Developing model systems for dinoflagellates in the post-genomic era

Dinoflagellates are a diverse group of eukaryotic microbes that are ubiquitous in aquatic environments. Largely photosynthetic, they encompass symbiotic, parasitic, and free-living lineages with a broad spectrum of trophism. Many free-living taxa can produce bioactive secondary metabolites such as biotoxins, some of which cause harmful algal blooms. In contrast, most symbiotic species are crucial for sustaining coral reef health. The year 2023 marked a decade since the first genome data of dinoflagellates became available. The growing genome-scale resources for these taxa are highlighting their remarkable evolutionary and genomic complexities. Here, we discuss the prospect of developing dinoflagellate models using the criteria of accessibility, tractability, resources, research support, and promise. Moving forward in the post-genomic era, we argue for the development of fit-to-purpose models that tailor to specific biological contexts, and that a one-size-fits-all model is inadequate for encapsulating the complex biology, ecology, and evolutionary history of dinoflagellates.

Economic Evaluations of Guideline-Directed Medical Therapies for Heart Failure With Reduced Ejection Fraction: A Systematic Review

OBJECTIVES

Decision-analytic models (DAMs) with varying structures and assumptions have been applied in economic evaluations (EEs) to assist decision making for heart failure with reduced ejection fraction (HFrEF) therapeutics. This systematic review aimed to summarize and critically appraise the EEs of guideline-directed medical therapies (GDMTs) for HFrEF.

METHODS

A systematic search of English articles and gray literature, published from January 2010, was performed on databases including MEDLINE, Embase, Scopus, NHSEED, health technology assessment, Cochrane Library, etc. The included studies were EEs with DAMs that compared the costs and outcomes of angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, angiotensin-receptor neprilysin inhibitors, beta-blockers, mineralocorticoid-receptor agonists, and sodium-glucose cotransporter-2 inhibitors. The study quality was evaluated using the Bias in Economic Evaluation (ECOBIAS) 2015 checklist and Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklists.

RESULTS

A total of 59 EEs were included. Markov model, with a lifetime horizon and a monthly cycle length, was most commonly used in evaluating GDMTs for HFrEF. Most EEs conducted in the high-income countries demonstrated that novel GDMTs for HFrEF were cost-effective compared with the standard of care, with the standardized median incremental cost-effectiveness ratio (ICER) of $21 361/quality-adjusted life-year. The key factors influencing ICERs and study conclusions included model structures, input parameters, clinical heterogeneity, and country-specific willingness-to-pay threshold.

CONCLUSIONS

Novel GDMTs were cost-effective compared with the standard of care. Given the heterogeneity of the DAMs and ICERs, alongside variations in willingness-to-pay thresholds across countries, there is a need to conduct country-specific EEs, particularly in low- and middle-income countries, using model structures that are coherent with the local decision context.

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BACKGROUND

Cultural competence resides at the core of undergraduate and postgraduate medical and health professional education. The evolution of studies on cultural competence has resulted in the existence of multiple theoretical frameworks and models, each emphasising certain elements of culturally appropriate care, but generally lacking in providing a coherent and systematic approach to teaching this subject.

METHODS

Following a meta-ethnographic approach, a systematic search of five databases was undertaken to identify relevant articles published between 1990 and 2022. After citation searching and abstract and full article screening, a consensus was reached on 59 articles for final inclusion. Key constructs and concepts of cultural competence were synthesised and presented as themes, using the lens of critical theory.

RESULTS

Three key themes were identified: competences; roles and identities; structural competency. Actionable concepts and themes were incorporated into a new transformative ACT cultural model that consists of three key domains: activate consciousness, connect relations, and transform to true cultural care.

CONCLUSION

This critical review provides an up-to-date synthesis of studies that conceptualise cultural competence frameworks and models in international medical and healthcare settings. The ACT cultural model provides a set of guiding principles for culturally appropriate care, to support high-quality educational interventions.

A novel non-linear approach for establishing a QSAR model of a class of 2-Phenyl-3-(pyridin-2-yl) thiazolidin-4-one derivatives

In this investigation, we aimed to address the pressing challenge of treating osteosarcoma, a prevalent and difficult-to-treat form of cancer. To achieve this, we developed a quantitative structure-activity relationship (QSAR) model focused on a specific class of compounds called 2-Phenyl-3-(pyridin-2-yl) thiazolidin-4-one derivatives. A set of 39 compounds was thoroughly examined, with 31 compounds assigned to the training set and 8 compounds allocated to the test set randomly. The goal was to predict the IC50 value of these compounds accurately. To optimize the compounds and construct predictive models, we employed a heuristic method of the CODESSA program. In addition to a linear model using four carefully selected descriptors, we also developed a nonlinear model using the gene expression programming method. The heuristic method resulted in correlation coefficients (R 2) of 0.603, 0.482, and 0.107 for R2 cv and S2, respectively. On the other hand, the gene expression programming method achieved higher R 2 and S2 values of 0.839 and 0.037 in the training set, and 0.760 and 0.157 in the test set, respectively. Both methods demonstrated excellent predictive performance, but the gene expression programming method exhibited greater consistency with experimental values. The successful nonlinear model generated through gene expression programming shows promising potential for designing targeted drugs to combat osteosarcoma effectively. This approach offers a valuable tool for optimizing compound selection and guiding future drug discovery efforts in the battle against osteosarcoma.

Can Organoid Model Reveal a Key Role of Extracellular Vesicles in Tumors? A Comprehensive Review of the Literature

Extracellular vesicles (EVs) are small membrane-bound vesicles that are released by cells into the extracellular environment. The role of EVs in tumors has been extensively studied, and they have been shown to play a crucial role in tumor growth, progression, and metastasis. Past research has mainly used 2D-cultured cell line models to investigate the role of EVs in tumors, which poorly simulate the tumor microenvironment. Organoid technology has gradually matured in recent years. Organoids are similar in composition and behavior to physiological cells and have the potential to recapitulate the architecture and function of the original tissue. It has been widely used in organogenesis, drug screening, gene editing, precision medicine and other fields. The integration of EVs and organoids has the potential to revolutionize the field of cancer research and represents a promising avenue for advancing our understanding of cancer biology and the development of novel therapeutic strategies. Here, we aimed to present a comprehensive overview of studies using organoids to study EVs in tumors.

Corrigendum: Institutional presence: toward a further developed Community of Inquiry model integrating institutional functions in online and blended learning environment

[This corrects the article DOI: 10.3389/fpsyg.2023.1132204.].

Engineered bone marrow as a clinically relevant ex vivo model for primary bone cancer research and drug screening

Osteosarcoma (OS) is the most common primary malignant bone cancer in children and adolescents. While numerous other cancers now have promising therapeutic advances, treatment options for OS have remained unchanged since the advent of standard chemotherapeutics and offer less than a 25% 5-y survival rate for those with metastatic disease. This dearth of clinical progress underscores a lack of understanding of OS progression and necessitates the study of this disease in an innovative system. Here, we adapt a previously described engineered bone marrow (eBM) construct for use as a three-dimensional platform to study how microenvironmental and immune factors affect OS tumor progression. We form eBM by implanting acellular bone-forming materials in mice and explanting the cellularized constructs after 8 wk for study. We interrogate the influence of the anatomical implantation site on eBM tissue quality, test ex vivo stability under normoxic (5% O2) and standard (21% O2) culture conditions, culture OS cells within these constructs, and compare them to human OS samples. We show that eBM stably recapitulates the composition of native bone marrow. OS cells exhibit differential behavior dependent on metastatic potential when cultured in eBM, thus mimicking in vivo conditions. Furthermore, we highlight the clinical applicability of eBM as a drug-screening platform through doxorubicin treatment and show that eBM confers a protective effect on OS cells that parallel clinical responses. Combined, this work presents eBM as a cellular construct that mimics the complex bone marrow environment that is useful for mechanistic bone cancer research and drug screening.

Two Pregnancy Care Models in Poland-A Descriptive-Comparative Study

Care for a pregnant woman can take various organizational forms. The World Health Organization (WHO) recommends leaders increase research into health systems. The aim of this is to manage the healthcare system in such a way as to provide beneficiaries with access to high-quality services with limited financial resources. The study presented in this paper was conducted using a diagnostic survey on a group of 1697 Polish women in the traditional model of care (TM) and 3216 women in the Coordinated Care for Pregnant Women Program (CCP). Two research tools were used in this study. The first is a survey prepared by the National Health Fund, the second is an author's survey. The results indicate that most women (85%) receiving care under the CCP received effective pain management, compared to 67% under the traditional care model (p < 0.001). In the CCP, women were significantly more likely to receive midwife assistance in infant care (90%) than women in the traditional care model (60%) (p < 0.001). Significantly more CCP patients want to return to the same hospital for their subsequent childbirth (74%) than women in the traditional model of care (43%) (p < 0.001). In patients' opinion, the new CCP model is superior in meeting their needs and providing higher-quality services. However, educating women that an obstetrician-gynecologist and a midwife can manage their pregnancy is still necessary. In addition to the CCP program, special attention should be paid to available pain management during childbirth since the use of analgesia is still insufficient in Polish hospitals.

Personal Health Tracking: A Paradigm Shift in the Self-Care Models in Nursing

The rapidly evolving digital health landscape necessitates updates to existing self-care models in nursing. This viewpoint paper revisits and evaluates prevalent models, recognizing their comprehensive exploration of self-care concepts while also identifying a gap in the incorporation of personal informatics. It underscores the missing link of human-technology interplay, an essential aspect in understanding self-care practices within digital generations. The author delineates the role of personal health tracking in self-care and the achievement of desired health outcomes. Based on these insights, the author proposes a refined, digitized self-care model that incorporates mobile health (mHealth) technologies and self-tracking behaviors. The paper concludes by advocating the application of this model for future mHealth nursing interventions, providing a framework for facilitating patient self-care and improving health and well-being in the era of digital health.

What Makes a Hospital Excellent? A Qualitative Study on the Organization and Management of Five Leading Public Hospitals in China

Purpose

To summarize the organizational and management experiences and explore the organizational theoretical model of five leading public hospitals in China.

Patients and Methods

Purposive sampling was used to select five leading hospitals in different regions of China under the National Health Commission and Provincial Health Commission as study hospitals for the study. From August 2021 to March 2022, 8 leaders and 39 managers from these hospitals were surveyed using semi-structured interviews. The data and information were analyzed in four dimensions using thematic analysis and grounded theory, focused on summarizing the experiences and practices of China's leading hospitals in organizational system, culture, operations and performance management, and employee development. This study complied with the COREQ guidelines for reporting qualitative research.

Results

An organizational system model of the characteristics of hospital excellence was developed using four core attributes: organizational system, organizational culture, operations and performance management, and employee development; the model was named the System-Culture-Operation-Performance-Employee (SCOPE) model. Organization and management among leaders and managers in China's leading hospitals are based on the SCOPE process, resulting in employees' well-being, patients' positive outcomes, and organizational excellence. In terms of hospital culture, while adhering to the Hippocratic Oath, the hospital is deeply influenced by traditional Chinese culture, which emphasizes "benevolence" and "love", leading all staff to adhere to "patient-centered care and service." In terms of operations management, a separate operations management department is responsible for hospital operations and performance assessment. As for employee development, the staff's sense of reverence for their profession is emphasized and a reasonable salary system and good practice environment are established to promote staff motivation.

Conclusion

The SCOPE model reveals the perspectives of leaders and managers in China's leading hospitals regarding organization and management under a Chinese cultural background. These findings can complement the existing literature on hospital management systems.

Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation

Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often, poor protein developability─quantified by expression, solubility, and stability─hinders utility. The ability to predict protein developability from amino acid sequence would reduce the experimental burden when selecting candidates. Recent advances in screening technologies enabled a high-throughput (HT) developability dataset for 105 of 1020 possible variants of protein ligand scaffold Gp2. In this work, we evaluate the ability of neural networks to learn a developability representation from a HT dataset and transfer this knowledge to predict recombinant expression beyond observed sequences. The model convolves learned amino acid properties to predict expression levels 44% closer to the experimental variance compared to a non-embedded control. Analysis of learned amino acid embeddings highlights the uniqueness of cysteine, the importance of hydrophobicity and charge, and the unimportance of aromaticity, when aiming to improve the developability of small proteins. We identify clusters of similar sequences with increased recombinant expression through nonlinear dimensionality reduction and we explore the inferred expression landscape via nested sampling. The analysis enables the first direct visualization of the fitness landscape and highlights the existence of evolutionary bottlenecks in sequence space giving rise to competing subpopulations of sequences with different developability. The work advances applied protein engineering efforts by predicting and interpreting protein scaffold expression from a limited dataset. Furthermore, our statistical mechanical treatment of the problem advances foundational efforts to characterize the structure of the protein fitness landscape and the amino acid characteristics that influence protein developability.

coli experiments

A simple agent-based model is presented that produces results matching the experimental data found by Lenski's group for ≤50,000 generations of Escherichia coli bacteria under continuous selective pressure. Although various mathematical models have been devised previously to model the Lenski data, the present model has advantages in terms of overall simplicity and conceptual accessibility. The model also clearly illustrates a number of features of the evolutionary process that are otherwise not obvious, such as the roles of epistasis and historical contingency in adaptation and why evolution is time irreversible ('Dollo's law'). The reason for this irreversibility is that genomes become increasingly integrated or organized, and this organization becomes a novel selective factor itself, against which future generations must compete. Selection for integrated or synergistic networks, systems or sets of mutations or traits, not for individual mutations, confers the main adaptive advantage. The result is a punctuated form of evolution that follows a logarithmic occurrence probability, in which evolution proceeds very quickly when interactomes begin to form but which slows as interactomes become more robust and the difficulty of integrating new mutations increases. Sufficient parameters exist in the game to suggest not only how equilibrium or stasis is reached but also the conditions in which it will be punctuated, the factors governing the rate at which genomic organization occurs and novel traits appear, and how population size, genome size and gene variability affect these.

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events.

OBJECTIVE

This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk.

METHODS

The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)-recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets.

RESULTS

The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model.

CONCLUSIONS

Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events.

Femoral Structure and Biomechanical Characteristics in Sanfilippo Syndrome Type-B Mice

Sanfilippo syndrome Type-B, also known as mucopolysaccharidosis IIIB (MPS IIIB), accounts for approximately one-third of all Sanfilippo syndrome patients and is characterized by a similar natural history as Type-A. Patients suffer from developmental regression, bone malformation, organomegaly, GI distress, and profound neurological deficits. Despite human trials of enzyme replacement therapy (ERT) (SBC-103, AX250) in MPS IIIB, there is currently no FDA approved treatment and a few palliative options. The major concerns of ERT and gene therapy for the treatment of bone malformation are the inadequate biodistribution of the missing enzyme, N-acetyl-α-glucosaminidase (NAGLU), and that the skeleton is a poorly hit target tissue in ERT and gene therapy. Each of the four known human types of MPS III (A, B, C, and D) is usually regarded as having mild bone manifestations, yet it remains poorly characterized. This study aimed to determine bone mineral content (BMC), volumetric bone mineral density (vBMD), and biomechanical properties in femurs MPS IIIB C57BL/6 mice compared to phenotypic control C57BL/6 mice. Significant differences were observed in MPS IIIB mice within various cortical and cancellous bone parameters for both males and females (p < 0.05). Here, we establish some osteogenic manifestations of MPS IIIB within the mouse model by radiographic and biomechanical tests, which are also differentially affected by age and sex. This suggests that some skeletal features of the MPS IIIB mouse model may be used as biomarkers of peripheral disease correction for preclinical treatment of MPS IIIB.

Estimating disease-free survival of thyroid cancer based on novel cuprotosis-related gene model

Background

Cuprotosis is a newly discovered form of cell death that differs from other types of cell death. The aim of this study was to investigate the functional role and a possible prognostic model for thyroid cancer.

Methods

TCGA and GEO were used to investigate the differential expression of CRGs in THCA. KEGG and GO enrichment analyses were applied to investigate the possible molecular functions. The features of CRGs were selected by LASSO regression. 20 pairs of samples were randomly collected from the hospital to compare expression between tumor and normal.

Results

Among the 19 CRGs related to thyroid cancer recurrence, 16 genes were differentially expressed in thyroid cancer. KEGG analysis showed that the 19 CRGs were mainly enriched in cell death, cell cycle and ribosomal pathways. K-M survival analysis and subsequent multiple logistic regression revealed that the expression of BUB1 and GINS2 were potential risk factors for disease-free survival (DFS) of thyroid cancer. In addition, further LASSO-regression selected the following three DFS-related CRGs: FDX1, BUB1 and RPL3. A novel prognostic prediction model was constructed by nomogram, and the prediction probability for 1-, 3- and 5-year survival approached the actual time. As for the possible mechanisms, FDX1, BUB1 and RPL3 were associated with immune infiltration. The cell model experiment illustrated that the ATM signaling pathway might be involved in thyroid cancer cell death.

Conclusion

Three CRG models (FDX1, BUB1, RPL3) could better predict the prognosis of thyroid cancer. Immune cell infiltration and the ATM pathway were the possible mechanisms.

Accuracy of placental growth factor alone or in combination with soluble fms-like tyrosine kinase-1 or maternal factors in detecting preeclampsia in asymptomatic women in the second and third trimesters: a systematic review and meta-analysis

OBJECTIVE

This study aimed to: (1) identify all relevant studies reporting on the diagnostic accuracy of maternal circulating placental growth factor) alone or as a ratio with soluble fms-like tyrosine kinase-1), and of placental growth factor-based models (placental growth factor combined with maternal factors±other biomarkers) in the second or third trimester to predict subsequent development of preeclampsia in asymptomatic women; (2) estimate a hierarchical summary receiver-operating characteristic curve for studies reporting on the same test but different thresholds, gestational ages, and populations; and (3) select the best method to screen for preeclampsia in asymptomatic women during the second and third trimester of pregnancy by comparing the diagnostic accuracy of each method.

DATA SOURCES

A systematic search was performed through MEDLINE, Embase, CENTRAL, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform databases from January 1, 1985 to April 15, 2021.

STUDY ELIGIBILITY CRITERIA

Studies including asymptomatic singleton pregnant women at >18 weeks' gestation with risk of developing preeclampsia were evaluated. We included only cohort or cross-sectional test accuracy studies reporting on preeclampsia outcome, allowing tabulation of 2×2 tables, with follow-up available for >85%, and evaluating performance of placental growth factor alone, soluble fms-like tyrosine kinase-1- placental growth factor ratio, or placental growth factor-based models. The study protocol was registered on the International Prospective Register Of Systematic Reviews (CRD 42020162460).

METHODS

Because of considerable intra- and interstudy heterogeneity, we computed the hierarchical summary receiver-operating characteristic plots and derived diagnostic odds ratios, β, θi, and Λ for each method to compare performances. The quality of the included studies was evaluated by the QUADAS-2 tool.

RESULTS

The search identified 2028 citations, from which we selected 474 studies for detailed assessment of the full texts. Finally, 100 published studies met the eligibility criteria for qualitative and 32 for quantitative syntheses. Twenty-three studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the second trimester, including 16 (with 27 entries) that reported on placental growth factor test alone, 9 (with 19 entries) that reported on the soluble fms-like tyrosine kinase-1-placental growth factor ratio, and 6 (16 entries) that reported on placental growth factor-based models. Fourteen studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the third trimester, including 10 (with 18 entries) that reported on placental growth factor test alone, 8 (with 12 entries) that reported on soluble fms-like tyrosine kinase-1-placental growth factor ratio, and 7 (with 12 entries) that reported on placental growth factor-based models. For the second trimester, Placental growth factor-based models achieved the highest diagnostic odds ratio for the prediction of early preeclampsia in the total population compared with placental growth factor alone and soluble fms-like tyrosine kinase-1-placental growth factor ratio (placental growth factor-based models, 63.20; 95% confidence interval, 37.62-106.16 vs soluble fms-like tyrosine kinase-1-placental growth factor ratio, 6.96; 95% confidence interval, 1.76-27.61 vs placental growth factor alone, 5.62; 95% confidence interval, 3.04-10.38); placental growth factor-based models had higher diagnostic odds ratio than placental growth factor alone for the identification of any-onset preeclampsia in the unselected population (28.45; 95% confidence interval, 13.52-59.85 vs 7.09; 95% confidence interval, 3.74-13.41). For the third trimester, Placental growth factor-based models achieved prediction for any-onset preeclampsia that was significantly better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1-placental growth factor ratio (placental growth factor-based models, 27.12; 95% confidence interval, 21.67-33.94 vs placental growth factor alone, 10.31; 95% confidence interval, 7.41-14.35 vs soluble fms-like tyrosine kinase-1-placental growth factor ratio, 14.94; 95% confidence interval, 9.42-23.70).

CONCLUSION

Placental growth factor with maternal factors ± other biomarkers determined in the second trimester achieved the best predictive performance for early preeclampsia in the total population. However, in the third trimester, placental growth factor-based models had predictive performance for any-onset preeclampsia that was better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1-placental growth factor ratio. Through this meta-analysis, we have identified a large number of very heterogeneous studies. Therefore, there is an urgent need to develop standardized research using the same models that combine serum placental growth factor with maternal factors ± other biomarkers to accurately predict preeclampsia. Identification of patients at risk might be beneficial for intensive monitoring and timing delivery.

Vaginal changes after ovariectomy in ewes: A large animal model for genitourinary syndrome of menopause

OBJECTIVE

To evaluate the effect of iatrogenic menopause on the physiology of the vagina of the ewe and to evaluate if vaginal changes in ewes can be translated to women with genitourinary syndrome of menopause (GSM).

METHODS

Preclinical research with Dohne Merino ewes. Iatrogenic menopause was induced by bilateral ovariectomy (OVX). Animals were randomized for surgery, blinded for allocation and outcome assessment. Differences between groups were determined by linear regression analyses at 5 months after OVX. Outcome measures were vaginal epithelial thickness, pH, vaginal maturation value, vaginal maturation index, epithelial glycogen accumulation, content of elastin fibers, collagen, and vascularity.

RESULTS

OVX ewes (n = 20) showed epithelial thinning of the vaginal wall from 146 μm to 47 μm (mean, P < 0.001). Furthermore, epithelial glycogen accumulation and vascularity of the vaginal wall significantly decreased (43% and 23%, respectively) as compared with the control group (no intervention; n = 5). No significant differences were found for other outcome measures.

CONCLUSION

This study established the ewe as a suitable large animal model for GSM. Furthermore, the similar relevant outcomes in humans and ewes hold great value for future translational research for the evaluation and optimization of different treatment modalities for GSM.

Using environmental factors to predict Rhipicephalus sanguineus s.l. (Acari: Ixodidae) mortality

BACKGROUND

Rhipicephalus sanguineus s.l. (Latreille, 1806) can establish indoor populations, which increases the risk of pathogen transmission to humans and companion dogs. Rhipicephalus sanguineus s.l. ticks spend most of their life cycle off the host, which subjects developmental timescale to abiotic factors. Previous studies showed that both temperature and relative humidity (RH) influenced Rhipicephalus sanguineus s.l. survival time across all life stages. However, quantified relationships between environmental factors and Rhipicephalus sanguineus s.l. mortality is not currently available. Here, three Rhipicephalus sanguineus s.l. strains were evaluated for mortality under 20 combinations of five temperatures and four RHs. The data obtained were analyzed to quantify the relationship between environmental factors and Rhipicephalus sanguineus s.l.

SURVIVAL

RESULTS

Mortality probabilities did not show a consistent pattern between the three tick strains. Temperature, RH, and their interaction influenced Rhipicephalus sanguineus s.l. mortality probabilities across all life stages, with mortality probability generally increasing with temperature but decreasing with RH. With 50% and lower RH, larvae cannot survive for more than 1 week. However, mortality probabilities in all strains and stages were more sensitive to temperature than to RH.

CONCLUSION

This study identified the predictive relationship between environmental factors and Rhipicephalus sanguineus s.l. survival, which enables estimations of tick survival time under varied residential situations, allows parameterization of population models, and provides guidance for pest control professionals to develop efficient management strategies. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignant tumor with high recurrence and mortality rates. This study aimed to develop and validate a prognostic model for patients with UCEC based on cuproptosis-related long non-coding RNA (lncRNA) signature.

Methods

Transcriptome and clinical UCEC data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis was conducted to screen out the cuproptosis-related lncRNAs, and univariate regression analysis was performed to determine prognostic factors associated with overall survival (OS). A cuproptosis-related lncRNA risk model was constructed through least absolute shrinkage and selection operator (LASSO) regression and cross-validation. The accuracy and reliability of the model were verified through Kaplan-Meier (KM), proportional hazards model (Cox) regression, nomogram, principal component analysis (PCA), and stage analysis. Gene Ontology (GO) enrichment, immune function, and tumor mutation burden (TMB) analyses were conducted between low-risk and high-risk groups, and antineoplastic drugs were predicted.

Results

By correlation analysis, 155 cuproptosis-related lncRNAs were acquired, and 9 lncRNAs were identified as independent prognostic factors. A 6-cuproptosis-related lncRNA model was established. The results revealed that patients in the high-risk group were more inclined to have a poor OS than those in the low-risk group. Risk score was an independent prognostic factor and had a high accuracy and predictive value. The extracellular structure and anchored components of membrane-related GO terms were significantly enriched. Immune function and TMB results were assumed to be different from each other, which might explain a better outcome in the low-risk group than that in the high-risk group. Eighteen compounds were predicted as chemotherapy drugs with high half maximal inhibitory concentration (IC50) in the high-risk group.

Conclusions

We successfully developed a cuproptosis-related lncRNA risk model for the prediction of prognosis, while simultaneously providing insights on new approaches for immunotherapy and chemotherapy for patients with UCEC.

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

BACKGROUND

Within the trauma system, the emergency department (ED) is the hospital's first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED.

OBJECTIVE

The aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED.

METHODS

We used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED.

RESULTS

Our proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320).

CONCLUSIONS

Our proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.

Prediction Model for Postoperative Quality of Life Among Breast Cancer Survivors Along the Survivorship Trajectory From Pretreatment to 5 Years: Machine Learning-Based Analysis

BACKGROUND

Breast cancer is the most common cancer and the most common cause of cancer death in women. Although survival rates have improved, unmet psychosocial needs remain challenging because the quality of life (QoL) and QoL-related factors change over time. In addition, traditional statistical models have limitations in identifying factors associated with QoL over time, particularly concerning the physical, psychological, economic, spiritual, and social dimensions.

OBJECTIVE

This study aimed to identify patient-centered factors associated with QoL among patients with breast cancer using a machine learning (ML) algorithm to analyze data collected along different survivorship trajectories.

METHODS

The study used 2 data sets. The first data set was the cross-sectional survey data from the Breast Cancer Information Grand Round for Survivorship (BIG-S) study, which recruited consecutive breast cancer survivors who visited the outpatient breast cancer clinic at the Samsung Medical Center in Seoul, Korea, between 2018 and 2019. The second data set was the longitudinal cohort data from the Beauty Education for Distressed Breast Cancer (BEST) cohort study, which was conducted at 2 university-based cancer hospitals in Seoul, Korea, between 2011 and 2016. QoL was measured using European Organization for Research and Treatment of Cancer QoL Questionnaire Core 30 questionnaire. Feature importance was interpreted using Shapley Additive Explanations (SHAP). The final model was selected based on the highest mean area under the receiver operating characteristic curve (AUC). The analyses were performed using the Python 3.7 programming environment (Python Software Foundation).

RESULTS

The study included 6265 breast cancer survivors in the training data set and 432 patients in the validation set. The mean age was 50.6 (SD 8.66) years and 46.8% (n=2004) had stage 1 cancer. In the training data set, 48.3% (n=3026) of survivors had poor QoL. The study developed ML models for QoL prediction based on 6 algorithms. Performance was good for all survival trajectories: overall (AUC 0.823), baseline (AUC 0.835), within 1 year (AUC 0.860), between 2 and 3 years (AUC 0.808), between 3 and 4 years (AUC 0.820), and between 4 and 5 years (AUC 0.826). Emotional and physical functions were the most important features before surgery and within 1 year after surgery, respectively. Fatigue was the most important feature between 1 and 4 years. Despite the survival period, hopefulness was the most influential feature on QoL. External validation of the models showed good performance with AUCs between 0.770 and 0.862.

CONCLUSIONS

The study identified important factors associated with QoL among breast cancer survivors across different survival trajectories. Understanding the changing trends of these factors could help to intervene more precisely and timely, and potentially prevent or alleviate QoL-related issues for patients. The good performance of our ML models in both training and external validation sets suggests the potential use of this approach in identifying patient-centered factors and improving survivorship care.

Predicting Undesired Treatment Outcomes With Machine Learning in Mental Health Care: Multisite Study

Background

Predicting which treatment will work for which patient in mental health care remains a challenge.

Objective

The aim of this multisite study was 2-fold: (1) to predict patients' response to treatment in Dutch basic mental health care using commonly available data from routine care and (2) to compare the performance of these machine learning models across three different mental health care organizations in the Netherlands by using clinically interpretable models.

Methods

Using anonymized data sets from three different mental health care organizations in the Netherlands (n=6452), we applied a least absolute shrinkage and selection operator regression 3 times to predict the treatment outcome. The algorithms were internally validated with cross-validation within each site and externally validated on the data from the other sites.

Results

The performance of the algorithms, measured by the area under the curve of the internal validations as well as the corresponding external validations, ranged from 0.77 to 0.80.

Conclusions

Machine learning models provide a robust and generalizable approach in automated risk signaling technology to identify cases at risk of poor treatment outcomes. The results of this study hold substantial implications for clinical practice by demonstrating that the performance of a model derived from one site is similar when applied to another site (ie, good external validation).

An EMG-to-Force Processing Approach to Estimating Knee Muscle Forces during Adult, Self-Selected Speed Gait

BACKGROUND

The purpose of this study was to determine the force production during self-selected speed normal gait by muscle-tendon units that cross the knee. The force of a single knee muscle is not directly measurable without invasive methods, yet invasive techniques are not appropriate for clinical use. Thus, an EMG-to-force processing (EFP) model was developed which scaled muscle-tendon unit (MTU) force output to gait EMG.

METHODS

An EMG-to-force processing (EFP) model was developed which scaled muscle-tendon unit (MTU) force output to gait EMG. Active muscle force power was defined as the product of MTU forces (derived from EFP) and that muscle's contraction velocity. Net knee EFP moment was determined by summing individual active knee muscle moments. Net knee moments were also calculated for these study participants via inverse dynamics (kinetics plus kinematics, KIN). The inverse dynamics technique used are well accepted and the KIN net moment was used to validate or reject this model. Closeness of fit of the moment power curves for the two methods (during active muscle forces) was used to validate the model.

RESULTS

The correlation between the EFP and KIN methods was sufficiently close, suggesting validation of the model's ability to provide reasonable estimates of knee muscle forces.

CONCLUSIONS

The EMG-to-force processing approach provides reasonable estimates of active individual knee muscle forces in self-selected speed walking in neurologically intact adults.

Protocol for establishing a model for integrated influenza surveillance in Tamil Nadu, India

The potential for influenza viruses to cause public health emergencies is great. The World Health Organisation (WHO) in 2005 concluded that the world was unprepared to respond to an influenza pandemic. Available surveillance guidelines for pandemic influenza lack the specificity that would enable many countries to establish operational surveillance plans. A well-designed epidemiological and virological surveillance is required to strengthen a country's capacity for seasonal, novel, and pandemic influenza detection and prevention. Here, we describe the protocol to establish a novel mechanism for influenza and SARS-CoV-2 surveillance in the four identified districts of Tamil Nadu, India. This project will be carried out as an implementation research. Each district will identify one medical college and two primary health centres (PHCs) as sentinel sites for collecting severe acute respiratory infections (SARI) and influenza like illness (ILI) related information, respectively. For virological testing, 15 ILI and 10 SARI cases will be sampled and tested for influenza A, influenza B, and SARS-CoV-2 every week. Situation analysis using the WHO situation analysis tool will be done to identify the gaps and needs in the existing surveillance systems. Training for staff involved in disease surveillance will be given periodically. To enhance the reporting of ILI/SARI for sentinel surveillance, trained project staff will collect information from all ILI/SARI patients attending the sentinel sites using pre-tested tools. Using time, place, and person analysis, alerts for abnormal increases in cases will be generated and communicated to health authorities to initiate response activities. Advanced epidemiological analysis will be used to model influenza trends over time. Integrating virological and epidemiological surveillance data with advanced analysis and timely communication can enhance local preparedness for public health emergencies. Good quality surveillance data will facilitate an understanding outbreak severity and disease seasonality. Real-time data will help provide early warning signals for prevention and control of influenza and COVID-19 outbreaks. The implementation strategies found to be effective in this project can be scaled up to other parts of the country for replication and integration.

Effects of Community-Based Caring Contact in Reducing Thwarted Belongingness Among Postdischarge Young Adults With Self-Harm: Randomized Controlled Trial

BACKGROUND

For patients with self-harm behaviors, the urge to hurt themselves persists after hospital discharge, leading to costly readmissions and even death. Hence, postdischarge intervention programs that reduce self-harm behavior among patients should be part of a cogent community mental health care policy.

OBJECTIVE

We aimed to determine whether a combination of a self-help mobile app and volunteer support could complement treatment as usual (TAU) to reduce the risk of suicide among these patients.

METHODS

We conducted a pragmatic randomized controlled trial on discharged patients aged between 18 and 45 years with self-harm episodes/suicide attempts, all of whom were recruited from 4 hospital emergency departments in Hong Kong. Participants were randomly assigned to one of three groups: (1) mobile app + TAU ("apps"), (2) mobile app + volunteer support + TAU ("volunteers"), or (3) TAU only as the control group ("TAU"). They were asked to submit a mobile app-based questionnaire during 4 measurement time points at monthly intervals.

RESULTS

A total of 40 participants were recruited. Blending volunteer care with a preprogrammed mobile app was found to be effective in improving service compliance. Drawing upon the interpersonal-psychological theory of suicide, our findings suggested that a reduction in perceived burdensomeness and thwarted belongingness through community-based caring contact are linked to improvement in hopelessness, albeit a transient one, and suicide risk.

CONCLUSIONS

A combination of volunteer care with a self-help mobile app as a strategy for strengthening the continuity of care can be cautiously implemented for discharged patients at risk of self-harm during the transition from the hospital to a community setting.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03081078; https://clinicaltrials.gov/study/NCT03081078.

Modelling the role of environmental circumscription in the evolution of inequality

Circumscription theory proposes that complex hierarchical societies emerged in areas surrounded by barriers to dispersal, e.g. mountains or seas. This theory has been widely influential but the lack of formal modelling has resulted in theoretical and empirical challenges. This theory shares parallels with reproductive skew models from evolutionary ecology where inequality depends on the capacity of subordinates to escape from despotic leaders. Building on these similarities, we extend reproductive skew models to simulate the concurrent evolution of inequality in many connected groups. Our results show that cost of migration does not directly limit inequality in the long term, but it does control the rate of increase in inequality. Second, we show that levels of inequality can be reduced if there are random errors made by dominants, as these lead to variations that propagate between polities. Third, our model clarifies the concept of circumscription by relating it to geographical features: the size of a region and the connectivity between polities. Overall, our model helps clarify some issues about how migration may affect inequality. We discuss our results in the light of anthropological and archaeological evidence and present the future extensions required to build towards a complete model of circumscription theory. This article is part of the theme issue 'Evolutionary ecology of inequality'.

A Model to Prevent Substance Use/Abuse by Student Nurses at Limpopo College of Nursing, South Africa

BACKGROUND

Substance use/abuse is a global challenge that has detrimental effects on nations' health, wealth, and security. Substance users in Africa make up roughly 17% to 21% of global illicit drug users in Africa and cannabis abuse (63%) (UNODC, 2010).

PURPOSE

We aimed to develop, describe, and evaluate a model that could be used as a framework of reference to prevent substance use/abuse by student nurses at Limpopo College of Nursing, South Africa.

METHOD

A mixed-method study approach was used following a qualitative exploratory, descriptive, and contextual design to explore factors contributing to substance use/abuse by student nurses and quantitatively examine the impact of substance use/abuse on students' academic achievements in Limpopo Province.

FINDINGS

The results of the first empirical phase reveal two themes and seven subthemes from the semi-structured interviews with lecturers, students, and support staff. The literature supported the results. In phase 2, we analyzed the concept of the "prevention" of substance use/abuse by student nurses following the process of a concept analysis by Walker and Avant (2016). The results were conceptualized within the six elements of practice theory: context, agent, recipient, dynamic, process and procedure, and outcome.

CONCLUSIONS

The relational statements provided the basis for the model description. A reliable method was used to describe and evaluate the model.

Closing the loop on morphogenesis: a mathematical model of morphogenesis by closed-loop reaction-diffusion

Morphogenesis, the establishment and repair of emergent complex anatomy by groups of cells, is a fascinating and biomedically-relevant problem. One of its most fascinating aspects is that a developing embryo can reliably recover from disturbances, such as splitting into twins. While this reliability implies some type of goal-seeking error minimization over a morphogenic field, there are many gaps with respect to detailed, constructive models of such a process. A common way to achieve reliability is negative feedback, which requires characterizing the existing body shape to create an error signal-but measuring properties of a shape may not be simple. We show how cells communicating in a wave-like pattern could analyze properties of the current body shape. We then describe a closed-loop negative-feedback system for creating reaction-diffusion (RD) patterns with high reliability. Specifically, we use a wave to count the number of peaks in a RD pattern, letting us use a negative-feedback controller to create a pattern with N repetitions, where N can be altered over a wide range. Furthermore, the individual repetitions of the RD pattern can be easily stretched or shrunk under genetic control to create, e.g., some morphological features larger than others. This work contributes to the exciting effort of understanding design principles of morphological computation, which can be used to understand evolved developmental mechanisms, manipulate them in regenerative-medicine settings, or engineer novel synthetic morphology constructs with desired robust behavior.

A multifactorial framework of psychobehavioral determinants of coping behaviors: an online survey at the early stage of the COVID-19 pandemic

Coronavirus disease 2019 dramatically changed people's behavior because of the need to adhere to infection prevention and to overcome general adversity resulting from the implementation of infection prevention measures. However, coping behavior has not been fully distinguished from risk perception, and a comprehensive picture of demographic, risk-perception, and psychobehavioral factors that influence the major coping-behavior factors remain to be elucidated. In this study, we recruited 2,885 Japanese participants. Major coping-behavior and risk-perception factors were identified via exploratory factor analysis of 50 candidate items. Then, we conducted a hierarchical multiple regression analysis to investigate factors associated with each coping-behavior factor. We identified four types of coping behavior [CB1 (mask-wearing), CB2 (information-seeking), CB3 (resistance to social stagnation), and CB4 (infection-prevention)] and three risk-perception factors [RP1 (shortages of daily necessities), RP2 (medical concerns), and RP3 (socioeconomic concerns)]. CB1 was positively associated with female sex and etiquette. CB2 was positively related to RP1 and RP3. CB3 was positively related to RP1 and leadership, and negatively associated with etiquette. CB4 was positively associated with female sex, etiquette, and active well-being. This parsimonious model may help to elucidate essential social dynamics and provide a theoretical framework for coping behavior during a pandemic.

A Micromechanical-Based Semi-Empirical Model for Predicting the Compressive Strength Degradation of Concrete under External Sulfate Attack

As one of the most harmful ions in the environment, sulfate could cause the deformation and material deterioration of concrete structures. Models that accurately describe the whole chemo-transport-mechanical process of an external sulfate attack (ESA) require substantial computational work and contain complex parameters. This paper proposes a semi-empirical model based on micromechanical theory for predicting the compressive strength degradation of concrete under an ESA with basic properties of the undamaged material and limited computational effort. A simplified exponential function is developed for the total amount of the invading sulfate, and a second-order equation governs the chemical reaction. A micromechanical model is implemented to solve the mechanical response caused by an ESA. The model is able to describe the compressive stress-strain behavior of concrete subject to uniaxial loading in good agreement with the experimental results. For the case of a sulfate-attacked material, the relationship between compressive strength and expansion is calculated and validated by the test results. Finally, the deterioration process of compressive strength is predicted with the test results of deformation.

Using advanced analytics to help identify women who are more likely to have a severe subjective experience of vulvovaginal atrophy: a modeling study

OBJECTIVE

To develop a model to identify women likely to be severely impacted by vulvovaginal atrophy (VVA), based on their experience of symptoms and non-clinical factors.

METHODS

Multivariate statistics and machine-learning algorithms were used to develop models using data from a cross-sectional, observational, multinational European survey. A set of independent variables were chosen to assess subjective VVA severity and its impact on daily activities.

RESULTS

A final composite model was selected that included three categories of variables: clinical severity, patient demographics/clinical characteristics and Day-to-Day Impact of Vaginal Aging (DIVA) variables related to emotion/mood, impact on lifestyle and frequency of sex. The model accurately classified 71% of women. Three DIVA variables (feeling bad about yourself, desire/interest in sex, physical comfort related to sitting) explained much of the variation in the dependent variable of the model. Over 90% of the impact of VVA relates to certain psychosocial and behavioral aspects that can be identified without the need to consider physical signs/symptoms.

CONCLUSION

Non-clinical factors can contribute significantly to the overall VVA burden.Questions used in developing the composite model could form the basis of an instrument to help screen women prior to clinical consultation and improve VVA management.

Short Culture of Bovine Hepatocytes Biopsied from Dairy Cows as a Model for Toxicological Studies-CYP 1A1 Activity Response to Zearalenone Treatment

This study presents a simple and cost-effective method for isolating hepatocytes from liver biopsies obtained from healthy and ketotic dairy cows, which can be utilized for studying cellular metabolism, drug toxicity, and hepatocyte-specific gene function and regulation. The expression of hepatocyte marker genes (G6PC, ALB, CYP1A2) was measured and found to be highest at 6 h post-isolation, with a subsequent decrease over time. Cells isolated from ketotic livers exhibited lower expression levels than those from healthy livers. Furthermore, for the functional characterization of ketotic hepatocytes, the cells were exposed to varying doses of zearalenone (ZEA). While doses of 10-50 µM did not affect cell viability, the highest dose of ZEA (100 µM) significantly decreased cell viability, as measured using XTT assay. Additionally, the potential induction of cytochrome P450 A1 (CYP1A1) by ZEA was found. Despite limitations such as a short-term culture, this model provides a useful tool for conducting toxicological research.

Experimental Induction of Acute Acanthamoeba castellanii Keratitis in Cats

Purpose

To develop a feline model of acute Acanthamoeba keratitis using methods that replicate natural routes of infection transmission.

Methods

Corneal Acanthamoeba castellanii inoculation was performed by three methods: topical inoculation with Acanthamoeba solution following corneal abrasion, placement of a contaminated contact lens for 7 days, and placement of a contaminated contact lens for 7 days following corneal abrasion. Sham inoculations with parasite-free medium and sterile contact lenses were also performed. Cats were monitored by ocular examination and in vivo corneal confocal microscopy for 21 days post-inoculation. Corneal samples were collected at intervals for microbiologic assessment, histopathology, and immunohistochemistry.

Results

All cats in the corneal abrasion groups developed clinical keratitis. Clinical ocular disease was inconsistently detected in cats from the contaminated contact lens only group. Initial corneal lesions were characterized by multifocal epithelial leukocyte infiltrates. Ocular lesions progressed to corneal epithelial ulceration and diffuse stromal inflammation. After 14 days, corneal ulcerations resolved, and stromal inflammation consolidated into multifocal subepithelial and stromal infiltrates. Corneal amoebae were detected by culture, in vivo confocal microscopy, histopathology, and immunohistochemistry in cats with keratitis. Neutrophilic and lymphocytic keratoconjunctivitis with lymphoplasmacytic anterior uveitis were identified by histopathology. Coinfection with aerobic bacteria was detected in some, but not all, cats with keratitis. Ocular disease was not detected in the sham inoculation groups.

Conclusions

Feline Acanthamoeba keratitis is experimentally transmissible by contaminated contact lenses and topical inoculation following corneal epithelial trauma.

Translational Relevance

Experimentally induced acute Acanthamoeba keratitis in cats is clinically and histopathologically similar to its human counterpart.

Establishment of a secondary infection laboratory model of Echinococcus shiquicus metacestode using BALB/c mice and Mongolian jirds (Meriones unguiculatus)

Echinococcus shiquicus is peculiar to the Qinghai–Tibet plateau of China. Research on this parasite has mainly focused on epidemiological surveys and life cycle studies. So far, limited laboratory studies have been reported. Here, experimental infection of E. shiquicus metacestode in BALB/c mice and Mongolian jirds (Meriones unguiculatus) was carried out to establish alternative laboratory animal models. Intraperitoneal inoculation of metacestode material containing protoscoleces (PSCs) obtained from infected plateau pikas were conducted on BALB/c mice. Furthermore, metacestode material without PSCs deriving from infected BALB/c mice was intraperitoneally inoculated to Mongolian jirds. Experimental animals were dissected for macroscopic and histopathological examination. The growth of cysts in BALB/c mice was infiltrative, and they invaded the murine entire body. Most of the metacestode cysts were multicystic, but a few were unilocular. The cysts contained sterile vesicles, which had no PSCs. The metacestode materials were able to successfully infect new mice. In the jirds model, E. shiquicus cysts were typically formed freely in the peritoneal cavity; the majority of these cysts were free while a small portion adhered loosely to nearby organs. The proportion of fertile cysts was high, and contained many PSCs. The PSCs produced in Mongolian jirds also successfully infected new ones, which confirms that jirds can serve as an alternative experimental intermediate host. In conclusion, a laboratory animal infection was successfully established for E. shiquicus using BALB/c mice and Mongolian jirds. These results provide new models for the in-depth study of Echinococcus metacestode survival strategy, host interactions and immune escape mechanism.

Establishment of a Rat Model of Capillary Leakage Syndrome Induced by Cardiopulmonary Resuscitation After Cardiac Arrest

OBJECTIVE

Cardiopulmonary resuscitation (CPR) after cardiac arrest (CA) is one of the main causes of capillary leakage syndrome (CLS). This study aimed to establish a stable CLS model following the CA and cardiopulmonary resuscitation (CA-CPR) model in Sprague-Dawley (SD) rats.

METHODS

We conducted a prospective, randomized, animal model study. All adult male SD rats were randomly divided into a normal group (group N), a sham operation group (group S), and a cardiopulmonary resuscitation group (group T). The SD rats of the three groups were all inserted with 24-G needles through their left femoral arteries and right femoral veins. In group S and group T, the endotracheal tube was intubated. In group T, CA induced by asphyxia (AACA) was caused by vecuronium bromide with the endotracheal tube obstructed for 8 min, and the rats were resuscitated with manual chest compression and mechanical ventilation. Preresuscitation and postresuscitation measurements, including basic vital signs (BVS), blood gas analysis (BG), routine complete blood count (CBC), wet-to-dry ratio of tissues (W/D), and the HE staining results after 6 h were evaluated.

RESULTS

In group T, the success rate of the CA-CPR model was 60% (18/30), and CLS occurred in 26.6% (8/30) of the rats. There were no significant differences in the baseline characteristics, including BVS, BG, and CBC, among the three groups (P>0.05). Compared with pre-asphyxia, there were significant differences in BVS, CBC, and BG, including temperature, oxygen saturation (SpO2), mean arterial pressure (MAP), central venous pressure (CVP), white blood cell count (WBC), hemoglobin, hematocrit, pH, pCO2, pO2, SO2, lactate (Lac), base excess (BE), and Na+ (P<0.05) after the return of spontaneous circulation (ROSC) in group T. At 6 h after ROSC in group T and at 6 h after surgery in groups N and S, there were significant differences in temperature, heart rate (HR), respiratory rate (RR), SpO2, MAP, CVP, WBC, pH, pCO2, Na+, and K+ among the three groups (P<0.05). Compared with the other two groups, the rats in group T showed a significantly increased W/D weight ratio (P<0.05). The HE-stained sections showed consistent severe lesions in the lung, small intestine, and brain tissues of the rats at 6 h after ROSC following AACA.

CONCLUSION

The CA-CPR model in SD rats induced by asphyxia could reproduce CLS with good stability and reproducibility.

An Ex Vivo Model of Posterior Tracheomalacia With Evaluation of Potential Treatment Modalities

OBJECTIVE

Posterior tracheomalacia (TM) is characterized by excessive intraluminal displacement of the tracheal membranous wall. Recently, novel surgical strategies for repair of posterior TM have been introduced. To our knowledge, these strategies have not been evaluated in a model of posterior TM. Thus, we sought to design an ex vivo mechanical model of posterior TM to evaluate potential repair interventions.

METHODS

A model for posterior TM was created with partial thickness longitudinal incisions to the posterior aspect of ex vivo porcine trachea. Three groups of tracheas were tested: (1) control (unmanipulated), (2) posterior TM (injury), and (3) intervention (repair). Interventions included external splinting with 0.3 and 0.5 mm bioresorbable plates, posterior tracheopexy, and injection tracheoplasty with calcium hydroxylapatite. An airtight tracheal system was created to measure tracheal wall collapse with changes in negative pressure. A bronchoscope and pressure transducer were connected to either end. Cross-sectional area of the tracheal lumen was analyzed using ImageJ software (National Institutes of Health, Bethesda, MD).

RESULTS

Average percent reduction in cross-sectional area of the tracheal lumen was compared using a two-tailed paired t-test. Significant differences were found between control and TM groups (p < 0.019). There was no significant difference between control and external splinting and posterior tracheopexy groups (p > 0.14).

CONCLUSION

We describe an ex vivo model for posterior TM that replicates airway collapse. External splinting and tracheopexy interventions showed recovery of the injured tracheal segment. Injection tracheoplasty did not improve the TM.

LEVEL OF EVIDENCE

N/A Laryngoscope, 133:2000-2006, 2023.

Prediction of birthweight and risk of macrosomia in pregnancies complicated by diabetes

BACKGROUND

Antenatal detection of accelerated fetal growth and macrosomia in pregnancies complicated by diabetes mellitus is important for patient counseling and management. Sonographic fetal weight estimation is the most commonly used tool to predict birthweight and macrosomia. However, the predictive accuracy of sonographic fetal weight estimation for these outcomes is limited. In addition, an up-to-date sonographic fetal weight estimation is often unavailable before birth. This may result in a failure to identify macrosomia, especially in pregnancies complicated by diabetes mellitus where care providers might underestimate fetal growth rate. Therefore, there is a need for better tools to detect and alert care providers to the potential risk of accelerated fetal growth and macrosomia.

OBJECTIVE

This study aimed to develop and validate prediction models for birthweight and macrosomia in pregnancies complicated by diabetes mellitus.

STUDY DESIGN

This was a completed retrospective cohort study of all patients with a singleton live birth at ≥36 weeks of gestation complicated by preexisting or gestational diabetes mellitus observed at a single tertiary center between January 2011 and May 2022. Candidate predictors included maternal age, parity, type of diabetes mellitus, information from the most recent sonographic fetal weight estimation (including estimated fetal weight, abdominal circumference z score, head circumference-to-abdomen circumference z score ratio, and amniotic fluid), fetal sex, and the interval between ultrasound examination and birth. The study outcomes were macrosomia (defined as birthweights >4000 and >4500 g), large for gestational age (defined as a birthweight >90th percentile for gestational age), and birthweight (in grams). Multivariable logistic regression models were used to estimate the probability of dichotomous outcomes, and multivariable linear regression models were used to estimate birthweight. Model discrimination and predictive accuracy were calculated. Internal validation was performed using the bootstrap resampling technique.

RESULTS

A total of 2465 patients met the study criteria. Most patients had gestational diabetes mellitus (90%), 6% of patients had type 2 diabetes mellitus, and 4% of patients had type 1 diabetes mellitus. The overall proportions of infants with birthweights >4000 g, >4500 g, and >90th percentile for gestational age were 8%, 1%, and 12%, respectively. The most contributory predictor variables were estimated fetal weight, abdominal circumference z score, ultrasound examination to birth interval, and type of diabetes mellitus. The models for the 3 dichotomous outcomes had high discriminative accuracy (area under the curve receiver operating characteristic curve, 0.929-0.979), which was higher than that achieved with estimated fetal weight alone (area under the curve receiver operating characteristic curve, 0.880-0.931). The predictive accuracy of the models had high sensitivity (87%-100%), specificity (84%-92%), and negative predictive values (84%-92%). The predictive accuracy of the model for birthweight had low systematic and random errors (0.6% and 7.5%, respectively), which were considerably smaller than the corresponding errors achieved with estimated fetal weight alone (-5.9% and 10.8%, respectively). The proportions of estimates within 5%, 10%, and 15% of the actual birthweight were high (52.3%, 82.9%, and 94.9%, respectively).

CONCLUSION

The prediction models developed in the current study were associated with greater predictive accuracy for macrosomia, large for gestational age, and birthweight than the current standard of care that includes estimated fetal weight alone. These models may assist care providers in counseling patients regarding the optimal timing and mode of delivery.

Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity

Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S-adenosyl-L-methionine binding region have been studied. Through topographical descriptors, quantitative structure-activity relationships (QSAR) were developed in order to identify the most effective PRMT1 inhibitors among 17 compounds. The model built using linear discriminant analysis allows us to accurately classify over 90% of the investigated active substances. Antileukemic activity is predicted using a multilinear regression analysis, and it can account for more than 56% of the variation. Both analyses are validated using an internal "leave some out" test. The developed model could be utilized in future preclinical experiments with novel drugs.

Development and Validation of Prediction Models for All-Cause Mortality and Cardiovascular Mortality in Patients on Hemodialysis: A Retrospective Cohort Study in China

Purpose

This study aimed to develop two predictive nomograms for the assessment of long-term survival status in hemodialysis (HD) patients by examining the prognostic factors for all-cause mortality and cardiovascular (CVD) event mortality.

Patients and methods

A total of 551 HD patients with an average age of over 60 were included in this study. The patients' medical records were collected from our hospital and randomly allocated to two cohorts: the training cohort (n=385) and the validation cohort (n=166). We employed multivariate Cox assessments and fine-gray proportional hazards models to explore the predictive factors for both all-cause mortality and cardiovascular event mortality risk in HD patients. Two nomograms were established based on predictive factors to forecast patients' likelihood of survival for 3, 5, and 8 years. The performance of both models was evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis.

Results

The nomogram for all-cause mortality prediction included seven factors: age ≥ 60, sex (male), history of diabetes and coronary artery disease, diastolic blood pressure, total triglycerides (TG), and total cholesterol (TC). The nomogram for cardiovascular event mortality prediction included three factors: history of diabetes and coronary artery disease, and total cholesterol (TC). Both models demonstrated good discrimination, with AUC values of 0.716, 0.722 and 0.725 for all-cause mortality at 3, 5, and 8 years, respectively, and 0.702, 0.695, and 0.677 for cardiovascular event mortality, respectively. The calibration plots indicated a good agreement between the predictions and the decision curve analysis demonstrated a favorable clinical utility of the nomograms.

Conclusion

Our nomograms were well-calibrated and exhibited significant estimation efficiency, providing a valuable predictive tool to forecast prognosis in HD patients.

Establishment of a Nipah Virus Disease Model in Hamsters, including a Comparison of Intranasal and Intraperitoneal Routes of Challenge

Nipah virus (NiV) is an emerging pathogen that can cause severe respiratory illness and encephalitis in humans. The main reservoir is fruit bats, distributed across a large geographical area that includes Australia, Southeast Asia, and Africa. Incursion into humans is widely reported through exposure of infected pigs, ingestion of contaminated food, or through contact with an infected person. With no approved treatments or vaccines, NiV poses a threat to human public health and has epidemic potential. To aid with the assessment of emerging interventions being developed, an expansion of preclinical testing capability is required. Given variations in the model parameters observed in different sites during establishment, optimisation of challenge routes and doses is required. Upon evaluating the hamster model, an intranasal route of challenge was compared with intraperitoneal delivery, demonstrating a more rapid dissemination to wider tissues in the latter. A dose effect was observed between those causing respiratory illness and those resulting in neurological disease. The data demonstrate the successful establishment of the hamster model of NiV disease for subsequent use in the evaluation of vaccines and antivirals.

Applicability of the model of inclusive education in early childhood education: a case study

Despite the development of policies and research supporting it, inclusion remains a challenge in contemporary education. We have developed a theoretical model for implementing inclusive education, thereby supporting early childhood education quality. It is necessary to establish the applicability of this model in order to apply it to improve the practices for adopting inclusive education. We conducted a case study, which showed that all levels and key characteristics of the theoretical model were also relevant in practice. However, as a result of the case study, the features describing the key characteristics were modified compared with the initial model. Additionally, the case study revealed that some of the features did not appear in practice. Those undetected features were mostly related to understanding the concept of inclusive education and the philosophy of inclusion. There appeared a need for a clearer understanding of inclusion on both the institutional and state level. The implementation of inclusive education does not in itself always increase inclusion or reduce exclusion. Therefore, when implementing inclusive education, it is necessary to think carefully about what is being done to allow all children to be meaningfully involved in the same classroom and by their teachers.

Face and Content Validity of an Artificial Eye Model for Secondary Intraocular Lens Fixation via Yamane Technique

Purpose

To assess the face and content validity of an artificial eye model for secondary intraocular lens (IOL) fixation via the Yamane technique.

Methods

Ophthalmologists and residents participated in a 90-minute simulation session on secondary IOL fixation via the Yamane technique. Hands-on practice of this technique was performed on an artificial eye, the Bioniko Okulo BR8. After, all ophthalmologists answered an 18-question survey assessing the face and content validity of the model. Survey responses were recorded on a 5-point double-headed Likert scale, ranging from strongly agree (1)-to-strongly disagree (5) (Figure 1).

Results

Twenty-three surveys were completed. Respondents rated the survey with a median response of 1 (strongly agree)-to-3 (neutral). Highest ratings for the model were received for "usefulness for training residents", and "easier to set up and clean-up compared to a cadaver". Lowest ratings were received for realism of the model compared to cadaveric eyes. Statistical analysis revealed no significant difference among identified groups. Ratings for face and content validity were viewed favorably, both with an overall median response of 2.00 (agree).

Conclusion

The Bioniko Okulo BR8 shows promise as a valid tool for practicing secondary IOL fixation via the Yamane technique. Considering recent guidelines in competency-based ophthalmology education programs, this model may be a valuable tool over traditional techniques for teaching and improving surgical skill amongst trainees.

Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study

Background

Preoperative prediction models for histologic subtype and grade of stage IA lung adenocarcinoma (LUAD) according to the update of the WHO Classification of Tumors of the Lung in 2021 and the 2020 new grade system are yet to be explored. We aim to develop the noninvasive pathology and grade evaluation approach for patients with stage IA LUAD via CT-based radiomics approach and evaluate their performance in clinical practice.

Methods

Chest CT scans were retrospectively collected from patients who were diagnosed with stage IA LUAD and underwent complete resection at two hospitals. A deep learning segmentation algorithm was first applied to assist lesion delineation. Expansion strategies such as bounding-box annotations were further applied. Radiomics features were then extracted and selected followed by radiomics modeling based on four classic machine learning algorithms for histologic subtype classification and grade stratification. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance.

Results

The study included 294 and 145 patients with stage IA LUAD from two hospitals for radiomics analysis, respectively. For classification of four histological subtypes, multilayer perceptron (MLP) algorithm presented no annotation strategy preference and achieved the average AUC of 0.855, 0.922, and 0.720 on internal, independent, and external test sets with 1-pixel expansion annotation. Bounding-box annotation strategy also enabled MLP an acceptable and stable accuracy among test sets. Meanwhile, logistic regression was selected for grade stratification and achieved the average AUC of 0.928, 0.837, and 0.748 on internal, independent, and external test sets with optimal annotation strategies.

Conclusions

DL-enhanced radiomics models had great potential to predict the fine histological subtypes and grades of early-stage LUADs based on CT images, which might serve as a promising noninvasive approach for the diagnosis and management of early LUADs.

Diversity of Ganglion Cell Responses to Saccade-Like Image Shifts in the Primate Retina

Saccades are a fundamental part of natural vision. They interrupt fixations of the visual gaze and rapidly shift the image that falls onto the retina. These stimulus dynamics can cause activation or suppression of different retinal ganglion cells, but how they affect the encoding of visual information in different types of ganglion cells is largely unknown. Here, we recorded spiking responses to saccade-like shifts of luminance gratings from ganglion cells in isolated marmoset retinas and investigated how the activity depended on the combination of presaccadic and postsaccadic images. All identified cell types, On and Off parasol and midget cells, as well as a type of Large Off cells, displayed distinct response patterns, including particular sensitivity to either the presaccadic or the postsaccadic image or combinations thereof. In addition, Off parasol and Large Off cells, but not On cells, showed pronounced sensitivity to whether the image changed across the transition. Stimulus sensitivity of On cells could be explained based on their responses to step changes in light intensity, whereas Off cells, in particular, parasol and the Large Off cells, seem to be affected by additional interactions that are not triggered during simple light-intensity flashes. Together, our data show that ganglion cells in the primate retina are sensitive to different combinations of presaccadic and postsaccadic visual stimuli. This contributes to the functional diversity of the output signals of the retina and to asymmetries between On and Off pathways and provides evidence of signal processing beyond what is triggered by isolated steps in light intensity.SIGNIFICANCE STATEMENT Sudden eye movements (saccades) shift our direction of gaze, bringing new images in focus on our retinas. To study how retinal neurons deal with these rapid image transitions, we recorded spiking activity from ganglion cells, the output neurons of the retina, in isolated retinas of marmoset monkeys while shifting a projected image in a saccade-like fashion across the retina. We found that the cells do not just respond to the newly fixated image, but that different types of ganglion cells display different sensitivities to the presaccadic and postsaccadic stimulus patterns. Certain Off cells, for example, are sensitive to changes in the image across transitions, which contributes to differences between On and Off information channels and extends the range of encoded stimulus features.

Cost-effectiveness analysis of introducing rotavirus vaccine into immunization program in Zhejiang province, China: A decision tree-Markov model study

Two RV vaccines have been licensed in Zhejiang province, China, including the pentavalent RotaTeq (RV5) and the monovalent RV vaccine (RV1). This study aimed to evaluate the cost-effectiveness of RV1 and RV5 to inform the prioritization of provincial immunization program. A decision tree-Markov model was applied to assess the cost-effectiveness of RV1 or RV5 vaccination, compared to no RV vaccination in a 5-year period. The incremental health cost per disability-adjusted life year (DALY) is averted when each kind of RV vaccine included in the immunization program was estimated. It was also compared to the willingness-to-pay (WTP) threshold of 1 time of the Gross Domestic Product (GDP) per capita of Zhejiang province in 2021 (16865.70US$). The uncertainties of these results were assessed through the one-way sensitivity analysis and probabilistic sensitivity analysis. In the base-case scenario, RV5 was the least costly RV vaccine with a cost-effectiveness ratio of 510 US$ per DALY averted. The other RV1 vaccines provided equivalent benefits but at higher costs, with a cost-effectiveness ratio of 4600 US$ per DALY averted. Compared to no vaccination, the probabilities of being cost-effective were 72.3% for RV1 and 88.4% for RV5, at a WTP threshold of 1 time of the GDP per capita. An RV vaccination program using any of the two China-licensed RV vaccines compared to no vaccination would avert 30-70% of outpatient visits, hospitalizations, and deaths from RVGE. RV5 was currently estimated to be the most cost-effective.

An IoT System Proposed for Higher Education: Approaches and Challenges in Economics, Computational Linguistics, and Engineering

The technological revolution and the evolution of technology have significantly facilitated the applicability of the IoT in various domains, such as healthcare, transportation, agriculture, retail, education, and, especially, higher education, which encompasses countless areas. Petri nets can be a useful tool to model the behavior of an IoT system. The main objective of this paper was to propose, model, and analyze a complex IoT system for higher education. The system involves the integration of IoT devices for monitoring data. An educational cloud was used as a support tool through which tracking, and control actions were implemented both internally, between the cloud and entities, and externally, between the cloud and the IoT. The system was modeled using Petri nets, which are systems with discrete events, and for simulation, we used the Visual Object Net++ package. Using this application, information was obtained in real time, and it was possible to intervene with changes even in the design phase. The diagrams were easy to read and interpret, which is an advantage for the decision-making system. The general structure of the application was based on n entities, where each entity represented a higher education field. In this paper, we discuss at least three fields: economics, computational linguistics, and engineering.

Research Methods and New Advances in Drug-Drug Interactions Mediated by Renal Transporters

The kidney is critical in the human body's excretion of drugs and their metabolites. Renal transporters participate in actively secreting substances from the proximal tubular cells and reabsorbing them in the distal renal tubules. They can affect the clearance rates (CLr) of drugs and their metabolites, eventually influence the clinical efficiency and side effects of drugs, and may produce drug-drug interactions (DDIs) of clinical significance. Renal transporters and renal transporter-mediated DDIs have also been studied by many researchers. In this article, the main types of in vitro research models used for the study of renal transporter-mediated DDIs are membrane-based assays, cell-based assays, and the renal slice uptake model. In vivo research models include animal experiments, gene knockout animal models, positron emission tomography (PET) technology, and studies on human beings. In addition, in vitro-in vivo extrapolation (IVIVE), ex vivo kidney perfusion (EVKP) models, and, more recently, biomarker methods and in silico models are included. This article reviews the traditional research methods of renal transporter-mediated DDIs, updates the recent progress in the development of the methods, and then classifies and summarizes the advantages and disadvantages of each method. Through the sorting work conducted in this paper, it will be convenient for researchers at different learning stages to choose the best method for their own research based on their own subject's situation when they are going to study DDIs mediated by renal transporters.

The Synthesis and Evaluation of Porous Carbon Material from Corozo Fruit (Bactris guineensis) for Efficient Propranolol Hydrochloride Adsorption

This study explores the potential of the corozo fruit (Bactris guineensis) palm tree in the Colombian Caribbean as a source for porous carbon material. Its specific surface area, pore volume, and average pore size were obtained using N₂ adsorption/desorption isotherms. The images of the precursor and adsorbent surface were obtained using scanning electron microscopy (SEM). Fourier transform infrared (FTIR) spectra were obtained to detect the main functional groups present and an X-ray diffraction analysis (XRD) was performed in order to analyze the structural organization of the materials. By carbonizing the fruit stone with zinc chloride, a porous carbon material was achieved with a substantial specific surface area (1125 m² g⁻¹) and pore volume (3.241 × 10-¹ cm³ g⁻¹). The material was tested for its adsorption capabilities of the drug propranolol. The optimal adsorption occurred under basic conditions and at a dosage of 0.7 g L⁻¹. The Langmuir homogeneous surface model effectively described the equilibrium data and, as the temperature increased, the adsorption capacity improved, reaching a maximum of 134.7 mg g⁻¹ at 328.15 K. The model constant was favorable to the temperature increase, increasing from 1.556 × 10^-1 to 2.299 × 10^-1 L mg^-1. Thermodynamically, the adsorption of propranolol was found to be spontaneous and benefited from higher temperatures, indicating an endothermic nature (12.39 kJ mol⁻¹). The negative ΔG⁰ values decreased from -26.28 to -29.99 kJ mol^-1, with the more negative value occurring at 328 K. The adsorbent material exhibited rapid kinetics, with equilibrium times ranging from 30 to 120 min, depending on the initial concentration. The kinetics data were well-represented by the general order and linear driving force models. The rate constant of the general order model diminished from 1.124 × 10^-3 to 9.458 × 10^-14 with an increasing concentration. In summary, the leftover stone from the Bactris guineensis plant can be utilized to develop activated carbon, particularly when activated using zinc chloride. This material shows promise for efficiently adsorbing propranolol and potentially other emerging pollutants.

'Critical circle of fundoplication' - A key concept towards creating identical, replicable and consistent fundoplication wraps

Background

There is a significant learning curve associated with laparoscopic Nissen's fundoplication, during which, complications due to surgical errors are more likely to occur. Moreover, length of the wrap, its situation on the lower esophagus and the points of the fundus brought together to create the wrap are arbitrary and as per the surgeon's judgement and estimation. This leads to a lot of variation in the wraps created, not only from surgeon to surgeon, but also from case to case for each individual surgeon. We propose a new concept of 'Critical Circle of Fundoplication' and the new approach to construct the fundoplication based on this concept, by application of which, a mathematically precise fundoplication can be created which is consistent, accurate and optimum.

Patients and Methods

A total of 132 patients were operated based on the concept of 'Critical Circle of Fundoplication' between 2015 and 2020, of which 116 patients completed one year of follow-up and are included in the paper.

Results

The demographic characteristics, intraoperative findings and postoperative are described in detail. An additional average time of 10.5 minutes (range: 8-26 minutes) was required in the precise measurements and markings that were required by this approach.

Conclusion

By application of the concept of 'Critical Circle of Fundoplication', precise, consistent and optimum wraps can be created. By eliminating the errors of structural construction and placement of the wrap, overall results can be improved.

A Survival Model for Patients with Upper Tract Urothelial Carcinoma

OBJECTIVES

We aimed to establish a survival model for patients with upper tract urothelial carcinoma (UTUC).

METHODS

A total of 241 patients with UTUC treated from January 2010 to December 2018 were selected. Their general clinical data were collected, and urological indices were measured. They were followed up after discharge, and divided into a death group (n = 51) and a survival group (n = 190) to compare the clinical data. Multivariate logistic regression analysis was performed to analyze the independent risk factors for postoperative death, based on which a nomogram prediction model was established and then validated.

RESULTS

The death group had significantly older age, larger tumor diameter, and higher tumor grade, pathological stage and proportion of no adjuvant chemotherapy than those of the control group (p < 0.01). The results of multivariate logistic analysis suggested that high tumor grade, tumor located in the ureter, large tumor diameter, high pathological stage, and lymph node metastasis were independent risk factors for postoperative death. A nomogram prediction model was established based on the prognostic independent risk factors. The area under the curve of receiver operating characteristic curve was 0.828 (95% confidence interval (CI): 0.801-0.845), so the model had good discrimination. The calibration curve showed that the model had high consistency.

CONCLUSIONS

The established nomogram model can be used to predict the mortality risk of patients with UTUC and postoperative survival, and to develop individualized treatment plans for improving the prognosis and survival.