Practical Algorithm Research Articles

Safe Reinforcement Learning With Dual Robustness.

Reinforcement learning (RL) agents are vulnerable to adversarial disturbances, which can deteriorate task performance or break down safety specifications. Existing methods either address safety requirements under the assumption of no adversary (e.g., safe RL) or only focus on robustness against performance adversaries (e.g., robust RL). Learning one policy that is both safe and robust under any adversaries remains a challenging open problem. The difficulty is how to tackle two intertwined aspects in the worst cases: feasibility and optimality. The optimality is only valid inside a feasible region (i.e., robust invariant set), while the identification of maximal feasible region must rely on how to learn the optimal policy. To address this issue, we propose a systematic framework to unify safe RL and robust RL, including the problem formulation, iteration scheme, convergence analysis and practical algorithm design. The unification is built upon constrained two-player zero-sum Markov games, in which the objective for protagonist is twofold. For states inside the maximal robust invariant set, the goal is to pursue rewards under the condition of guaranteed safety; for states outside the maximal robust invariant set, the goal is to reduce the extent of constraint violation. A dual policy iteration scheme is proposed, which simultaneously optimizes a task policy and a safety policy. We prove that the iteration scheme converges to the optimal task policy which maximizes the twofold objective in the worst cases, and the optimal safety policy which stays as far away from the safety boundary. The convergence of safety policy is established by exploiting the monotone contraction property of safety self-consistency operators, and that of task policy depends on the transformation of safety constraints into state-dependent action spaces. By adding two adversarial networks (one is for safety guarantee and the other is for task performance), we propose a practical deep RL algorithm for constrained zero-sum Markov games, called dually robust actor-critic (DRAC). The evaluations with safety-critical benchmarks demonstrate that DRAC achieves high performance and persistent safety under all scenarios (no adversary, safety adversary, performance adversary), outperforming all baselines by a large margin.

A General Neurologist's Practical Diagnostic Algorithm for Atypical Parkinsonian Disorders: A Consensus Statement.

The most common four neurodegenerative atypical parkinsonian disorders (APDs) are progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal syndrome (CBS), and dementia with Lewy bodies (DLB). Their formal diagnostic criteria often require subspecialty experience to implement as designed and all require excluding competing diagnoses without clearly specifying how to do that. Validated diagnostic criteria are not available at all for many of the other common APDs, including normal pressure hydrocephalus (NPH), vascular parkinsonism (VP), or drug-induced parkinsonism (DIP). APDs also include conditions of structural, genetic, vascular, toxic/metabolic, infectious, and autoimmune origin. Their differential diagnosis can be challenging early in the course, if the presentation is atypical, or if a rare or non-neurodegenerative condition is present. This review equips community general neurologists to make an early provisional diagnosis before, or in place of, referral to a tertiary center. Early diagnosis would allay diagnostic uncertainty, allow prompt symptomatic management, provide disease-specific information and support resources, avoid further pointless testing and treatments, and create the possibility of trial referral. We address 64 APDs using one over-arching flow diagram and a series of detailed tables. Most instances of APDs can be diagnosed with a careful history and neurological exam, along with a non-contrast brain MRI. Additional diagnostic tests are rarely needed but are delineated where applicable. Our diagnostic algorithm encourages referral to a tertiary center whenever the general neurologist feels it would be in the patient's best interest. Our algorithm emphasizes that the diagnosis of APDs is an iterative process, refined with the appearance of new diagnostic features, availability of new technology, and advances in scientific understanding of the disorders. Clinicians' proposals for all diagnostic tests for the APDs, including repeat visits, should be discussed with patients and their families to ensure that the potential information to be gained aligns with their larger clinical goals. We designed this differential diagnostic algorithm for the APDs to enhance general neurologists' diagnostic skills and confidence and to help them address the less common or more ambiguous cases.

Game Pathfinding System Design based on A-Star Pathfinding Algorithm

Abstract. Pathfinding systems are used in many applications today, including navigation systems, autonomous driving systems, and games. The core part of the pathfinding system is the pathfinding algorithm, A Star pathfinding algorithm is a kind of efficient pathfinding algorithm, which is especially suitable for the design of game pathfinding. In this paper, based on the A Star algorithm, Godot game engine and C Sharp language are used to write a pathfinding program. After comparing different pathfinding methods and the estimated function, the A Star algorithm is improved according to the needs of the actual game pathfinding system, so that the pathfinding program can avoid crossing the obstacles through the diagonal by searching the surrounding obstacles and excluding unavailable nodes, and find a shorter path by using the Euclidean distance estimated function. The program applies the improved A Star algorithm in practice, and finally can complete the basic pathfinding needed in the game, which is practical for the design of the game's pathfinding system.

Pharmacological management of pediatric metabolic dysfunction-associated steatotic liver disease.

Pediatric obesity, characterized by a body mass index (BMI) at or above the 95th percentile for age, affects a substantial number of children and adolescents worldwide. Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease, represents a prominent hepatic manifestation of obesity and metabolic syndrome, emerging as the most prevalent hepatic disorder among pediatric patients and a significant contributor to liver transplantation in adults. The escalating prevalence of pediatric MASLD mirrors the alarming rise in childhood obesity rates over recent decades. While lifestyle modifications focusing on dietary changes and increased physical activity constitute the cornerstone of MASLD management, achieving and maintaining significant weight reduction remains challenging. Moreover, disease progression often persists despite standard-of-care interventions, warranting exploration into alternative therapeutic strategies. Pharmacological interventions, particularly, glucagon-like peptide-1 receptor agonists (GLP-1RA), have shown promise in addressing pediatric obesity and its associated comorbidities, including MASLD. Recent studies have demonstrated the efficacy of GLP-1RA in inducing weight loss and improving liver enzyme levels, suggesting a potential role in halting disease progression, and reducing the risk of major adverse liver outcomes. This review provides a comprehensive overview of the current pharmacotherapy landscape for pediatric MASLD, with a focus on novel agents such as GLP-1RA. Furthermore, the manuscript proposes a practical algorithm to assist in integrating GLP-1RA into the clinical management of pediatric patients with obesity and MASLD. Despite promising results, further research is warranted to elucidate the long-term efficacy and safety of GLP-1RA in pediatric populations.

Merged-nets enumeration for the systematic design of multicomponent reticular structures.

Rational design of intricate multicomponent reticular structures is often hindered by the lack of suitable blueprint nets. We established the merged-net approach, proffering optimal balance between designability and complexity, as a systematic solution for the rational assembly of multicomponent structures. In this work, by methodically mapping node-net relationships among 53 basic edge-transitive nets, we conceived a signature net map to identify merging net pairs, resulting in the enumeration of 53 merged nets. We developed a practical design algorithm and proposed more than 100 multicomponent metal-organic framework platforms. The effectiveness of this approach is commended by the successful synthesis of four classes of materials, which is based on merging three-periodic nets with the four possible net periodicities. The construction of multicomponent materials based on derived nets of merged nets highlights the potential of the merged-net approach in accelerating the discovery of intricate reticular materials.

On Lattice-Based Broadcasting for Massive-User MIMO: Practical Algorithms and Optimization

On Lattice-Based Broadcasting for Massive-User MIMO: Practical Algorithms and Optimization

A calibration-informed deep learning model for three-dimensional particle reconstruction of volumetric particle image velocimetry

Accurately reconstructing three-dimensional particle fields is essential in fluid velocity measurement research. This study addresses the limitations of current three-dimensional (3D) particle reconstruction methods, such as computational efficiency, precision at high particle density, and particle morphology issues, by introducing a calibration-informed deep learning model named the calibrated pixel to voxel convolutional neural network (CPV-CNN) for 3D Particle Reconstruction. This innovative neural network framework employs a unique Gaussian attention mechanism that bridges pixels and voxels, enabling the mapping of pixel features from two-dimensional (2D) particle images to 3D voxel features. This approach eliminates the need for an initial particle field for particle reconstruction, while significantly enhancing reconstruction efficiency. Additionally, the neural network incorporates camera calibration parameters and the physical coordinates of the reconstructed domain, thereby improving the model's generalization capability and flexibility. Numerical experiments demonstrate that CPV-CNN delivers superior results in terms of accuracy, generalization, and robustness in 3D particle reconstruction. The reconstructed particles exhibit favorable morphology, without the elongation issues commonly observed with conventional methods. This achievement illustrates a practical particle reconstruction algorithm based on artificial intelligence (AI) techniques and represents an important step toward developing an end-to-end AI-based particle reconstruction method in the future.

ESC Heart Failure Association age-adjusted natriuretic peptide thresholds for a new diagnosis of heart failure: diagnostic accuracy study

Abstract Background Natriuretic peptide (NP) testing is an important part of the heart failure (HF) diagnostic pathway. The European Society of Cardiology (ESC) HF guidelines currently recommend a NT-proBNP cut-off <125pg/mL to rule out HF in the community.1 However, NP level increases with age and an NT-proBNP above 125pg/mL is common in older people in the general population. The ESC Heart Failure Association (HFA) recently published a practical algorithm for early diagnosis of HF recommending age-specific rule-in NT-proBNP thresholds: ≥125pg/ml for patients aged under 50 years, ≥250pg/ml for patients aged 50-75 years, and ≥500pg/ml for patients over 75 years (Figure 1).2 Purpose Our aim was to assess NT-proBNP test performance for HF diagnosis at the ESC HFA age-specific thresholds. Method We analysed our existing diagnostic accuracy study dataset containing primary care data from the Clinical Practice Research Datalink (CPRD) linked to inpatient Hospital Episode Statistics (HES) inpatient data between 2004 and 2018. NT-proBNP results were taken from the GP record (index test) and the primary outcome (reference standard) was a HF diagnostic code entered in CPRD or HES within six months of the test. NT-proBNP accuracy for HF was assessed by calculating sensitivity, specificity, positive predictive values (PPV), and negative predictive value (NPV). Receiver operating characteristic (ROC) curves were plotted to assess overall test performance. Results In total, 229,580 patients had an NP test and 21,102 (9.2%) were diagnosed with HF. The current ESC NT-proBNP threshold ≥125pg/mL had sensitivity 94.6% (95% confidence intervals [CI] 94.2 to 95.0), specificity 50.0% (95%CI 49.7 to 50.3), PPV 16.4% (16.1 to 16.6), NPV 98.9% (98.8 to 99.0)). Area under the ROC curve was 0.874 (0.871 to 0.877). Age-specific NT-proBNP thresholds performance: Below 50 years (≥125pg/mL): 32,882 people tested, HF prevalence 3.8%. Sensitivity 83.5 % (81.3 to 85.5), specificity 77.6% (77.2 to 78.1), PPV 13.0% (12.2 to 13.7), NPV 99.2% (99.0 to 99.3). 50 to 75 years ≥250pg/mL: 105,771 people tested, HF prevalence 9.8%. Sensitivity 88.5% (87.9 to 89.1), specificity 67.8% (67.5 to 68.0), PPV 23.1% (22.6 to 23.5), NPV 98.2% (98.1 to 98.3). Above 75 years ≥500pg/mL: 16,694 people tested, HF prevalence 17.5%. Sensitivity 84.4% (83.0 to 85.7), specificity 63.5% (62.7 to 64.3), PPV 32.8% (31.7 to 33.9), NPV 95.0% (94.6 to 95.5). Conclusion The age-specific NT-proBNP thresholds in the HFA practical algorithm had higher specificity so fewer people without HF would be referred, reducing demand for echocardiography and cardiology assessment overall meaning people with HF could be seen earlier. However, lower sensitivity means some cases of HF would initially be missed. NT-proBNP was a reliable ‘rule-out’ test at population level. The optimal NT-proBNP threshold will depend on the priorities and capacity of the national healthcare system.

Cluster mechanism for the initiative scientific and technical results implementation in Belarus

The article considers the prerequisites for the emergence of a cluster mechanism for the introduction of initiative developments in the Republic of Belarus. A basic (reference) model of the implementation mechanism is proposed, according to which a new cluster mechanism is formed at the level of its core, an algorithm for integration and interaction of implementation organizations and six conditions for the implementation of this algorithm in practice. It is shown that the launch of a new cluster mechanism will allow the introduction of 14 to 26 initiative developments annually, which will contribute to the socio-economic development of Belarus.

Smartphone-based thermal imaging for pedicled skin flaps: a pilot study toward objective perfusion assessment in facial, head, and neck reconstruction.

This study aims to develop a practical algorithm for utilizing smartphone-based thermal imaging (SBTI) in the perioperative setting and to establish a standardized evaluation method for objectively assessing SBTI images for cutaneous perfusion of pedicled flaps in the face, head, and neck. This prospective conducted study, integrated SBTI into the assessment of 16 patients undergoing reconstructive surgery for face and neck defects. Thermal images were captured at four timepoints: after marking (T1), after flap elevation (T2), upon completion of surgery (T3), and 24h postoperatively (T4). The flap areas were divided into three flap zones and graded based on temperature differences (ΔT), with a grading system where grade 1 indicated perfect perfusion and grades 2 to 5 indicated increasing perfusion impairment. 6 male and 10 female patients aged 64-93 years (mean 78.5 years) undergoing reconstructive pedicled skin flap surgery for facial and neck defects (1 × 1cm to 11 × 8cm) due to diagnoses of malignant cutaneous lesions were investigated. Intraoperative assessments indicated good perfusion across flaps. One postoperative dehiscence occurred in an 83-year-old male with a cervical advancement flap, correlated with a significant temperature difference (ΔT > 4°C) intraoperatively. Statistical analysis revealed a strong positive correlation (p = 0.0003) between clinical assessment grades and ΔT values between specific flap zones at T3. SBTI is an easy-to-apply, low-cost, real-time and reproducible technique for indirect perfusion assessment in pedicled skin flaps of the head and neck region. Further studies are needed implementing this methodology in large and free flaps.

On the Definition of Velocity in Discrete-Time, Stochastic Langevin Simulations

We systematically develop beneficial and practical velocity measures for accurate and efficient statistical simulations of the Langevin equation with direct applications to computational statistical mechanics and molecular dynamics sampling. Recognizing that the existing velocity measures for the most statistically accurate discrete-time Verlet-type algorithms are inconsistent with the simulated configurational coordinate, we seek to create and analyze new velocity companions that both improve existing methods as well as offer practical options for implementation in existing computer codes. The work is based on the set of GJ methods that, of all methods, for any time step within the stability criteria correctly reproduces the most basic statistical features of a Langevin system; namely correct Boltzmann distribution for harmonic potentials and correct transport in the form of drift and diffusion for linear potentials. Several new and improved velocities exhibiting correct drift are identified, and we expand on an earlier conclusion that, generally, only half-step velocities can exhibit correct, time-step independent Maxwell–Boltzmann distributions. Specific practical and efficient algorithms are given in familiar forms, and these are used to numerically validate the analytically derived expectations. One especially simple algorithm is highlighted, and the ability of one of the new on-site velocities to produce statistically correct averages for a particular damping value is specified.

Prevention and management of infectious and tropical diseases in kidney transplant recipients residing in European outermost and overseas territories.

The European Union encompasses 30 outermost and overseas countries and territories (OCTs). Despite a recent increasing activity of renal transplantation in these territories, many patients still undergo transplantation in continental Europe, with follow-up care coordinated between health professionals from both their transplant center and their home region. Each territory has its unique infectious epidemiology which must be known to ensure appropriate care for kidney transplant recipients (KTRs). This paper proposes a pragmatic approach to optimize pre-transplant check-up and to provide an overview of the specific epidemiological features of each region. It offers practical algorithms to help practitioners in managing infected KTR living in these territories. This work advocates for increased collaborative research among European OCTs.

Implementation of an AI Algorithm in Clinical Practice to Reduce Missed Incidental Pulmonary Embolisms on Chest CT and Its Impact on Short-Term Survival.

A substantial number of incidental pulmonary embolisms (iPEs) in computed tomography scans are missed by radiologists in their daily routine. This study analyzes the radiological reports of iPE cases before and after implementation of an artificial intelligence (AI) algorithm for iPE detection. Furthermore, we investigate the anatomic distribution patterns within missed iPE cases and mortality within a 90-day follow-up in patients before and after AI use. This institutional review board-approved observational single-center study included 5298 chest computed tomography scans performed for reasons other than suspected pulmonary embolism (PE). We compared 2 cohorts: cohort 1, consisting of 1964 patients whose original radiology reports were generated before the implementation of an AI algorithm, and cohort 2, consisting of 3334 patients whose scans were analyzed after the implementation of an Food and Drug Administration-approved and CE-certified AI algorithm for iPE detection (Aidoc Medical, Tel Aviv, Israel). For both cohorts, any discrepancies between the original radiology reports and the AI results were reviewed by 2 thoracic imaging subspecialized radiologists. In the original radiology report and in case of discrepancies with the AI algorithm, the expert review served as reference standard. Sensitivity, specificity, prevalence, negative predictive value (NPV), and positive predictive value (PPV) were calculated. The rates of missed iPEs in both cohorts were compared statistically using STATA (Version 17.1). Kaplan-Meier curves and Cox proportional hazards models were used for survival analysis. In cohort 1 (mean age 70.6 years, 48% female [n = 944], 52% male [n = 1020]), the prevalence of confirmed iPE was 2.2% (n = 42), and the AI detected 61 suspicious iPEs, resulting in a sensitivity of 95%, a specificity of 99%, a PPV of 69%, and an NPV of 99%. Radiologists missed 50% of iPE cases in cohort 1. In cohort 2 (mean age 69 years, 47% female [n = 1567], 53% male [n = 1767]), the prevalence of confirmed iPEs was 1.7% (56/3334), with AI detecting 59 suspicious cases (sensitivity 90%, specificity 99%, PPV 95%, NPV 99%). The rate of missed iPEs by radiologists dropped to 7.1% after AI implementation, showing a significant improvement (P < 0.001). Most overlooked iPEs (61%) were in the right lower lobe. The survival analysis showed no significantly decreased 90-day mortality rate, with a hazards ratio of 0.95 (95% confidence interval, 0.45-1.96; P = 0.88). The implementation of an AI algorithm significantly reduced the rate of missed iPEs from 50% to 7.1%, thereby enhancing diagnostic accuracy. Despite this improvement, the 90-day mortality rate remained unchanged. These findings highlight the AI tool's potential to assist radiologists in accurately identifying iPEs, although its implementation does not significantly affect short-term survival. Notably, most missed iPEs were located in the right lower lobe, suggesting that radiologists should pay particular attention to this area during evaluations.

Neural Network Algorithms in Current Processes of Transformation of Traditional Worldview and Ideological Systems.

This study is devoted to identifying potential risks, challenges, and threats of intensive development and active implementation of artificial intelligence technologies and in particular self-learning neural network algorithms in the current practice of socio-political communications associated with active information impact on mass consciousness. This study proves that contemporary digital technological transformations and the active spread of digital technologies most directly influence the transformation of traditional worldview systems and ideologies. Based on the results of the study, it is concluded that with the help of digital tools, there is a significant information and communication impact on mass consciousness in terms of influencing traditional worldview and ideological systems. In this regard, the author puts forward the thesis about the need to form and maintain the digital sovereignty of contemporary states as a key conclusion. It is viewed as a critical condition for preserving and protecting national worldview and ideological spaces in the conditions of current global technological transformations

Monte Carlo safeguarding of key links through multiple random walks in large network

Safeguarding important links is a useful way to promote network vulnerability, especially in sparse networks where random link failures can disconnect the network. Sustaining network connectivity is the main goal of safeguarding and can be handled by selectively safeguarding links belonging to certain cuts of a network. However, due to the inherent high complexity of cut enumeration, existing algorithms can only handle small‐scale networks with limited nodes. It is important to find practical algorithms that are feasible for large‐scale graphs, as a significant portion of real‐world graphs under investigation are not as small as dozens of nodes. To address the problem, we propose to use a high‐precision approximate approach to accelerate the first step of the two‐step safeguard process and thus accelerate the whole process. A Monte Carlo algorithm is proposed to exploit efficient random paths for small cuts enumeration, which can help locate important edges with high probability in large‐scale sparse networks. These edges will then be utilized to find the approximated minimum cost edge set whose safeguarding can sustain the expected network connectivity. The algorithm is validated using various sizes of graphs of random/Barbasi–Albert scale‐free/Clustered scale‐free/unit disk/Watts–Strogatz small‐world and real‐world graphs. The experimental results show that the algorithm can perfectly sustain the network connectivity, and the acceleration ratio of more than 105 can be achieved with a little additional overhead. Specifically, in large graphs of one million nodes, approximated safeguard solutions survive at least 99.9% random link failures, and the solution time can be further reduced to dozens of seconds when the algorithm steps are easily implemented in full parallel.

A fundus vessel segmentation method based on double skip connections combined with deep supervision.

Fundus vessel segmentation is vital for diagnosing ophthalmic diseases like central serous chorioretinopathy (CSC), diabetic retinopathy, and glaucoma. Accurate segmentation provides crucial vessel morphology details, aiding the early detection and intervention of ophthalmic diseases. However, current algorithms struggle with fine vessel segmentation and maintaining sensitivity in complex regions. Challenges also stem from imaging variability and poor generalization across multimodal datasets, highlighting the need for more advanced algorithms in clinical practice. This paper aims to explore a new vessel segmentation method to alleviate the above problems. We propose a fundus vessel segmentation model based on a combination of double skip connections, deep supervision, and TransUNet, namely DS2TUNet. Initially, the original fundus images are improved through grayscale conversion, normalization, histogram equalization, gamma correction, and other preprocessing techniques. Subsequently, by utilizing the U-Net architecture, the preprocessed fundus images are segmented to obtain the final vessel information. Specifically, the encoder firstly incorporates the ResNetV1 downsampling, dilated convolution downsampling, and Transformer to capture both local and global features, which upgrades its vessel feature extraction ability. Then, the decoder introduces the double skip connections to facilitate upsampling and refine segmentation outcomes. Finally, the deep supervision module introduces multiple upsampling vessel features from the decoder into the loss function, so that the model can learn vessel feature representations more effectively and alleviate gradient vanishing during the training phase. Extensive experiments on publicly available multimodal fundus datasets such as DRIVE, CHASE_DB1, and ROSE-1 demonstrate that the DS2TUNet model attains F1-scores of 0.8195, 0.8362, and 0.8425, with Accuracy of 0.9664, 0.9741, and 0.9557, Sensitivity of 0.8071, 0.8101, and 0.8586, and Specificity of 0.9823, 0.9869, and 0.9713, respectively. Additionally, the model also exhibits excellent test performance on the clinical fundus dataset CSC, with F1-score of 0.7757, Accuracy of 0.9688, Sensitivity of 0.8141, and Specificity of 0.9801 based on the weight trained on the CHASE_DB1 dataset. These results comprehensively validate that the proposed method obtains good performance in fundus vessel segmentation, thereby aiding clinicians in the further diagnosis and treatment of fundus diseases in terms of effectiveness and feasibility.

SOLVING LINEAR AND NON-LINEAR EQUATIONS IN INTEGERS

This study explores the methods for solving linear and non-linear equations in integers, focusing on their mathematical significance and applications in various fields. The article examines both theoretical frameworks and practical algorithms, highlighting the challenges and advancements in integer solutions. Results from different approaches are presented, demonstrating the efficacy of each method.

Discovering Top-k Relevant and Diversified Rules

This paper studies the problem of discovering top- k relevant and diversified rules. Given a real-life dataset, it is to mine a set of k rules that are as close to users' interest as possible, and meanwhile, as diverse to each other as possible. It aims to reduce excessive irrelevant rules commonly returned by rule discovery. As a testbed, we consider Entity Enhancing Rules (REEs), which subsume popular data quality rules as special cases. We train a relevance model to learn users' prior knowledge, rank rules based on users' need, and propose four diversity measures to assess the diversity between rules. Based on these measures, we formulate a new discovery problem. We show that the bi-criteria discovery problem is NP-complete and hard to approximate. This said, we develop a practical algorithm for the problem, and prove its approximation bounds under certain conditions. Moreover, we develop optimization techniques to speed up the process, and parallelize the algorithm such that it guarantees to reduce runtime when given more processors. Using real-life data, we empirically verify that on average, the top-10 REEs discovered by our algorithm is able to catch 77.5% of errors detected by the entire set Σ all of REEs and achieve F_1 = 0.74 for real error detection; moreover, discovering top-ranked REEs is 62.4X faster than mining Σ all .

Is It Time for a New Algorithm for the Pharmacotherapy of Steroid-Induced Diabetes?

Glucocorticoids (GS) are widely used in multiple medical indications due to their anti-inflammatory, immunosuppressive, and antiproliferative effects. Despite their effectiveness in treating respiratory, skin, joint, renal, and neoplastic diseases, they dysregulate glucose metabolism, leading to steroid-induced diabetes (SID) or a significant increase of glycemia in people with previously diagnosed diabetes. The risk of adverse event development depends on the prior therapy, the duration of the treatment, the form of the drug, and individual factors, i.e., BMI, genetics, and age. Unfortunately, SID and steroid-induced hyperglycemia (SIH) are often overlooked, because the fasting blood glucose level, which is the most commonly used diagnostic test, is insufficient for excluding both conditions. The appropriate control of post-steroid hyperglycemia remains a major challenge in everyday clinical practice. Recently, the most frequently used antidiabetic strategies have been insulin therapy with isophane insulin or multiple injections in the basal-bolus regimen. Alternatively, in patients with lower glycemia, sulphonylureas or glinides were used. Taking into account the pathogenesis of post-steroid-induced hyperglycemia, the initiation of therapy with glucagon-like peptide 1 (GLP-1) analogs and dipeptidyl peptidase 4 (DPP-4) inhibitors should be considered. In this article, we present a universal practical diagnostic algorithm of SID/SIH in patients requiring steroids, in both acute and chronic conditions, and we present a new pharmacotherapy algorithm taking into account the use of all currently available antidiabetic drugs.

Causal message-passing for experiments with unknown and general network interference

Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occurs when the treatment of one unit impacts not only its outcome but also that of connected units, biasing traditional treatment effect estimations. Our study introduces a framework to accommodate complex and unknown network interference, moving beyond specialized models in the existing literature. Our framework, termed causal message-passing, is grounded in high-dimensional approximate message-passing methodology. It is tailored for multiperiod experiments and is particularly effective in settings with many units and prevalent network interference. The framework models causal effects as a dynamic process where a treated unit's impact propagates through the network via neighboring units until equilibrium is reached. This approach allows us to approximate the dynamics of potential outcomes over time, enabling the extraction of valuable information before treatment effects reach equilibrium. Utilizing causal message-passing, we introduce a practical algorithm to estimate the total treatment effect, defined as the impact observed when all units are treated compared to the scenario where no unit receives treatment. We demonstrate the effectiveness of this approach across five numerical scenarios, each characterized by a distinct interference structure.