Handling missing data and measurement error for early-onset myopia risk prediction models.

BACKGROUND: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction. METHODS: We explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage and selection operator logistic regression) in myopia risk prediction. We apply these models to the Shanghai Jinshan Myopia Cohort Study and also conduct a simulation study to investigate the impact of missing mechanisms, the degree of ME, and the importance of predictors on model performance. Model performance is evaluated using the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS: Our findings indicate that in scenarios with missing data and ME, using MI-ME in combination with logistic regression yields the best prediction results. In scenarios without ME, employing MI-MAR to handle missing data outperforms SI regardless of the missing mechanisms. When ME has a greater impact on prediction than missing data, the relative advantage of MI-MAR diminishes, and MI-ME becomes more superior. Furthermore, our results demonstrate that statistical models exhibit better prediction performance than machine-learning models. CONCLUSION: MI-ME emerges as a reliable method for handling missing data and ME in important predictors for early-onset myopia risk prediction.

Differential effect of China's Zero Markup Drug Policy on provider-induced demand in secondary and tertiary hospitals.

Following the marketization of China's health system in the 1980's, the government allowed public hospitals to markup the price of certain medications by 15% to compensate for reduced revenue from government subsidies. This incentivized clinicians to induce patient demand for drugs which resulted in higher patient out-of-pocket payments, higher overall medical expenditure, and poor health outcomes. In 2009, China introduced the Zero Markup Drug Policy (ZMDP) which eliminated the 15% markup. Using Shanghai as a case study, this paper analyzes emerging and existing evidence about the impact of ZMDP on hospital expenditure and revenue across secondary and tertiary public hospitals. We use data from 150 public hospitals across Shanghai to examine changes in hospital expenditure and revenue for various health services following the implementation of ZMDP. Our analysis suggests that, across both secondary and tertiary hospitals, the implementation of ZMDP reduced expenditure on drugs but increased expenditure on medical services, exams, and tests thereby increasing hospital revenue and keeping inpatient and outpatient costs unchanged. Moreover, our analysis suggests that tertiary facilities increased their revenue at a faster rate than secondary facilities, likely due to their ability to prescribe more advanced and, therefore, more costly procedures. While rigorous experimental designs are needed to confirm these findings, it appears that ZMDP has not reduced instances of medical expenditure provoked by provider-induced demand (PID) but rather shifted the effect of PID from one revenue source to another with differential effects in secondary vs. tertiary hospitals. Supplemental policies are likely needed to address PID and reduce patient costs.

Increased prevalence of vertebrobasilar dolichoectasia in Parkinson's disease and its effect on white matter microstructure and network.

This study aimed to investigate the prevalence of vertebrobasilar dolichoectasia (VBD) in Parkinson's disease (PD) patients and analyze its role in gray matter changes, white matter (WM) microstructure and network alterations in PD. This is a cross-sectional study including 341 PD patients. Prevalence of VBD in these PD patients was compared with general population. Diffusion tensor imaging and T1-weighted imaging analysis were performed among 174 PD patients with or without VBD. Voxel-based morphometry analysis was used to estimate gray matter volume changes. Tract-based spatial statistics and region of interest-based analysis were used to evaluate WM microstructure changes. WM network analysis was also performed. Significantly higher prevalence of VBD in PD patients was identified compared with general population. Lower fractional anisotropy and higher diffusivity, without significant gray matter involvement, were found in PD patients with VBD in widespread areas. Decreased global and local efficiency, increased hierarchy, decreased degree centrality at left Rolandic operculum, increased betweenness centrality at left postcentral gyrus and decreased average connectivity strength between and within several modules were identified in PD patients with VBD. VBD is more prevalent in PD patients than general population. Widespread impairments in WM microstructure and WM network involving various motor and nonmotor PD symptom-related areas are more prominent in PD patients with VBD compared with PD patients without VBD.

Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation.

The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010-2019. The burdens of five categories of disease causes - cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases - were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.

Numerical simulation study on the evolution law of mechanical properties of different metamorphic coals after heat treatment.

In order to study the effect of temperature on the structure and mechanical properties of coal with different metamorphic degree. Three coal samples with varying degrees of metamorphism were chosen for analysis. The discrete element software PFC2D is used to simulate the heat treatment and compression of coal. The findings indicate that during the heating process, low-order coal exhibits noticeable thermal cracks at an early stage, while thermal crack development in middle-order coal is concentrated in the later stages. In contrast, high-order coal demonstrates a more stable macroscopic structure. The strength and stiffness of low rank coal show the lowest value and decrease significantly within 135 °C. However, the strength and stiffness of medium rank coal decrease significantly after 135 °C. The changes of mechanical properties and damage modes of coal caused by thermal damage are often ignored, which may lead to the deviation of design and research results from the actual situation. Therefore, this study is of great significance to the prevention and control of coal mine disasters.

Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning.

Introduction: Leaves are important organs for photosynthesis in plants, and the restriction of leaf growth is among the earliest visible effects under abiotic stress such as nutrient deficiency. Rapidly and accurately monitoring plant leaf area is of great importance in understanding plant growth status in modern agricultural production. Method: In this paper, an image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed. A total of 1,080 rapeseed leaf image areas from five nutrient amendment treatments were continuously collected using the automatic leaf acquisition device and the commonly used area measurement methods (manual and stretching methods). Results: The average error rate of the manual method is 12.12%, the average error rate of the stretching method is 5.63%, and the average error rate of the splint method is 0.65%. The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with the manual and stretching methods, respectively, and had the advantages of speed and automation. Experiments on the effects of the manual method, stretching method, and splinting method on the growth of rapeseed are conducted, and the growth rate of rapeseed leaves under the stretching method treatment is considerably greater than that of the normal treatment rapeseed. Discussion: The growth rate of leaves under the splinting method treatment was less than that of the normal rapeseed treatment. The mean intersection over union (mIoU) of the UNet-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed.

Cerebellar and cerebral white matter changes in Parkinson's disease with resting tremor.

PURPOSE: Cerebellum modulates the amplitude of resting tremor in Parkinson's disease (PD) via cerebello-thalamo-cortical (CTC) circuit. Tremor-related white matter alterations have been identified in PD patients by pathological studies, but in vivo evidence is limited; the influence of such cerebellar white matter alterations on tremor-related brain network, including CTC circuit, is also unclear. In this study, we investigated the cerebral and cerebellar white matter alterations in PD patients with resting tremor using diffusion tensor imaging (DTI). METHODS: In this study, 30 PD patients with resting tremor (PDWR), 26 PD patients without resting tremor (PDNR), and 30 healthy controls (HCs) from the Parkinson's Progression Markers Initiative (PPMI) cohort were included. Tract-based spatial statistics (TBSS) and region of interest-based analyses were conducted to determine white matter difference. Correlation analysis between DTI measures and clinical characteristics was also performed. RESULTS: In the whole brain, TBSS and region of interest-based analyses identified higher fractional anisotropy (FA) value, lower mean diffusivity (MD) value, and lower radial diffusivity (RD) in multiple fibers. In the cerebellum, TBSS analysis revealed significantly higher FA value, decreased RD value as well as MD value in multiple cerebellar tracts including the inferior cerebellar peduncle (ICP) and middle cerebellar peduncle (MCP) when comparing the PDWR with HC, and higher FA value in the MCP when compared with PDNR. CONCLUSION: We identified better white matter integrity in the cerebrum and cerebellum in PDWR indicating a potential association between the cerebral and cerebellar white matter and resting tremor in PD.

Study of the relationship between onset lateralization and hemispheric white matter asymmetry in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is characterized by a lateralized onset, but its cause and mechanism are still unclear. METHODS: Obtaining diffusion tensor imaging (DTI) data from the Parkinson's Progression Markers Initiative (PPMI). Tract-based spatial statistics analysis and region-of-interest-based analysis were performed to evaluate the white matter (WM) asymmetry using original DTI parameters, Z Score normalized parameters, or the asymmetry index (AI). Hierarchical cluster analysis and least absolute shrinkage and selection operator regression were performed to construct predictive models for predicting the PD onset side. DTI data from The Second Affiliated Hospital of Chongqing Medical University were obtained for external validation of the prediction model. RESULTS: 118 PD patients and 69 healthy controls (HC) from PPMI were included. Right-onset PD patients presented more asymmetric areas than left-onset PD patients. The inferior cerebellar peduncle (ICP), superior cerebellar peduncle (SCP), external capsule (EC), cingulate gyrus (CG), superior fronto-occipital fasciculus (SFO), uncinate fasciculus (UNC), and tapetum (TAP) showed significant asymmetry in left-onset and right-onset PD patients. An onset-side-specific pattern of WM alterations exists in PD patients, and a prediction model was constructed. The predicting models based on AI and ΔZ Score presented favorable efficacy in predicting PD onset side by external validation in 26 PD patients and 16 HCs from our hospital. CONCLUSIONS: Right-onset PD patients may have more severe WM damage than left-onset PD patients. WM asymmetry in ICP, SCP, EC, CG, SFO, UNC, and TAP may predict PD onset side. Imbalances in the WM network may underlie the mechanism of lateralized onset in PD.

Novel Ethyl-3-Aryl-2-Nitroacrylate Derivatives as Potential T3SS Inhibitors against Xanthomonas oryzae pv. oryzae on Rice.

Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae (Xoo) is a highly destructive bacterial disease. Traditional prevention methods have utilized antibiotics to target bacterial growth, which has accelerated the emergence of resistant strains. New prevention techniques are developing agents such as type III secretion system (T3SS) inhibitors that target bacterial virulence factors without affecting bacterial growth. To explore novel T3SS inhibitors, a series of ethyl-3-aryl-2-nitroacrylate derivatives were designed and synthesized. Preliminary screening of T3SS inhibitors was based on the inhibition of the hpa1 gene promoter and showed no effect on bacterial growth. Compounds B9 and B10, obtained in the primary screening, significantly inhibited the hypersensitive response (HR) in tobacco and the expression of T3SS genes in the hrp cluster including key regulatory genes. In vivo bioassays showed that T3SS inhibitors obviously inhibited BLB and appeared to be more effective when combined with quorum quenching bacteria F20.

Effects of Anti-Parkinsonian Drugs on Verbal Fluency in Patients with Parkinson's Disease: A Network Meta-Analysis.

Verbal fluency impairment is common in patients with Parkinson's disease (PD), but the effect of drugs on verbal fluency in PD patients has not been comprehensively evaluated. We conducted a network meta-analysis based on four online databases to compare the effect of drugs on verbal fluency in PD patients. This study was performed and reported according to PRISMA-NMA guidelines. In total, 6 out of 3707 articles (three RCTS and three cross-sectional studies) covering eight drug regimens were included (five for letter fluency, five for semantic fluency). In terms of letter fluency, the ranking of the overall efficacy of included drug regimens was: levodopa, levodopa combined with pramipexole, rotigotine, cabergoline, pramipexole, pergolide, but no drug regimen presented a significant advantage over the others. In terms of semantic fluency, the ranking of the overall efficacy of included drug regimens was: rotigotine, levodopa, cabergoline, pergolide, pramipexole, among which, levodopa alone (SMD = 0.93, 95%CI: 0.28-1.59) and rotigotine alone (SMD = 1.18, 95%CI: 0.28-2.09) were statistically superior to pramipexole, while no significant difference was identified between all the other drug regimens. Levodopa and rotigotine seem to be more appropriate choices for PD patients with verbal fluency impairment. Further study is needed to illustrate the efficacy of drugs on verbal fluency in PD patients.

The impacts and unintended consequences of the nationwide pricing reform for drugs and medical services in the urban public hospitals in China.

BACKGROUND: Since 2015, China has been rolling out the pricing reform for drugs and medical services (PRDMS) in the urban public hospitals in order to reduce drug expenditures and to relieve financial burdens of patients. This study aims at evaluating the effectiveness of the reform and investigating its positive impacts and unintended consequences to provide evidence basis for further policy making. METHODS: The Difference-in-difference (DID) approach was employed to analyze the reform impacts on the 31 provincial administrative areas in China based on data abstracted from China Statistics Yearbooks and China Health Statistics Yearbooks from 2012 to 2018. RESULTS: The reform resulted in a decrease of 7.59% in drug cost per outpatient visit, a decrease of 5.73% in drug cost per inpatient admission, a decrease of 3.63% in total cost per outpatient visit and an increase of 9.10% in surgery cost per inpatient admission in the intervention group. However, no significant change in examination cost was found. The reduction in the medical cost per inpatient admission was not yet demonstrated, nor was that in the total outpatient/ inpatient expenses. The nationwide pricing reform for drugs and medical services in urban public hospitals (PRDMS-U) in China is demonstrated to be effective in cutting down the drug expenditures. However, the revealed unintended consequences indicate that there are still significant challenges for the reform to reach its ultimate goal of curbing the medical expenditures. CONCLUSION: We conclude that the pricing reform alone may not be enough to change the profit-driven behavior of medical service providers as the root cause lies in the unchanged incentive scheme for providers in the service delivery. This holds lessons for policy making of other low- and middle-income countries (LMICs) with similar health systems set up in the achievement of Universal Health Coverage (UHC).

Further Validation of the Inventory of Callous-Unemotional Traits in Chinese Children: Cross-Informants Invariance and Longitudinal Invariance.

The present study examined the factor structure and measurement invariance of the shortened versions of the Inventory of Callous-Unemotional Traits (ICU) with data from multiple informants. Five short versions of the ICU proposed in previous studies were tested and compared through confirmatory factor analysis. The measurement invariance across different informants (i.e., self-report, parent-report, and teacher-report) and longitudinal measurement invariance for the resulting best-fitting model were tested thoroughly. Results indicated that a shortened form that consists of 11 items (ICU-11) to assess callousness and uncaring factors had excellent overall fit. Moreover, the ICU-11 was invariant across informant and occasions. However, the ICU-11 was not without limitations; the internal consistency α for the uncaring factor with self-report scores was marginal. In conclusion, our findings suggest that the ICU-11 was an excellent fit for our data and displayed measurement invariance across informants and over time. The ICU-11 may be a promising assessment tool that could be used in research to assess callous-uncaring traits in Chinese children.

[Resting-state electroencephalogram classification of patients with schizophrenia or depression].

The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, ß). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector P performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the ß rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector P in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.

Influence of EEG References on N170 Component in Human Facial Recognition.

The choice of the reference electrode scheme is an important step in event-related potential (ERP) analysis. In order to explore the optimal electroencephalogram reference electrode scheme for the ERP signal related to facial recognition, we investigated the influence of average reference (AR), mean mastoid reference (MM), and Reference Electrode Standardization Technique (REST) on the N170 component via statistical analysis, statistical parametric scalp mappings (SPSM) and source analysis. The statistical results showed that the choice of reference electrode scheme has little effect on N170 latency (p > 0.05), but has an significant impact on N170 amplitude (p < 0.05). ANOVA results show that, for the three references scheme, there was statistically significant difference between N170 latency and amplitude induced by the unfamiliar face and that induced by the scrambled face (p < 0.05). Specifically, the SPSM results show an anterior and a temporo-occipital distribution for AR and REST, whereas just anterior distribution shown for MM. However, the circumstantial evidence provided by source analysis is more consistent with SPSM of AR and REST, compared with that of MM. These results indicate that the experimental results under the AR and REST references are more objective and appropriate. Thus, it is more appropriate to use AR and REST reference scheme settings in future facial recognition experiments.

The factor structure and construct validity of the inventory of callous-unemotional traits in Chinese undergraduate students.

The current study assesses the factor structure and construct validity of the self-reported Inventory of Callous-Unemotional Traits (ICU) in 637 Chinese community adults (mean age = 25.98, SD = 5.79). A series of theoretical models proposed in previous studies were tested through confirmatory factor analyses. Results indicated that a shortened form that consists of 11 items (ICU-11) to assess callousness and uncaring factors has excellent overall fit. Additionally, correlations with a wide range of external variables demonstrated that this shortened form has similar construct validity compared to the original ICU. In conclusion, our findings suggest that the ICU-11 may be a promising self-report tool that could be a good substitute for the original form to assess callous-uncaring traits in adults.

N-Glycan Branching Affects the Subcellular Distribution of and Inhibition of Matriptase by HAI-2/Placental Bikunin.

The gene product of SPINT 2, that encodes a transmembrane, Kunitz-type serine protease inhibitor independently designated as HAI-2 or placenta bikunin (PB), is involved in regulation of sodium absorption in human gastrointestinal track. Here, we show that SPINT 2 is expressed as two species of different size (30-40- versus 25-kDa) due to different N-glycans on Asn-57. The N-glycan on 25-kDa HAI-2 appears to be of the oligomannose type and that on 30-40-kDa HAI-2 to be of complex type with extensive terminal N-acetylglucosamine branching. The two different types of N-glycan differentially mask two epitopes on HAI-2 polypeptide, recognized by two different HAI-2 mAbs. The 30-40-kDa form may be mature HAI-2, and is primarily localized in vesicles/granules. The 25-kDa form is likely immature HAI-2, that remains in the endoplasmic reticulum (ER) in the perinuclear regions of mammary epithelial cells. The two different N-glycans could, therefore, represent different maturation stages of N-glycosylation with the 25-kDa likely a precursor of the 30-40-kDa HAI-2, with the ratio of their levels roughly similar among a variety of cells. In breast cancer cells, a significant amount of the 30-40-kDa HAI-2 can translocate to and inhibit matriptase on the cell surface, followed by shedding of the matriptase-HAI-2 complex. The 25-kDa HAI-2 appears to have also exited the ER/Golgi, being localized at the cytoplasmic face of the plasma membrane of breast cancer cells. While the 25-kDa HAI-2 was also detected at the extracellular face of plasma membrane at very low levels it appears to have no role in matriptase inhibition probably due to its paucity on the cell surface. Our study reveals that N-glycan branching regulates HAI-2 through different subcellular distribution and subsequently access to different target proteases.

Differential subcellular localization renders HAI-2 a matriptase inhibitor in breast cancer cells but not in mammary epithelial cells.

The type 2 transmembrane serine protease matriptase is under tight control primarily by the actions of the integral membrane Kunitz-type serine protease inhibitor HAI-1. Growing evidence indicates that HAI-2 might also be involved in matriptase inhibition in some contexts. Here we showed that matriptase inhibition by HAI-2 depends on the subcellular localizations of HAI-2, and is observed in breast cancer cells but not in mammary epithelial cells. HAI-2 is co-expressed with matriptase in 21 out of 26 human epithelial and carcinoma cells examined. HAI-2 is also a potent matriptase inhibitor in solution, but in spite of this, HAI-2 inhibition of matriptase is not observed in all contexts where HAI-2 is expressed, unlike what is seen for HAI-1. Induction of matriptase zymogen activation in mammary epithelial cells results in the formation of matriptase-HAI-1 complexes, but matriptase-HAI-2 complexes are not observed. In breast cancer cells, however, in addition to the appearance of matriptase-HAI-1 complex, three different matriptase-HAI-2 complexes, are formed following the induction of matriptase activation. Immunofluorescent staining reveals that activated matriptase is focused at the cell-cell junctions upon the induction of matriptase zymogen activation in both mammary epithelial cells and breast cancer cells. HAI-2, in contrast, remains localized in vesicle/granule-like structures during matriptase zymogen activation in human mammary epithelial cells. In breast cancer cells, however, a proportion of the HAI-2 reaches the cell surface where it can gain access to and inhibit active matriptase. Collectively, these data suggest that matriptase inhibition by HAI-2 requires the translocation of HAI-2 to the cell surface, a process which is observed in some breast cancer cells but not in mammary epithelial cells.