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Background and objective: Ureteral stones are currently treated with holmium:yttrium-aluminum-garnet (Ho:YAG) laser lithotripsy using rigid and flexible ureteroscopes. This study aims to evaluate the efficacy and safety of semirigid ureteroscopy combined with Ho:YAG laser lithotripsy for treating proximal ureteral stones in real-world studies. Methods: The PubMed, Embase, and Web of Science databases were searched systematically to retrieve all relevant studies up to April 2024. A meta-analysis of the retrieved studies was performed using Stata 14.0 software and expressed as rate, mean difference, and 95% confidence interval (CI). The main outcomes of this meta-analysis were stone-free rate (SFR) and perioperative outcomes including operation time, hospital stay, postoperative ureteral stent placement, auxiliary procedures, and intra- or postoperative complications. Key findings and limitations: A total of 24 studies were included in this study, including 2058 patients with proximal ureteral stones. According to the results of the combined analysis, the SFR of semirigid ureteroscopy combined with Ho:YAG laser lithotripsy for the treatment of proximal ureteral stones was 78% (95% CI [75%, 82%]), operation time was 51.03 min (95% CI [43.5, 58.56]), hospital stay was 1.62 d (95% CI [1.54, 1.70]), auxiliary procedure rate was 20% (95% CI [15%, 25%]), and postoperative complication rate was 16% (95% CI [12%, 22%]). Conclusions and clinical implications: The results of this study show that semirigid ureteroscopic Ho:YAG laser lithotripsy is a commonly used treatment for proximal ureteral calculi, but its efficacy and safety need to be improved further, and its efficacy is not related to the diameter of the endoscope. Patient summary: This study showed that Ho:YAG laser lithotripsy with semirigid ureteroscopes is commonly used for treating ureteral stones. However, its efficacy and safety need to be improved further, and future research should focus on these.

In this paper, a novel as-cast CrFeNiAl0.35Si0.1Ti0.1 high entropy alloy (HEA) with micron-to-nano hierarchical heterostructure was designed, and its microstructure, mechanical properties and corrosion resistance have been systematically investigated. It is found that, this novel HEA exhibits well-dispersed L21 phase with various sizes from micron-scale to nano-scale in the BCC matrix, while nano-sized BCC lamellas could also emerge in the L21 phase with large diameter. Moreover, this HEA exhibits not only excellent corrosion resistance in 3.5 wt% NaCl solution that close to super-stainless steels, but also high mechanical properties combining yield strength of 1712 ± 56 MPa, compressive plasticity of 21.2% ± 0.5% and hardness of ∼560 Hv. Such outstanding comprehensive properties for this HEA are possibly resulted from the complex hierarchical BCC-L21-BCC heterostructure with stable passive film and low diversity in work function between different phases. This work provides a novel route in the development of high-performance corrosion-resistant HEAs for structural and coating applications.

Abstract In many hematologic malignancies, the adoptive transfer of chimeric antigen receptor (CAR) T cells has demonstrated notable success; nevertheless, further improvements are necessary to optimize treatment efficacy. Current CAR-T therapies are particularly discouraging for solid tumor treatment. The immunosuppressive microenvironment of tumors affects CAR-T cells, limiting the treatment’s effectiveness and safety. Therefore, enhancing CAR-T cell infiltration capacity and resolving the immunosuppressive responses within the tumor microenvironment could boost the anti-tumor effect. Specific strategies include structurally altering CAR-T cells combined with targeted therapy, radiotherapy, or chemotherapy. Overall, monitoring the tumor microenvironment and the status of CAR-T cells is beneficial in further investigating the viability of such strategies and advancing CAR-T cell therapy.

Objective To investigate the association between preoperative nutritional scores and moderate-to-severe acute kidney injury (AKI) after coronary artery bypass graft (CABG) surgery and the predictive significance of nutritional indices for moderate to severe AKI.Methods This study retrospectively included older patients underwent CABG surgery from the Medical Information Mart for Intensive Care (MIMIC) database. Nutritional scores were calculated by the Geriatric Nutritional Risk Index (GNRI) and the Prognostic Nutritional Index (PNI), respectively. Moderate-to-severe injury was determined by KDIGO criteria. Logistic regression, subgroup analysis, and restricted cubic splines were utilized to investigate the association. The predictive value was also assessed by the area under the curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI).Results A total of 1,007 patients were retrospectively included, of which 100 (9.9%) and 380 (37.7%) had malnutrition calculated by GNRI and PNI scores. The incidence of moderate-to-severe AKI was 524 (52.0%). After adjustment for selected risk factors, worse nutritional scores were associated with a higher incidence of moderate-to-severe AKI (PGNRI

Abstract Objectives To compare the efficacy and safety of thulium fiber laser (TFL) to holmium: YAG (Ho: YAG) laser in ureteroscopic lithotripsy for urolithiasis. Methods PubMed, Web of Science, Embase, CENTRAL, SinoMed, CNKI database, VIP and Wanfang Database were systematically searched for all relevant clinical trials until September 2023. References were explored to identify the relevant articles. Meta-analysis was carried out for the retrieved studies using RevMan5.4.1 software, and the risk ratio, mean difference and 95% confidence interval were expressed. Statistical significance was set at p

The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of “image input and result output”. Initially, we constructed a dataset that encompasses colorimetric sensor data under varying lighting conditions to enhance model generalization. Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. Furthermore, compared to YOLOv8n, the parameter count and computational load were reduced by 25.8% and 25.6%, respectively. Additionally, precision improved by 2.8%, recall increased by 3.5%, mAP50 enhanced by 2%, and mAP95 rose by 1.9%. These results affirm the substantial potential of our proposed method for rapid on-site water quality detection, offering new technological insights for future water quality monitoring.

The industrialization of Fe-based amorphous alloys with high a saturation magnetic flux density (Bs) has been limited so far due to their inadequate amorphous forming ability (AFA). In this study, the effects of substituting Si with C on the AFA, thermal stability, and magnetic properties of Fe82Si6−xB9P3Cx (x = 0–6) alloys were systematically investigated. The experimental results demonstrate that the AFA, thermal stability, and soft magnetic properties can be significantly enhanced by the addition of C. Specifically, at a copper wheel velocity of 40 m/s, the Fe82Si6−xB9P3Cx (x = 2, 3, 4, 5 and 6) alloy ribbons exhibit a fully amorphous structure in the as-spun state. The activation energy required for the α-Fe phase crystallization process in Fe82Si6−xB9P3Cx (x = 0, 2, 4, and 6) alloys is determined to be 326.74, 390.69, 441.06, and 183.87 kJ/mol, respectively. Among all of the compositions studied, the Fe82Si4B9P3C2 alloy exhibits optimized soft magnetic properties, including a low coercivity (Hc) of 1.7 A/m, a high effective permeability (μe) of 10608 (f = 1 kHz), and a relatively high Bs of 1.61 T. These improvements may be attributed to a more homogeneous and optimized magnetic domain structure being achieved through proper C addition. This work holds significant implications for the advancement of Fe-based soft magnetic amorphous alloys with high Bs.

Misalignment has a significant impact on the fatigue performance of circumferential weld joints in pipelines, which can significantly reduce the fatigue life. Misalignment generates a structural stress concentration on the pipeline, which proportionally reduces its fatigue strength. Moreover, due to the misalignment, the reinforcement of the root and the transition angle of the pipeline inwall are significantly reduced, increasing its notch stress concentration factor and further reducing its fatigue performance. This work investigates the effect of misalignment on stress concentration in the circumferential welds of pipelines, and it is used to predict the fatigue life. The structural stress method is proposed in the present work, and finite element analysis technology with Abaqus is used to calculate the structural stress concentration factor kj at the root-pass toe of misaligned circumferential weld joints, and a formula for the relationship between the structural stress concentration factor kj and the misalignment is established. The total stress concentration factor k of weld joints with different misalignments under several welding processes are calculated, and are compared with the structural stress concentration factor kj. The fatigue test data of weld joints with different misalignments are studied, and it is shown that the fatigue performance could be predicted by the fitting result.

This study presents an innovative integrated structure that combines a cabinet with a vertical falling film evaporator, enabling efficient natural evaporative cooling and effectively reducing energy loss in data centers. To in-depth understand the proposed structure's cooling mechanism, a novel inserted liquid film distributor is initially devised in the experiment, which can make the water flowing into the tube form an intact liquid film. Then, the visualization experiments are carried out in the tube to observe the water film flow dynamics, demonstrating that a minimum spray density of 0.048 kg·m−1·s−1 can be attained while maintaining liquid film integrity. Meanwhile, when the vibration frequency reaches 2700 Hz, a significant water film rupture in the tube occurs. Finally, building upon the experimental result analysis, at a spray density increment of 0.024 kg·m−1·s−1, the maximum increments of heat transfer rate and heat transfer coefficient corresponding to the intact liquid film are 16.2% and 23.3%, respectively. By contrast, those two indexes corresponding to the ruptured liquid film are 63.4% and 50.9%, respectively. This research provides reliable guidance and valuable insights for the design and efficient operation of natural evaporation falling film evaporators.

BackgroundMounting evidence has demonstrated that an imbalance in liquid–liquid phase separation (LLPS) can induce alteration in the spatiotemporal coordination of biomolecular condensates, which plays a role in carcinogenesis and cachexia. However, the role of LLPS in the occurrence and progression of bladder cancer (BLCA) remains to be elucidated. Identifying the role of LLPS in carcinogenesis may aid in cancer therapeutics.MethodsA total of 1,351 BLCA samples from six cohorts were retrieved from publicly available databases like The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress. The samples were divided into three distinct clusters, and their multi-dimensional heterogeneities were explored. The LLPS patterns of all patients were determined based on the LLPS-related risk score (LLPSRS), and its multifaceted landscape was depicted and experimentally validated at the multi-omics level. Finally, a cytotoxicity-related and LLPSRS-based classifier was established to predict the patient’s response to immune checkpoint blockade (ICB) treatment.ResultsThree LLPS-related subtypes were identified and validated. The differences in prognosis, tumor microenvironment (TME) features, cancer hallmarks, and certain signatures of the three LLPS-related subtypes were validated. LLPSRS was calculated, which could be used as a prognostic biomarker. A close correlation was observed between clinicopathological features, genomic variations, biological mechanisms, immune infiltration in TME, chemosensitivity, and LLPSRS. Furthermore, our classifier could effectively predict immunotherapy response in patients with BLCA.ConclusionsOur study identified a novel categorization of BLCA patients based on LLPS. The LLPSRS could predict the prognosis of patients and aid in designing personalized medicine. Further, our binary classifier could effectively predict patients’ sensitivity to immunotherapy.

With the development of wind turbine，high-speed railway，aero-engine and other large equipment towards the direction of high reliability，long life and intelligence，it has put forward higher requirements for the life of basic components such as gears and bearings. It is urgent to use more scientific and efficient fatigue life prediction method. The life prediction methods of mechanical components can be divided into physical failure model，data-driven model and fusion model（physical failure and data-driven model fusion） three types. With the development of components life prediction research towards high precision and high efficiency，physical model is difficult to meet modern needs due to its complexity，time-consuming and non-universal disadvantages. With the rapid development of machine learning，deep learning and other technologies，data-driven model has become a hot topic in the research of components fatigue life prediction of due to the advantages of no need to know detailed failure mechanism and accurate prediction results. In view of this，the fatigue life prediction method of components based on data-driven is described. The application of these methods in the life prediction of components is introduced，including neural network，support vector machine，random forest and deep learning，and the characteristics of each method are summarized，and the developing trend of the life prediction method of components based on data-driven is discussed. And a case study of gear contact fatigue life prediction based on GA-BP neural network is presented.

The traditional lateral flow immunoassay (LFIA) detection method suffers from issues such as unstable detection results and low quantitative accuracy. In this study, we propose a novel multi-test line lateral flow immunoassay quantitative detection method using smartphone-based SAA immunoassay strips. Following the utilization of image processing techniques to extract and analyze the pigments on the immunoassay strips, quantitative analysis of the detection results was conducted. Experimental setups with controlled lighting conditions in a dark box were designed to capture samples using smartphones with different specifications for analysis. The algorithm’s sensitivity and robustness were validated by introducing noise to the samples, and the detection performance on immunoassay strips using different algorithms was determined. The experimental results demonstrate that the proposed lateral flow immunoassay quantitative detection method based on image processing techniques achieves an accuracy rate of 94.23% on 260 samples, which is comparable to the traditional methods but with higher stability and lower algorithm complexity.

Characteristic of high heat dissipation of semiconductor electronic products require the cooling systems operating throughout the year, which inevitably causes a huge energy consumption. Although the small-scale channel (i.e., mini-channel or microchannel) liquid cooling heat sink has been applied in semiconductor electronic products cooling accounting for its advantages of outstanding heat exchange performance and easy integration, the existing small-scale channel liquid cooling heat sink still has problems of uneven temperature distribution and excessive pressure loss. In this paper, a Tree-shaped mini-channel liquid cooling heat sink (TSMLCHS) with four levels branches is proposed. The models for determining the dimension of each branch are established. The effect of flowrates on the heat transfer efficiency and pressure loss for the proposed TSMLCHS is discussed in detail under different heat fluxes and flowrates. The experimental results show that the TSMLCHS is suitable for applying on the electronic chip cooling since the average surface temperature is only 23.8 °C–66.6 °C when the heat flux is varied from 5 W/cm2 to 8 W/cm2.

The shape of the weld pool surface contains a lot of important features to reflect the quality of the weld. However, it is difficult to obtain the weld pool boundary precisely. In this paper, a boundary extension method is designed first to optimize the boundary profile of the interpolated weld pool surface calculated by the long-short-term memory (LSTM)-based measurement method. Experimental results show that after boundary extension, the errors of the left and right part of the boundary of the weld pool are slightly improved. The weld width error in the X direction is reduced to 2.43%, and the weld width error in the Y direction is slightly increased to 8.68%. Then the robustness of the LSTM-based model is analyzed by studying the phenomena of missing points and more points. To solve the problem caused by the missing/adding the first few imaging points, a forward-reverse united reconstruction optimization method is then designed. After optimization by forward-reverse united reconstruction optimization method, the boundary error of the reconstructed weld pool surface is obviously reduced. The weld width error is reduced to 1.62% in the X direction and 3.94% in the Y direction.

Heavy metal pollution is an important problem in current water treatments. Traditional methods for treating chromium-containing wastewater have limitations such as having complicated processes and causing secondary pollution. Therefore, seeking efficient and fast processing methods is an important research topic at present. Photocatalysis is an efficient method to remove Cr(VI) from aqueous solutions; however, conventional photocatalysts suffer from a low metal absorption capacity, high investment cost, and slow desorption of trivalent chromium from the catalyst surface. In this study, a novel composite gel was synthesized by chemically modifying thiourea onto sodium alginate, which was then mixed with biochar. The composite gel (T-BSA) can effectively remove 99.98% of Cr(VI) in aqueous solution through synergistic adsorption and photocatalytic reduction under UV light irradiation. The removal mechanism of Cr(VI) was analyzed by FT-IR, FESEM, UV-DRS and XPS. The results show that under acidic conditions, the amino group introduced by chemical modification can be protonated to adsorb Cr(VI) through electrostatic interaction. In addition, the biochar as a functional material has a large specific surface area and pore structure, which can provide active sites for the adsorption of Cr(VI), while the photo-reduced Cr(III) is released into the solution through electrostatic repulsion, regenerating the adsorption sites, thereby improving the removal performance of Cr(VI). Biochar significantly intensifies the Cr(VI) removal performance by providing a porous structure and transferring electrons during photoreduction. This study demonstrates that polysaccharide-derived materials can serve as efficient photocatalysts for wastewater treatment.

Tumor cells rewire metabolism to meet their increased nutritional demands, allowing the maintenance of tumor survival, proliferation, and expansion. Enhancement of glycolysis and glutaminolysis is identified in most, if not all cancers, including multiple myeloma (MM), which interacts with a hypoxic, acidic, and nutritionally deficient tumor microenvironment (TME). In this review, we discuss the metabolic changes including generation, depletion or accumulation of metabolites and signaling pathways, as well as their relationship with the TME in MM cells. Moreover, we describe the crosstalk among metabolism, TME, and changing function of immune cells during cancer progression. The overlapping metabolic phenotype between MM and immune cells is discussed. In this sense, targeting metabolism of MM cells is a promising therapeutic approach. We propose that it is important to define the metabolic signatures that may regulate the function of immune cells in TME in order to improve the response to immunotherapy.

During the last twenty years, the complex network modeling approach has been introduced to assess the reliability of rail transit networks, in which the dynamic performance involving passenger flows have attracted more attentions during operation stages recently. This paper proposes the passenger-flow-weighted network reliability evaluation indexes, to assess the impact of passenger flows on network reliability. The reliability performances of the rail transit network and passenger-flow-weighted one are analyzed from the perspective of a complex network. The actual passenger flow weight of urban transit network nodes was obtained from the Shanghai Metro public transportation card data, which were used to assess the reliability of the passenger-flow-weighted network. Furthermore, the dynamic model of the Shanghai urban rail transit network was constructed based on the coupled map lattice (CML) model. Then, the processes of cascading failure caused by network nodes under different destructive situations were simulated, to measure the changes of passenger-flow-weighted network reliability during the processes. The results indicate that when the scale of network damage attains 50%, the reliability of the passenger-flow-weighted network approaches zero. Consequently, taking countermeasures during the initial stage of network cascading may effectively prevent the disturbances from spreading in the network. The results of the paper could provide guidelines for operation management, as well as identify the unreliable stations within passenger-flow-weighted networks.

With the rapid development of wireless communication and micro-power technologies, smart wearable devices with various functionalities appear more and more in our daily lives. Nevertheless, they normally possess short battery life and need to be recharged with external power sources with a long charging time, which seriously affects the user experience. To help extend the battery life or even replace it, a non-resonant piezoelectric–electromagnetic–triboelectric hybrid energy harvester is presented to effectively harvest energy from low-frequency human motions. In the designed structure, a moving magnet is used to simultaneously excite the three integrated energy collection units (i.e., piezoelectric, electromagnetic, and triboelectric) with a synergistic effect, such that the overall output power and energy-harvesting efficiency of the hybrid device can be greatly improved under various excitations. The experimental results show that with a vibration frequency of 4 Hz and a displacement of 200 mm, the hybrid energy harvester obtains a maximum output power of 26.17 mW at 70 kΩ for one piezoelectric generator (PEG) unit, 87.1 mW at 500 Ω for one electromagnetic generator (EMG) unit, and 63 μW at 140 MΩ for one triboelectric nanogenerator (TENG) unit, respectively. Then, the generated outputs are adopted for capacitor charging, which reveals that the performance of the three-unit integration is remarkably stronger than that of individual units. Finally, the practical energy-harvesting experiments conducted on various body parts such as wrist, calf, hand, and waist indicate that the proposed hybrid energy harvester has promising application potential in constructing a self-powered wearable system as the sustainable power source.

A 64-year-old man was admitted to our hospital with hematemesis and melena. Six years ago, he had undergone total gastrectomy with Roux-en-Y esophagojejunostomy for gastric cancer. Endoscopic examination revealed varicose veins at the anastomotic sites with cherry-red spots and hemorrhage. Abdominal computed tomography showed that the varices were supplied by a dilated jejunal vein. Transjugular intrahepatic portosystemic shunt (TIPS) and variceal embolization were performed. There were no major complications or episodes of bleeding during the three-month follow-up. We conclude that TIPS in combination with varices obliteration is an effective alternative method for treatment of ruptured esophagojejunal varices after total gastrectomy. Keywords: Esophagojejunal varices, Transjugular intrahepatic portosystemic shunt, Varices obliteration, Total gastrectomy

Ferrite number (FN) is a crucial parameter for austenite steel-welding products, since it has a specific relationship with crack sensitivity and other important properties. In this paper, artificial neural network (ANN) models were built to predict FN, based on the GTAW tests of 304L plates produced by two different steelworks, Dongfang Special Steel hot-rolled sheet (DFSS) and Anshan Iron and Steel cold-rolled sheet (ASIS). The results show that a high performance, of more than 98% accuracy, can be achieved when the models of DFSS and ASIS are modeled separately, and that accuracy is also above 96% when an integrated model is built. The influences of nitrogen content and multiwelding parameters, such as travel speed, wire-feed rate, welding current and arc length, on FN are also analyzed through the FN-prediction model for DFSS. The results show that FN increases monotonously with the increase of nitrogen content, but the influences of either of the other two parameters on FN are nonlinear.