Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

Five-year analysis of neoadjuvant dabrafenib and trametinib for stage III melanoma.

BACKGROUND: Neoadjuvant dabrafenib plus trametinib has a high pathological response rate and impressive short-term survival in patients with resectable stage III melanoma. We report 5-year outcomes from the phase II NeoCombi trial. PATIENTS AND METHODS: NeoCombi (NCT01972347) was a single-arm, open-label, single-centre, phase II trial. Eligible patients were adults (aged ≥18 years) with histologically confirmed, resectable, RECIST-measurable, American Joint Committee on Cancer seventh edition clinical stage IIIB-C BRAF V600E/K-mutant melanoma and Eastern Cooperative Oncology Group performance status ≤1. Patients received 52 weeks of treatment with dabrafenib 150 mg (orally twice per day) plus trametinib 2 mg (orally once per day), with complete resection of the pre-therapy tumour bed at week 12. RESULTS: Between 20 August 2014 and 19 April 2017, 35 patients were enrolled. At data cut-off (17 August 2021), the median follow-up was 60 months [95% confidence interval (CI) 56-72 months]. Overall, 21 of 35 (60%) patients recurred, including 12 (57%) with first recurrence in locoregional sites (followed by later distant recurrence in 6) and 9 (43%) with first recurrence in distant sites, including 3 in the brain. Most recurrences occurred within 2 years, with no recurrences beyond 3 years. At 5 years, recurrence-free survival (RFS) was 40% (95% CI 27% to 60%), distant metastasis-free survival (DMFS) was 57% (95% CI 42% to 76%), and overall survival was 80% (95% CI 67% to 94%). Five-year survival outcomes were stratified by pathological response: RFS was 53% with pathological complete response (pCR) versus 28% with non-pCR (P = 0.087), DMFS was 59% versus 55% (P = 0.647), and overall survival was 88% versus 71% (P = 0.205), respectively. CONCLUSIONS: Neoadjuvant dabrafenib plus trametinib has high pathological response rates in clinical stage III melanoma, but low rates of RFS, similar to those achieved with adjuvant targeted therapy alone. Patients with a pCR to dabrafenib plus trametinib still had a high risk of recurrence, unlike that seen with immunotherapy where recurrences are rare.

The evolving phenotype of autoimmune hepatitis across the millennium: The 40-year experience of a referral centre in Italy.

BACKGROUND AND AIMS: During recent years, there have been major insight into the pathogenesis, diagnosis and treatment of autoimmune hepatitis (AIH). We aim to evaluate modifications of the clinical-epidemiological phenotype of AIH patients from 1980 to our days. METHODS: Single-centre, tertiary care retrospective study on 507 consecutive Italian patients with AIH. Patients were divided into four subgroups according to the decade of diagnosis: 1981-1990, 1991-2000, 2001-2010 and 2011-2020. We assessed clinical, laboratory and histological features at diagnosis, response to treatment and clinical outcomes. Acute presentation is defined as transaminase levels >10-fold the upper limit and/or bilirubin >5 mg/dL. Complete response is defined as the normalization of transaminases and IgG after 12 months. Clinical progression is defined as the development of cirrhosis in non-cirrhotic patients and hepatic decompensation/hepatocellular carcinoma development in compensated cirrhosis. RESULTS: Median age at diagnosis increased across decades (24, 31, 39, 52 years, p < .001). Acute onset became more common (39.6%, 44.4%, 47.7%, 59.5%, p = .019), while cirrhosis at diagnosis became less frequent (36.5%, 16.3%, 10.8%, 8.7%, p < .001). Complete response rates rose (11.1%, 49.4%, 72.7% 76.2%, p < .001) and clinical progression during follow-up decreased (54.3%, 29.9%, 16.9%, 11.2%, p < .001). Anti-nuclear antibodies positivity increased (40.7%, 52.0%, 73.7%, 79.3%, p < .001), while IgG levels/upper limit progressively decreased (1.546, 1.515, 1.252, 1.120, p < .001). Liver-related death and liver transplantation reduced from 17.1% to 2.1% (p < .001). CONCLUSIONS: In the new millennium, the typical AIH patient in Italy is older at diagnosis, more often presents with acute hepatitis, cirrhosis is less frequent and response to treatment is more favourable.

Update on Cognitive Enhancers Among the Older Adult Population: A Clinical Review.

PURPOSE OF REVIEW: The purpose of this review is to identify key classes of medications that are used for the treatment of older adults with neurocognitive disorders. RECENT FINDINGS: Clinical factors play a critical role in the prescribing of these medication classes for the treatment of dementia. The variation in prescribing trends is determined by the presence of medical and psychiatric comorbidities commonly occurring in older adults and is based on the consideration of potential interactions between pharmacotherapies for the comorbidities and for the dementia. Six medication classes currently exist to address the neurocognitive aspect of dementia, with varying pharmacokinetic and pharmacodynamic profiles. We review these six classes in this report and provide a provision of clinical insights regarding the use of these agents. While literature exists on the safety and efficacy of individual medication options for the treatment of dementia in the older adult population, further research is needed to provide clearer guidance regarding the specific use of these agents in clinical practice.

Primary cutaneous cribriform tumor: A case report and literature review.

In the updated 5th edition of the WHO Classification of Skin Tumors, primary cutaneous cribriform carcinoma has been renamed cribriform tumor. This entity is a rare sweat gland neoplasm with undetermined malignant potential, with only 46 cases reported to date. Herein, we present a case of a 30-year-old female with a solitary nodule in the left thigh subcutaneous tissue. Histopathological examination revealed a well-defined dermal nodule composed of monomorphic, deeply staining cells arranged in solid nests, tubular, and cribriform patterns, with no recurrence or distant metastasis observed during a 1-year follow-up. Summarizing all 47 cases, they exhibited consistent, reproducible histological morphology and similar immunohistochemistry. Although the tumor nests lacked myoepithelial cells peripherally, all cleanly excised cases showed no recurrence or distant metastasis, suggesting a benign biological behavior. We argue against overtreatment.

Use of Hydrochlorothiazide in the United States Following Label Update About Skin Cancer Risk.

PURPOSE: On August 20, 2020, the United States (U.S.) Food and Drug Administration (FDA) issued a Drug Safety Communication (DSC) along with labeling updates to inform the public about a small increased risk of non-melanoma skin cancer (NMSC) associated with hydrochlorothiazide (HCTZ) use. This study aims to assess whether the DSC impacted HCTZ use in the U.S. METHODS: We conducted a trend analysis in the Sentinel Distributed Database using national healthcare administrative data from January 2017 to November 2022. We identified two cohorts each month: An overall cohort of all enrollees and a skin cancer cohort of those with a history of NMSC. For each cohort, we plotted the monthly proportion of patients receiving HCTZ-containing products among those receiving any thiazide diuretics. We performed interrupted time series analyses to quantify the impact of the DSC on these monthly proportions. Secondary analyses were conducted on the proportion of HCTZ users among patients receiving any antihypertensives. RESULTS: In the overall cohort, the DSC was only associated with a statistically significant but clinically negligible trend change of monthly HCTZ proportion within this cohort (0.018%; 95% CI, 0.012%-0.025%). Similar results were observed in the skin cancer cohort. The secondary analysis found no significant level change or trend change in the monthly proportion of HCTZ use among antihypertensive users. CONCLUSIONS: We did not observe significant changes in HCTZ use following the DSC about its NMSC risk, among the overall population and those with a history of NMSC. Our findings were in accordance with the DSC recommendation.

Inter-laboratory comparison of eleven quantitative or digital PCR assays for detection of proviral bovine leukemia virus in blood samples.

Bovine leukemia virus (BLV) is the etiological agent of enzootic bovine leukosis and causes a persistent infection that can leave cattle with no symptoms. Many countries have been able to successfully eradicate BLV through improved detection and management methods. However, with the increasing novel molecular detection methods there have been few efforts to standardize these results at global scale. This study aimed to determine the interlaboratory accuracy and agreement of 11 molecular tests in detecting BLV. Each qPCR/ddPCR method varied by target gene, primer design, DNA input and chemistries. DNA samples were extracted from blood of BLV-seropositive cattle and lyophilized to grant a better preservation during shipping to all participants around the globe. Twenty nine out of 44 samples were correctly identified by the 11 labs and all methods exhibited a diagnostic sensitivity between 74 and 100%. Agreement amongst different assays was linked to BLV copy numbers present in samples and the characteristics of each assay (i.e., BLV target sequence). Finally, the mean correlation value for all assays was within the range of strong correlation. This study highlights the importance of continuous need for standardization and harmonization amongst assays and the different participants. The results underscore the need of an international calibrator to estimate the efficiency (standard curve) of the different assays and improve quantitation accuracy. Additionally, this will inform future participants about the variability associated with emerging chemistries, methods, and technologies used to study BLV. Altogether, by improving tests performance worldwide it will positively aid in the eradication efforts.

Guidelines in Neurosurgery: a critical appraisal.

BACKGROUND: The process of grading and stratifying evidence in the extensive literature on neurosurgical guidelines has evolved significantly, ranging from high-quality standards to suggested options. However, the methodology for guideline development has become increasingly complex, leading to challenges in their application across various neurosurgical specialties and settings. This mini review aims to explore the practical implications of published suggestions for managing neurosurgical patients. METHODS: A critical and focused collection of published literature concerning guidelines in different neurosurgical topics, from Pubmed and other sources formed the basis of this non-systematic narrative review. Only guidelines produced by neurosurgeons in the era of evidence based medicine (after 1996) were included. RESULTS: Neurosurgical guidelines often rely on a limited number of Randomized Controlled Trials (RCTs) and Class I evidence, particularly in surgical and emergency contexts where randomization of patient treatments may conflict with established clinical practices. Challenges also include the timely update of guidelines, which sometimes lags behind rapid shifts in evidence, and varying methodologies in guideline production that can result in divergent recommendations. Geographical disparities in disease burden and literature production further influence guideline applicability, suggesting a need for greater inclusion of authors from Low- and Middle-Income Countries (LMICs) to enhance realism and global relevance. Consensus conferences and expert reviews may serve as viable alternatives to address these challenges. CONCLUSION: While Evidence-Based Medicine remains pivotal, critical appraisal and practical application of guidelines must consider these complexities to optimize patient care and outcomes.

A Cautionary Tale: Malaligned Incentives in Total Hip and Knee Arthroplasty Payment Model Reforms Threaten Promising Innovation and Access to Care.

Over the past several years, there have been notable changes and controversies involving Medicare reimbursement for total hip (THA) and total knee arthroplasty (TKA). We have seen the development and implementation of experimental bundled payment model pilot programs goals of improving quality and decreasing overall costs of care during the last decade. Many orthopaedic surgeons have embraced these programs and have demonstrated the ability to succeed in these new models by implementing strategies, such as preservice optimization, to shift care away from inpatient or postdischarge settings and reduce postoperative complications. However, these achievements have been met with continual reductions in surgeon reimbursement rates, lower bundle payment target pricings, modest increases in hospital reimbursement rates, and inappropriate valuations of THA and TKA Common Procedural Terminology (CPT) codes. These challenges have led to an organized advocacy movement and spurred research involving the methods by which improvements have been made throughout the entire episode of arthroplasty care. Collectively, these efforts have recently led to a novel application of CPT codes recognized by payers to potentially capture presurgical optimization work. In this paper, we present an overview of contemporary payment models, summarize notable events involved in the review of THA and TKA CPT codes, review recent changes to THA and TKA reimbursement, and discuss future challenges faced by arthroplasty surgeons that threaten access to high-quality THA and TKA care.

Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed.

The capacity to update firmware is a vital component in the lifecycle of Internet of Things (IoT) devices, even those with restricted hardware resources. This paper explores the best way to wirelessly (Over The Air, OTA) update low-end IoT nodes with difficult access, combining the use of unicast and broadcast communications. The devices under consideration correspond to a recent industrial IoT project that focuses on the installation of intelligent lighting systems within ATEX (potentially explosive atmospheres) zones, connected via LoRa to a gateway. As energy consumption is not limited in this use case, the main figure of merit is the total time required for updating a project. Therefore, the objective is to deliver all the fragments of the firmware to each and all the nodes in a safe way, in the least amount of time. Three different methods, combining unicast and broadcast transmissions in different ways, are explored analytically, with the aim of obtaining the expected update time. The methods are also tested via extensive simulations, modifying different parameters such as the size of the scenario, the number of bytes of each firmware chunk, the number of nodes, and the number of initial broadcast rounds. The simulations show that the update time of a project can be significant, considering the limitations posed by regulations, in terms of the percentage of airtime consumption. However, significant time reductions can be achieved by using the proper method: in some cases, when the number of nodes is high, the update time can be reduced by two orders of magnitude if the correct method is chosen. Moreover, one of the proposed methods is implemented using actual hardware. This real implementation is used to perform firmware update experiments in a lab environment. Overall, the article illustrates the advantage of broadcast approaches in this kind of technology, in which the transmission rate is constant despite the distance between the gateway and the node. However, the advantage of these broadcast methods with respect to the unicast one could be mitigated if the nodes do not run exactly the same firmware version, since the control of the broadcast update would be more difficult and the total update time would increase.

Enhanced Predefined-Time Control for Spacecraft Attitude Tracking: A Dynamic Predictive Approach.

This study presents a predefined-time control strategy for rigid spacecraft, employing dynamic predictive techniques to achieve robust and precise attitude tracking within predefined time constraints. Advanced predictive algorithms are used to effectively mitigate system uncertainties and environmental disturbances. The main contributions of this work are introducing adaptive global optimization for period updates, which relaxes the original restrictive conditions; ensuring easier parameter adjustments in predefined-time control, providing a nonconservative upper bound on system stability; and developing a continuous, robust control law through terminal sliding mode control and predictive methods. Extensive simulations confirm the control scheme reduces attitude tracking errors to less than 0.01 degrees at steady state, demonstrating the effectiveness of the proposed control strategy.

River Surface Velocity Measurement for Rapid Levee Breach Emergency Response Based on DFP-P-LK Algorithm.

In recent years, the increasing frequency of climate change and extreme weather events has significantly elevated the risk of levee breaches, potentially triggering large-scale floods that threaten surrounding environments and public safety. Rapid and accurate measurement of river surface velocities is crucial for developing effective emergency response plans. Video image velocimetry has emerged as a powerful new approach due to its non-invasive nature, ease of operation, and low cost. This paper introduces the Dynamic Feature Point Pyramid Lucas-Kanade (DFP-P-LK) optical flow algorithm, which employs a feature point dynamic update fusion strategy. The algorithm ensures accurate feature point extraction and reliable tracking through feature point fusion detection and dynamic update mechanisms, enhancing the robustness of optical flow estimation. Based on the DFP-P-LK, we propose a river surface velocity measurement model for rapid levee breach emergency response. This model converts acquired optical flow motion to actual flow velocities using an optical flow-velocity conversion model, providing critical data support for levee breach emergency response. Experimental results show that the method achieves an average measurement error below 15% within the velocity range of 0.43 m/s to 2.06 m/s, demonstrating high practical value and reliability.

On the Adaptive Buffer-Aided TDMA Uplink System with AoI-Aware Status-Update Services and Timely Throughput Traffics.

In this paper, we study a buffer-aided TDMA uplink network, where multiple status-update devices and throughput-demand devices are supposed to upload their data to one information access point (AP), and all devices are assumed to be provisioned with a data buffer to temporarily store the randomly generated data from either the installed sensor or upper-layer applications. To fulfill the communication requirements using two types of devices, the average Age of Information (AoI) is utilized to characterize the data freshness of the status-update devices, while the average sum rate is employed to capture the average transmission performance of the throughput-demand devices. On this basis, a joint-optimization problem was formulated to minimize the average AoI for status-update devices and to maximize the average sum rate for the throughput-demand devices. Lyapunov optimization framework was used to solve the problem of obtaining an AoI-aware adaptive TDMA uplink scheme. Numerical results are presented to show that an AoI-aware adaptive TDMA uplink scheme can effectively fulfill the heterogeneous service requirements using status-update devices and throughput-demand devices.

Mapless Path Planning for Mobile Robot Based on Improved Deep Deterministic Policy Gradient Algorithm.

In the traditional Deep Deterministic Policy Gradient (DDPG) algorithm, path planning for mobile robots in mapless environments still encounters challenges regarding learning efficiency and navigation performance, particularly adaptability and robustness to static and dynamic obstacles. To address these issues, in this study, an improved algorithm frame was proposed that designs the state and action spaces, and introduces a multi-step update strategy and a dual-noise mechanism to improve the reward function. These improvements significantly enhance the algorithm's learning efficiency and navigation performance, rendering it more adaptable and robust in complex mapless environments. Compared to the traditional DDPG algorithm, the improved algorithm shows a 20% increase in the stability of the navigation success rate with static obstacles along with a 25% reduction in pathfinding steps for smoother paths. In environments with dynamic obstacles, there is a remarkable 45% improvement in success rate. Real-world mobile robot tests further validated the feasibility and effectiveness of the algorithm in true mapless environments.

The Construction and Application of a Digital Coal Seam for Shearer Autonomous Navigation Cutting.

Accurately obtaining the geological characteristic digital model of a coal seam and surrounding rock in front of a fully mechanized mining face is one of the key technologies for automatic and continuous coal mining operation to realize an intelligent unmanned working face. The research on how to establish accurate and reliable coal seam digital models is a hot topic and technical bottleneck in the field of intelligent coal mining. This paper puts forward a construction method and dynamic update mechanism for a digital model of coal seam autonomous cutting by a coal mining machine, and verifies its effectiveness in experiments. Based on the interpolation model of drilling data, a fine coal seam digital model was established according to the results of geological statistical inversion, which overcomes the shortcomings of an insufficient lateral resolution of lithology and physical properties in a traditional geological model and can accurately depict the distribution trend of coal seams. By utilizing the numerical derivation of surrounding rock mining and geological SLAM advanced exploration, the coal seam digital model was modified to achieve a dynamic updating and optimization of the model, providing an accurate geological information guarantee for intelligent unmanned coal mining. Based on the model, it is possible to obtain the boundary and inclination information of the coal seam profile, and provide strategies for adjusting the height of the coal mining machine drum at the current position, achieving precise control of the automatic height adjustment of the coal mining machine.

Priority-Aware Actuation Update Scheme in Heterogeneous Industrial Networks.

In heterogeneous wireless networked control systems (WNCSs), the age of information (AoI) of the actuation update and actuation update cost are important performance metrics. To reduce the monetary cost, the control system can wait for the availability of a WiFi network for the actuator and then conduct the update using a WiFi network in an opportunistic manner, but this leads to an increased AoI of the actuation update. In addition, since there are different AoI requirements according to the control priorities (i.e., robustness of AoI of the actuation update), these need to be considered when delivering the actuation update. To jointly consider the monetary cost and AoI with priority, this paper proposes a priority-aware actuation update scheme (PAUS) where the control system decides whether to deliver or delay the actuation update to the actuator. For the optimal decision, we formulate a Markov decision process model and derive the optimal policy based on Q-learning, which aims to maximize the average reward that implies the balance between the monetary cost and AoI with priority. Simulation results demonstrate that the PAUS outperforms the comparison schemes in terms of the average reward under various settings.

Load Prediction in Double-Channel Residual Self-Attention Temporal Convolutional Network with Weight Adaptive Updating in Cloud Computing.

When resource demand increases and decreases rapidly, container clusters in the cloud environment need to respond to the number of containers in a timely manner to ensure service quality. Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed, the Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW), in order to make the response of the container cluster more rapid and accurate. A Double-channel Temporal Convolution Network model (DTN) has been developed to capture long-term sequence dependencies and enhance feature extraction capabilities when the model handles long load sequences. Double-channel dilated causal convolution has been adopted to replace the single-channel dilated causal convolution in the DTN. A residual temporal self-attention mechanism (SM) has been proposed to improve the performance of the network and focus on features with significant contributions from the DTN. DTN and SM jointly constitute a dual-channel residual self-attention temporal convolutional network (DSTN). In addition, by evaluating the accuracy aspects of single and stacked DSTNs, an adaptive weight strategy has been proposed to assign corresponding weights for the single and stacked DSTNs, respectively. The experimental results highlight that the developed method has outstanding prediction performance for cloud computing in comparison with some state-of-the-art methods. The proposed method achieved an average improvement of 24.16% and 30.48% on the Container dataset and Google dataset, respectively.

Concept Drift Mitigation in Low-Cost Air Quality Monitoring Networks.

Future air quality monitoring networks will integrate fleets of low-cost gas and particulate matter sensors that are calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of data quality loss in machine learning application operational scenarios. The present study focuses on addressing the calibration model update of low-cost NO2 sensors once they are triggered by a concept drift detector. It also defines which data are the most appropriate to use in the model updating process to gain compliance with the relative expanded uncertainty (REU) limits established by the European Directive. As the examined methodologies, the general/global and the importance weighting calibration models were applied for concept drift effects mitigation. Overall, for all the devices under test, the experimental results show the inadequacy of both models when performed independently. On the other hand, the results from the application of both models through a stacking ensemble strategy were able to extend the temporal validity of the used calibration model by three weeks at least for all the sensor devices under test. Thus, the usefulness of the whole information content gathered throughout the original co-location process was maximized.

New staging of endometrial carcinoma - FIGO 2023.

AIM: To review the changes in the new version of the FIGO 2023 staging system for endometrial cancer. METHODS AND RESULTS: The new FIGO 2023 endometrial cancer staging system provides key updates for the diagnosis and treatment of endometrial cancer. An important step in diagnosis is molecular classification, which allows more accurate risk stratification for recurrence and the identification of targeted therapies. The new staging system, based on the recommendations of the international societies ESGO, ESTRO and ESP, incorporates not only the description of the pathological and anatomical extent of the disease, but also the histopathological characteristics of the tumour, including the histological type and the presence of lymphovascular space invasion. In addition, the staging system uses molecular testing to classify endometrial cancers into four prognostic groups: POLEmut, MMRd, NSMP and p53abn. Each group has its own specific characteristics and prognosis. The most significant changes have occurred in stages I and II, in which the sub-staging better reflects the biological behaviour of the tumour. This update increases the accuracy of prognosis and improves individualized treatment options for patients with endometrial cancer. CONCLUSION: The updated FIGO staging of endometrial cancer for 2023 incorporates different histologic types, tumour features, and molecular classifications to better reflect the current improved understanding of the complex nature of several endometrial cancer types and their underlying bio logic behaviour. The aim of the new endometrial cancer staging system is to better define stages with similar prognosis, allowing for more precise indication of individualised adjuvant radiation or systemic treatment, including the use of immunotherapy.

WI-TMLEGA: Weight Initialization and Training Method Based on Entropy Gain and Learning Rate Adjustment.

Addressing the issues of prolonged training times and low recognition rates in large model applications, this paper proposes a weight training method based on entropy gain for weight initialization and dynamic adjustment of the learning rate using the multilayer perceptron (MLP) model as an example. Initially, entropy gain was used to replace random initial values for weight initialization. Subsequently, an incremental learning rate strategy was employed for weight updates. The model was trained and validated using the MNIST handwritten digit dataset. The experimental results showed that, compared to random initialization, the proposed initialization method improves training effectiveness by 39.8% and increases the maximum recognition accuracy by 8.9%, demonstrating the feasibility of this method in large model applications.