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Mitotic slippage involves cells exiting mitosis without proper chromosome segregation. Although degradation of cyclin B1 during prolonged mitotic arrest is believed to trigger mitotic slippage, its upstream regulation remains obscure. Whether mitotic slippage is caused by APC/CCDC20 activity that is able to escape spindle-assembly checkpoint (SAC)-mediated inhibition, or is actively promoted by a change in SAC activity remains an outstanding issue. We found that a major culprit for mitotic slippage involves reduction of MAD2 at the kinetochores, resulting in a progressive weakening of SAC during mitotic arrest. A further level of control of the timing of mitotic slippage is through p31comet-mediated suppression of MAD2 activation. The loss of kinetochore MAD2 was dependent on APC/CCDC20, indicating a feedback control of APC/C to SAC during prolonged mitotic arrest. The gradual weakening of SAC during mitotic arrest enables APC/CCDC20 to degrade cyclin B1, cumulating in the cell exiting mitosis by mitotic slippage.

Objective This study aims to identify the risk factors for systemic inflammatory response syndrome (SIRS) after minimally invasive percutaneous nephrolithotomy (PCNL) with a controlled irrigation pressure and to find which patients undergoing PCNL are likely to develop SIRS under the pressure-controlled condition. Methods A total of 303 consecutive patients who underwent first-stage PCNL in our institute between July 2016 and June 2018 were retrospectively reviewed. All the procedures were performed with an 18 F tract using an irrigation pump setting the irrigation fluid pressure at 110 mmHg and the flow rate of irrigation at 0.4 L/min. SIRS and sepsis were recorded after PCNL. The demographic data, clinical features, and test results were analyzed. Results 52 patients (17.2%) developed SIRS and only 3 patients (0.99%) further progressed to severe sepsis. The results of univariate analysis showed that the stone size, operative time, history of DM, the value of glycosylated hemoglobin, history of ipsilateral surgery, preoperative urine culture, Staghorn calculi, pelvic urine culture, stone culture, number of tracts, blood transfusion, and residual stones were found to have a significant correlation with post-PCNL SIRS ( p  < 0.05). In multivariate analysis, the stone size (OR = 3.743, p  = 0.012), preoperative urine culture (OR = 2.526, p  = 0.042), pelvic urine culture (OR = 13.523, p  < 0.001), the number of access tracts (OR = 8.945, p  = 0.002), blood transfusion (OR = 26.308, p  < 0.001) were identified as the independent risk factors for post-PCNL SIRS. Conclusion The stone size (>4cm 2 ), positive preoperative urine culture, positive pelvic urine culture, multiple tracts, receipt of a blood transfusion are the independent risk factors for SIRS under the pressure-controlled condition. More attention should be paid when the PCNL patients have these risk factors.

Background Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short-term COVID-19 reinfection in patients with rheumatic diseases. Methods We developed four prediction models using explainable machine learning to assess the risk of short-term COVID-19 reinfection in 543 patients with rheumatic diseases. Psychological health was evaluated using the Functional Assessment of Chronic Illness Therapy Fatigue (FACIT-F) scale, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder 7-item (GAD-7) questionnaire, and the Pittsburgh Sleep Quality Index (PSQI) scale. Health status and disease activity were assessed using the EuroQol-5 Dimension-3 Level (EQ-5D-3L) descriptive system and the Visual Analogue Score (VAS) scale. The model performance was assessed by Area Under the Receiver Operating Characteristic Curve (AUC), Area Under the Precision-Recall Curve (AUPRC), and the geometric mean of sensitivity and specificity (G-mean). SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of each predictor to the model outcomes. Results The eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance with an AUC of 0.91 (95% CI 0.87–0.95). Significant factors of short-term reinfection included glucocorticoid taper (OR = 2.61, 95% CI 1.38–4.92), conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) taper (OR = 2.97, 95% CI 1.90–4.64), the number of symptoms (OR = 1.24, 95% CI 1.08–1.42), and GAD-7 scores (OR = 1.07, 95% CI 1.02–1.13). FACIT-F scores were associated with a lower likelihood of short-term reinfection (OR = 0.95, 95% CI 0.93–0.96). Besides, we found that the GAD-7 score was one of the most important predictors. Conclusion We developed explainable machine learning models to predict the risk of short-term COVID-19 reinfection in patients with rheumatic diseases. SHAP analysis highlighted the importance of clinical and psychological factors. Factors included anxiety, fatigue, depression, poor sleep quality, high disease activity during initial infection, and the use of glucocorticoid taper were significant predictors. These findings underscore the need for targeted preventive measures in this patient population.

Including apolipoprotein E-ε4 (APOE-ε4) status and older age into consideration may increase the accuracy of plasma Aβ 42 /Aβ 40 detecting Aβ+ individuals, but the rationale behind this remains to be fully understood. Besides, both Aβ pathology and vascular diseases are related to neurodegeneration and cognitive decline, but it is still not fully understood how APOE-ε4 modulates these relationships. In this study, we examined 241 non-demented Alzheimer’s Disease Neuroimaging Initiative participants to investigate the associations among age, white matter hyperintensities (WMH), hypertension, hyperlipidemia, body mass index (BMI), plasma Aβ 42 /Aβ 40 measured by liquid chromatography tandem mass spectrometry, and 18 F-florbetapir Aβ PET as well as their prediction of longitudinal adjusted hippocampal volume (aHCV) and cognition in APOE-ε4 carriers and non-carriers. We found older age predicted faster WMH increase ( p  = 0.024) and cortical Aβ accumulation ( p  = 0.043) in APOE-ε4 non-carriers only, whereas lower plasma Aβ 42 /Aβ 40 predicted faster cortical Aβ accumulation ( p  < 0.018) regardless of APOE-ε4 status. While larger WMH and underweight predicted ( p  < 0.05) faster decreases in aHCV and cognition in APOE-ε4 non-carriers, lower plasma Aβ 42 /Aβ 40 predicted ( p  < 0.031) faster decreases in aHCV and cognition in APOE-ε4 carriers. Higher Aβ PET also predicted faster rates of aHCV ( p  = 0.010) in APOE-ε4 carriers only, but was related to faster rates of cognitive decline ( p  < 0.022) regardless of APOE-ε4 status. These findings may provide novel insights into understanding different mechanisms underlie neurodegeneration and cognitive decline in non-demented elderly adults with and without APOE-ε4 allele, which may help the design of anti-Alzheimer’s clinical trials.

ObjectiveTo investigate the two-dimensional speckle-tracking echocardiography (2D-STE) parameters associated with early impaired left ventricular systolic function in SLE patients and to estimate the potential clinical factors that may trigger and influence left ventricular systolic dysfunction.MethodsThis study collected a total of 36 patients admitted to the rheumatology and immunology department of Sun Yat-sen University between January 2020 and December 2021, who were newly diagnosed with SLE and had a Systemic Lupus Erythematosus Disease Activity Index 2000 Score≥4 points. An equal number of healthy controls matched for gender and age were included. All participants underwent routine echocardiography and two-dimensional speckle-tracking echocardiography (2D-STE) examinations. Various clinical data were also collected. Machine learning and regressions were used to estimate potential risk factors for left ventricular systolic dysfunction in SLE patients.ResultsSignificant differences in 2D-STE parameters were found, including global longitudinal peak systolic strain (GLPS) (p-adjust<0.001), GLPS strain obtained from the apical two-chamber view and GLPS strain obtained from the apical four-chamber view (GLPS-A4C) (p-adjust=0.005), and GLPS strain obtained from the apical long-axis view (GLPS-APLAX) (p-adjust=0.003) between SLE patients and controls. Machine learning models, particularly GLPS-APLAX, showed excellent discrimination ability with an AUC of 0.93 (95% CI: 0.89 to 0.96) and an area under the precision-recall curve of 0.96. Multivariate regression further highlighted the inverse relationship between anti-U1 small nuclear ribonucleoprotein (U1RNP) antibodies and four GLPS-related continuous variable measures, with GLPS, GLPS-A4C and GLPS-APLAX measures having statistically significant effects (eg, GLPS coefficient=−3.71, 95% CI: −5.91 to −1.51, p=0.002).ConclusionsThis case-control study revealed that 2D-STE parameters can be used to predict subclinical cardiac dysfunction in SLE patients, and anti-U1RNP antibodies may be an essential predictive clinical factor. Machine learning may further assist in preliminary screening and quantifying left ventricular systolic dysfunction reasons in SLE patients.

Portable radiation detectors are widely used in environmental radiation detection and medical imaging due to their portability feature, high detection efficiency, and large field of view. Lutetium-yttrium oxyorthosilicate (LYSO) is a widely used scintillator in gamma radiation detection. However, the structure and the arrangement of scintillators limit the sensitivity and detection accuracy of these radiation detectors. In this study, a novel portable sensor based on a monolithic LYSO ring was developed for the detection of environmental radiation through simulation, followed by construction and assessments. Monte Carlo simulations were utilized to prove the detection of gamma rays at 511 keV by the developed sensor. The simulations data, including energy resolutions, decoding errors, and sensitivity, showed good potential for the detection of gamma rays by the as-obtained sensor. The experimental results using the VA method revealed decoding errors in the energy window width of 50 keV less than 2°. The average error was estimated at 0.67°, a sufficient value for the detection of gamma radiation. In sum, the proposed radiation sensor appears promising for the construction of high-performance radiation detectors and systems.

BackgroundAnti-C1q autoantibodies can disrupt normal complement function, contributing to the formation of pathogenic immune complexes and end-organ damage. Although their role in SLE is well-established, their detection rate and clinical relevance across a broader spectrum of diseases remain insufficiently characterised. This study aimed to investigate the distribution of abnormal anti-C1q levels in the population and examine their associations with age, sex and specific clinical subtypes.MethodsThis retrospective study included patients who underwent anti-C1q testing at our hospital between September 2020 and September 2023. The primary outcome was the detection rate of abnormal anti-C1q levels (>10 U/mL) categorised by patient sex, age and disease diagnosis. One-way and two-way fixed-effect models were used to assess associations between odds of abnormal antibody levels and demographic factors. Multivariate logistic regression was performed to identify disease-specific correlates.ResultsAmong 15 363 patients (median (IQR) age, 38 (28–53) years; 79.02% female; 52.19% aged <40 years) representing 67 distinct diagnoses, 7.88% showed abnormal anti-C1q levels. Female sex and younger age showed higher median anti-C1q levels and a greater proportion of abnormal results. SLE subtypes showed the highest detection rate of abnormal anti-C1q levels, with SLE without severe complications (853 of 3760, 22.69%) and lupus nephritis (88 of 294, 29.93%) being the most obvious. Lupus haematological and encephalopathic manifestations were associated with elevated antibody levels. Additionally, autoimmune cirrhosis (7 of 59, 11.86%) and systemic sclerosis (19 of 165, 11.52%) also showed high detection rates of abnormal anti-C1q levels. Both univariate and multivariate analyses indicated that male sex and younger age were significantly associated with increased odds of abnormal anti-C1q levels.ConclusionElevations in anti-C1q levels extend beyond SLE and are influenced by both demographic factors and specific disease phenotypes. Male sex and younger age emerged as significant predictors of abnormal anti-C1q status. Our findings underscore the potential utility of anti-C1q testing for improving diagnostic precision and risk stratification across a wide range of clinical conditions.

The performance of radiation detectors used in positron-emission tomography (PET) is determined by the intrinsic properties of the scintillators, the geometry and surface treatment of the scintillator crystals and the electrical and optical characteristics of the photosensors. Experimental studies were performed to assess the timing resolution and energy resolution of detectors constructed with samples of different scintillator materials (LaBr3, CeBr3, LFS, LSO, LYSO: Ce, Ca and GAGG) that were fabricated into different shapes with various surface treatments. The saturation correction of SiPMs was applied for tested detectors based on a Tracepro simulation. Overall, we tested 28 pairs of different forms of scintillators to determine the one with the best CTR and light output. Two common high-performance silicon photomultipliers (SiPMs) provided by SensL (J-series, 6 mm) or AdvanSiD (NUV, 6 mm) were used for photodetectors. The PET detector constructed with 6 mm CeBr3 cubes achieved the best CTR with a FWHM of 74 ps. The 4 mm co-doped LYSO: Ce, Ca pyramid crystals achieved 88.1 ps FWHM CTR. The 2 mm, 4 mm and 6 mm 0.2% Ce, 0.1% Ca co-doped LYSO cubes achieved 95.6 ps, 106 ps and 129 ps FWHM CTR, respectively. The scintillator crystals with unpolished surfaces had better timing than those with polished surfaces. The timing resolution was also improved by using certain geometric factors, such as a pyramid shape, to improve light transportation in the scintillator crystals.

As a commonly used solution, the multi-ended readout can measure the depth-of-interaction (DOI) for positron emission tomography (PET) detectors. In the present study, the effects of the multi-ended readout design were investigated using the leading-edge discriminator (LED) triggers on the timing performance of time-of-flight (TOF) PET detectors. At the very first, the photon transmission model of the four detectors, namely, single-ended readout, dual-ended readout, side dual-ended readout, and triple-ended readout, was established in Tracepro. The optical simulation revealed that the light output of the multi-ended readout was higher. Meanwhile, the readout circuit could be triggered earlier. Especially, in the triple-ended readout, the light output at 0.5 ns was observed to be nearly twice that of the single-ended readout after the first scintillating photon was generated. Subsequently, a reference detector was applied to test the multi-ended readout detectors that were constructed from a 6 × 6 × 25 mm3 LYSO crystal. Each module is composed of a crystal coupled with multiple SiPMs. Accordingly, its timing performance was improved by approximately 10% after the compensation of fourth-order polynomial fitting. Finally, the compensated full-width-at-half-maximum (FWHM) coincidence timing resolutions (CTR) of the dual-ended readout, side dual-ended readout, and triple-ended readout were 216.9 ps, 231.0 ps, and 203.6 ps, respectively.

Positron emission tomography (PET) is widely used in the diagnosis of tumors, cardiovascular system diseases, and neurological diseases. Scintillation crystals are an important part of PET scanners; they can convert γ photons into fluorescent photons to obtain their energy, time, and position information. Currently, an important research goal in PET is to find scintillation crystals with better performance. In this work, the principle of scintillation crystals is introduced, and the properties and requirements of scintillation crystals in different PET scanners are analyzed. At present, Lu2(1−x)Y2xSiO5 (LYSO) is the scintillation crystal with the best comprehensive properties. LaBr3 performs even better regarding the timing characteristics and light output; however, LaBr3 has not been used in any PET scanner because of its deliquescence. Detectors made of Gd3(Ga, Al)5O12 (GAGG) exhibit a high depth of interaction (DOI) resolution and have considerable application potential. The application fields of PET are constantly expanding, and its future development aims to achieve high spatial resolution and high sensitivity, which require scintillation crystals with better performance.