Operational Features Research Articles

Screening and identification of the hub genes in severe acute pancreatitis and sepsis.

Severe acute pancreatitis (SAP) is accompanied with acute onset, rapid progression, and complicated condition. Sepsis is a common complication of SAP with a high mortality rate. This research aimed to identify the shared hub genes and key pathways of SAP and sepsis, and to explore their functions, molecular mechanism, and clinical value. We obtained SAP and sepsis datasets from the Gene Expression Omnibus (GEO) database and employed differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify the shared differentially expressed genes (DEGs). Functional enrichment analysis and protein-protein interaction (PPI) was used on shared DEGs to reveal underlying mechanisms in SAP-associated sepsis. Machine learning methods including random forest (RF), least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) were adopted for screening hub genes. Then, receiver operating characteristic (ROC) curve and nomogram were applied to evaluate the diagnostic performance. Finally, immune cell infiltration analysis was conducted to go deeply into the immunological landscape of sepsis. We obtained a total of 123 DEGs through cross analysis between Differential expression analysis and WGCNA important module. The Gene Ontology (GO) analysis uncovered the shared genes exhibited a significant enrichment in regulation of inflammatory response. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the shared genes were primarily involved in immunoregulation by conducting NOD-like receptor (NLR) signaling pathway. Three machine learning results revealed that two overlapping genes (ARG1, HP) were identified as shared hub genes for SAP and sepsis. The immune infiltration results showed that immune cells played crucial part in the pathogenesis of sepsis and the two hub genes were substantially associated with immune cells, which may be a therapy target. ARG1 and HP may affect SAP and sepsis by regulating inflammation and immune responses, shedding light on potential future diagnostic and therapeutic approaches for SAP-associated sepsis.

Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning

Emergency control is essential for maintaining the stability of power systems, serving as a key defense mechanism against the destabilization and cascading failures triggered by faults. Under-voltage load shedding is a popular and effective approach for emergency control. However, with the increasing complexity and scale of power systems and the rise in uncertainty factors, traditional approaches struggle with computation speed, accuracy, and scalability issues. Deep reinforcement learning holds significant potential for the power system decision-making problems. However, existing deep reinforcement learning algorithms have limitations in effectively leveraging diverse operational features, which affects the reliability and efficiency of emergency control strategies. This paper presents an innovative approach for real-time emergency voltage control strategies for transient stability enhancement through the integration of edge-graph convolutional networks with reinforcement learning. This method transforms the traditional emergency control optimization problem into a sequential decision-making process. By utilizing the edge-graph convolutional neural network, it efficiently extracts critical information on the correlation between the power system operation status and node branch information, as well as the uncertainty factors involved. Moreover, the clipped double Q-learning, delayed policy update, and target policy smoothing are introduced to effectively solve the issues of overestimation and abnormal sensitivity to hyperparameters in the deep deterministic policy gradient algorithm. The effectiveness of the proposed method in emergency control decision-making is verified by the IEEE 39-bus system and the IEEE 118-bus system.

Field effect transistor biosensors for healthcare monitoring

AbstractThe burgeoning demand for healthcare monitoring has brought field effect transistor (FET) biosensors into the spotlight as a highly efficient detection technology. FET biosensors offer inherent advantages including high sensitivity, rapid response times, operational simplicity, integrability, and label‐free detection. These characteristics render them particularly well‐suited for detecting a diverse array of physiological parameters and biomarkers, thereby furnishing real‐time data crucial for personalized medicine and disease prevention. This review aims to elucidate recent advancements in FET biosensors within the realm of healthcare management encompassing several facets. Initially, this review systematically analyzes the device architecture, sensing mechanisms, and performance evaluation methods of FET biosensors to gain an in‐depth understanding of their operational principles and features. Subsequently, it focuses on the application of FET biosensors for detecting health‐related biomarkers encompassing nucleic acids, proteins, exosomes, viruses, etc. Lastly, the review presents engineered medical sensor prototypes predicated on FET biosensors, such as point‐of‐care testing devices, wearable sensors, and implantable sensors, underscoring their practical utility and potential in health management. In addition, the review addresses critical issues and prospects of using FET biosensors in healthcare monitoring, aiming to provide some references and insights for research and innovation in this field.

Radiomics-Based Risk Assessment Correlates With Outcomes in Patients With Acute Type B Aortic Dissection Undergoing Thoracic Endovascular Repair.

Our study aimed to investigate the correlations between radiomics-based assessment and outcomes, including positive aortic remodeling (PAR), reintervention for dissection at 1 year, and overall survival, in patients with Type B aortic dissection (TBAD) who underwent thoracic endovascular aortic repair (TEVAR). This was a single-center, retrospective, cohort study. The cohort comprised 104 patients who had undergone TEVAR of TBAD in our institution between January 2010 and October 2022. We segmented preoperative computed tomography (CT) images of the patients' descending aorta regions, then extracted a comprehensive set of radiomic features, including first-order features, shape features (2D and 3D), gray-level co-occurrence matrix (GLCM), gray-level size zone matrix, gray-level run length matrix, gray-level dependence matrix, neighborhood gray-tone difference matrix, from the regions of interest. Next, we selected radiomics features associated with total descending aorta positive aortic remodeling (TDA-PAR) and reintervention by least absolute shrinkage and selection operator (LASSO) regression and features associated with survival by LASSO-Cox regression. This enabled us to calculate radiomics-based risk scores for each patient. We then allocated the patients to high and low radiomics-based risk groups, the cutoff being the median score. We used 3 different models to validate the radiomics-based risk scores. The patients' baseline characteristics did not differ between those who achieved TDA-PAR and those who did not. The radiomics-based risk scores were significantly and independently associated with all 3 outcomes. As to the impact of specific radiomics features, we found that GLSZM_SmallAreaLowGrayLevelEmphasis and shape_Maximum2DDiameterColumn had positive impacts on both reintervention and survival outcomes, whereas GLCM_Idmn positively affected survival but negatively affected reintervention. We found that radiomics-based risk for TDA-PAR correlated most significantly with zone 6 PAR. Radiomics-based risk scores were significantly associated with the outcomes of TDA-PAR, reintervention, and overall survival. Radiomics has the potential to make significant contributions to prediction of outcomes in patients with TBAD undergoing TEVAR. In this study of 104 patients with Type B aortic dissection, we demonstrated associations between radiomics-based risk and postoperative outcomes, including total descending aorta positive aortic remodeling, reintervention and survival. These findings highlight radiomics' potential as a tool for risk stratification and prognostication in acute Type B aortic dissection management.

A novel electrochemical sensor based on aspartic acid polymer and carbonized-ZIF-67 for rapid sensing of arbutin

A novel electrochemical sensor modified by aspartic acid polymer and carbonized-ZIF-67 was prepared for arbutin detection. ZIF-67 was synthesized and carbonized, and then used as the substrate supporting of electrochemical sensor. Carbonized-ZIF-67 had a large specific surface and good electrical conductivity, which provided large electroactive area and promoted the electron transfer at the sensing interface. Aspartic acid polymer was prepared by cyclic voltammetry. Aspartic acid polymer could effectively absorb arbutin, resulting in the preconcentration of arbutin on sensor surface. Benefiting from the synergistic effect between carbonized-ZIF-67 and aspartic acid polymer, the prepared electrochemical sensor exhibited sensitive electrochemical response for arbutin. Under the optimum conditions, the electrochemical sensor achieved a wide linear range in the range from 5 to 110 μmol/L and a low detection limit of 0.41 μmol/L for arbutin. In practical application, the electrochemical sensor was employed to detect arbutin in green tea and whitening cream samples, exhibiting satisfactory recovery rates. With the outstanding features of easy operation, rapid response and high sensitivity, the prepared electrochemical sensor provided a convenient and effective tool for arbutin detection.

Screening, identification and targeted intervention of necroptotic biomarkers of asthma

BackgroundAs a pivotal pathway of programmed cell death, necroptosis significantly contributes to the pathogenesis of respiratory disorders. However, its role in asthma is not yet fully elucidated. Therefore, this study aimed to identify markers associated with necroptosis, evaluate their functions in asthma, and explore potential therapeutic agents targeting necroptosis for the management of asthma. MethodsFirstly, machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest, and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were utilized to identify necroptosis-related differentially expressed genes (NRDEGs) in asthma patients compared to healthy controls. Concurrently, the expression of NRDEGs was validated using external datasets, Western blot, and quantitative real-time polymerase chain reaction (qPCR). Secondly, the clinical relevance of NRDEGs was assessed through Receiver Operating Characteristic (ROC) curve analysis and correlation with clinical indicators. Thirdly, the relationship between NRDEGs and pulmonary immune cell infiltration, as well as the signaling interactions between different cells types, were analyzed through immune infiltration and single-cell analysis. Fourthly, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), were conducted to elucidate the functional roles of NRDEGs. Finally, compounds targeting NRDEGs were screened, and their binding affinities were evaluated using molecular docking studies. ResultsIn asthma, necroptosis is activated, leading to the identification of four NRDEGs: NLRP3, PYCARD, ALOX15, and VDAC3. Among these, NLRP3, PYCARD, and ALOX15 are upregulated, whereas VDAC3 is downregulated in asthma. Comprehensive clinical evaluations indicated that NRDEGs hold diagnostic value for asthma. Specifically, NLRP3 was inversely correlated with forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC), while VDAC3 showed an inverse correlation with sputum neutrophils. Conversely, ALOX15 expression was positively correlated with fractional exhaled nitric oxide (FeNO) levels, as well as sputum eosinophils, blood eosinophils, and blood IgE levels. Subsequent immune infiltration analysis revealed associations between NRDEGs and activated dendritic cells, mast cells, and eosinophils. Single-cell RNA sequencing (scRNA-seq) further confirmed the communication signals between myeloid dendritic cells, fibroblasts, neutrophils, and helper T cells, predominantly related to fibrosis and immune-inflammatory responses. Pathway enrichment analysis demonstrated that NRDEGs are involved in ribosomal function, oxidative phosphorylation, and fatty acid metabolism. Finally, resveratrol and triptonide were identified as potential therapeutic agents targeting the proteins encoded by NRDEGs for asthma treatment. ConclusionsThe necroptosis pathway is activated in asthma, with NRDEGs—namely PYCARD, NLRP3, ALOX15, and VDAC3—correlated with declines in lung function and airway inflammation. These genes serve as reliable predictors of asthma risk and are involved in the regulation of the immune-inflammatory microenvironment. Resveratrol and triptolide have been identified as promising therapeutic candidates due to their potential to target the proteins encoded by these genes.

SUS 2 - Routine histological examination of haemorrhoidal tissue – an unnecessary environmental and economic expense?

Abstract Background Modern, less invasive methods of haemorrhoid treatment, such as haemorrhoid artery ligation and radiofrequency ablation, are increasingly employed. These do not yield specimens for histological analysis. However, when open haemorrhoidectomy is performed, even in the absence of suspicious operative features and in low-risk patients, the haemorrhoid specimen is routinely sent. This has both environmental and financial costs in an increasingly pressured NHS, and negatively impacts sustainability with no clinical advantage. Methods We retrospectively studied 112 open haemorrhoidectomies performed at our UK hospital from February 2018 to June 2023, reviewing clinic letters, operation notes and post-operative histology. Aims To assess the frequency of incidental pathology identified during routine histological assessment, and the associated financial costs and carbon footprint generated. Results 112 open haemorrhoidectomies were performed in the 5-year study period. In total, two patients with risk factors for AIN/Anal cancer were identified. There were no documented concerns of abnormal intra-operative findings. Histology mainly revealed haemorrhoid tissue alone (103) and benign polyps (8). AIN3 was detected in one of the high-risk patients. The cost per histology specimen was £93.32 (Total cost £10,451.84). Each specimen is estimated to generate 0.29kg of Carbon Dioxide equivalents (Total 32.48kg CO2e). Conclusion Routine histological assessment of haemorrhoidectomy specimens is time-consuming, expensive and has an avoidable carbon footprint for a low detection of incidental pathology. Our study suggests that histology should be reserved for patients with risk factors for AIN/Anal cancer, or concerning intra-operative findings. Larger scale studies, and development of national guidelines would aid clinician decision-making.

Sufficient condition for reliable logic operations in an over damped bistable system driven by Gaussian white noise.

In this paper, we investigate an overdamped bistable system subject to Gaussian white noise and logical inputs. By solving and estimating the steady distribution of the corresponding Fokker-Planck equation and considering the two essential features of the reliable logic operations (RLOs)-initial value independence and sign invariance-we establish the sufficient condition for RLO occurrence and identify parametric resonance phenomena in the system. Our numerical simulations confirm the reliability and accuracy of the theoretical results. This work offers insights into enhancing the accuracy of stochastic systems, particularly in the realm of logical stochastic resonance, thereby contributing to advancements in understanding and controlling stochastic dynamical systems.

Flexible Job-shop Scheduling Problem with parallel operations using Reinforcement Learning: An approach based on Heterogeneous Graph Attention Networks

The Flexible Job-shop Scheduling Problem (FJSP) has received considerable scholarly attention as a classic problem. However, in practical industrial manufacturing scenarios, it is common for an operation to have multiple preceding parallel operations. This not only necessitates adhering to the sequential relationships inherent in FJSP but also requires ensuring that preceding operations are completed simultaneously whenever feasible. We term this scenario as the Flexible Job-shop Scheduling Problem with Parallel Operations (FJSP-PO), a pervasive challenge encountered across nearly every production line in real-world discrete manufacturing applications. Despite its prevalence, there is a noticeable scarcity of research on FJSP-PO in existing literature. Given the objective of synchronizing multiple preceding operations, FJSP-PO presents a broader solution space and more intricate optimization challenges compared to traditional FJSP. To address this, we propose an Attention Restart method based on Heterogeneous Graph Attention Networks (AR-HGAT). Leveraging a heterogeneous graph network structure and reinforcement learning, AR-HGAT learns the implicit features of operations and machines through node-level and semantic-level attention mechanisms. The AR mechanism is utilized to determine the optimal scheduling of operations at specific time slots. Compared to existing FJSP methods, our AR-HGAT approach demonstrates superior performance in terms of inference time and solution effectiveness. Furthermore, we conducted a comparative analysis using authentic operational data from companies and contrasted it with results obtained from an online tree search algorithm, thereby providing empirical validation of the effectiveness of the proposed AR-HGAT method.

Substrate-Switched Dual-Signal Self-Powered Sensing System Based on Dual-Nanozyme Activity of Bimetal-Doped CeO2 Nanospheres for Electrochemical Assay of Aflatoxin B1.

The long-term operation feature of enzymatic biofuel cell-based self-powered biosensor (EBFC-SPB) endows them with the potential to execute dual-signal biosensing without having to integrate an extra signal acquisition device. Herein, cobalt and manganese codoped CeO2 nanospheres (CoMn-CeO2 NSs) with glucose-oxidase-like and peroxidase-like activities have been developed as substrate-switched dual-channel signal transduction components in EBFC-SPB for a dual-signal assay of aflatoxin B1 (AFB1). The CoMn-CeO2 NSs modified with aptamer are anchored to a complementary DNA-attached bioanode of EBFC-SPB by base complementary pairing, which catalyze the glucose oxidation together with the glucose oxidase (GOx) on the bioanode. Once the AFB1 appears, CoMn-CeO2 NSs will be released from the bioanode due to the binding specificity of the aptamer, resulting in a decreased catalytic efficiency and the first declining stage of EBFC-SPB. Accompanied by the introduction of H2O2, the residual CoMn-CeO2 NSs on the bioanode switch to peroxidase-like activity and mediate the production of benzo-4-chlorohexadienone (4-CD) precipitate, which increases the steric hindrance and yields another declining stage of EBFC-SPB. By assessing the variation amplitudes during these two declining stages, the dual-signal assay of AFB1 has been realized with satisfying results. This work not only breaks ground in dual-signal bioassays but also deepens the application of nanozymes in EBFC-SPB.

Algorithm for 3D point cloud steganalysis based on composite operator feature enhancement

Algorithm for 3D point cloud steganalysis based on composite operator feature enhancement

Flexible and simultaneous multi-scale bioparticle manipulation to actuate cell and drug delivery utilizing AC electrokinetic -powered microswimmer

Bioparticle trapping and transporting are essential processes for developing an integrated cell and drug delivery system. Herein, we present a unique platform that effectively combines magnetic field and alternating current (AC) electrokinetics for the simultaneous manipulation of bioparticles across scales. The magnetic field is used to control the moving direction of Janus swimmers, and the AC electrokinetics incorporating dielectrophoresis (DEP) and induced charge electroosmosis (ICEO) flow are utilized to power Janus swimmers to manipulate bioparticles. Numerical characterization of the stagnant regions generated by DEP and ICEO elucidates the trapping mechanisms for both micro and nano bioparticles. We demonstrate that the Janus swimmers are capable of navigating along varied trajectories at speeds of up to hundreds of micrometers per second. Developed phase maps, derived from AC frequency and medium conductivity parameters, delineate three distinct bioparticle loading positions, offering a versatile guide for the manipulation of cells and nanodrugs. The platform’s adaptability is further highlighted by its success in performing mobile trapping of cells and nanodrugs along user-defined trajectories, targeted trapping of nanoparticle aggregations, and simultaneous multi-scale bioparticle capture. The presented multi-scale bioparticle trapping strategy, together with its unique features of simple geometric configuration, facile operation and broad applicability, can broaden utility in cellular and pharmaceutical research.

Multi-objective evolutionary algorithm based on transfer learning and neural networks: Dual operator feature fusion and weight vector adaptation

In multi-objective evolutionary algorithms, one of the focal points is finding a balance between diversity and convergence. In decomposition-based algorithms, the role of weight vectors is crucial. Despite numerous studies dedicated to these aspects, there is a scarcity of utilizing transfer learning algorithms for dual-operator feature fusion and employing neural networks for accurate partitioning of the objective space population. To address the aforementioned issues, this paper proposes the following improvements: (1) Implementing a Balanced Distribution Adaptation (BDA) transfer learning algorithm to achieve dual-operator feature fusion, resulting in a transfer population guiding the adaptive adjustment of weight vectors. (2) Integrating the BDA algorithm with multi-objective algorithms requires labeling the data, a challenge in the multi-objective evolutionary algorithm. To tackle this issue, non-dominated sorting is introduced as a bridge connecting the BDA and multi-objective evolutionary algorithms. This serves as a method to combine the advantages of decomposition-based and Pareto-dominance principle-based multi-objective algorithms. (3) To overcome the impact of traditional Euclidean distance on population sparsity, a neural network is employed to determine the population's distribution in the objective space accurately. This ensures the precise identification of individuals to be removed from the current population and the areas where additions are needed. In order to fully validate the effectiveness of the proposed algorithm, four different sets of experiments are conducted in the experimental section, where three sets of benchmarking problems are compared to a variety of algorithms that have received much attention in recent years, as well as ablation experiments.

(Anti)Causativization of psych verbs in Spanish and Japanese

There is a notable typological contrast between psych verbs in Japanese and Spanish. Japanese derives Experiencer-Object verbs (e.g. yorokob-ase-ru ‘to please’) from specific Experiencer-Subject verbs (e.g. yorokobu ‘to become pleased’) via a morphological causativization. Spanish, on the other hand, presents so-called reflexive psych verbs (e.g. alegrarse ‘to feel happy’), most of which can be analyzed as outputs of an anticausativization from certain Experiencer-Accusative verbs (e.g. alegrar ‘to make happy’). Simply put, these languages derive psych verbs with procedures that reversely mirror each other. This paper will elucidate the characteristics of the causativization used to produce Japanese Experiencer-Object causatives and the anticausativization associated with Spanish Reflexive Psych Verbs and demonstrate that the typological contrast between Japanese and Spanish psych verbs results in semantic variation, e.g. differences in the entailment relation, absence/presence of ambiguity in negation, aspectual diversity. Semantic differences between psych verbs in these languages are ascribed to specific features of the (anti)causative operations employed to generate the predicates.

Reversible solid oxide cells-based hydrogen energy storage system for renewable solar power plants

The power-H2-power system based on reversible solid oxide cell is a promising pathway for large-scale renewable energy storage but not well understood due to the absence of comprehensive system analyses. In this study, a reversible solid oxide cell-based H2 energy storage system for a 100 % renewable solar power plant is proposed and analyzed through detailed modeling approach and optimization framework. The detailed parametric analyses and economic evaluations are performed in electrolysis (12:00 pm, sufficient solar energy) and power generation (6:30 am, insufficient solar energy) conditions. Notably, it is found that larger stack capacity (Ncell) and higher operating temperature (TReSOC) of the cell enhances system net H2 production and system economics despite additional investment cost. Afterwards, the optimal system performance is obtained (VH2,produce = 498.40 Nm3·h−1, Z=263.59 $·h−1 for electrolysis, and VH2,consume = 380.33 Nm3·h−1 for power generation) through multi-objective optimization by fully considering cell internal operating characteristics and system energy-exergy-economic factors. Besides, it is also found that the performance of power-H2-power system is still limited by remarkable fuel (H2) costs for nighttime power generation (daytime H2 production is smaller than nighttime H2 consumption when Wtot = 1 MW). Therefore, the critical power capacity (Pcritical) is obtained, offering a convenient approach to determine the maximum output capacity (Pcritical = 880 kW) to make the power-H2-power system profitable at specific solar energy input. This study provides valuable insights between system capacity, economics, and cell operating features in solar power plants, which are useful for the design and optimization of practical power-H2-power systems for renewable energy storage.

Performance analysis and optimization of a zero-emission solar-driven hydrogen production system based on solar power tower plant and protonic ceramic electrolysis cells

The solar-driven high-temperature steam electrolysis is promising for efficient large-scale H2 production. In this study, a comprehensive component-to-system model and optimization framework is developed to investigate the performance of a zero-emission H2 production system based on solar power plant and protonic ceramic electrolysis cell. Compared to previous system studies, the detailed description of cell internal operating characteristics is realized by integrating multi-physics simulation and artificial neural network. After parametric analyses, it is found that the system energy/exergy efficiency and co-generation performance are complicated by each subsystem. And the optimal system performance (ηth = 50.63 %, Z = 179.63 $·h−1 and ηex = 33.03 %, Z = 178.94 $·h−1, with LCOE = 0.172 $·kWh−1 and ZH2 = 6.497 $·kg−1) is obtained considering cell operating features and system energy-exergy-economic factors through multi-objective optimizations. Besides, the tradeoff between system maximum H2 production capacity and cell internal thermal conditions is revealed. This study can facilitate the development of zero-emission green H2 production driven by renewable energy.

Identification and Validation of IFI44 as a Novel Biomarker for Primary Sjögren's Syndrome.

Primary Sjögren's syndrome (pSS) is an autoimmune condition marked by lymphocyte infiltration in the exocrine glands. Our study aimed to identify a novel biomarker for pSS to improve its diagnosis and treatment. The gene expression profiles of pSS were obtained from the Gene Expression Omnibus (GEO) database. The specific differentially expressed genes (DEGs) were screened by the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Recursive Feature Elimination with Support Vector Machines (SVM-RFE). A biomarker was picked out based on correlation and diagnostic performance, the connection between the biomarker and clinical traits and immune infiltrating cells was explored, and the biomarker's protein expression level in the serum of pSS patients was detected by enzyme-linked immunosorbent assay (ELISA). The competitive endogenous RNA (ceRNA) network regulated by the biomarker was predicted to verify the reliability of the biomarker in diagnosing pSS. IFI44, XAF1, GBP1, EIF2AK2, IFI27, and IFI6 showed prominent diagnostic ability, with the high accuracy (AUC = 0.859) and significance (R ≥ 0.8) of IFI44 within the training dataset. IFI44 strongly exhibited a negative correlation with resting NK cells, macrophages M0, and eosinophils, and a positive correlation with activated dendritic cells, naive B cells, and activated CD4 memory T cells. Furthermore, IFI44 was significantly positively correlated with clinical traits such as IgG, SSA, SSB, ANA, and ESSDAI, with its protein expression level in the serum of pSS patients being notably elevated compared to controls (p < 0.001). Finally, the ceRNA regulatory network showed that hsa-miR-944, hsa-miR-9-5p, hsa-miR-126-5p, and hsa-miR-335-3p were significantly targeted IFI44, suggesting that IFI44 may serve as a dependable biomarker for pSS. In this study, we dug out IFI44 as a biomarker for pSS, systematically studied the potential regulatory mechanism of IFI44, and verified its reliability as a biomarker for pSS.

Incorporating patients’ appointment date preferences into decision-making: A simulation and optimization study

A recent trend in health care is to give patients more flexibility by taking their preferences into account. While this patient-centered approach adds further complexity to the management of operations, it also generates new opportunities for potential improvements in the system. In this study, we show that such an improvement can be obtained via appointment scheduling (AS) systems which are the critical component of any health care delivery system as they can easily be a source of dissatisfaction for the patients as well as for the providers. Accordingly, we propose a novel patient-oriented AS strategy that utilizes patients’ appointment date preferences. The main idea of the strategy is to accumulate patients’ preferences for some amount of time before deciding on their appointments via mathematical optimization, rather than traditional first-call first-booked strategy in which patients are appointed at the time they call. By this way, we aim to exploit the advantage of giving patients preferences to improve the system performance. To examine the proposed AS system with different model settings and problem parameters, we perform a comprehensive simulation study that incorporates several realistic operational features as well as an optimization model for patient to time-slot assignments. Computational results show that using this system can improve not only clinic utility but also patients’ AS experience significantly since it allows more patients to be appointed to one of their convenient dates. This simulation study presents a proof-of-concept for the proposed strategy while providing valuable managerial insights for implementing and operating such an AS system.

Microsurgery of Cerebral Arteriovenous Malformations in a Resource-Limited Setting: The First Case-Series from Iraq

Cerebral arteriovenous malformations (AVMs) can lead to significant morbidity and are particularly challenging to manage in resource-limited settings where endovascular treatment modalities are unaffordable for most patients. To describe the first case series of AVM from Iraq with an analysis of the related clinicoradiologic characteristics, operative features, and outcomes. A single-center database from October 2018 to December 2022 was reviewed to analyze the characteristics of cerebral AVMs who underwent surgical treatment in Baghdad, Iraq. We collected patient demographics, clinical, radiologic, operative, and the follow-up combined outcome results (modified Rankin Scale score and the presence of AVM remnants). Of the 54 AVM patients treated with microsurgery, the majority of lesions have Spetzler-Martin grade of 3 (31.5%), followed by grade 1 (29.6%). The parietal lobe was the most common location of AVM in 25.9% of the cases, and the temporal location had better outcomes. The mean duration of surgery was 8.5 hours, ranging from 3 to 14 hours, with 20.3% of cases having undergone preoperative stereotactic radiosurgery, and just one patient received preoperative embolization. Good combined outcome (modified Rankin Scale 0-2 and no AVM remnant) was associated with lower SM grades (P=0.003); location in the nondominant hemisphere (P=0.036), and noneloquent regions (P=0.006); absence of deep venous drainage (P=0.042) and no intraoperative brain swelling (P=0.004). The mortality rate in our series was 5.5%. Good clinicoradiologic outcomes can be achieved through microsurgery in a setting where endovascular treatment is inaccessible to patients due to limited resources.

Pedestrian and Car Occupant Crash Casualties Over a 9-Year Span of Vision Zero in New York City

Vision Zero has been increasingly embraced by jurisdictions across the United States. Existing research primarily focuses on the theoretical principles and effectiveness of specific engineering measures. However, there is limited understanding of the holistic effects of Vision Zero treatments, in the context of street type and urban environment. We developed a street typology framework to categorize street segments using four design and operational features: street width, traffic direction (one- versus two-way), number of travel lanes, and presence of on-street parking. We applied a sample-based partitioning around medoids algorithm to classify 90,327 street segments in New York City. This process results in six distinctive types of street segment. To integrate neighborhood-level factors (e.g., land use variables and sociodemographics), we aggregated street segments of a given street type for each neighborhood. Negative binomial regression models were developed for pedestrian and car occupant crash injuries and fatalities separately for three periods: 2014 to 2016, 2017 to 2019, and 2020 to 2022. Our findings showed that street-segment groups with narrower, two-way sections and greater tree canopy coverage were significantly associated with a lower risk of casualties for both pedestrians and motorized users. Street-segment groups located in neighborhoods with a larger percentage of African American and Hispanic American residents experienced a significantly greater risk of casualties. Vision Zero treatments had mixed effects on safety outcomes. Streets treated with leading pedestrian intervals showed a lower risk of casualties. Neighborhood- and arterial slow zones were associated with a lower risk of car occupant casualties.