection / 中山大学学报(医学科学版)

ObjectiveTo investigate the prevention strategy of bilateral vocal cord adhesion after simultaneous Renke space edema resection under CO2 laser. MethodsSeventy patients who underwent CO2 laser resection of bilateral Renke space edema of vocal cords from June 2018 to June 2021 in our hospital were retrospectively selected for this study. According to their postoperative vocal cord adhesion， patients were divided into vocal cord adhesion group （35 cases） and silent band adhesion group （35 cases）， and the general data of the two groups were compared. Multivariate logistic regression analysis was used to evaluate the risk factors for postoperative vocal cord adhesion. The prediction model of postoperative morbidity risk of vocal cord adhesion was established by using chisquared automatic interaction detection （CHAID） classification tree algorithm， and the application value of the model was evaluated by benefit graph and index graph. ResultsMultivariate analysis showed that surgical range and depth of Ⅱ， laser power≥5 W and anterior connection involvement were the risk factors for postoperative vocal cord adhesion ［OR 95%CI： 6.113 （2.346， 17.451）； 5.214 （1.469， 15.263）； 18.651 （1.689， 36.203）］. The classification tree model showed that anterior articulation involvement was an important predictor of postoperative vocal cord adhesion （76.92%； χ2=11.993， P=0.001）， and the benefit graph and index graph showed good models. ConclusionClinical attention should be paid to surgical scope and depth， laser power and anterior union involvement， and timely prevention strategies should be formulated to reduce the risk of vocal cord adhesion in patients.

Development and validation of the prediction models for preeclampsia: a retrospective, single-center, case-control study.

Background: Preeclampsia (PE) is a major cause of adverse maternal and infant outcomes. Accurate screening of PE is currently the focus of clinical attention. This study aimed to develop a model for predicting PE. Methods: A retrospective case-control study was conducted with 916 pregnant women who received care at the Second Hospital of Tianjin Medical University (October 2018 to July 2020). Women were randomly divided into the training (n=680) and testing (n=236) sets based on a ratio of 3:1. Demographic and clinical data of women were collected. In training set, logistic regression (LR), classification tree (CT) model, and random forest (RF) algorithm were used to develop prediction models for PE. Using the testing set was to validate these prediction models. The predictive performance of three models were assessed by the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Of the total 916 women, 237 had PE. The family history of hypertension, pre-pregnancy body mass index (pBMI), blood pressure (BP) ≥130/80 mmHg in early pregnancy, age, chronic hypertension, and duration of hypertension were the predictors of PE. The AUCs for the LR, CT, and RF models were 0.778, 0.850, and 0.871, respectively (all P<0.05 for all pair-wise comparisons). The RF had the best predictive efficiency with sensitivity, specificity, PPV, and NPV of 79.6%, 94.7%, 79.6%, and 94.7%, respectively. Conclusions: The RF model could be a practical screening approach for predicting PE, which is helpful for clinicians to identify high-risk individuals and prevent the occurrence of adverse pregnancy outcomes.

Depressive disorders in older Chinese adults with essential hypertension: A classification tree analysis.

Background: Although there has been accumulating evidence on the elevated risk of depression in hypertensive patients, data regarding depressive disorders in older adults with hypertension and the interplay between factors associated with depression in this population are very limited. Disentangling the mutual influences between factors may help illuminate the pathways involved in the pathogenesis of the comorbidity of depression in hypertension. This study investigated the prevalence of depressive disorders in older Chinese adults with hypertension and examined major correlates of depressive disorders and the interactions between correlates by using classification tree analysis (CTA). Methods: In total, 374 older adults with essential hypertension were enrolled from seven urban and six rural primary care centers in Wuhan, China, and interviewed with the Chinese Mini-international Neuropsychiatric Interview 5.0. Family relationship and feelings of loneliness were assessed with standardized questions. A checklist was used to assess the presence of six major medical conditions: diabetes mellitus, heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, chronic gastric ulcer, and arthritis. Results: The 1-month prevalence rate of depressive disorders was 25.7%. The CTA model identified four major correlates of depressive disorders: loneliness was the most salient, followed by arthritis, family relationship, and heart disease. There were statistically significant interactions between loneliness and arthritis, loneliness and family relationship, and arthritis and heart disease. Conclusion: Over one out of every four older Chinese adults with hypertension suffer from depressive disorders. Collaborative multidisciplinary management services are needed to reduce the burden of depression in hypertensive older adults, which may include social work outreach services to promote family relationship, mental health services to relive loneliness, and primary care services to manage arthritis and heart disease.

Analysis of the eutrophication in a wetland using a data-driven model.

Eutrophication is a major problem in the international Anzali wetland (northern Iran). The present research initially aimed to determine the trophic state index (TSI) in ten sampling sites in the main parts of the Anzali wetland (western, eastern, central, and Siahkeshim parts). After determining the TSI in the wetland, a data-driven method (classification tree model with a J48 algorithm) was implemented to predict the trophic condition in the wetland based on a set of water quality and physical-structural variables. One hundred twenty samples related to chlorophyll-a (the model's output) and environmental variables (the model's inputs) were measured monthly during 1-year study period (2017-2018). Based on the TSI calculation, the western, Siahkeshim, eastern, and central parts of the wetland are classified as eutrophic, super-eutrophic, hyper-eutrophic, and hyper-eutrophic, respectively. When all environmental variables were introduced to the model (with five-time randomization effort, pruning confidence factor = 0.01, and seven-fold cross-validation), eight variables (bicarbonate, pH, water temperature, electric conductivity, dissolved oxygen, total phosphorus, water depth, and water turbidity) were predicted by the model. The model predicted that an increase in total phosphate, water turbidity, and electric conductivity concentration may contribute to the hyper-eutrophic state of the wetland. In contrast, the hyper-eutrophic of the wetland is associated with a decrease in water depth, dissolved oxygen, and pH concentration. According to ANOVA test, the trophic condition in the wetland can be affected by spatial and temporal patterns. Anthropogenic pressures such as the influx of chemicals particularly the nutrients (phosphorus and nitrogen) are the main cause of water enrichment (eutrophication problem) in main parts of the Anzali wetland ecosystem.

Potential Predictive Value of miR-125b-5p, miR-155-5p and Their Target Genes in the Course of COVID-19.

Purpose: This study aimed to provide new biomarkers for predicting the disease course of COVID-19 by analyzing the dynamic changes of microRNA (miRNA) and its target gene expression in the serum of COVID-19 patients at different stages. Methods: Serum samples were collected from all COVID-19 patients at three time points: the acute stage, the turn-negative stage, and the recovery stage. The expression level of miRNA and the target mRNA was measured by Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR). The classification tree model was established to predict the disease course, and the prediction efficiency of independent variables in the model was analyzed using the receiver operating characteristic (ROC) curve. Results: The expression of miR-125b-5p and miR-155-5p was significantly up-regulated in the acute stage and gradually decreased in the turn-negative and recovery stages. The expression of the target genes CDH5, STAT3, and TRIM32 gradually down-regulated in the acute, turn-negative, and recovery stages. MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 constituted a classification tree model with 100% accuracy of prediction and AUC >0.7 for identification and prediction in all stages. Conclusion: MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 could be useful biomarkers to predict the time nodes of the acute, turn-negative, and recovery stages of COVID-19.