Impaired brain ability of older adults to transit and persist to latent states with well-organized structures at wakeful rest.

The intrinsic brain functional network organization continuously changes with aging. By integrating spatial and temporal information, the process of how brain networks temporally reconfigure and remain well-organized spatial structure largely reflects the brain function, thereby holds the potential to capture its age-related declines. In this study, we examined the spatiotemporal brain dynamics from resting-state functional Magnetic Resonance Imaging (fMRI) data of healthy young and older adults using a Hidden Markov Model (HMM). Six brain states were generated by HMM, with the young group showing higher fractional occupancy and mean dwell time in states 1, 3, and 4 (SY1, SY2 and SY3), and the older group in states 2, 5, and 6 (SO1, SO2 and SO3). Importantly, comparisons of transition probabilities revealed that the older group showed a reduced brain ability to transition into states dominated by the younger group, as well as a diminished capacity to persist in them. Moreover, graph analysis revealed that these young-specific states exhibited higher modularity and k-coreness. Collectively, these findings suggested that the older group showed impaired brain ability of both transition into and sustain well spatially organized states. This emphasized that the temporal changes in brain state organization, rather than its static mode, could be a key biomarker for detecting age-related functional decline. These insights may pave the way for targeted interventions aimed at mitigating cognitive decline in the aging population.

Landscape pattern prediction method based on ANN-CA-Markov coupling model.

The simulation and prediction of landscape patterns can provide a reference for the formulation of ecological space security policies and management and control measures. Therefore, it is necessary to use objective and accurate scientific methods to make accurate simulation predictions. In this study, the quantitative prediction of the Markov model is combined with the advantages of the space-time simulation of the CA model, and the artificial neural network (ANN) module is used for calculation and coupling correction. The Kappa coefficient and the FoM (figure of merit) index are used to test the simulation accuracy. Based on this process, the changes of the landscape pattern in Mudanjiang City at three time points in 2000, 2010 and 2020 were analyzed, and the simulation prediction was made based on this. The experimental results show that the CA-Markov model using the ANN module for coupling correction can be tested for accuracy in the study area, and its Kappa coefficient is 0.834 and the FoM index is 0.001. The model meets the requirements of the landscape pattern for the accuracy of simulation and prediction, and the prediction and analysis of the study area is carried out, and the accurate prediction results are obtained. The results of the study were: Looking at the change in landscape pattern over the 20 years from 2000 to 2020, significant changes occurred in the areas of arable land and artificial surface. The total area of arable land decreased by nearly 130 km2, while the area of artificial surface (i.e., built-up land) increased by about 167 km2. The forest area initially decreased and then increased, with an overall increase of approximately 56 km2, while the water and grassland areas first increased and then decreased. In terms of spatial distribution, the changes in agricultural and forest land are mainly concentrated in the central area where urban development is intensive and in the northern forest area. The results of this study are quite important for policy formulation, according to which it was found that human activities, especially urbanization, have led to a decrease in the area of forested land, and therefore the implementation of the natural forest protection policy can lead to a sustained increase in the area of forested land.

A Markov cost-effectiveness modeling framework for evaluating wound dressings: A concept for practical implementation of economic evaluations in an informed dressing selection process.

AIMS: Exemplify the potential of using health economy modeling and simulations to support and optimize wound dressing purchasing decisions. MATERIALS AND METHODS: We developed a Markov cost-effectiveness modeling framework fusing clinical and industry sources of healing and cost outcomes for evaluating dressings, focusing on polymeric membrane dressings compared to passive foam dressings without active inflammation modulation components. We calculated the wound care costs for patients with and without diabetes, as well as for infected and non-infected wounds, to illustrate the effectiveness of this model in supporting decision-making. RESULTS: The model results demonstrated that polymeric membrane dressings reduce the cumulative treatment costs compared to passive foam dressings, due to fewer dressing changes and lower associated labor costs, regardless of the initial product price differences. CONCLUSION: Cost-effectiveness calculations should be performed in healthcare facilities to support purchasing decisions based on true cost analyses. Making purchasing decisions focusing on the dressing price alone may provide wrong estimates of the real cost differences.

Cost-effectiveness of expanding national health insurance coverage for composite resin restorations in cancer patients in South Korea.

OBJECTIVES: Cancer patients often have compromised oral health, making them vulnerable to severe dental caries and restoration failures. Due to the nature of cervical or anterior caries in cancer patients, the use of adequate restorative materials is important. However, public dental insurance coverage for composite treatments varies among countries and only glass ionomer cements (GICs) are covered in all age groups in South Korea. This study examined the cost-effectiveness of expanding national health insurance coverage to include resin composite (RC) restorations as compared with GIC in cancer patients. METHODS: Data from cancer patients who received direct restoration using GIC were identified from the National Health Screening Cohort. The relative effect of RC compared to GIC was determined through a meta-analysis, which was then utilized in calculating corresponding transition probabilities within a multi-state model. A Markov-chain Monte Carlo microsimulation was performed to estimate useful life-years and total treatment costs at the tooth level. The incremental cost-effectiveness ratio (ICER) of RC versus GIC was calculated, considering scenarios with and without expanded national health insurance coverage. The robustness of the results was confirmed through various sensitivity analyses. RESULTS: Between the two materials, RC resulted in a 0.4-year longer useful life. From a limited societal perspective, it cost $9.6 less with expanded coverage but $24.3 more without expansion, resulting in an ICER of -$25.2 and $63.9 per tooth-year, respectively. From a patient's perspective, the ICER values were -$72.7 versus $138.8 per tooth-year, respectively, translating into $200 more in savings with the expansion. Various sensitivity analyses consistently demonstrated a smaller ICER when insurance coverage was expanded. CONCLUSIONS: The expansion of national health insurance coverage to include RC restorations for cancer patients appears to be clearly cost-effective. This emphasizes the need for further policy considerations to ensure access to dental care for cancer patients. CLINICAL SIGNIFICANCE: Timely management of dental caries is crucial for cancer patients, as untreated caries can escalate into severe oral conditions, negatively impacting treatment outcomes and increasing care costs. Expanding a national health insurance coverage for cancer patients in the treatment of early dental lesions is necessary to prevent advanced dental diseases.

Predator-prey space use and landscape features influence movement behaviors in a large-mammal community.

Predator hunting strategies, such as stalking versus coursing behaviors, are hypothesized to influence antipredator behaviors of prey and can describe the movement behaviors of predators themselves. Predators and prey may alter their movement in relation to predator hunting modes, yet few studies have evaluated how these strategies influence movement behaviors of free-ranging animals in a multiple-predator, multiple-prey system. We fit hidden Markov models (HMM) with movement data derived from >400 GPS-collared ungulates and large predators in eastern Washington, USA. We used these models to test our hypotheses that stalking (cougars [Puma concolor]) and coursing (gray wolves [Canis lupus]) predators would exhibit different broad-scale movement behaviors consistent with their respective hunting strategies in areas that increased the likelihood of encountering or capturing ungulate prey (e.g., habitats selected by deer [Odocoileus spp.]). Similarly, we expected that broadscale movement behaviors of prey would change in response to background levels of predation risk associated with each predator's hunting strategy. We found that predators and ungulate prey adjusted their broadscale movements in response to one another's long-term patterns of habitat selection but not based on differences in predator-hunting strategies. Predators changed their movement behaviors based on the type of prey, whereas ungulates generally reduced movement in areas associated with large predators, regardless of the predator's hunting strategy. Both predator and prey movements varied in response to landscape features but not necessarily based on habitat that would facilitate specific hunting behaviors. Our results suggest that predators and prey adjust their movements at broad temporal scales in relation to long-term patterns of risk and resource distributions, potentially influencing their encounter rates with one another at finer spatiotemporal scales. Habitat features further influenced changes in movement, resulting in a complex combination of movement behaviors in multiple-predator, multiple-prey systems.

A Nationwide Physical Activity Intervention for 654,500 Adults in Singapore: Cost-Utility Analysis.

Background: Increasing physical inactivity is a primary risk factor for diabetes and hypertension, contributing to rising health care expenditure and productivity losses. As Singapore's aging population grows, there is an increased disease burden on Singapore's health systems. Large-scale physical activity interventions could potentially reduce the disease burden but face challenges with the uncertainty of long-term health impact and high implementation costs, hindering their adoption. Objective: We examined the cost-effectiveness of the Singapore National Steps Challenge (NSC), an annual nationwide mobile health (mHealth) intervention to increase physical activity, from both the health care provider perspective, which only considers the direct costs, and the societal perspective, which considers both the direct and indirect costs. Methods: We used a Markov model to assess the long-term impact of increased physical activity from the NSC on adults aged 17 years and older. A Monte Carlo simulation with 1000 samples was conducted to compare two situations: the NSC conducted yearly for 10 years against a no-intervention situation with no NSC. The model projected inpatient and outpatient costs and mortality arising from diabetes and hypertension, as well as their complications. Health outcomes were expressed in terms of the quality-adjusted life-years (QALYs) gained. All future costs and QALYs were discounted at 3% per annum. Sensitivity analyses were done to test the robustness of our model results. Results: We estimated that conducting the NSC yearly for 10 years with a mean cohort size of 654,500 participants was projected to prevent 6200 diabetes cases (95% credible interval 3700 to 9100), 10,500 hypertension cases (95% credible interval 6550 to 15,200), and 4930 deaths (95% credible interval 3260 to 6930). This led to a reduction in health care costs of SGD (Singapore dollar) 448 million (95% credible interval SGD 132 million to SGD 1.09 billion; SGD 1=US $0.73 for the year 2019). There would be 78,800 (95% credible interval 55,700 to 102,000) QALYs gained. Using a willingness-to-pay threshold of SGD 10,000 per QALY gained, the NSC would be cost-saving. When indirect costs were included, the NSC was estimated to reduce societal costs by SGD 1.41 billion (95% credible interval SGD 353 million to SGD 3.80 billion). The model was most sensitive to changes in the inpatient cost of treatment for diabetes complications, time horizon, and program compliance. Conclusions: In this modeling study, increasing physical activity by conducting a yearly nationwide physical activity intervention was cost-saving, preventing diabetes and hypertension and reducing mortality from these diseases. Our results provide important information for decision-making in countries that may consider introducing similar large-scale physical activity programs.

Cancer detection with various classification models: A comprehensive feature analysis using HMM to extract a nucleotide pattern.

This work presents a novel feature extraction method for identifying complex patterns in genomic sequences by employing the Hidden Markov Model (HMM). In this study, we use HMM to identify gene nucleotide patterns that are specific to malignant and non-malignant cells. Crucial genetic components DNA and RNA are involved in many biological processes that impact both healthy and malignant cells. Early patient identification is essential to successful cancer diagnosis and therapy. Varying nucleotide patterns indicate different cellular responses, which are important to understanding the molecular causes of cancer and associated disorders. We present a detailed study of nucleotide patterns in whole raw nucleotide sequences with variations in both protein sequence (CDS) and non-protein sequence (NCDS) in both malignant and non-malignant cells. Nucleotide prediction has been achieved while computational expenses are reduced by using the proposed HMM for feature extraction and selection. The classification models implemented in this work for cancer detection are Gradient-Boosted Decision Trees (GBDT), Random Forests (RF), Decision Trees (DT), and Support Vector Machines (SVM) with kernels. The suggested classification model's accuracy and 10-fold cross-validation have been validated via comprehensive case studies. The results reveal that DT and ensemble learning techniques significantly differentiate between malignant and non-malignant DNA sequences. SVM with suitable kernels improves cancer detection accuracy significantly. Combining feature reduction approaches with nucleotide pattern classifiers based on Hidden Markov models improves performance and ensures reliable cancer detection.

MMG4: Recognition of G4-Forming Sequences Based on Markov Model.

G-quadruplexes (G4s) are special nucleic acid structures with various important biological functions. Existing tools and technologies for G4-forming sequences recognition are limited to time-consuming and costly methods such as circular dichroism and nuclear magnetic resonance. Developing a fast and accurate model for G4-forming sequences recognition has far-reaching significance. In this study, MMG4, a novel model to recognize G4-forming sequences based on Markov model (MM), was developed and the phenomenon of high recognition accuracy in the central region of the sequence and low accuracy in the two end regions was discovered. It was further found that the differences in base transfer probabilities, ratio distribution, and G4-motif structural content in different regions may be the causes of this phenomenon. The study also explored the impact of sequence length on recognition accuracy and found the optimal recognition interval to be [910-1049], with the highest recognition accuracy reaching 85.95%. By extracting sequence features, the study constructed three types of machine learning models: random forest (RF), support vector machine, and back-propagation neural network. It was found that recognition performance of MM was significantly better than that of the other three machine learning models, proving that the recognition method based on MM can effectively capture the correlation information between adjacent nucleotides of G4. By combining MM with the three machine learning models, the predictive performance of MMG4 improved. Among them, the RF model combined with MM has the best performance, achieving an area under the receiver operating characteristic curve value of 0.93 and an area under the precision-recall curve value of 0.9. Finally, the study validated the model robustness and generalization ability through independent testing dataset.

Comparing Recurrence Rates and the Cost-Effectiveness of Arthroscopic Labral Repair and Nonoperative Management for Primary Anterior Shoulder Dislocations in Young Patients: A Decision-Analytic Markov Model-Based Analysis.

BACKGROUND: Value-based decision-making regarding nonoperative management versus early surgical stabilization for first-time anterior shoulder instability (ASI) events remains understudied. PURPOSE: To perform (1) a systematic review of the current literature and (2) a Markov model-based cost-effectiveness analysis comparing an initial trial of nonoperative management to arthroscopic Bankart repair (ABR) for first-time ASI. STUDY DESIGN: Economic and decision analysis; Level of evidence, 3. METHODS: A Markov chain Monte Carlo probabilistic model was developed to evaluate the outcomes and costs of 1000 simulated patients (mean age, 20 years; range, 12-26 years) with first-time ASI undergoing nonoperative management versus ABR. Utility values, recurrence rates, and transition probabilities were derived from the published literature. Costs were determined based on the typical patient undergoing each treatment strategy at the authors' institution. Outcome measures included costs, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER). RESULTS: The Markov model with Monte Carlo microsimulation demonstrated mean (± standard deviation) 10-year costs for nonoperative management and ABR of $38,649 ± $10,521 and $43,052 ± $9352, respectively. Total QALYs acquired over the 10-year time horizon were 7.67 ± 0.43 and 8.44 ± 0.46 for nonoperative management and ABR, respectively. The ICER comparing ABR with nonoperative management was found to be just $5725/QALY, which falls substantially below the $50,000 willingness-to-pay (WTP) threshold. The mean numbers of recurrences were 2.55 ± 0.31 and 1.17 ± 0.18 for patients initially assigned to the nonoperative and ABR treatment groups, respectively. Of 1000 samples run over 1000 trials, ABR was the optimal strategy in 98.7% of cases, with nonoperative management the optimal strategy in 1.3% of cases. CONCLUSION: ABR reduces the risk for recurrent dislocations and is more cost-effective despite higher upfront costs when compared with nonoperative management for first-time ASI in the young patient. While all these factors are important to consider in surgical decision-making, ultimate treatment decisions should be made on an individual basis and occur through a shared decision-making process.

Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI.

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16 hr fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Insights into Blue Whale (Balaenoptera musculus L.) Population Movements in the Galapagos Archipelago and Southeast Pacific.

The Galapagos Marine Reserve is vital for cetaceans, serving as both a stopover and residency site. However, blue whales, occasionally sighted here, exhibit poorly understood migratory behavior within the Galapagos and the broader Eastern Tropical Pacific. This study, the first to satellite tag blue whales in the Galapagos (16 tagged between 2021 and 2023), explored their behavior in relation to environmental variables like chlorophyll-a concentration, sea surface temperature (SST), and productivity. Key findings show a strong correlation between foraging behavior, high chlorophyll-a levels, productivity, and lower SSTs, indicating a preference for food-rich areas. Additionally, there is a notable association with geomorphic features like ridges, which potentially enhance food abundance. Most tagged whales stayed near the Galapagos archipelago, with higher concentrations observed around Isabela Island, which is increasingly frequented by tourist vessels, posing heightened ship strike risks. Some whales ventured into Ecuador's exclusive economic zone, while one migrated southward to Peru. The strong 2023 El Niño-Southern Oscillation event led to SST and primary production changes, likely impacting whale resource availability. Our study provides crucial insights into blue whale habitat utilization, informing adaptive management strategies to mitigate ship strike risks and address altered migration routes due to climate-driven environmental shifts.

A novel method for sparse dynamic functional connectivity analysis from resting-state fMRI.

BACKGROUND: There is growing interest in understanding the dynamic functional connectivity (DFC) between distributed brain regions. However, it remains challenging to reliably estimate the temporal dynamics from resting-state functional magnetic resonance imaging (rs-fMRI) due to the limitations of current methods. NEW METHODS: We propose a new model called HDP-HSMM-BPCA for sparse DFC analysis of high-dimensional rs-fMRI data, which is a temporal extension of probabilistic principal component analysis using Bayesian nonparametric hidden semi-Markov model (HSMM). Specifically, we utilize a hierarchical Dirichlet process (HDP) prior to remove the parametric assumption of the HMM framework, overcoming the limitations of the standard HMM. An attractive superiority is its ability to automatically infer the state-specific latent space dimensionality within the Bayesian formulation. RESULTS: The experiment results of synthetic data show that our model outperforms the competitive models with relatively higher estimation accuracy. In addition, the proposed framework is applied to real rs-fMRI data to explore sparse DFC patterns. The findings indicate that there is a time-varying underlying structure and sparse DFC patterns in high-dimensional rs-fMRI data. COMPARISON WITH EXISTING METHODS: Compared with the existing DFC approaches based on HMM, our method overcomes the limitations of standard HMM. The observation model of HDP-HSMM-BPCA can discover the underlying temporal structure of rs-fMRI data. Furthermore, the relevant sparse DFC construction algorithm provides a scheme for estimating sparse DFC. CONCLUSION: We describe a new computational framework for sparse DFC analysis to discover the underlying temporal structure of rs-fMRI data, which will facilitate the study of brain functional connectivity.

Molecular Dynamics Simulation Combined with Neural Relationship Inference and Markov Model to Reveal the Relationship between Conformational Regulation and Bioluminescence Properties of Gaussia Luciferase.

Gaussia luciferase (Gluc) is currently known as the smallest naturally secreted luciferase. Due to its small molecular size, high sensitivity, short half-life, and high secretion efficiency, it has become an ideal reporter gene and is widely used in monitoring promoter activity, studying protein-protein interactions, protein localization, high-throughput drug screening, and real-time monitoring of tumor occurrence and development. Although studies have shown that different Gluc mutations exhibit different bioluminescent properties, their mechanisms have not been further investigated. The purpose of this study is to reveal the relationship between the conformational changes of Gluc mutants and their bioluminescent properties through molecular dynamics simulation combined with neural relationship inference (NRI) and Markov models. Our results indicate that, after binding to the luciferin coelenterazine (CTZ), the α-helices of the 109-119 residues of the Gluc Mutant2 (GlucM2, the flash-type mutant) are partially unraveled, while the α-helices of the same part of the Gluc Mutant1 (GlucM1, the glow-type mutant) are clearly formed. The results of Markov flux analysis indicate that the conformational differences between glow-type and flash-type mutants when combined with luciferin substrate CTZ mainly involve the helicity change of α7. The most representative conformation and active pocket distance analysis indicate that compared to the flash-type mutant GlucM2, the glow-type mutant GlucM1 has a higher degree of active site closure and tighter binding. In summary, we provide a theoretical basis for exploring the relationship between the conformational changes of Gluc mutants and their bioluminescent properties, which can serve as a reference for the modification and evolution of luciferases.

Cost-effectiveness analysis of durvalumab plus tremelimumab as first-line therapy in patients with unresectable hepatocellular carcinoma.

Background: The HIMALAYA trial found that durvalumab plus tremelimumab significantly prolonged progression-free survival and overall survival in patients with unresectable hepatocellular carcinoma (HCC) compared with sorafenib. Objective: This study aimed to investigate the cost-effectiveness of durvalumab plus tremelimumab compared with sorafenib in the first-line HCC setting. Design: A Markov model-based cost-effectiveness analysis. Methods: We created a Markov model to compare healthcare costs and clinical outcomes of HCC patients treated with durvalumab plus tremelimumab in the first-line setting compared with sorafenib. We estimated transition probabilities from randomized trials. Lifetime direct healthcare costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios were calculated for first-line durvalumab plus tremelimumab compared with sorafenib from a US payer's perspective. Results: In the base case, first-line durvalumab plus tremelimumab was associated with an improvement of 0.29 QALYs compared with sorafenib. While both treatment strategies were associated with considerable lifetime expenditures, first-line durvalumab plus tremelimumab was less expensive than sorafenib ($188,405 vs $218,584). The incremental net monetary benefit for durvalumab plus tremelimumab versus sorafenib was $72,762 (valuing QALYs at $150,000 each). The results of durvalumab plus tremelimumab were better in terms of costs and health outcomes in patients with HBV-related HCC and high alpha-fetoprotein levels. Conclusion: First-line durvalumab plus tremelimumab was estimated to be dominant for the treatment of unresectable HCC compared with sorafenib from a US payer's perspective.

Naivety dies with the calf: calf loss to human hunters imposes behavioral change in a long-lived but heavily harvested ungulate.

BACKGROUND: In prey, patterns of individual habitat selection and movement can be a consequence of an individuals' anti-predator behavior. Adjustments of anti-predator behavior are important for prey to increase their survival. Hunters may alter the anti-predator behavior of prey. In long-lived animals, experience may cause behavioral changes during individuals' lifetime, which may result in altered habitat selection and movement. Our knowledge of which specific events related to hunting activity induce behavioral changes in solitary living species is still limited. METHODS: We used offspring loss in a solitary and long-lived ungulate species, moose (Alces alces), as our model system. We investigated whether offspring loss to hunters induces behavioral changes in a species subjected to heavy human harvest but free from natural predation. To test for behavioral change in relation to two proxies for experience (calf fate and age), we combined movement data from 51 adult female moose with data on their offspring survival and female age. We tested for adjustments in females' habitat selection and movement following calf harvest using Hidden Markov Models and integrated Step Selection Analysis to obtain behavioral state specific habitat selection coefficients. RESULTS: We found that females with a harvested calf modified habitat selection and movement during the following hunting season. Female moose selected for shorter distance to roads during the night, selected for shorter distance to forests and greater distance to human settlements following calf harvest than females who had not lost a calf. The survival of twins in a given hunting season was related to female age. Older females we more likely to have twins survive the hunting season. CONCLUSIONS: Our findings suggest that losing offspring to human harvest imposes behavioral changes in a long-lived ungulate species, leading to adjustments in females' habitat selection and movement behavior, which may lower the risk of encountering hunters. In our study, female moose that experienced calf loss selected for lower distance to forest and selected for greater distance to human settlements during periods of high hunting pressure compared to females without the experience of calf loss during the previous hunting season. We interpret this as potential learning effects.

Exact Decoding of a Sequentially Markov Coalescent Model in Genetics.

In statistical genetics, the sequentially Markov coalescent (SMC) is an important family of models for approximating the distribution of genetic variation data under complex evolutionary models. Methods based on SMC are widely used in genetics and evolutionary biology, with significant applications to genotype phasing and imputation, recombination rate estimation, and inferring population history. SMC allows for likelihood-based inference using hidden Markov models (HMMs), where the latent variable represents a genealogy. Because genealogies are continuous, while HMMs are discrete, SMC requires discretizing the space of trees in a way that is awkward and creates bias. In this work, we propose a method that circumvents this requirement, enabling SMC-based inference to be performed in the natural setting of a continuous state space. We derive fast, exact procedures for frequentist and Bayesian inference using SMC. Compared to existing methods, ours requires minimal user intervention or parameter tuning, no numerical optimization or E-M, and is faster and more accurate.

Cost-effectiveness analysis of tislelizumab plus chemotherapy versus standard chemotherapy in first-line treatment for extensive-stage small cell lung cancer: perspectives from the United States and China.

BACKGROUND: Tislelizumab combined with chemotherapy has shown significant clinical benefits in improving overall survival compared to chemotherapy alone for patients with extensive-stage small-cell lung cancer (ES-SCLC). AIM: This study aimed to evaluate the cost-effectiveness of tislelizumab plus chemotherapy versus standard chemotherapy as a first-line treatment for ES-SCLC from the US payer perspective and the perspective of the Chinese healthcare system. METHOD: We conducted an economic evaluation using a Markov state-transition model, reflecting the US payer perspective and the perspective of the Chinese healthcare system. Baseline patient characteristics and essential clinical data were obtained from the RATIONALE-312 trial. The costs and utilities were derived from open-access databases and published literature. The primary outcomes measured included quality-adjusted life years (QALYs), incremental cost-effectiveness ratio (ICER), incremental net health benefit (INHB), and incremental net monetary benefit (INMB). Uncertainties in the model were addressed by probabilistic sensitivity analysis (PSA) and one-way sensitivity analysis (OWSA). RESULTS: In the base-case analysis, the addition of tislelizumab to chemotherapy provided an incremental gain of 0.16 QALYs at an additional cost of $7430.73, resulting in an ICER of $46,132.33 per QALY. Although above the willingness-to-pay (WTP) threshold of China of $38,042.49 per QALY, the cost-effectiveness was marginal, with an INHB of - 0.03 QALYs and an INMB of $- 1303.06. In the US, despite a slightly higher effectiveness gain of 0.28 QALYs, the increased cost of $45,157.35 resulted in an unfavorable ICER of $163,885.06 per QALY, exceeding the US WTP threshold of $150,000.00. PSA showed probabilities of cost-effectiveness of tislelizumab plus chemotherapy at 17.18% in China and 40.41% in the US. CONCLUSION: Tislelizumab combined with chemotherapy was not a cost-effective first-line treatment option for ES-SCLC in China or the US; however, the margin of cost-effectiveness was narrow.

Health impact of rotavirus vaccination in China.

Rotavirus (RV) vaccines have demonstrated substantial effectiveness in reducing the healthcare burden caused by gastroenteritis (RVGE) worldwide. This study aims to understand the differential impact of RV vaccination in reducing RVGE burden in children under 7 years old in China. A Markov Model was used to investigate the health impact of introducing two different RV vaccines into the Chinese population. The analysis was conducted for RV5, a live pentavalent human-bovine reassortant vaccine, and Lanzhou Lamb RV (LLR), a live-attenuated monovalent RV vaccine, separately, by comparing the strategy of each vaccine to no vaccination within a Chinese birth cohort, including 100,000 children modeled until 7 years of age. The vaccination scenario assumed a vaccination coverage of 2.5%, 2.5%, 90% and 5% for doses one, two, three and no vaccine, respectively, for both vaccines. Strategies with RV5, LLR, and no vaccination were associated with 9,895, 49,069, and 64,746 symptomatic RV infections, respectively. RV5 and LLR were associated with an 85% and 24% reduction in the total symptomatic RV infections, respectively, suggesting that the health benefits of RV5 are at least three-fold greater than those associated with the LLR. Further, strategies with RV5 and LLR resulted in an estimated 206 and 59-year increase in quality-adjusted life years (QALYs), respectively. Sensitivity and scenario analyses supported the robustness of the base-case findings. Use of RV vaccine is expected to improve RV-associated health outcomes and its adoption will help alleviate the burden of RVGE in China. RV5 use will result in significantly better health outcomes.

Dynamic transient brain states in preschoolers mirror parental report of behavior and emotion regulation.

The temporal dynamics of resting-state networks may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan. However, little is known about brain dynamics during the preschool period, which is a sensitive time window for cognitive control development. The fast timescale of synchronization and switching characterizing cortical network functional organization gives rise to quasi-stable patterns (i.e., brain states) that recur over time. These can be inferred at the whole-brain level using hidden Markov models (HMMs), an unsupervised machine learning technique that allows the identification of rapid oscillatory patterns at the macroscale of cortical networks. The present study used an HMM technique to investigate dynamic neural reconfigurations and their associations with behavioral (i.e., parental questionnaires) and cognitive (i.e., neuropsychological tests) measures in typically developing preschoolers (4-6 years old). We used high-density EEG to better capture the fast reconfiguration patterns of the HMM-derived metrics (i.e., switching rates, entropy rates, transition probabilities and fractional occupancies). Our results revealed that the HMM-derived metrics were reliable indices of individual neural variability and differed between boys and girls. However, only brain state transition patterns toward prefrontal and default-mode brain states, predicted differences on parental-report questionnaire scores. Overall, these findings support the importance of resting-state brain dynamics as functional scaffolds for behavior and cognition. Brain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers.

Movement decisions driving metapopulation connectivity respond to social resources in a long-lived ungulate, bighorn sheep (Ovis canadensis).

The spatial availability of social resources is speculated to structure animal movement decisions, but the effects of social resources on animal movements are difficult to identify because social resources are rarely measured. Here, we assessed whether varying availability of a key social resource-access to receptive mates-produces predictable changes in movement decisions among bighorn sheep in Nevada, the United States. We compared the probability that males made long-distance 'foray' movements, a critical driver of connectivity, across three ecoregions with varying temporal duration of a socially mediated factor, breeding season. We used a hidden Markov model to identify foray events and then quantified the effects of social covariates on the probability of foray using a discrete choice model. We found that males engaged in forays at higher rates when the breeding season was short, suggesting that males were most responsive to the social resource when its existence was short lived. During the breeding season, males altered their response to social covariates, relative to the non-breeding season, though patterns varied, and age was associated with increased foray probability. Our results suggest that animals respond to the temporal availability of social resources when making the long-distance movements that drive connectivity. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.