In-silico study of the impact of system design parameters on microcalcification detection in wide-angle digital breast tomosynthesis.

Purpose: Accurate detection of microcalcifications ( µ Calcs ) is crucial for the early detection of breast cancer. Some clinical studies have indicated that digital breast tomosynthesis (DBT) systems with a wide angular range have inferior µ Calc detectability compared with those with a narrow angular range. This study aims to (1) provide guidance for optimizing wide-angle (WA) DBT for improving µ Calcs detectability and (2) prioritize key optimization factors. Approach: An in-silico DBT pipeline was constructed to evaluate µ Calc detectability of a WA DBT system under various imaging conditions: focal spot motion (FSM), angular dose distribution (ADS), detector pixel pitch, and detector electronic noise (EN). Images were simulated using a digital anthropomorphic breast phantom inserted with 120 µ m µ Calc clusters. Evaluation metrics included the signal-to-noise ratio (SNR) of the filtered channel observer and the area under the receiver operator curve (AUC) of multiple-reader multiple-case analysis. Results: Results showed that FSM degraded µ Calcs sharpness and decreased the SNR and AUC by 5.2% and 1.8%, respectively. Non-uniform ADS increased the SNR by 62.8% and the AUC by 10.2% for filtered backprojection reconstruction with a typical clinical filter setting. When EN decreased from 2000 to 200 electrons, the SNR and AUC increased by 21.6% and 5.0%, respectively. Decreasing the detector pixel pitch from 85 to 50 µ m improved the SNR and AUC by 55.6% and 7.5%, respectively. The combined improvement of a 50 µ m pixel pitch and EN200 was 89.2% in the SNR and 12.8% in the AUC. Conclusions: Based on the magnitude of impact, the priority for enhancing µ Calc detectability in WA DBT is as follows: (1) utilizing detectors with a small pixel pitch and low EN level, (2) allocating a higher dose to central projections, and (3) reducing FSM. The results from this study can potentially provide guidance for DBT system optimization in the future.

The effect of adsorbent textural and functional properties on model naphthenic acid adsorption.

Naphthenic acids, NAs, are a major contaminant of concern and a focus of much research around remediation of oil sand process affected waters, OSPW. Using activated carbon adsorbents are an attractive option given their low cost of fabrication and implementation. A deeper evaluation of the effect NA structural differences have on uptake affinity is warranted. Here we provide an in-depth exploration of NA adsorption including many more model NA species than have been assessed previously with evaluation of adsorption kinetics and isotherms at the relevant alkaline pH of OSPW using several different carbon adsorbents with pH buffering to simulate the behaviour of real OSPW. Uptake for the NA varied considerably regardless of the activated carbon used, ranging from 350 mg/g to near zero highlighting recalcitrant NAs. The equilibrium data was explored to identify structural features of these species and key physiochemical properties that influence adsorption. We found that certain NA will be resistant to adsorption when hydrophobic adsorbents are used. Adsorption isotherm modelling helped explore interactions occurring at the interface between NA and adsorbent surfaces. We identified the importance of NA hydrophobicity for activated carbon uptake. Evidence is also presented that indicates favorable hydrogen bonding between certain NA and surface site hydroxyl groups, demonstrating the importance of adsorbent surface functionality for NA uptake. This research highlights the challenges associated with removing NAs from OSPW through adsorption and also identifies how adsorbent surface chemistry modification can be used to increase the removal efficiency of recalcitrant NA species.

Insight into the sorption and desorption pattern of pyrrolizidine alkaloids and their N-oxides in acidic tea (Camellia sinensis) plantation soils.

Pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) are phytotoxins produced by various plant species and have been emerged as environmental pollutants. The sorption/desorption behaviors of PAs/PANOs in soil are crucial due to the horizontal transfer of these natural products from PA-producing plants to soil and subsequently absorbed by plant roots. This study firstly investigated the sorption/desorption behaviors of PAs/PANOs in tea plantation soils with distinct characteristics. Sorption amounts for seneciphylline (Sp) and seneciphylline-N-oxide (SpNO) in three acidic soils ranged from 2.9 to 5.9 µg/g and 1.7 to 2.8 µg/g, respectively. Desorption percentages for Sp and SpNO were from 22.2% to 30.5% and 36.1% to 43.9%. In the mixed PAs/PANOs systems, stronger sorption of PAs over PANOs was occurred in tested soils. Additionally, the Freundlich models more precisely described the sorption/desorption isotherms. Cation exchange capacity, sand content and total nitrogen were identified as major influencing factors by linear regression models. Overall, the soils exhibiting higher sorption capacities for compounds with greater hydrophobicity. PANOs were more likely to migrate within soils and be absorbed by tea plants. It contributes to the understanding of environmental fate of PAs/PANOs in tea plantations and provides basic data and clues for the development of PAs/PANOs reduction technology.

On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles.

Thermodynamic modeling is still the most widely used method to characterize aerosol acidity, a critical physicochemical property of atmospheric aerosols. However, it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamic models are employed to estimate the acidity of coarse particles. In this work, field measurements were conducted at a coastal city in northern China across three seasons, and covered wide ranges of temperature, relative humidity and NH3 concentrations. We examined the performance of different modes of ISORROPIA-II (a widely used aerosol thermodynamic model) in estimating aerosol acidity of coarse and fine particles. The M0 mode, which incorporates gas-phase data and runs the model in the forward mode, provided reasonable estimation of aerosol acidity for coarse and fine particles. Compared to M0, the M1 mode, which runs the model in the forward mode but does not include gas-phase data, may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles; M2, which runs the model in the reverse mode, results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity calculations. However, M1 significantly underestimates liquid water contents for both fine and coarse particles, while M2 provides reliable estimation of liquid water contents. In summary, our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity, and thus may help improve our understanding of acidity of coarse particles.

How sediment dredging alters phosphorus dynamics in a lowland rural river?

China's lowland rural rivers are facing severe eutrophication problems due to excessive phosphorus (P) from anthropogenic activities. However, quantifying P dynamics in a lowland rural river is challenging due to its complex interaction with surrounding areas. A P dynamic model (River-P) was specifically designed for lowland rural rivers to address this challenge. This model was coupled with the Environmental Fluid Dynamics Code (EFDC) and the Phosphorus Dynamic Model for lowland Polder systems (PDP) to characterize P dynamics under the impact of dredging in a lowland rural river. Based on a two-year (2020-2021) dataset from a representative lowland rural river in the Lake Taihu Basin, China, the coupled model was calibrated and achieved a model performance (R2>0.59, RMSE<0.04 mg/L) for total P (TP) concentrations. Our research in the study river revealed that (1) the time scale for the effectiveness of sediment dredging for P control was ∼300 days, with an increase in P retention capacity by 74.8 kg/year and a decrease in TP concentrations of 23% after dredging. (2) Dredging significantly reduced P release from sediment by 98%, while increased P resuspension and settling capacities by 16% and 46%, respectively. (3) The sediment-water interface (SWI) plays a critical role in P transfer within the river, as resuspension accounts for 16% of TP imports, and settling accounts for 47% of TP exports. Given the large P retention capacity of lowland rural rivers, drainage ditches and ponds with macrophytes are promising approaches to enhance P retention capacity. Our study provides valuable insights for local environmental departments, allowing a comprehensive understanding of P dynamics in lowland rural rivers. This enable the evaluation of the efficacy of sediment dredging in P control and the implementation of corresponding P control measures.

The effect of fine grits and fine debris concentrations on the MLVSS/MLSS ratio of an activated sludge system.

This study embarks on an explorative investigation into the effects of typical concentrations and varying particle sizes of fine grits (FG, the involatile portion of suspended solids) and fine debris (FD, the volatile yet unbiodegradable fraction of suspended solids) within the influent on the mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio of an activated sludge system. Through meticulous experimentation, it was discerned that the addition of FG or FD, the particle size of FG, and the concentration of FD bore no substantial impact on the pollutant removal efficiency (denoted by the removal rate of COD and ammonia nitrogen) under constant operational conditions. However, a notable decrease in the MLVSS/MLSS ratio was observed with a typical FG concentration of 20 mg/L, with smaller FG particle sizes exacerbating this reduction. Additionally, variations in FD concentrations influenced both MLSS and MLVSS/MLSS ratios; a higher FD concentration led to an increased MLSS and a reduced MLVSS/MLSS ratio, indicating FD accumulation in the system. A predictive model for MLVSS/MLSS was constructed based on quality balance calculations, offering a tool for foreseeing the MLVSS/MLSS ratio under stable long-term influent conditions of FG and FD. This model, validated using data from the BXH wastewater treatment plant (WWTP), showcased remarkable accuracy.

Establishment of a swine model of delayed bleeding after endoscopic procedure.

Objectives: Although delayed bleeding after endoscopic procedures has become a problem, currently, there are no appropriate animal models to validate methods for preventing it. This study aimed to establish an animal model of delayed bleeding after endoscopic procedures of the gastrointestinal tract. Methods: Activated coagulation time (ACT) was measured using blood samples drawn from a catheter inserted into the external jugular vein of swine (n = 7; age, 6 months; mean weight, 13.8 kg) under general anesthesia using the cut-down method. An upper gastrointestinal endoscope was inserted orally, and 12 mucosal defects were created in the stomach by endoscopic mucosal resection using a ligating device. Hemostasis was confirmed at this time point. The heparin group (n = 4) received 50 units/kg of unfractionated heparin via a catheter; after confirming that the ACT was ≥200 s 10 min later, continuous heparin administration (50 units/kg/h) was started. After 24 h, an endoscope was inserted under general anesthesia to evaluate the blood volume in the stomach and the degree of blood adherence at the site of the mucosal defect. Results: Delayed bleeding was observed in three swine (75%) in the heparin-treated group, who had a maximum ACT of >220 s before the start of continuous heparin administration. In the non-treated group (n = 3), no prolonged ACT or delayed bleeding was observed at 24 h. Conclusion: An animal model of delayed bleeding after an endoscopic procedure in the gastrointestinal tract was established using a single dose of heparin and continuous heparin administration after confirming an ACT of 220 s.

Absorption mechanism of SO3 on various alkaline absorbents in the presence of SO2.

Sulfur trioxide (SO3) as a condensable particle matter has a significant influence on atmospheric visibility, which easily arouses formation of haze. It is imperative to control the SO3 emission from the industrial flue gas. Three commonly used basic absorbents, including Ca(OH)2, MgO and NaHCO3 were selected to explore the effects of temperature, SO2 concentration on the SO3 absorption, and the reaction mechanism of SO3 absorption was further illustrated. The suitable reaction temperature for various absorbents were proposed, Ca(OH)2 at the high temperatures above 500°C, MgO at the low temperatures below 320°C, and NaHCO3 at the temperature range of 320-500°C. The competitive absorption between SO2 and SO3 was found that the addition of SO2 reduced the SO3 absorption on Ca(OH)2 and NaHCO3, while had no effect on MgO. The order of the absorption selectivity of SO3 follows MgO, NaHCO3 and Ca(OH)2 under the given conditions in this work. The absorption process of SO3 on NaHCO3 follows the shrinking core model, thus the absorption reaction continues until NaHCO3 was exhausted with the utilization rate of nearly 100%. The absorption process of SO3 on Ca(OH)2 and MgO follows the grain model, and the dense product layer hinders the further absorption reaction, resulting in low utilization of about 50% for Ca(OH)2 and MgO. The research provides a favorable support for the selection of alkaline absorbent for SO3 removal in application.

Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions.

The presence of aluminum (Al3+) and fluoride (F-) ions in the environment can be harmful to ecosystems and human health, highlighting the need for accurate and efficient monitoring. In this paper, an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum (Al3+) and fluoride (F-) ions in aqueous solutions. The proposed method involves the synthesis of sulfur-functionalized carbon dots (C-dots) as fluorescence probes, with fluorescence enhancement upon interaction with Al3+ ions, achieving a detection limit of 4.2 nmol/L. Subsequently, in the presence of F- ions, fluorescence is quenched, with a detection limit of 47.6 nmol/L. The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python, followed by data preprocessing. Subsequently, the fingerprint data is subjected to cluster analysis using the K-means model from machine learning, and the average Silhouette Coefficient indicates excellent model performance. Finally, a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions. The results demonstrate that the developed model excels in terms of accuracy and sensitivity. This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment, making it a valuable tool for safeguarding our ecosystems and public health.

CRISPR-Mediated Construction of Gene-Knockout Mice for Investigating Antiviral Innate Immunity.

With the rapid development of CRISPR-Cas9 technology, gene editing has become a powerful tool for studying gene function. Specifically, in the study of the mechanisms by which natural immune responses combat viral infections, gene knockout mouse models have provided an indispensable platform. This article describes a detailed protocol for constructing gene knockout mice using the CRISPR-Cas9 system. This field focuses on the design of single-guide RNAs (sgRNAs) targeting the antiviral immune gene cGAS, embryo microinjection, and screening and verification of gene editing outcomes. Furthermore, this study provides methods for using cGAS gene knockout mice to analyze the role of specific genes in natural immune responses. Through this protocol, researchers can efficiently generate specific gene knockout mouse models, which not only helps in understanding the functions of the immune system but also offers a powerful experimental tool for exploring the mechanisms of antiviral innate immunity.

Unveiling the impact of estrogen exposure on ovarian cancer: A comprehensive risk model and immune landscape analysis.

This study examines the impact of estrogenic compounds like bisphenol A (BPA), estradiol (E2), and zearalenone (ZEA) on human ovarian cancer, focusing on constructing a risk model, conducting Gene Set Variation Analysis (GSVA), and evaluating immune infiltration. Differential gene expression analysis identified 980 shared differentially expressed genes (DEGs) in human ovarian cells exposed to BPA, E2, and ZEA, indicating disruptions in ribosome biogenesis and RNA processing. Using the Cancer Genome Atlas Ovarian Cancer (TCGA-OV) dataset, a least absolute shrinkage and selection operator (LASSO)-based risk model was developed incorporating prognostic genes 4-Hydroxyphenylpyruvate Dioxygenase Like (HPDL), Thy-1 Cell Surface Antigen (THY1), and Peptidase Inhibitor 3 (PI3). This model effectively stratified ovarian cancer patients into high-risk and low-risk categories, showing significant differences in overall survival, disease-specific survival, and progression-free survival. GSVA analysis linked HPDL expression to pathways related to the cell cycle, DNA damage, and repair, while THY1 and PI3 were associated with apoptosis, hypoxia, and proliferation pathways. Immune infiltration analysis revealed distinct immune cell profiles for high and low expression groups of HPDL, THY1, and PI3, indicating their influence on the tumor microenvironment. The findings demonstrate that estrogenic compounds significantly alter gene expression and oncogenic pathways in ovarian cancer. The risk model integrating HPDL, THY1, and PI3 offers a strong prognostic tool, with GSVA and immune infiltration analyses providing insights into the interplay between these genes and the tumor microenvironment, suggesting potential targets for personalized therapies.

Driving forces of herbaceous species diversity in natural broadleaf forests from in Maoershan from Northeast China.

The understory herbaceous flora plays a pivotal role in regulating the structural stability, complexity, and ecological function of forest communities. It is crucial to investigate the impact of the intricate connections between these factors and the forces driving the diversity of herbaceous species within natural broadleaf understory forests can assist forest managers in developing optimal forest structure optimization techniques, allowing them to adjust the forest species diversity. In this study, Pearson correlation analysis, conventional correlation analysis, and multiple linear regression were employed to elucidate the relationship between stand structure, soil nutrients, and understory herbaceous species richness in natural broadleaved forests. Structural equation modeling was utilized to ascertain the influence of multiple factors on understory herbaceous species diversity and to evaluate the underlying pathways. The results indicated a significant negative correlation between stand closure and the Simpson's and Shannon-Wiener's indices, and between the mixing degree and the Pielou evenness index, Simpson's index, and Shannon-Wiener's index (p<0.05). Furthermore, a significant positive correlation was observed between soil nutrients, specifically organic matter and total phosphorus, and the Pielou evenness index and Shannon-Wiener's index (p<0.05). It was found that total phosphorus was significantly positively correlated with both the Pielou evenness index and the Shannon-Wiener index (p<0.05). The correlation coefficients of the first group of typical variables in the typical correlation analysis were 0.498 and 0.585, respectively (p<0.05). From the set of typical variables of stand structure, it can be seen that the Hegyi competition index and the canopy density affected the diversity of understory herbaceous plants. The composite index demonstrated the greatest impact, with loadings of 0.872 and -0.506, respectively. The Simpson and Shannon-Wiener indices exhibited the most sensitive loadings of -0.441 and -0.408, respectively. The soil nutrients of SOM and TN affected the understory herbaceous plant species diversity composite index, with greater loadings of -0.184 and 1.002, respectively. The path coefficient of the understory herbaceous diversity stand structure was 0.35. The path coefficient with soil nutrient content was found to be 0.23 following structural equation analysis and the path coefficient between stand structure and soil nutrient content was 0.21, which indirectly affect the diversity of understory herbaceous species. To enhance the diversity of herbaceous species, it is recommended that the canopy density and tree density of the upper forest be reduced appropriately, while the degree of mixing and the level of spatial distribution of trees be adjusted in a manner that maintains a reasonable stand structure. Furthermore, a comprehensive forest management program for improving soil nutrients should be considered.

Maximum likelihood estimation for semiparametric regression models with interval-censored multistate data.

Interval-censored multistate data arise in many studies of chronic diseases, where the health status of a subject can be characterized by a finite number of disease states and the transition between any two states is only known to occur over a broad time interval. We relate potentially time-dependent covariates to multistate processes through semiparametric proportional intensity models with random effects. We study nonparametric maximum likelihood estimation under general interval censoring and develop a stable expectation-maximization algorithm. We show that the resulting parameter estimators are consistent and that the finite-dimensional components are asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we demonstrate through extensive simulation studies that the proposed numerical and inferential procedures perform well in realistic settings. Finally, we provide an application to a major epidemiologic cohort study.

Leveraging diverse cellular stress patterns for predicting clinical outcomes and therapeutic responses in patients with multiple myeloma.

Tumour microenvironment harbours diverse stress factors that affect the progression of multiple myeloma (MM), and the survival of MM cells heavily relies on crucial stress pathways. However, the impact of cellular stress on clinical prognosis of MM patients remains largely unknown. This study aimed to provide a cell stress-related model for survival and treatment prediction in MM. We incorporated five cell stress patterns including heat, oxidative, hypoxic, genotoxic, and endoplasmic reticulum stresses, to develop a comprehensive cellular stress index (CSI). Then we systematically analysed the effects of CSI on survival outcomes, clinical characteristics, immune microenvironment, and treatment sensitivity in MM. Molecular subtypes were identified using consensus clustering analysis based on CSI gene profiles. Moreover, a prognostic nomogram incorporating CSI was constructed and validated to aid in personalised risk stratification. After screening from five stress models, a CSI signature containing nine genes was established by Cox regression analyses and validated in three independent datasets. High CSI was significantly correlated with cell division pathways and poor clinical prognosis. Two distinct MM subtypes were identified through unsupervised clustering, showing significant differences in prognostic outcomes. The nomogram that combined CSI with clinical features exhibited good predictive performances in both training and validation cohorts. Meanwhile, CSI was closely associated with immune cell infiltration level and immune checkpoint gene expression. Therapeutically, patients with high CSI were more sensitive to bortezomib and antimitotic agents, while their response to immunotherapy was less favourable. Furthermore, in vitro experiments using cell lines and clinical samples verified the expression and function of key genes from CSI. The CSI signature could be a clinically applicable indicator of disease evaluation, demonstrating potential in predicting prognosis and guiding therapy for patients with MM.

Global, regional, and national burdens of leukemia from 1990 to 2019: A systematic analysis of the global burden of disease in 2019 based on the APC model.

BACKGROUND: Leukemia is the tenth most common cause of cancer death worldwide and one of the most important causes of disability. To understand the current status and changing trends of the disease burden of leukemia at the global, regional, and national levels, and to provide a scientific basis for the development of leukemia prevention and treatment strategies. METHODS: Based on open data from the Global Burden of Disease Study 2019 (GBD 2019), R software was used to calculate estimated annual percentage changes to estimate trends in the age-standardized incidence (ASIR) and the age-standardized disability-adjusted life years (DALY) rate due to leukemia and its major subtypes from 1990 to 2019. RESULTS: In 2019, globally, the number of incidences and DALYs of leukemia were 643.6 × 103 (587.0 × 103, 699.7 × 103) and 11,657.5 × 103 (10529.1 × 103, 12700.7 × 103), respectively. The ASIR (estimated annual percentage change (EAPC) = -0.37, 95%UI -0.46 to -0.28) and the age-standardized DALY rate (EAPC = -1.72, 95%UI -1.80 to -1.65) of leukemia showed a decreasing trend from 1990 to 2019. The APC model analysis showed that the age effect of leukemia risk was a "U"-shaped distribution of relative risk (RR) with increasing age from 1990 to 2019, globally. The time effect was an increase in incidence rate with increasing years but a decrease in DALY rate with increasing years. The cohort effects of both incidence and DALY rates tended to increase and then decrease with the development of the birth cohort. In 1990 and 2019, smoking, high body-mass index, occupational exposure to benzene, and occupational exposure to formaldehyde were risk factors for DALY in leukemia, especially in areas with high SDI. CONCLUSIONS: From 1990 to 2019, the disease burden of leukemia showed a decreasing trend, but it is worth noting that its overall severity is still very high. The disease burden of leukemia varies greatly from region to region, and exclusive strategies for the prevention and treatment of leukemia should be developed according to the economic and cultural development of each region.

A Method for Predicting Allelic Variants of Single Nucleotide Polymorphisms.

INTRODUCTION: Single nucleotide polymorphisms (SNPs) are pivotal in clinical genetics, serving to link genotypes with disease susceptibility and response to environmental factors, including pharmacogenetics. They also play a crucial role in population genetics for mapping the human genome and localizing genes. Despite their utility, challenges arise when molecular genetic studies yield insufficient or uninformative data, particularly for socially significant diseases. This study aims to address these gaps by proposing a method to predict allelic variants of SNPs. METHOD: Using quantitative PCR and analyzing body composition data from 150 patients with their voluntary informed consent, we employed IBM SPSS Statistics 29.0 for data analysis. Our prototype formula, exemplified by allelic variant ADRB2 (rs1042713) = 0.257 + 0.639 * allelic variant ADRB2 (rs1042714) - 0.314 * allelic variant ADRB3 (rs4994) + 0.191 * allelic variant PPARA (rs4253778) - 0.218 * allelic variant PPARD (rs2016520) + 0.027 * body weight + 0.00001 * body weight², demonstrates the feasibility of predicting SNP allelic variants. RESULTS: This method holds promise for diverse diseases, including those of significant social impact, due to its potential to streamline and economize molecular genetic research. Its ability to stratify disease risk in the absence of complete SNP data makes it particularly compelling for clinical and laboratory geneticists. CONCLUSION: However, its translation into clinical practice necessitates the establishment of a comprehensive SNP database, especially for frequently analyzed SNPs within the implementing institution.

The dynamics of tuberculosis transmission model with different genders.

The dynamics of tuberculosis transmission model with different genders are to be established and studied. The basic regeneration numbers R0=RF+RM are to be defined, where RF and RM to be the basic reproduction number of tuberculosis transmission in female and male populations, respectively. The existence and global stability of the disease-free equilibrium was discussed when R0<1. The global dynamic behaviours of the corresponding limit system under some conditions are to be provided, including the existence, uniqueness, and global stability of the disease-free equilibrium and endemic equilibrium. The numerical simulation shows that the endemic equilibrium may be unique and stable when R0>1, and the system will undergo Hopf bifurcation based on some parameter values. Finally, we applied this model to analyse the transmission of tuberculosis in China, estimated the incidence of tuberculosis in China in 2035, and gave the conclusion that controlling the incidence of tuberculosis in male populations could better reduce the incidence of tuberculosis in China.

The effectiveness of clinical thinking using a problem-solving model.

[Purpose] No established method for appropriately developing clinical thinking in physical therapy currently exists. This study examined whether clinical thinking can be appropriately developed using a problem-solving model. [Participants and Methods] Physical therapy students were asked to develop clinical thinking in the same two cases: one class using the problem-solving model and the other class using International Classification of Functioning, Disability and Health concepts. Each clinical thought was scored and compared based on consistency. [Results] In both cases, students who used the problem-solving model scored higher. [Conclusion] The hierarchical structure of the problem-solving model clarified the relationship between each element and this was easy to maintain, suggesting that it facilitated appropriate clinical thinking.

Constructing a Hospital Department Development-Level Assessment Model: Machine Learning and Expert Consultation Approach in Complex Hospital Data Environments.

BACKGROUND: Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data. OBJECTIVE: This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management. METHODS: Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department's development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm. RESULTS: Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital's training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective. CONCLUSIONS: This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions.