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Abstract Background Membranous nephropathy (MN) is a common type of nephrotic syndrome (NS) in adults, accounting for about 20–30% of cases. Although secondary to specific factors, the coexistence of MN and mantle cell lymphoma (MCL) has been scarcely reported in clinical literature. Case presentation A 59-year-old Chinese male was admitted to the hospital with a generalized pruritic rash with bilateral lower extremity edema, which did not improve significantly after symptomatic treatment. He had undergone renal biopsy, and the diagnosis was thought to be secondary MN (SMN), therefore, we did a lymph node biopsy on the patient and found that MN was complicated with MCL. Soon after, the patient was admitted to the hematology department for a BR chemotherapy regimen (composed of bendamustine 90 mg/m2 BSA (body surface area), rituximab 375 mg/m2 BSA and dexamethasone 5 mg), and during the post-treatment follow-up, both his symptoms and renal function improved. Conclusions The mechanism underlying the combination of SMN and MCL remains elusive and exceedingly rare, consequently often overlooked in clinical practice. This case serves to offer valuable clinical insights for diagnosis and treatment, while emphasizing the pivotal role of renal pathology in clinical assessment.

Acute kidney injury (AKI) is a significant issue in public health, displaying a high occurrence rate and mortality rate. Ferroptosis, a form of programmed cell death (PCD), is characterized by iron accumulation and intensified lipid peroxidation. Recent studies have demonstrated the pivotal significance of ferroptosis in AKI caused by diverse stimuli, including ischemia-reperfusion injury (IRI), sepsis and toxins. Autophagy, a multistep process that targets damaged organelles and macromolecules for degradation and recycling, also plays an essential role in AKI. Previous research has demonstrated that autophagy deletion in proximal tubules could aggravate tubular injury and renal function loss, indicating the protective function of autophagy in AKI. Consequently, finding ways to stimulate autophagy has become a crucial therapeutic strategy. The recent discovery of the role of selective autophagy in influencing ferroptosis has identified new therapeutic targets for AKI and has highlighted the importance of understanding the cross-talk between autophagy and ferroptosis. This study aims to provide an overview of the signaling pathways involved in ferroptosis and autophagy, focusing on the mechanisms and functions of selective autophagy and autophagy-dependent ferroptosis. We hope to establish a foundation for future investigations into the interaction between autophagy and ferroptosis in AKI as well as other diseases.

Summary: The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological features of symptom networks, the control relationships between symptoms remain largely unclear. Here, we present a novel systematizing concept, module control, to analyze the control principle of the symptom network at a module level. We introduce Module Control Network (MCN) to identify key modules that regulate the network’s behavior. By applying our approach to a multivariate psychological dataset, we discover that non-emotional modules, such as sleep-related and stress-related modules, are the primary controlling modules in the symptom network. Our findings indicate that module control can expose central symptom cluster governing psychopathology network, offering novel insights into the underlying mechanisms of mental disorders and individualized approach to psychological interventions.

To solve the problem that how users choose access points in cell-free massive multiple-input multiple-output (CF-mMIMO) network, a prioritized access strategy for poorer users based on channel coefficient ranking was proposed.First, users were evaluated and ranked for their channel quality and stability after channel sensing, and suitable access points were selected in sequence according to the order of the channel state information.Second, considering issues such as users' energy consumption and data security, a federal learning framework was used to enhance user's data privacy and security.Meanwhile, an alternating optimization variables algorithm based on energy consumption optimization was proposed to optimize the multi-dimensional variables, for the purpose of minimizing the total energy consumption of the system.Simulation results show that compared with the traditional user-centric in massive MIMO, the proposed access strategy can improve the average uplink reachable rate of users by 20%, and the uplink rate of users with poor channels can be double improved; in terms of energy consumption optimization, the total energy consumption can be reduced by much more than 50% after optimization.

Abstract Background The active pursuit of network medicine for drug repurposing, particularly for combating Covid-19, has stimulated interest in the concept of structural controllability in cellular networks. We sought to extend this theory, focusing on the defense rather than control of the cell against viral infections. Accordingly, we extended structural controllability to total structural controllability and introduced the concept of control hubs. Perturbing any control hub may render the cell uncontrollable by exogenous stimuli like viral infections, so control hubs are ideal drug targets. Results We developed an efficient algorithm to identify all control hubs, applying it to a largest homogeneous network of human protein interactions, including interactions between human and SARS-CoV-2 proteins. Our method recognized 65 druggable control hubs with enriched antiviral functions. Utilizing these hubs, we categorized potential drugs into four groups: antiviral and anti-inflammatory agents, drugs acting on the central nervous system, dietary supplements, and compounds enhancing immunity. An exemplification of our approach’s effectiveness, Fostamatinib, a drug initially developed for chronic immune thrombocytopenia, is now in clinical trials for treating Covid-19. Preclinical trial data demonstrated that Fostamatinib could reduce mortality rates, ICU stay length, and disease severity in Covid-19 patients. Conclusions Our findings confirm the efficacy of our novel strategy that leverages control hubs as drug targets. This approach provides insights into the molecular mechanisms of potential therapeutics for Covid-19, making it a valuable tool for interpretable drug discovery. Our new approach is general and applicable to repurposing drugs for other diseases.

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

Abstract Introduction The current challenge for the treatment of varicocele is identifying patients who could benefit the most from surgery. We aimed to develop and validate a nomogram for predicting spontaneous pregnancy following microscopic varicocelectomy in infertile men, based on a large cohort. Methods Two hundred eighty-two consecutive patients who underwent microscopic varicocelectomy from January 2018 to December 2020 were enrolled as participants in the study. Xiang Hua center (206 patients) as a development cohort. Hu Nan center (76 patients) as a validation cohort. Patient clinicopathologic data were recorded. Multivariate logistic regression was used to build a predictive model with regression coefficients. Then, backward stepwise selection was applied, and the likelihood ratio test with Akaike’s information criterion was used as the stopping rule. The performance of this predictive model was assessed for discrimination, calibration, and clinical usefulness. Results Predictors of this model included the age of female partners, diameter of veins, initial and increased total progressively motile sperm count. The model demonstrated good discrimination with an AUROC of 0.925 (p

Summary: Finding cancer-driver genes has been a central theme of cancer research. We took a different perspective; instead of considering normal cells, we focused on cancerous cells and genes that maintained abnormal cell growth, which we named cancer-keeper genes (CKGs). Intervening CKGs may rectify aberrant cell growth, making them potential cancer therapeutic targets. We introduced control-hub genes and developed an efficient algorithm by extending network controllability theory. Control hub are essential for maintaining cancerous states and thus can be taken as CKGs. We applied our CKG-based approach to bladder cancer (BLCA). All genes on the cell-cycle and p53 pathways in BLCA were identified as CKGs, showing their importance in cancer. We discovered that sensitive CKGs — genes easily altered by structural perturbation — were particularly suitable therapeutic targets. Experiments on cell lines and a mouse model confirmed that six sensitive CKGs effectively suppressed cancer cell growth, demonstrating the immense therapeutic potential of CKGs.

Isoprene is a highly reactive volatile organic compound that significantly affects atmospheric oxidant capacity, regional air quality, and climate change. Moso bamboo (Phyllostachys edulis), a species widely distributed in tropical and subtropical regions, particularly in China, is a strong isoprene emitter with great potential for carbon sequestration. Carbon sequestration is negatively correlated with culm age; however, the effect of this correlation on isoprene emissions remains unknown. In this study, we investigated the photosynthetic and isoprene emission characteristics of Moso bamboo at different culm ages. The results showed that the age effect on isoprene emission was different from that on photosynthesis; the net photosynthesis rate (Pn) was the highest in young, followed by mature, and then old bamboo, whereas the isoprene emission rate (Iso) was the highest in young, followed by old, and then mature bamboo. Moreover, the percentage of carbon loss as isoprene emission (C-loss) during photosynthesis of old bamboo was 35% higher than that of mature bamboo under standard conditions (leaf temperature: 30°C; light intensity: 1000 µmol m-2 s-1). Therefore, we strongly recommend considering the culm age when establishing an isoprene emission model of Moso bamboo. Additionally, because the Iso and C-loss of old bamboo were higher than those of mature bamboo, we suggest that attention should be paid to the management of bamboo age structure and timely felling of aged bamboo to reduce environmental risk.

Industrial Internet mobile edge computing (MEC) deploys edge servers near base stations to bring computing resources to the edge of industrial networks to meet the energy-saving requirements of Industrial Internet terminal devices. This paper considers a wireless MEC system in an intelligent factory that has multiple edge servers and mobile smart industrial terminal devices. In this paper, the terminal device has the choice of either offloading the task in whole or in part to the edge server, or performing it locally. Through combined optimization of the task offload ratio, number of subcarriers, transmission power, and computing frequency, the system can achieve minimum total energy consumption. A computing offloading and resource allocation approach that combines federated learning (FL) and deep reinforcement learning (DRL) is suggested to address the optimization problem. According to the simulation results, the proposed algorithm displays fast convergence. Compared with baseline algorithms, this algorithm has significant advantages in optimizing the performance of energy consumption.

This paper focuses on reducing execution delays of dynamic computing tasks in UAV-assisted fault-prone mobile edge computing (FP-MEC) systems, which combine mobile edge computing (MEC) and network function virtualization (NFV) technologies. FP-MEC is suited to meet Industrial Internet (IIN) requirements such as data privacy, low latency, and low-cost industrial scalability in specific scenarios. However, the reliability of virtual network functions (VNFs) deployed on UAVs could impact system performance. Thus, this paper proposes the dynamic task scheduling optimization algorithm (DTSOA) based on deep reinforcement learning (DRL) for resource allocation design. The formulated execution delay optimization problem is described as an integer linear programming problem and it is an NP-hard problem. To overcome the intractable problem, this paper discretizes it into a series of single-time slot optimization problems. Furthermore, the experimental rigor is improved by constructing a real-time server state update system to calculate the real-time server load situation and crash probability. Theoretical analysis and experiments show that the DTSOA has better application prospects than Q-learning and the recent search method (RSM), and it is closer to the traversal search method (TSM).

Abstract Background The aim of this study is to describe a modified technique in ileal orthotopic bladder substitution with a new ureteroileal anastomosis. Case presentation After a classic open radical cystectomy with bilateral pelvic lymphadenectomy was performed extraperitoneally, a 56-cm ileal segment was used to construct the spheroidal shape bladder substitution. The 8-cm long proximal and distal ends of the ileal segment were used as afferent limbs. Two-centimeter ileal segments of afferent limbs were detubularized and transversally tubularized. The elongated ileal tube was anastomosed with the ureter in an end-to-end fashion. The pathway of the ureteroileal anastomosis was placed between the abdominal cutaneous fat and the anterior rectus muscular sheath. Perioperative data and long-term functional outcomes were assessed. Between December 2011 and December 2015, seven male patients underwent this procedure with a median 46 (30–77) months follow-up in our hospital. There was no difference between preoperative and postoperative estimated glomerular filtration rates (Z = − 1.693, P = 0.09). One of 14 sides had ureteroileal anastomotic stenosis; two of 14 sides in one patient had ureteroileal anastomotic stenosis caused by invasion of pelvic recurrence 15 months postoperatively. Reflux was completely prevented by placing pressure on the corresponding point on the abdominal surface when voiding urine in all patients. Conclusions We describe a feasible technique modification in detail, which provides some advantages for effective anti-reflux by mechanical finger pressing and abdominal contraction, a low incidence of stricture, and ease for a secondary operation in the long-term follow-up period.

Abstract The nuclear speckle-type pox virus and zinc finger (POZ) protein (SPOP), a representative substrate-recognition subunit of the cullin-RING E3 ligase, has been characterized to play a dual role in tumorigenesis and cancer progression. Numerous studies have determined that SPOP suppresses tumorigenesis in a variety of human malignancies such as prostate, lung, colon, gastric, and liver cancers. However, several studies revealed that SPOP exhibited oncogenic function in kidney cancer, suggesting that SPOP could exert its biological function in a cancer type-specific manner. The role of SPOP in thyroid, cervical, ovarian, bone and neurologic cancers has yet to be determined. In this review article, we describe the structure and regulation of SPOP in human cancer. Moreover, we highlight the critical role of SPOP in tumorigenesis based on three major categories: physiological evidence (animal models), pathological evidence (human cancer specimens) and biochemical evidence (downstream ubiquitin substrates). Furthermore, we note that SPOP could be a promising therapeutic target for cancer treatment.

An unmanned aerial vehicle (UAV) swarm together with a large-scale heterogeneous Internet of Things (IoT) network consisting of macrocells and energy-constrained IoT transmitters (IoT-Ts) is investigated. The UAVs are utilized as flying robot swarms that intelligently transfer energy to the energy-constrained IoT-Ts on the ground. Each IoT-T has an associated IoT device (IoT-D) that is placed at a fixed distance in a random direction. The transmission probability of the energy-constrained IoT-Ts is derived by considering one-slot charging and two-slot charging according to three dimensional (3D) locations, respectively. The coverage probability of each type of IoT-D is investigated. The energy efficiency is derived by considering the transmission power of the active IoT-Ts and the effect of the association biasing factor, and the energy efficiency is also maximized by deploying the optimal density of IoT-Ts. Simulation results are examined to validate the accuracy of our theoretical analysis. Results illustrate the insightful effects of the network parameters, and the helpful guidelines for practical UAV swarms and IoT system design.

Cellular networks assisted by flexibly placed high-maneuverability unmanned aerial vehicles (UAVs) have attracted virtual interests recently. In this paper, the utility maximization problem is investigated to determine how to improve the performance of multi-UAV enabled software-defined cellular networks (SDCNs) with wireless backhaul. The formulated problem jointly optimizes the three dimensional (3D) UAV placement, user scheduling and association, and spectrum resource allocation. The proposed problem is intractable since it is a mixed-integer combined non-convex problem. Thus, an efficient distributed alternating maximization (AM) iterative algorithm is developed to solve the proposed problem. Then, the original optimization problem is decomposed into three subproblems that are solved alternatively via the successive convex optimization (SCO) technique and the modified alternating direction method of multipliers (ADMM) in the proposed algorithm. The theoretical analysis and the simulation results confirm the convergence performance of the proposed algorithm. The extensive numerical results substantiate the superiority of the proposed algorithm, which significantly increases the throughput and utility of the overall users relative to the traditional overlaid ground base station (GBS) and UAV structure and other benchmark methods. The maximal throughput gain is as large as 74.9% on average for all users, in contrast to other benchmark schemes.

The aim of the current resting-state functional magnetic resonance imaging (fMRI) study was to investigate the potential mechanism of schizophrenia through the posterior–anterior cerebrum imbalance in methylazoxymethanol acetate (MAM) rats and to evaluate the effectiveness of repetitive transcranial magnetic stimulation (rTMS) as an early-stage intervention. The rats were divided into four groups: the MAM-sham group, vehicle-sham group, MAM-rTMS group, and vehicle-rTMS group. The rTMS treatment was targeted in the visual cortex (VC) in adolescent rats. Granger Causality Analysis (GCA) was used to evaluate the effective connectivity between regions of interest. Results demonstrated a critical right VC–nucleus accumbens (Acb)–orbitofrontal cortex (OFC) pathway in MAM rats; significant differences of effective connectivity (EC) were found between MAM-sham and vehicle-sham groups (from Acb shell to OFC: t = −2.553, p = 0.021), MAM-rTMS and MAM-sham groups (from VC to Acb core: t = −2.206, p = 0.043; from Acb core to OFC: t = 4.861, p < 0.001; from Acb shell to OFC: t = 4.025, p = 0.001), and MAM-rTMS and vehicle-rTMS groups (from VC to Acb core: t = −2.482, p = 0.025; from VC to Acb shell: t = −2.872, p = 0.012; from Acb core to OFC: t = 4.066, p = 0.001; from Acb shell to OFC: t = 3.458, p = 0.004) in the right hemisphere. Results of the early-stage rTMS intervention revealed that right nucleus accumbens played the role as a central hub, and VC was a potentially novel rTMS target region during adolescent schizophrenia. Moreover, the EC of right nucleus accumbens shell and orbitofrontal cortex was demonstrated to be a potential biomarker. To our knowledge, this was the first resting-state fMRI study using GCA to assess the deficits of a visual-reward neural pathway and the effectiveness of rTMS treatment in MAM rats. More randomized controlled trials in both animal models and schizophrenia patients are needed to further elucidate the disease characteristics.

BackgroundMYCN is an oncogenic transcription factor of the MYC family and plays an important role in the formation of tissues and organs during development before birth. Due to the difficulty in drugging MYCN directly, revealing the molecules in MYCN regulatory networks will help to identify effective therapeutic targets.MethodsWe utilized network controllability theory, a recent developed powerful tool, to identify the potential drug target around MYCN based on Protein-Protein interaction network of MYCN. First, we constructed a Protein-Protein interaction network of MYCN based on public databases. Second, network control analysis was applied on network to identify driver genes and indispensable genes of the MYCN regulatory network. Finally, we developed a novel integrated approach to identify potential drug targets for regulating the function of the MYCN regulatory network.ResultsWe constructed an MYCN regulatory network that has 79 genes and 129 interactions. Based on network controllability theory, we analyzed driver genes which capable to fully control the network. We found 10 indispensable genes whose alternation will significantly change the regulatory pathways of the MYCN network. We evaluated the stability and correlation analysis of these genes and found EGFR may be the potential drug target which closely associated with MYCN.ConclusionTogether, our findings indicate that EGFR plays an important role in the regulatory network and pathways of MYCN and therefore may represent an attractive therapeutic target for cancer treatment.

In this study, the fatigue performance of butt-welded austenitic stainless-steel joints is investigated by fatigue test, nominal-stress method and peak-stress method. Welding profiles and misalignment were analyzed, residual stresses were measured, and fatigue tests were conducted. The experimental fatigue data are regressed to the stress range versus life-to-failure ( S–N ) curve of the nominal stress method, and the factor affected dispersion is analyzed by using the peak-stress method. The following conclusion can be drawn: (1) The fatigue strength is 155 MPa in nominal stress method, which is higher than that of structural steel (80 MPa) in the international fatigue design standards. (2) The influence of the welding misalignment and distortion can be accurately estimated by the combing the reference S-N curve and stress magnification factor formulae from the international fatigue design standards.