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Abstract Background Patient experiences and survival outcomes can be influenced by the circumstances related to dialysis initiation and subsequent modality choices. This systematic review and meta-analysis aimed to explore the rate and reasons for peritoneal dialysis (PD) dropout following haemodialysis (HD) to PD switch. Method This systematic review conducted searches in four databases, including Medline, PubMed, Embase, and Cochrane. The protocol was registered on PROSPERO (study ID: CRD42023405718). Outcomes included factors leading to the switch from HD to PD, the rate and reasons for PD dropout and mortality difference in two groups (PD first group versus HD to PD group). The Critical Appraisal Skills Programme (CASP) checklist and the GRADE tool were used to assess quality. Results 4971 papers were detected, and 13 studies were included. On meta-analysis, there was no statistically significant difference in PD dropout in the PD first group (OR: 0.81; 95%CI: 0.61, 1.09; I2 = 83%; P = 0.16), however, there was a statistically significant reduction in the rate of mortality (OR: 0.48; 95%CI: 0.25, 0.92; I2 = 73%; P = 0.03) compared to the HD to PD group. The primary reasons for HD to PD switch, included vascular access failure, patient preference, social issues, and cardiovascular disease. Causes for PD dropout differed between the two groups, but inadequate dialysis and peritonitis were the main reasons for PD dropout in both groups. Conclusion Compared to the PD first group, a previous HD history may not impact PD dropout rates for patients, but it could impact mortality in the HD to PD group. The reasons for PD dropout differed between the two groups, with no statistical differences. Psychosocial reasons for PD dropout are valuable to further research. Additionally, establishing a consensus on the definition of PD dropout is crucial for future studies.

In order to address the potential collision issue arising from multiple fixed-wing unmanned aerial vehicles (UAVs) intercepting targets in n-on-n and n-on-1 pursuit-evasion scenarios, we propose a collision-avoidance guidance strategy for UAVs based on high-order control barrier functions (HOCBFs). Initially, a two-dimensional model of multiple UAVs and targets is established, and the interaction between UAVs is determined. Subsequently, the collision-avoidance problem within a UAV swarm is formulated as a mathematical problem involving multiple constraints in the form of higher-order control obstacle functions. Multiple HOCBF constraints are then simplified into a single linear constraint for computational convenience. By integrating HOCBF constraints with quadratic programming problems, we obtain a closed-form solution for UAVs that incorporates collision-avoidance guidance terms alongside nominal guidance terms. Simulations with different numbers of pursuers and different target motion states are conducted. The results demonstrate an excellent experimental effect, ensuring that the multi-UAVs consistently remain above the minimum safe distance and ultimately hit the targets accurately.

BackgroundRobotic assistance in thyroidectomy is a developing field that promises enhanced surgical precision and improved patient outcomes. This study investigates the impact of the da Vinci Surgical System on operative efficiency, learning curve, and postoperative outcomes in thyroid surgery.MethodsWe conducted a retrospective cohort study of 104 patients who underwent robotic thyroidectomy between March 2018 and January 2022. We evaluated the learning curve using the Cumulative Sum (CUSUM) analysis and analyzed operative times, complication rates, and postoperative recovery metrics.ResultsThe cohort had a mean age of 36 years, predominantly female (68.3%). The average body mass index (BMI) was within the normal range. A significant reduction in operative times was observed as the series progressed, with no permanent hypoparathyroidism or recurrent laryngeal nerve injuries reported. The learning curve plateaued after the 37th case. Postoperative recovery was consistent, with no significant difference in hospital stay duration. Complications were minimal, with a noted decrease in transient vocal cord palsy as experience with the robotic system increased.ConclusionRobotic thyroidectomy using the da Vinci system has demonstrated a significant improvement in operative efficiency without compromising safety. The learning curve is steep but manageable, and once overcome, it leads to improved surgical outcomes and high patient satisfaction. Further research with larger datasets and longer follow-up is necessary to establish the long-term benefits of robotic thyroidectomy.

Abstract During the multistage fracturing of shale gas horizontal wells, casing damage frequently occurs, making it essential to evaluate casing stress and deformation. This paper establishes a mathematical model of thermo‐hydro‐mechanical coupling and a finite element model of casing‐cement sheath‐formation (CCF). This paper examines the effects of temperature, casing thickness, nonuniform in situ stress, mechanical properties of cement sheath, and treating pressure on the stress and deformation of CCF. Finally, the study investigates the variation of local in situ stress at the heel and casing stress from toe to heel. The results show that the low temperature of CCF, high elastic modulus of cement sheath, small casing thickness, high treating pressure, and large nonuniform in situ stress can exacerbate casing stress. After pumping off, the local in situ stress and its nonuniformity near heel gradually increased, and the effective stress of casing increases compared with the pumping on the process. As the fracturing stages increase, the effective stress of casing at heel increases, and the casing at heel preferentially damages. The results provide a theoretical basis and guidance for maintaining wellbore integrity and preventing casing damage in the multistage fracturing of shale gas horizontal wells.

Improper drilling parameters may cause severe vibration of drill string which leads to reduce the rate of penetration and drilling tool premature failure accidents in the drilling process of ultra-deep well. The study on dynamic characteristics of drill string plays an important role in increasing the safety of drilling tool and optimizing the drilling parameters. Considering the influences of real borehole trajectory, interaction between bit and formation, contact between drill string and borehole wall, stiction of drilling fluid and other factors, a comprehensive drill string dynamic model was established to simulate the changes of wellhead hook load, torque, equivalent stress of drill string and BHA (bottom-hole assembly) section acceleration and motion trajectory with time at different WOBs (weights on bit) and rotary speeds. The safety factor and overpull margin of wellhead drill string were calculated and the strength of drilling tool in ultra-deep well was checked using the fourth strength theory. The analysis results show that, in the drilling process of ultra-deep well, the transverse motion amplitude of the drill string near the wellhead is relatively small and vibration of drill string mainly occurs in the lower well section. As the rotary speed increases, the number of collision between lower drilling tool and borehole wall increases, wellhead transverse stress increases, change in torque is not large and change in wellhead equivalent stress is relatively small. As the WOB increases, wellhead torque will increase, axial load and equivalent stress will decrease and vibration acceleration of BHA will increase sharply. Wellhead overpull margin and safety factor will decrease with the increase of rotary speed and increase with the increase of WOB. Wellhead safety factor of S135 drilling tool in an Φ190.5 mm ultra-deep well on the south margins of Junggar basin changes around 1.8. The drilling tool is safe and has relatively sufficient ability to deal with the downhole accidents if a large size high steel grade drill string (Φ139.7 mm S135) is used. However, in view of BHA safety, neither rotary speed shall be too high nor WOB shall be too large.

AbstractPlatelets contribute to COVID-19 clinical manifestations, of which microclotting in the pulmonary vasculature has been a prominent symptom. To investigate the potential diagnostic contributions of overall platelet morphology and their α-granules and mitochondria to the understanding of platelet hyperactivation and micro-clotting, we undertook a 3D ultrastructural approach. Because differences might be small, we used the high-contrast, high-resolution technique of focused ion beam scanning EM (FIB-SEM) and employed deep learning computational methods to evaluate nearly 600 individual platelets and 30 000 included organelles within three healthy controls and three severely ill COVID-19 patients. Statistical analysis reveals that the α-granule/mitochondrion-to-plateletvolume ratio is significantly greater in COVID-19 patient platelets indicating a denser packing of organelles, and a more compact platelet. The COVID-19 patient platelets were significantly smaller –by 35% in volume – with most of the difference in organelle packing density being due to decreased platelet size. There was little to no 3D ultrastructural evidence for differential activation of the platelets from COVID-19 patients. Though limited by sample size, our studies suggest that factors outside of the platelets themselves are likely responsible for COVID-19 complications. Our studies show how deep learning 3D methodology can become the gold standard for 3D ultrastructural studies of platelets.

Abstract Objective Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE). Materials and methods ML models were developed and validated based on a public database named Medical Information Mart for Intensive Care (MIMIC)-IV. Models were compared by the area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and Hosmer–Lemeshow good of fit test. Results Of 6994 patients in MIMIC-IV included in the final cohort, a total of 1232 (17.62%) patients died following SAE. Recursive feature elimination (RFE) selected 15 variables, including acute physiology score III (APSIII), Glasgow coma score (GCS), sepsis related organ failure assessment (SOFA), Charlson comorbidity index (CCI), red blood cell volume distribution width (RDW), blood urea nitrogen (BUN), age, respiratory rate, PaO2, temperature, lactate, creatinine (CRE), malignant cancer, metastatic solid tumor, and platelet (PLT). The validation cohort demonstrated all ML approaches had higher discriminative ability compared with the bagged trees (BT) model, although the difference was not statistically significant. Furthermore, in terms of the calibration performance, the artificial neural network (NNET), logistic regression (LR), and adapting boosting (Ada) models had a good calibration—namely, a high accuracy of prediction, with P-values of 0.831, 0.119, and 0.129, respectively. Conclusions The ML models, as demonstrated by our study, can be used to evaluate the prognosis of SAE patients in the intensive care unit (ICU). Online calculator could facilitate the sharing of predictive models.

Super-absorbent polymers (SAPs) are widely used chemical water-saving materials, which play an active role in the accumulation of soil water and the improvement of soil structure. Little is known about their performance with repeated usage or about factors influencing their efficiency under alternate wetting and drying cycles. In this study, various concentrations of SAP (0, 0.1, 0.2 and 0.3%) in soil following three continuous wetting and drying cycles (T1, T2 and T3), were studied to determine effects on soil structure stability and hydro-physical properties. The results indicated that the SAP improved soil water supply capacity under conditions of mild drought (T2) and sufficient irrigation (T3) at concentrations of 0.2 and 0.3%, but a reduction was observed under severe drought conditions (T1), which was negatively correlated with the SAP concentration. The physical adsorption of the SAP by soil and the chemical connection between the SAP and soil mineral colloids as Si-O-Si bonds, -OH bonds and different crystalline silica were the important factors that directly lead to the reduction of water retention capacities of the SAP with alternating wet and dry conditions. Compared with the control, the soil liquid phase ratios of the SAP treatments were increased by 8.8–202.7% in the T1 and T2 cycles, which would have led to a decrease in the soil air phase ratios. After repeated wetting and drying cycles, the SAP treatments increased the amount of >0.25 mm soil aggregates and the contents of water-stable macro-aggregate (R0.25), and decreased the amount of

Fluid components in cores are crucial parameters in evaluating the quality of a shale reservoir in both laboratory analyses and log interpretation. In the Gulong area, shale reservoirs are characterized by a high clay content, with clay spaces hosting both oil and water phases, complicating the occurrence mechanism of fluid components, as a result, traditional research methods are no longer applicable. As an advanced technique, nuclear magnetic resonance (NMR) has been applied in oilfields to determine the specific petrophysical properties of rocks. To more accurately identify the types of fluid components, this study carried out a new, well-designed 2D NMR experiment, rock pyrolysis experiment, and quantitative oil and water detection experiment (QOWDE) to study the Gulong shale. This study measured the 2D NMR map of the original state, saturation state, centrifugal state, and pyrolysis at different temperatures, and conducted mutual verification between the QOWDE and 2D NMR pyrolysis experiments to obtain the distribution of different components of Gulong shale on the 2D NMR map. Based on the experimental results, this study developed a component identification template suitable for the Gulong area and calculated the 2D NMR porosity and saturation from it. This lays a foundation for the analysis and application of fluid components in the Gulong region and provides a new experimental basis and methodological support for porosity and saturation calculations. [ABSTRACT FROM AUTHOR]

BackgroundSARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility.MethodsThis observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy.ResultsAmong the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities; lower Ct value at recurrence; more persistent cough; and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials.ConclusionQuarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.