Complicated Patterns Research Articles

Complications of femoral access for hemodialysis: A surgical perspective

Background: Vascular access, especially arteriovenous fistula (AVF), is critical for patients requiring haemodialysis. Due to delayed diagnosis, late referral, poverty and limited availability of competent access surgeons, many patients commence haemodialysis without an AVF, hence the frequent use of venous catheters. Objectives: To assess the pattern of complications associated with repeated femoral punctures for haemodialysis. Materials and Methods: This is a multicentre prospective study conducted at the Cardiovascular units of the University of Port Harcourt Teaching Hospital, Port Harcourt and Federal Medical Centre, Umuahia between January 2017 and December 2022. 66 patients were recruited for the study. Data obtained were patients’ demography, complications of femoral access, surgical approach and outcome. Results: 15 patients presented with severe lower limb pains and inability to walk, 10 with fibrosis/ hardening of the skin of both groins, 8 with skin infection at the repeated puncture sites, 12 had Pseudoaneurysm of the superficial femoral artery (SFA), 6 had iatrogenic femoro-femoral Arteriovenous Fistula (AVF) of the SFA and superficial femoral vein (SFV), 5 had groin abscess. All the patients had internal jugular venous catheters inserted. Some patients had Fogarty catheter thrombectomies and interposition Great Saphenous Vein (GSV) graft repairs, while some others had successful takedown of the AVF, Fogarty thrombectomy and direct prolene 6/0 repair of the vein & arterial wall defects. All those that underwent surgical treatment had successful recovery. Conclusion: Femoral vein cannulation, apparently easy to perform, should not be used as the vascular access of choice for chronic haemodialysis.

Autonomous Healthcare Systems: Deep Learning-Based IoT Solutions for Continuous Monitoring and Adaptive Treatment

Autonomous healthcare systems are a big change in the way medicine is done. They use deep learning algorithms and Internet of Things (IoT) devices to keep an eye on patients all the time and change their treatment as needed. This new way of doing things could change the way patients are cared for by giving real-time information and personalized treatments. With the help of deep learning, these systems can look at huge amounts of data produced by IoT sensors, like those in medical implants and smart tech, to spot small changes in health and spot problems before they get worse.Autonomous healthcare systems are based on their ability to constantly gather and analyze data from a variety of sources, such as vital signs, biological markers, and patient-reported complaints. Deep learning algorithms are very important to this process because they can find complicated patterns and connections in the data. These algorithms can get useful information from raw sensor data by using methods like CNN, MobileNet, and InceptionV3. This algorithm lets healthcare professionals move quickly and proactively.Also, independent healthcare systems are made to change based on the wants and interests of each patient by using personalized treatment plans. These systems can improve results and patient happiness by constantly checking how patients respond to actions and making changes to treatment plans on the fly. Adding IoT devices also makes it easier for patients and healthcare workers to talk to each other, allowing for distant discussions and quick solutions. The automated healthcare systems are a revolutionary way to provide medical care. They use deep learning-based IoT solutions to keep an eye on patients all the time and adjust their treatment as needed. These systems might be able to improve patient results, lower healthcare costs, and raise general quality of life by using the power of data-driven insights and individual actions

Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment

Multi-hop fact verification aims to detect the veracity of the given claim by integrating and reasoning over multiple pieces of evidence. Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets. Among the various debiasing works, the causal inference-based methods become popular by performing theoretically guaranteed debiasing such as casual intervention or counterfactual reasoning. However, existing causal inference-based debiasing methods, which mainly formulate fact verification as a single-hop reasoning task to tackle shallow bias patterns, cannot deal with the complicated bias patterns hidden in multiple hops of evidence. To address the challenge, we propose Causal Walk, a novel method for debiasing multi-hop fact verification from a causal perspective with front-door adjustment. Specifically, in the structural causal model, the reasoning path between the treatment (the input claim-evidence graph) and the outcome (the veracity label) is introduced as the mediator to block the confounder. With the front-door adjustment, the causal effect between the treatment and the outcome is decomposed into the causal effect between the treatment and the mediator, which is estimated by applying the idea of random walk, and the causal effect between the mediator and the outcome, which is estimated with normalized weighted geometric mean approximation. To investigate the effectiveness of the proposed method, an adversarial multi-hop fact verification dataset and a symmetric multi-hop fact verification dataset are proposed with the help of the large language model. Experimental results show that Causal Walk outperforms some previous debiasing methods on both existing datasets and the newly constructed datasets. Code and data will be released at https://github.com/zcccccz/CausalWalk.

Dof1.7 and NIGT1 transcription factors mediate multilayered transcriptional regulation for different expression patterns of NITRATE TRANSPORTER2 genes under nitrogen deficiency stress.

Elucidating the mechanisms regulating nitrogen (N) deficiency responses in plants is of great agricultural importance. Previous studies revealed that decreased expression of NITRATE-INDUCIBLE GARP-TYPE TRANSCRIPTIONAL REPRESSOR1 (NIGT1) transcriptional repressor genes upon N deficiency is involved in N deficiency-inducible gene expression in Arabidopsis thaliana. However, our knowledge of the mechanisms controlling N deficiency-induced changes in gene expression is still limited. Through the identification of Dof1.7 as a direct target of NIGT1 repressors and a novel N deficiency response-related transcriptional activator gene, we here show that NIGT1 and Dof1.7 transcription factors (TFs) differentially regulate N deficiency-inducible expression of three high-affinity nitrate transporter genes, NRT2.1, NRT2.4, and NRT2.5, which are responsible for most of the soil nitrate uptake activity of Arabidopsis plants under N-deficient conditions. Unlike NIGT1 repressors, which directly suppress NRT2.1, NRT2.4, and NRT2.5 under N-sufficient conditions, Dof1.7 directly activated only NRT2.5 but indirectly and moderately activated NRT2.1 and NRT2.4 under N-deficient conditions, probably by indirectly decreasing NIGT1 expression. Thus, Dof1.7 converted passive transcriptional activation into active and potent transcriptional activation, further differentially enhancing the expression of NRT2 genes. These findings clarify the mechanism underlying different expression patterns of NRT2 genes upon N deficiency, suggesting that time-dependent multilayered transcriptional regulation generates complicated expression patterns of N deficiency-inducible genes.

Modeling the Interactive Patterns of International Migration Network through a Reverse Gravity Approach

The international migration network, comprising the movements of people between countries, is one of the most important global systems of interaction, which can reflect the complex international relations of economics, cultures, and politics and has huge impacts on global sustainability. However, the conventional gravity model cannot model its complicated interactions accurately. In this article, we propose a novel reverse gravity model using genetic algorithm to reconstruct the complicated interaction patterns with high accuracy. To verify the feasibility of our method, it was applied to a series of international migration networks. We found that the derived node attractions were highly correlated with socioeconomic factors and network metrics, and the calculated node positions outperformed the geometric centers from the perspective of human migration that related to economy and demography. Our approach could be a preferred choice to investigate the spatial–temporal interactive patterns in geographical space, facilitating comprehension of the mechanisms underlying their generation and evolution.

Comparing multi-class classifier performance by multi-class ROC analysis: A nonparametric approach

The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. Real world applications often require classification into multiple (more than two) classes. For multi-class classifiers that produce class membership scores, a popular multi-class AUC (MAUC) variant is to average the pairwise AUC values (Hand and Till, 2001). Due to the complicated correlation patterns, the variance of MAUC is often estimated numerically using resampling techniques. This work is a generalization of DeLong’s nonparameteric approach for binary AUC analysis (DeLong et al., 1988) to MAUC. We first derive the closed-form expression of the covariance matrix of the pairwise AUCs within a single MAUC. Then by dropping higher order terms, we obtain an approximate covariance matrix with a compact, matrix factorization form, which then serves as the basis for variance estimation of a single MAUC. We further extend this approach to estimate the covariance of correlated MAUCs that arise from multiple competing classifiers. For the special case of binary correlated AUCs, our results coincide with that of DeLong. Our numerical studies confirm the accuracy of the variance and covariance estimates. We provide the source code of the proposed covariance estimation of correlated MAUCs on GitHub (https://tinyurl.com/euj6wvsz) for its easy adoption by machine learning and statistical analysis packages to quantify and compare multi-class classifiers.

Spatiotemporal optical vortex reconnections of multi-vortices

Vortex reconnections are ubiquitous events found in diverse media. Here we show that vortex reconnections also occur between spatiotemporal vortices in optical waves. Since vortices exhibit orbital angular momentum (OAM), the reconnections of optical vortices create a variety of connected OAM states. Dispersion and diffraction can cause different reconnection pairs, depending on the orientation of the vortices. The transverse crossing of two vortices with a topological charge of one can produce unique vortex loop reconnection patterns. Higher topological charges result in arrays of vortex loops and connection points. Crossing of three vortices produces spherical structures made of three symmetrical vortex arms. A three vortices reconnection with higher topological charges develops complicated patterns similar to turbulence cascade phenomena in other media. Studying optical vortex interactions may bring insight into vortex reconnections in other fields. We also provide experimental results of two-vortex loop interaction.

Light-assisted patterning of salt precipitation on photovoltaic LiNbO3 substrates

The control of salt crystallization on a surface has important implications in many technological and industrial applications. In this work, we propose and demonstrate an optoelectrical method to define and control the spatial distribution of salt crystallization on a lithium niobate photovoltaic substrate. It is based on the bulk photovoltaic effect that generates an electric field on the illuminated regions of the crystal. The salt only crystallizes on these illuminated regions of the substrate. Single salt spots or more complicated spatial patterns, defined by the light intensity spatial distribution, have been achieved. In particular, some results have been obtained using scanning/moving laser beams, i.e., “drawing” the saline patterns. The role of light exposure time and salt concentration in the aqueous solution has been studied. The method has been checked with several salts with successful results showing its general applicability. A discussion on the possible physical mechanisms behind the method and their implication for the operation of photovoltaic platforms in other applications is also included.

The effects of fractures on porous flow and heat transfer in a reservoir of a U-shaped closed geothermal system

In geothermal reservoirs, fractures, serving as preferential channels for heat transfer fluid circulation, can cause complicated migration patterns and heat transfer characteristics of the fluids. However, the crucial issue of whether and how fractures within a hydrothermal reservoir of a closed geothermal system (CGS) affect reservoir heat transfer fluid migration and the system’s heat transfer is still poorly understood. In this study, we construct a thermo-hydraulic coupling finite-element model to investigate the effects of fractures on fluid flow and heat transfer enhanced by natural convection in a U-shaped closed geothermal system (UCGS) with a horizontal well. We systematically analyze the responses of fluid migration and heat extraction in the system to key factors, such as fracture aperture, fracture azimuth, fracture distribution, and reservoir permeability. The results show that, in a high permeability reservoir (such as 2 × 10−13 m2), the fracture with an aperture of a millimeter scale can greatly strengthen and control the natural convection induced by heat transfer around the horizontal well in a UCGS, achieving a heat recovery efficiency of six times higher than conduction alone within the reservoir. The fracture azimuth can control the heat production region by guiding the evolution of natural convection. Intersecting network-distributed fractures can effectively improve heat recovery area and performance (more than 15%) compared to a single fracture. Some specific fracture segments within a fracture network may exhibit synergy or inhibition in different heat extraction stages, while a fracture connecting in the deep reservoir and orienting to the horizontal well may lead to heat breakthrough. In addition, a low-permeability interlayer below the horizontal well can restrict the heat recovery (nearly 20% drop in our models), but a fracture penetrating the layer can compensate for this hindrance. The findings of this study can contribute to understanding the thermodynamic mechanisms of fluid flow and heat transfer in a fractured reservoir, and then utilizing them to maximize heat recovery rather than considering conduction only, thus providing scientific insights into engineering practices of the UCGS.

RAINFALL FORECASTING WITH AN INTERMITTENT APPROACH USING HYBRID EXPONENTIAL SMOOTHING NEURAL NETWORK

Rainfall forecasting is crucial in agriculture, water resource management, urban planning, and disaster preparation. Traditional approaches fail to capture complicated and intermittent rainfall patterns. The “Hybrid Exponential Smoothing Neural Network” is introduced in this study to handle intermittent rainfall forecasting issues. Exponential Smoothing, an established approach for discovering underlying patterns and seasonal fluctuations in time series data, is combined with Neural Networks, which are good at capturing complex linkages and nonlinearities. Using these two methods, this model hopes to deliver a complete rainfall forecasting solution that accounts for short-term changes and long-term patterns. This research uses residuals from the exponential smoothing model and is modeled using a Neural Network. The residual input is transformed using rolling mean. The results show that the hybrid model is able to capture patterns well, but there are still patterns that experience time lag. Experimental results obtained reveal that the hybrid methodology performs better than the model exponential smoothing, implying that the proposed model hybrid synergy approach can be used as an alternative solution to the rainfall time series forecasting. The results show that the Hybrid method can form patterns better than individual exponential smoothing models or neural networks. The RMSSE values for all areas are 1.0185, 1.55092, 1.0872.

Risk factor analysis for perioperative complications in impacted third molar surgery - a single center experience.

The surgical removal of impacted third molars is usually carried out by an oral/maxillofacial surgeon. Two specific risks of surgical removal of impacted third molars are oroantral communication (OAC) when extracting ​​upper third molars and hypesthesia of the inferior alveolar nerve (IAN) ​​when extracting lower third molars. The aim of this study is to determine the distribution of complications in deeply impacted third molar surgery, to identify specific risk factors influencing the most common perioperative (OAC, IAN hypesthesia) and to compare these results with other studies. The clinical findings, digital panoramic radiographs, intra- and postoperative data of 80 patients with a total of 232 impacted third molars that had been subjected for tooth extraction, from December 2022 and August 2023, were collected and analyzed. Perioperative complications (IAN hypesthesia, OAC, hypesthesia lingual nerve, postoperative bleeding, postoperative infection) were identified. A risk analysis for OAC and IAN hypesthesia was performed regarding perioperative data. Overall, the rate of OAC for the right upper third molar was 12.8% and for the left upper third molar 15.6%. The complication rates regarding transient hypesthesia were 8.1% for the left IAN and 7.3% for the right IAN. The distance to maxillary sinus, the depth score according to Pell and Gregory, the bone coverage score, the operation time, the tooth's angulation and the type of surgeon (oral surgeon, DMD) were identified as significant risk factors for the occurrence of OAC. The minimum distance to IAN, the bone coverage score, the total operation time and the operation by an oral surgeon (DMD) were identified as significant risk factors for hypesthesia of the IAN. Next to the risk factors from above, the present study is one of the first showing that patients who were primarily operated on by an oral surgeon (DMD) and not a maxillofacial surgeon (MD, DMD) showed higher rates of OAC and IAN hypesthesia in impacted third molar extraction. The results of this study can serve as a baseline for further studies to investigate complication patterns in impacted third molar surgery.

Probing the interaction of an antiviral drug 8-methoxy-3-nitro-2-(4-methoxyphenyl)-2H-chromene (MNC) with DNA by experimental and computational approach

Deoxyribonucleic acid (DNA) is a major target in research pertaining to antiviral and anticancer therapies since it is the principal genetic material that controls vital biological functions. The complex relationship between calf thymus DNA (ct-DNA) and 8-Methoxy-3-nitro-2-(4-methoxyphenyl)-2H-chromene (MNC), an anticancer drug derivative of 2-aryl-3-nitro-2H-chromenes, is explored in this work. Using various spectroscopic methods such as fluorescence, circular dichroism (CD), UV–visible, isothermal titration calorimetry (ITC) experiment, and molecular docking calculations, this study elucidates the binding characteristics and affinity between MNC and ct-DNA. Through predominant groove binding and moderate intercalation, MNC demonstrates a complicated binding pattern with DNA at the minor groove, as shown by the UV–Vis spectra and emission-based dye displacement investigations. DNA undergoes a little conformational change that MNC induces, as shown by CD spectroscopy. The thermodynamics parameters such as the binding constant, binding enthalpy, and binding free energy of DNA-MNC interaction, as calculated by the ITC experiment, were approximately ∼ 104 M−1, −26 kcal mol−1, and 6 kcal/mol, respectively. The binding process is enthalpy driven implying a stabilizing contact of MNC with DNA and the entropic penalty (−TΔS = +20.1 kcal mol−1) points to a reduction in the degree of freedom of movement. The molecular docking investigation also estimates the similar interaction energy and binding locations of MNC with DNA. Interestingly, MNC attaches to the surface of the DNA via electrostatic interactions; a stronger attachment through van der Waals and hydrophobic contacts within the minor groove follows this. These studies are anticipated to provide researchers with important direction for designing new drug molecules for further biomolecular investigations.

Image Denoising Techniques Using Unsupervised Machine Learning and Deep Learning Algorithms: A Review

The continuous evolution of imaging technologies has accentuated the demand for robust and efficient image denoising techniques. Unsupervised machine learning algorithms have emerged as promising tools for addressing this challenge. This review scrutinizes the efficacy, versatility, and limitations of various unsupervised machine learning approaches in the area of image denoising. The paper commences with a clarification of the foundational concepts of image denoising and the pivotal role unsupervised machine learning plays in enhancing its efficacy. Traditional denoising methods, encompassing filters and transforms, are briefly outlined, highlighting their insufficiencies in handling complicated noise patterns prevalent in modern imaging systems. Subsequently, the review delves into an exploration of unsupervised machine learning techniques tailored for image denoising. This includes an in-depth analysis of methodologies such as clustering deep learning. Each technique is surveyed for its architectural variation, adaptability, and performance in denoising diverse image datasets. Additionally, the review encompasses an evaluation of prevalent metrics used for quantifying denoising performance, discussing their relevance and applicability across varying noise types and image characteristics. Furthermore, it delineates the challenges faced by unsupervised techniques in this domain and charts prospective avenues for future research, emphasizing the fusion of unsupervised methods with other learning paradigms for heightened denoising efficacy. This review merges empirical insights, critical analysis, and future perspectives, serving as a roadmap for researchers and practitioners navigating the landscape of image denoising through unsupervised machine learning methodologies.

BrGDGT-based seasonal paleotemperature reconstruction for the last 15 000 years from a shallow lake on the eastern Tibetan Plateau

Abstract. Understanding Holocene temperature changes is vital for resolving discrepancies between proxy reconstructions and climate models. The intricate temperature variations across the Tibetan Plateau (TP) add complexity to studying continental climate change during this period. Discrepancies between model-based and proxy-based reconstructions might stem from seasonal biases and environmental uncertainties in the proxies. Employing multiple proxies from a single sediment core for quantitative temperature reconstructions offers an effective method for cross-validation in terrestrial environments. Here, we present an ice-free-season temperature record for the past 15 kyr from a shallow, freshwater lake on the eastern TP, based on brGDGTs (branched glycerol dialkyl glycerol tetraethers). This record shows that the Holocene Thermal Maximum lags the pollen-based July temperature recorded in the same sediment core. We conclude that the mismatch between the brGDGT-based and pollen-based temperatures is primarily the result of seasonal variations in solar irradiance. The overall pattern of temperature changes is supported by other summer temperature records, and the Younger Dryas cold event and the Bølling–Allerød warm period are also detected. A generally warm period occurred during 8–3.5 ka, followed by a cooling trend in the late Holocene. Our findings have implications for understanding the seasonal signal of brGDGTs in shallow lakes and provide critical data for confirming the occurrence of seasonal biases in different proxies from high-elevation lakes. To further investigate the significance of the brGDGTs and temperature patterns on the TP, we examined existing brGDGT-based Holocene temperature records, which interpret these compounds as indicators of mean annual or growing season temperatures. The existing and available temperature records show complicated patterns of variation, some with general warming trends throughout the Holocene, some with cooling trends, and some with a warm middle Holocene. We analyzed the possible reasons for the diverse brGDGTs records on the TP and emphasize the importance of considering lake conditions and modern investigations of brGDGTs in lacustrine systems when using brGDGTs to reconstruct paleoenvironmental conditions.

Complication Patterns of Loop Ileostomy

Background: Loop ileostomy is a temporary diversion of small intestinal contents towards exterior through anterior abdominal wall. Though it is a good procedure for a better outcome of primary surgery it has itself many complications. Objective: To determine the clinical spectrum of loop ileostomy complications. Materials and method: This prospective study was conducted from January, 2017 to December, 2017. Consecutive 30 patients requiring loop ileostomy were enrolled in the study by purposive sampling and were categorized into Group A requiring emergency surgery and Group B planned for elective surgery. Data regarding sociodemographic, clinical, surgical and outcome profile were recorded in a pre-structured, interview and observation based, peer reviewed data collection sheet. Data were compiled, edited and analyzed with SPSS version 23. Data were presented as mean and standard deviation, frequency percentage and median with range. Results: The mean age of the patients were 32.79±5.19 years (age range: 20-43 years) and 49.16±6.17 years (age range: 28-76 years) in Group A and Group B respectively with sex ratio of male to female of 4:1 and 3:1. Out of 10 patients in Group A, 4(40%) patients underwent resection anastomosis with ileostomy and primary repair with loop ileostomy whereas 2(20%) patients underwent exteriorization of multiple perforation site. In Group B among 20 patients, 9(45%) underwent low anterior resection with loop ileostomy and 5(25%) patients underwent left hemicolectomy with loop ileostomy. Out of 10 patients in Group A, 5(50%) patients each suffered from skin excoriation and major wound infection. On the contrary, among 20 patients in Group B, 11(55%) and 4(20%) patients suffered from skin excoriation and stomal obstruction. Only skin excoriation was evident as statistically significantly higher in Group B than in Group A (p 0.03). Among the general complications, electrolyte imbalance (60% vs 40% in Group A and B respectively) and respiratory tract infection (10% each in Group A and B) were evident. Conclusion: Skin discoloration, skin edema, major and minor wound infection, prolapse, skin excoriation and stomal obstruction are the different spectrum of loop ileostomy complications in our perspective. Among them skin excoriation is much higher in routine cases than emergency surgery. Delta Med Col J. Jan 2021;9(1):17-22

Deepfake Creation and Detection of Multimedia Data

As the increasing number of deepfake content poses a growing threat to multimedia integrity, this paper proposes a robust deepfake detection approach based on a hybrid architecture. The proposed framework combines the power of Residual Networks (ResNet) for spatial feature extraction and Long Short-Term Memory (LSTM) with Convolutional Neural Networks (CNN) for temporal dependency modeling. The ResNet component captures complicated patterns in facial and contextual information, whereas the LSTM-CNN module identifies dynamic facial expressions and movements across multiple frames. Transfer learning strategies are used to improve model generalization by combining pre-training on a large dataset with fine-tuning on deepfake data. Experimental evaluations on a variety of deepfake datasets show superior accuracy, precision, and recall, demonstrating the hybrid architecture's efficiency in dealing with the evolving challenges posed by more advanced deepfake generation techniques. In conclusion, our novel deepfake detection framework employs an effective combination of ResNet and LSTM-CNN, demonstrating a promising solution that not only advances the state-of-the-art in multimedia forensics but is also resistant to adversarial attacks. This hybrid model, which effectively combines spatial and temporal information, has the potential to significantly improve the accuracy and reliability of deepfake detection systems in the face of emerging digital threats.

Multi-objective and multi-solution source mask optimization using NSGA-II for more direct process window enhancement.

Source and mask optimization (SMO) technology is increasingly relied upon for resolution enhancement of photolithography as critical dimension (CD) shrinks. In advanced CD technology nodes, little process variation can impose a huge impact on the fidelity of lithography. However, traditional source and mask optimization (SMO) methods only evaluate the imaging quality in the focal plane, neglecting the process window (PW) that reflects the robustness of the lithography process. PW includes depth of focus (DOF) and exposure latitude (EL), which are computationally intensive and unfriendly to gradient-based SMO algorithms. In this study, we propose what we believe to be a novel process window enhancement SMO method based on the Nondominated Sorting Genetic Algorithm II (NSGA-II), which is a multi-objective optimization algorithm that can provide multiple solutions. By employing the variational lithography model (VLIM), a fast focus-variation aerial image model, our method, NSGA-SMO, can directly optimize the PW performance and improve the robustness of SMO results while maintaining the in-focus image quality. Referring to the simulations of two typical patterns, NSGA-SMO showcases an improvement of more than 20% in terms of DOF and EL compared to conventional multi-objective SMO, and even four times superior to single-objective SMO for complicated patterns.

Outcomes of robot-assisted laparoscopic extended pelvic lymph node dissection for prostate Cancer

IntroductionExtended pelvic lymph node dissection (ePLND) in men undergoing robot-assisted laparoscopic radical prostatectomy (RARP) is a widely used procedure. However, little is known about anatomical site-specific yields and subsequent metastatic patterns in these patients.Patients and methodsData on a consecutive series of 1107 patients undergoing RARP at our centre between 2004 and 2018 were analysed. In men undergoing LN dissection, the internal, external and obturator nodes were removed and sent in separately. We performed an analysis of LN yields in total and for each anatomical zone, patterns of LN metastases and complications. Oncological outcome in pN+ disease was assessed including postoperative PSA persistence and survival.ResultsA total of 823 ePLNDs were performed in the investigated cohort resulting in 98 men being diagnosed as pN+ (8.9%). The median (IQR) LN yield was 19 (14–25), 10 (7–13) on the right and 9 (6–12) on the left side (P < 0.001). A median of six (4–8) LNs were retrieved from the external, three (1–6) from the internal iliac artery, and eight (6–12) from the obturator fossa. More men had metastatic LNs on the right side compared to the left (41 vs. 19). Symptomatic lymphoceles occurred exclusively in the ePLND group (2.3% vs. 0%, p = 0.04). Postoperatively, 47 (47.9%) of men with pN+ reached a PSA of < 0.1μg/ml. There was no association between a certain pN+ region and postoperative PSA persistence or BCRFS. The estimated cancer specific survival rate at 5 years was 98.5% for pN+ disease.ConclusionRobot-assisted laparoscopic ePLND with a high LN yield and low complication rate is feasible. However, we observed an imbalance in more removed and positive LNs on the right side compared to the left. A high rate of postoperative PSA persistence and early recurrence in pN+ patients might indicate a possibly limited therapeutical value of the procedure in already spread disease. Yet, these men demonstrated an excellent survival.

Differentiated genomic footprints suggest isolation and long-distance migration of Hmong-Mien populations

BackgroundThe underrepresentation of Hmong-Mien (HM) people in Asian genomic studies has hindered our comprehensive understanding of the full landscape of their evolutionary history and complex trait architecture. South China is a multi-ethnic region and indigenously settled by ethnolinguistically diverse HM, Austroasiatic (AA), Tai-Kadai (TK), Austronesian (AN), and Sino-Tibetan (ST) people, which is regarded as East Asia’s initial cradle of biodiversity. However, previous fragmented genetic studies have only presented a fraction of the landscape of genetic diversity in this region, especially the lack of haplotype-based genomic resources. The deep characterization of demographic history and natural-selection-relevant genetic architecture of HM people was necessary.ResultsWe reported one HM-specific genomic resource and comprehensively explored the fine-scale genetic structure and adaptative features inferred from the genome-wide SNP data of 440 HM individuals from 33 ethnolinguistic populations, including previously unreported She. We identified solid genetic differentiation between HM people and Han Chinese at 7.64‒15.86 years ago (kya) and split events between southern Chinese inland (Miao/Yao) and coastal (She) HM people in the middle Bronze Age period and the latter obtained more gene flow from Ancient Northern East Asians. Multiple admixture models further confirmed that extensive gene flow from surrounding ST, TK, and AN people entangled in forming the gene pool of Chinese coastal HM people. Genetic findings of isolated shared unique ancestral components based on the sharing alleles and haplotypes deconstructed that HM people from the Yungui Plateau carried the breadth of previously unknown genomic diversity. We identified a direct and recent genetic connection between Chinese inland and Southeast Asian HM people as they shared the most extended identity-by-descent fragments, supporting the long-distance migration hypothesis. Uniparental phylogenetic topology and network-based phylogenetic relationship reconstruction found ancient uniparental founding lineages in southwestern HM people. Finally, the population-specific biological adaptation study identified the shared and differentiated natural selection signatures among inland and coastal HM people associated with physical features and immune functions. The allele frequency spectrum of cancer susceptibility alleles and pharmacogenomic genes showed significant differences between HM and northern Chinese people.ConclusionsOur extensive genetic evidence combined with the historical documents supported the view that ancient HM people originated from the Yungui regions associated with ancient “Three-Miao tribes” descended from the ancient Daxi-Qujialing-Shijiahe people. Then, some have recently migrated rapidly to Southeast Asia, and some have migrated eastward and mixed respectively with Southeast Asian indigenes, Liangzhu-related coastal ancient populations, and incoming southward ST people. Generally, complex population migration, admixture, and adaptation history contributed to the complicated patterns of population structure of geographically diverse HM people.

Stratification of Kidney Transplant Recipients Into Five Subgroups Based on Temporal Disease Trajectories.

Kidney transplantation is the treatment of choice for patients with end-stage renal disease. Considerable clinical research has focused on improving graft survival and an increasing number of kidney recipients die with a functioning graft. There is a need to improve patient survival and to better understand the individualized risk of comorbidities and complications. Here, we developed a method to stratify recipients into similar subgroups based on previous comorbidities and subsequently identify complications and for a subpopulation, laboratory test values associated with survival. First, we identified significant disease patterns based on all hospital diagnoses from the Danish National Patient Registry for 5752 kidney transplant recipients from 1977 to 2018. Using hierarchical clustering, these longitudinal patterns of diseases segregate into 3 main clusters of glomerulonephritis, hypertension, and diabetes. As some recipients are diagnosed with diseases from >1 cluster, recipients are further stratified into 5 more fine-grained trajectory subgroups for which survival, stratified complication patterns as well as laboratory test values are analyzed. The study replicated known associations indicating that diabetes and low levels of albumin are associated with worse survival when investigating all recipients. However, stratification of recipients by trajectory subgroup showed additional associations. For recipients with glomerulonephritis, higher levels of basophils are significantly associated with poor survival, and these patients are more often diagnosed with bacterial infections. Additional associations were also found. This study demonstrates that disease trajectories can confirm known comorbidities and furthermore stratify kidney transplant recipients into clinical subgroups in which we can characterize stratified risk factors. We hope to motivate future studies to stratify recipients into more fine-grained, homogenous subgroups to better discover associations relevant for the individual patient and thereby enable more personalized disease-management and improve long-term outcomes and survival.