A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,t...A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,the aging rates between two age groups are set to be constant.The existence-and-uniqueness of global positive solution is firstly showed.Then,by constructing several appropriate Lyapunov functions and using the high-dimensional Itô’s formula,the sufficient conditions for the stochastic extinction and stochastic persistence of the exposed individuals and the infected individuals are obtained.The stochastic extinction indicator and the stochastic persistence indicator are less-valued expressions compared with the basic reproduction number.Meanwhile,the main results of this study are modified into multi-age groups.Furthermore,by using the surveillance data for Fujian Provincial Center for Disease Control and Prevention,Fuzhou COVID-19 epidemic is chosen to carry out the numerical simulations,which show that the age group of the population plays the vital role when studying infectious diseases.展开更多
A 3D crustal model was constructed using a combination of cutting-edge techniques,which were integrated to provide a density model for Egypt and address the sporadic distribution of seismic data.These techniques inclu...A 3D crustal model was constructed using a combination of cutting-edge techniques,which were integrated to provide a density model for Egypt and address the sporadic distribution of seismic data.These techniques include obtaining gravity data from the Gravity Field and Steady-State Ocean Circulation Explorer(GOCE),creating seismic profiles,analyzing the receiver functions of seismic data,obtaining information from boreholes,and providing geological interpretations.GOCE satellite gravity data were processed to construct a preliminary model based on nonlinear inversions of the data.A regional crustal thickness model was developed using receiver functions,seismic refraction profiles,and geological insights.The inverted model was validated using borehole data and compared with seismic estimates.The model exhibited strong consistency and revealed a correlation between crustal thickness,geology,and tectonics of Egypt.It showed that the shallowest depths of the Moho are located in the north along the Mediterranean Sea and in the eastern part along the Red Sea,reflecting an oceanic plate with a thin,high-density crust.The deepest Moho depths are located in the southwestern part of Egypt,Red Sea coastal mountains,and Sinai Peninsula.The obtained 3D model of crustal thickness provided finely detailed Moho depth estimates that aligned closely with geology and tectonic characteristics of Egypt,contributing valuable insights into the subsurface structure and tectonic processes of region.展开更多
Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism rem...Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.展开更多
Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model...Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.展开更多
To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precis...To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.展开更多
Bangladesh has a denser population in comparison with many other countries. Though the rate of population increase has been regarded as a concerning issue, estimation of the population instability in the upcoming year...Bangladesh has a denser population in comparison with many other countries. Though the rate of population increase has been regarded as a concerning issue, estimation of the population instability in the upcoming years may be useful for national planning. To predict Bangladesh’s future population, this study compares the estimated populations of two popular population models, the Malthusian and the logistic population models, with the country’s census population published by BBS. We also tried to find out which model gives a better approximation for forecasting the past, present, and future population between these two models.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination ...The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.展开更多
This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV i...This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.展开更多
Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-spec...Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
In this paper,we propose an RLC equivalent circuit model theory which can accurately predict the spectral response and resonance characteristics of metamaterial absorption structures,extend its design,and characterize...In this paper,we propose an RLC equivalent circuit model theory which can accurately predict the spectral response and resonance characteristics of metamaterial absorption structures,extend its design,and characterize the parameters of the model in detail.By employing this model,we conducted computations to characterize the response wavelength and bandwidth of variously sized metamaterial absorbers.A comparative analysis with Finite Difference Time Domain(FDTD)simulations demonstrated a remarkable level of consistency in the results.The designed absorbers were fabricated using micro-nano fabrication processes,and were experimentally tested to demonstrate absorption rates exceeding 90%at a wavelength of 9.28μm.The predicted results are then compared with test results.The comparison reveals good consistency in two aspects of the resonance responses,thereby confirming the rationality and accuracy of this model.展开更多
Terrestrial invasive alien weed plants are known to infest cultivated land, potentially releasing allelochemicals into the rhizosphere during decomposition, negatively impacting crop growth. This study aimed to evalua...Terrestrial invasive alien weed plants are known to infest cultivated land, potentially releasing allelochemicals into the rhizosphere during decomposition, negatively impacting crop growth. This study aimed to evaluate: (1) the allelopathic activity of five invasive weed species (Ageratum conyzoides, Bidens pilosa, Cecropia peltata, Tithonia diversifolia, and Chromolaena odorata) on Lactuca sativa and Phaseolus vulgaris growth;and (2) the effects of A. conyzoides rhizospheric soil on P. vulgaris seed germination. Bioassays of aqueous and leachate extracts were prepared from fresh leaves of the invasive species at concentrations of 0%, 25%, 50%, 75%, and 100% to assess allelopathic effects on L. sativa and P. vulgaris seed germination. Additionally, rhizospheric soil from A. conyzoides stands was collected, processed, and applied at varied weights (0.5 - 7 kg) to P. vulgaris seeds, with germination observed over nine days. Polynomial regression analysis was applied to model the data. High-concentration extracts (75% and 100%) significantly inhibited germination, root, and shoot growth in both L. sativa and P. vulgaris (P 2 − 80.294922x3 + 41.541115x4 − 11.747532x5 + 1.8501702x6 − 0.1519795x7 + 0.0050631x8. Allelopathic effects were concentration-dependent, with roots more sensitive than shoots to the invasive extracts. L. sativa was the most susceptible, while P. vulgaris showed greater tolerance. Modelling the allelopathic impact of A. conyzoides rhizospheric soil offers valuable insight into the allelochemical dosage necessary to affect seed germination, informing potential agricultural management strategies for invasive plant control.展开更多
The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical r...The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.展开更多
Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein functio...Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.展开更多
Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction mode...Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.展开更多
Diabetes mellitus is an important chronic disease that affects the health of the population worldwide, causing a serious impact on patients’ quality of life and increasing the burden on the healthcare system. With th...Diabetes mellitus is an important chronic disease that affects the health of the population worldwide, causing a serious impact on patients’ quality of life and increasing the burden on the healthcare system. With the increasing number of diabetic patients, the traditional healthcare model is under tremendous pressure. In recent years, the nurse-led diabetes management model, as an innovative approach to nursing intervention, has gradually become an important part of comprehensive diabetes management. This article reviews the conceptual model, specific types of nurse-led diabetes management models, barriers faced by nurses during implementation, and corresponding strategies, with a view to providing a reference for the management of diabetic patients and the development of diabetes specialty nurses.展开更多
To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these me...To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.展开更多
基金Supported by National Natural Science Foundation of China(61911530398,12231012)Consultancy Project by the Chinese Academy of Engineering(2022-JB-06,2023-JB-12)+3 种基金the Natural Science Foundation of Fujian Province of China(2021J01621)Special Projects of the Central Government Guiding Local Science and Technology Development(2021L3018)Royal Society of Edinburgh(RSE1832)Engineering and Physical Sciences Research Council(EP/W522521/1).
文摘A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,the aging rates between two age groups are set to be constant.The existence-and-uniqueness of global positive solution is firstly showed.Then,by constructing several appropriate Lyapunov functions and using the high-dimensional Itô’s formula,the sufficient conditions for the stochastic extinction and stochastic persistence of the exposed individuals and the infected individuals are obtained.The stochastic extinction indicator and the stochastic persistence indicator are less-valued expressions compared with the basic reproduction number.Meanwhile,the main results of this study are modified into multi-age groups.Furthermore,by using the surveillance data for Fujian Provincial Center for Disease Control and Prevention,Fuzhou COVID-19 epidemic is chosen to carry out the numerical simulations,which show that the age group of the population plays the vital role when studying infectious diseases.
文摘A 3D crustal model was constructed using a combination of cutting-edge techniques,which were integrated to provide a density model for Egypt and address the sporadic distribution of seismic data.These techniques include obtaining gravity data from the Gravity Field and Steady-State Ocean Circulation Explorer(GOCE),creating seismic profiles,analyzing the receiver functions of seismic data,obtaining information from boreholes,and providing geological interpretations.GOCE satellite gravity data were processed to construct a preliminary model based on nonlinear inversions of the data.A regional crustal thickness model was developed using receiver functions,seismic refraction profiles,and geological insights.The inverted model was validated using borehole data and compared with seismic estimates.The model exhibited strong consistency and revealed a correlation between crustal thickness,geology,and tectonics of Egypt.It showed that the shallowest depths of the Moho are located in the north along the Mediterranean Sea and in the eastern part along the Red Sea,reflecting an oceanic plate with a thin,high-density crust.The deepest Moho depths are located in the southwestern part of Egypt,Red Sea coastal mountains,and Sinai Peninsula.The obtained 3D model of crustal thickness provided finely detailed Moho depth estimates that aligned closely with geology and tectonic characteristics of Egypt,contributing valuable insights into the subsurface structure and tectonic processes of region.
文摘Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.
基金supported by the National Key R&D Program of China (Grant No.2022YFF0503700)the National Natural Science Foundation of China (42074196, 41925018)
文摘Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.
基金Yongxian Huang supported by Projects of Guangzhou Science and Technology Plan(2023A04J0409)。
文摘To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.
文摘Bangladesh has a denser population in comparison with many other countries. Though the rate of population increase has been regarded as a concerning issue, estimation of the population instability in the upcoming years may be useful for national planning. To predict Bangladesh’s future population, this study compares the estimated populations of two popular population models, the Malthusian and the logistic population models, with the country’s census population published by BBS. We also tried to find out which model gives a better approximation for forecasting the past, present, and future population between these two models.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
文摘The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-RP23066).
文摘This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary order.The HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered class.The total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,respectively.The proposed model is tested separately for unique solutions and existence on bi intervals.The numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons Polynomial.The proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler Law.The cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said disease.This study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered 1000.The obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.
文摘Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金Supported by the National Natural Science Foundation of China(62174092)the Open Fund of State Key Laboratory of Infrared Physics(SITP-NLIST-ZD-2023-04)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0580000)。
文摘In this paper,we propose an RLC equivalent circuit model theory which can accurately predict the spectral response and resonance characteristics of metamaterial absorption structures,extend its design,and characterize the parameters of the model in detail.By employing this model,we conducted computations to characterize the response wavelength and bandwidth of variously sized metamaterial absorbers.A comparative analysis with Finite Difference Time Domain(FDTD)simulations demonstrated a remarkable level of consistency in the results.The designed absorbers were fabricated using micro-nano fabrication processes,and were experimentally tested to demonstrate absorption rates exceeding 90%at a wavelength of 9.28μm.The predicted results are then compared with test results.The comparison reveals good consistency in two aspects of the resonance responses,thereby confirming the rationality and accuracy of this model.
文摘Terrestrial invasive alien weed plants are known to infest cultivated land, potentially releasing allelochemicals into the rhizosphere during decomposition, negatively impacting crop growth. This study aimed to evaluate: (1) the allelopathic activity of five invasive weed species (Ageratum conyzoides, Bidens pilosa, Cecropia peltata, Tithonia diversifolia, and Chromolaena odorata) on Lactuca sativa and Phaseolus vulgaris growth;and (2) the effects of A. conyzoides rhizospheric soil on P. vulgaris seed germination. Bioassays of aqueous and leachate extracts were prepared from fresh leaves of the invasive species at concentrations of 0%, 25%, 50%, 75%, and 100% to assess allelopathic effects on L. sativa and P. vulgaris seed germination. Additionally, rhizospheric soil from A. conyzoides stands was collected, processed, and applied at varied weights (0.5 - 7 kg) to P. vulgaris seeds, with germination observed over nine days. Polynomial regression analysis was applied to model the data. High-concentration extracts (75% and 100%) significantly inhibited germination, root, and shoot growth in both L. sativa and P. vulgaris (P 2 − 80.294922x3 + 41.541115x4 − 11.747532x5 + 1.8501702x6 − 0.1519795x7 + 0.0050631x8. Allelopathic effects were concentration-dependent, with roots more sensitive than shoots to the invasive extracts. L. sativa was the most susceptible, while P. vulgaris showed greater tolerance. Modelling the allelopathic impact of A. conyzoides rhizospheric soil offers valuable insight into the allelochemical dosage necessary to affect seed germination, informing potential agricultural management strategies for invasive plant control.
文摘The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.
基金supported by Warren Alpert Foundation and Houston Methodist Academic Institute Laboratory Operating Fund(to HLC).
文摘Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.
文摘Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.
文摘Diabetes mellitus is an important chronic disease that affects the health of the population worldwide, causing a serious impact on patients’ quality of life and increasing the burden on the healthcare system. With the increasing number of diabetic patients, the traditional healthcare model is under tremendous pressure. In recent years, the nurse-led diabetes management model, as an innovative approach to nursing intervention, has gradually become an important part of comprehensive diabetes management. This article reviews the conceptual model, specific types of nurse-led diabetes management models, barriers faced by nurses during implementation, and corresponding strategies, with a view to providing a reference for the management of diabetic patients and the development of diabetes specialty nurses.
基金supported by University of Macao,China,Nos.MYRG2022-00054-FHS and MYRG-GRG2023-00038-FHS-UMDF(to ZY)the Macao Science and Technology Development Fund,China,Nos.FDCT0048/2021/AGJ and FDCT0020/2019/AMJ and FDCT 0011/2018/A1(to ZY)Natural Science Foundation of Guangdong Province of China,No.EF017/FHS-YZ/2021/GDSTC(to ZY)。
文摘To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.
