Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy.

OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL). METHODS: We retrospectively reviewed the clinical data of 337 patients who received PCNL between May 2020 and June 2022. In our study, 80% of the data were used as the training set, and the remaining data were used as the testing set. Separate prediction models based on the six machine learning algorithms were created using the training set. The predictive performance of each machine learning model was determined by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity using the testing set. We used coefficients to interpret the contribution of each variable to the predictive performance. RESULTS: Among the six machine learning algorithms, the support vector machine (SVM) delivered the best performance with accuracy of 0.868, AUC of 0.942 (95% CI 0.890-0.994) in the testing set. Further analysis using the SVM model showed that prealbumin contributed the most to the prediction of the outcome, followed by preoperative urine culture, systemic immune-inflammation (SII), neutrophil to lymphocyte ratio (NLR), staghorn stones, fibrinogen, operation time, preoperative urine white blood cell (WBC), preoperative urea nitrogen, hydronephrosis, stone burden, sex and preoperative lymphocyte count. CONCLUSION: Machine learning-based prediction models can accurately predict the possibility of SIRS after PCNL in advance by learning patient clinical data, and should be used to guide surgeons in clinical decision-making.

Structural basis for recognition of 26RFa by the pyroglutamylated RFamide peptide receptor.

The neuropeptide 26RFa, a member of the RF-amide peptide family, activates the pyroglutamylated RF-amide peptide receptor (QRFPR), a class A GPCR. The 26RFa/QRFPR system plays critical roles in energy homeostasis, making QRFPR an attractive drug target for treating obesity, diabetes, and eating disorders. However, the lack of structural information has hindered our understanding of the peptide recognition and regulatory mechanism of QRFPR, impeding drug design efforts. In this study, we determined the cryo-EM structure of the Gq-coupled QRFPR bound to 26RFa. The structure reveals a unique assembly mode of the extracellular region of the receptor and the N-terminus of the peptide, and elucidates the recognition mechanism of the C-terminal heptapeptide of 26RFa by the transmembrane binding pocket of QRFPR. The study also clarifies the similarities and distinctions in the binding pattern of the RF-amide moiety in five RF-amide peptides and the RY-amide segment in neuropeptide Y. These findings deepen our understanding of the RF-amide peptide recognition, aiding in the rational design of drugs targeting QRFPR and other RF-amide peptide receptors.

Multi-omics analysis of renal vein serum with Ischemia-Reperfusion injury.

BACKGROUND: Acute kidney injury (AKI) is frequently caused by renal ischemia-reperfusion injury (IRI). Identifying potential renal IRI disease biomarkers would be useful for evaluating AKI severity. OBJECTIVE: We used proteomics and metabolomics to investigate the differences in renal venous blood between ischemic and healthy kidneys in an animal model by identifying differentially expressed proteins (DEPs) and differentially expressed protein metabolites (DEMs). METHODS: Nine pairs of renal venous blood samples were collected before and at 20, 40, and 60 min post ischemia. The ischemia time of Group A, B and C was 20,40 and 60 min. The proteome and metabolome of renal venous blood were evaluated to establish the differences between renal venous blood before and after ischemia. RESULTS: We identified 79 common DEPs in all samples of Group A, 80 in Group B, and 131 in Group C. Further common DEPs among all three groups were Tyrosineprotein kinase, GPR15LG, KAZALD1, ADH1B. We also identified 81, 64, and 83 common DEMs in each group respectively, in which 30 DEMs were further common to all groups. Bioinformatic analysis of the DEPs and DEMs was conducted. CONCLUSION: This study demonstrated that different pathological processes occur during short- and long-term renal IRI. Tyrosine protein kinase, GPR15LG, Kazal-type serine peptidase inhibitor domain 1, and all-trans-retinol dehydrogenase are potential biomarkers of renal IRI.

Using machine learning to develop preoperative model for lymph node metastasis in patients with bladder urothelial carcinoma.

BACKGROUND: Lymph node metastasis (LNM) is associated with worse prognosis in bladder urothelial carcinoma (BUC) patients. This study aimed to develop and validate machine learning (ML) models to preoperatively predict LNM in BUC patients treated with radical cystectomy (RC). METHODS: We retrospectively collected demographic, pathological, imaging, and laboratory information of BUC patients who underwent RC and bilateral lymphadenectomy in our institution. Patients were randomly categorized into training set and testing set. Five ML algorithms were utilized to establish prediction models. The performance of each model was assessed by the area under the receiver operating characteristic curve (AUC) and accuracy. Finally, we calculated the corresponding variable coefficients based on the optimal model to reveal the contribution of each variable to LNM. RESULTS: A total of 524 and 131 BUC patients were finally enrolled into training set and testing set, respectively. We identified that the support vector machine (SVM) model had the best prediction ability with an AUC of 0.934 (95% confidence interval [CI]: 0.903-0.964) and accuracy of 0.916 in the training set, and an AUC of 0.855 (95%CI: 0.777-0.933) and accuracy of 0.809 in the testing set. The SVM model contained 14 predictors, and positive lymph node in imaging contributed the most to the prediction of LNM in BUC patients. CONCLUSIONS: We developed and validated the ML models to preoperatively predict LNM in BUC patients treated with RC, and identified that the SVM model with 14 variables had the best performance and high levels of clinical applicability.

Dimerization and antidepressant recognition at noradrenaline transporter.

The noradrenaline transporter has a pivotal role in regulating neurotransmitter balance and is crucial for normal physiology and neurobiology1. Dysfunction of noradrenaline transporter has been implicated in numerous neuropsychiatric diseases, including depression and attention deficit hyperactivity disorder2. Here we report cryo-electron microscopy structures of noradrenaline transporter in apo and substrate-bound forms, and as complexes with six antidepressants. The structures reveal a noradrenaline transporter dimer interface that is mediated predominantly by cholesterol and lipid molecules. The substrate noradrenaline binds deep in the central binding pocket, and its amine group interacts with a conserved aspartate residue. Our structures also provide insight into antidepressant recognition and monoamine transporter selectivity. Together, these findings advance our understanding of noradrenaline transporter regulation and inhibition, and provide templates for designing improved antidepressants to treat neuropsychiatric disorders.

Synthesis, Photophysical, and Optical Limiting Properties of Twistacene-Functionalized Arenes.

To realize the relationship of structure and property, four novel twistacene-functionalized arenes, namely, 1,4-bis(2,7-di-tert-butyl-9,14-bis(4-(tert-butyl)phenyl)dibenzo[de,qr]tetracen-11-yl)buta-1,3-diyne (4), 1,4-bis(3,5,10,12-tetra-tert-butyltribenzo[a,d,g]coronen-16-yl)buta-1,3-diyne (7), 1,4-bis(2,7-di-tert-butyl-9,14-bis(4-(tert-butyl)phenyl)dibenzo[de,qr]tetracen-10-yl)buta-1,3-diyne (10), 1,4-bis(3,5,10,12-tetra-tert-butyltribenzo[a,d,g]coronen-15-yl)buta-1,3-diyne (13), linked with butadiyne as π bridges have been strategically synthesized and characterized. The nonlinear optical properties are detailly examined in solution through the open-aperture Z-scan method in a comparative manner, indicating that molecules 4 and 7 exhibit better nonlinear optical responses than 10 and 13. Among them, 4 and 7 exhibit excellent optical limiting responses with limiting thresholds of 0.17 and 0.19 J/cm2, respectively, being superior to the state-of-the-art material C60. The ultrafast transient absorption test and DFT calculations suggest that the nonlinear absorption mechanisms belong to TPA-induced ESA. In addition, the effective percentage calculated from TD-DFT can provide a brief glance to evaluate the optical limiting performance.

Study of two-photon absorption and excited-state dynamics of coumarin derivatives: the effect of monomeric and dimeric structures.

Intramolecular charge transfer (ICT) and π-electron delocalization are two key factors affecting the nonlinear optical absorption of organic molecules. To clarify the different influences of ICT and π-electron delocalization on two-photon absorption (TPA) and excited-state absorption (ESA), monomeric coumarin C1 and dimeric coumarin C2 are synthesized and studied. Transient absorption spectroscopy (TAS) analysis of these coumarin derivatives in solvents of varying polarities describes the polarity-dependent excited-state dynamics and reveals the ESA signals of the charge transfer state (CTS) and local excited state (LES) with different spectral features. Femtosecond broadband Z-scan experiments indicate that dimeric coumarin C2 has a more significant TPA response than monomeric coumarin C1 in the near-infrared region. Natural transition orbital (NTO) analysis further theoretically characterizes the electron transition feature induced by TPA. Our results reveal that the TPA of these coumarin derivatives can be significantly enhanced by expanding π-electron delocalization, but their ESA is mainly dominated by ICT performance. This study indicates that coumarin derivatives will exhibit extremely broad application prospects in the field of ultrafast optical limiting (OL) through reasonable molecular design.

CsBr-Triggered Reversible Phase Transition of Perovskite Nanocrystals for Advanced Information Encryption and Decryption.

Luminescent perovskite nanocrystals (NCs), possessing the advantages of low cost, easy detection, and excellent luminescence, are becoming more and more significant in the fields of information encryption and decryption. Most hydrochromic perovskite NCs for information encryption have moderate reversibility and are easily passively decrypted by water in the moist air, limiting their practical applications. Herein, a lyochromic material is synthesized based on reversible phase transition between luminescent CsPbBr3-HBr (pretreating CsPbBr3 with HBr) and nonluminescent Cs4PbBr6, exhibiting excellent reversibility in 50 cycles triggered by CsBr solution. HBr treatment boosts the ion migration of NCs via diminishing surface ligands and passivating Br vacancy, assisting CsBr concentration acting as a crucial factor in dynamic ion exchange equilibrium between the trigger solution and CsPbBr3-HBr. By utilizing CsPbBr3-HBr as a safety ink, the CsBr-triggered photoluminescence switch has been demonstrated to be reproducible, stable, and reliable for information encryption and decryption.

Intravenous metastasis of unexpected uterine sarcoma in the context of uterine fibroids: case report and literature review.

Objective: Endometrial stromal tumors are rare and complex mesenchymal tumors that often present with clinical symptoms similar to uterine leiomyomas. Due to their atypical nature, they are prone to be misdiagnosed or overlooked by healthcare professionals. This study presents a case report of an incidentally discovered endometrial stromal sarcoma with venous metastasis, which was initially misdiagnosed as a uterine leiomyoma. In addition, this study reviews previously documented cases of similar tumors. Case report: During a routine medical examination in 2016, a 50-year-old woman was diagnosed with uterine fibroids. In June 2020, she began experiencing moderate, irregular vaginal bleeding. Nevertheless, a histopathological examination indicated an endometrial stromal sarcoma with a striking amalgamation of both low-grade and high-grade features. Molecular analysis identified a rare MED12 gene mutation. The patient underwent total hysterectomy, bilateral salpingectomy, and resection of the metastatic lesions. Postoperative management included radiotherapy, chemotherapy, and hormone therapy. After completion of chemotherapy, the patient was followed up for 27 months with no evidence of tumor recurrence. Conclusion: This case report highlights the importance of pathological, immunohistochemical, and molecular aspects of this rare tumor involving the inferior vena cava and showing the presence of atypical gene mutations. The successful treatment outcome further emphasizes the importance of advances in diagnostic modalities for managing rare tumors like this.

Self-supervised learning for medical image data with anatomy-oriented imaging planes.

Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical image data (e.g., three dimensionality), which are more relevant to medical image analysis than generic ones for natural images. However, previous work rarely paid attention to data with anatomy-oriented imaging planes, e.g., standard cardiac magnetic resonance imaging views. As these imaging planes are defined according to the anatomy of the imaged organ, pretext tasks effectively exploiting this information can pretrain the networks to gain knowledge on the organ of interest. In this work, we propose two complementary pretext tasks for this group of medical image data based on the spatial relationship of the imaging planes. The first is to learn the relative orientation between the imaging planes and implemented as regressing their intersecting lines. The second exploits parallel imaging planes to regress their relative slice locations within a stack. Both pretext tasks are conceptually straightforward and easy to implement, and can be combined in multitask learning for better representation learning. Thorough experiments on two anatomical structures (heart and knee) and representative target tasks (semantic segmentation and classification) demonstrate that the proposed pretext tasks are effective in pretraining deep networks for remarkably boosted performance on the target tasks, and superior to other recent approaches.

Exploration of the molecular biological mechanisms and review of postoperative radiotherapy cases in tenosynovial giant cell tumors.

Tendon Sheath Giant Cell Tumor (TGCT) is a benign tumor that primarily grows within joints and bursae. However, it has a high postoperative recurrence rate, ranging from 15% to 45%. Although radiotherapy may reduce this recurrence rate, its applicability as a standard treatment is still controversial. Furthermore, the pathogenic mechanisms of TGCT are not clear, which limits the development of effective treatment methods. The unpredictable growth and high recurrence rate of TGCT adds to the challenges of disease management. Currently, our understanding of TGCT mainly depends on pathological slice analysis due to a lack of stable cell models. In this study, we first reviewed the medical records of two female TGCT patients who had undergone radiotherapy. Then, by combining bioinformatics and machine learning, we interpreted the pathogenesis of TGCT and its associations with other diseases from multiple perspectives. Based on a deep analysis of the case data, we provided empirical support for postoperative radiotherapy in TGCT patients. Additionally, our further analysis revealed the signaling pathways of differentially expressed genes in TGCT, as well as its potential associations with osteoarthritis and synovial sarcomas.

Retracted: Marchantin M Induces Apoptosis of Prostate Cancer Cells Through Endoplasmic Reticulum Stress.

The Editors of Medical Science Monitor wish to inform you that the above manuscript has been retracted from publication due to concerns with the credibility and originality of the study, the manuscript content, and the Figure images. Reference: Tian-Wei Zhang, Li Xing, Jun-Long Tang, Jing-Xiao Lu, Chun-Xiao Liu. Marchantin M Induces Apoptosis of Prostate Cancer Cells Through Endoplasmic Reticulum Stress. Med Sci Monit, 2015; 21: 3570-3576. DOI: 10.12659/MSM.894476.

Preparation and thermal responsiveness of microencapsulated fluorinated liquids for automatic fire extinguishing.

Most early-stage fires originating in small confined spaces may not be effectively mitigated by automatic fire-extinguishing systems. Leveraging the unique controlled release capability and barrier properties of microcapsules presents a promising avenue for developing multifunctional and intelligent fire-extinguishing agents tailored for early-stage fire suppression. This paper introduces two types of microcapsules that integrate automatic detection and fire extinguishing functions, utilizing fluorinated liquids specifically perfluoro(2-methyl-3-pentanone) and 1,1,1,2,2,3,4,5,5,5 decafluoro-3-methoxy-4(trifluoromethyl)-pentane as core materials. The preparation process was optimized, and the thermal response of the microcapsules was evaluated by directly incorporating them into combustible materials. The results indicated a correlation between the preparation method, coating efficiency, and thermal stability of microcapsules with the core-wall materials. When the fluoride solution in the core material reaches the thermal response threshold temperature, the gas pressure generated during vaporization and phase change can break through the shell, enabling early active fire protection. Beyond a specific threshold of additive microcapsules in the material, the material exhibits self-extinguishing potential during combustion. In cases where the additive amount falls short of achieving self-extinguishing, the fire-resistant performance of materials can be enhanced through various measures. For instance, reducing the amount of fire-extinguishing agents, delaying the ignition time of fuel, and lowering the heat release rate during combustion are effective strategies. Moreover, the degree of improvement is related to the additional amount and the type of core-wall materials. The thermal-response mechanism of microcapsules constitutes a comprehensive mechanism with physical and chemical effects. The finding of this research offer a new technical approach for microencapsulating high-boiling-point gas extinguishing agents, facilitating intelligent and precise prevention of early fires resulting from combustible materials.

Recent Progress of Biodegradable Polymer Package Materials: Nanotechnology Improving Both Oxygen and Water Vapor Barrier Performance.

Biodegradable polymers have become a topic of great scientific and industrial interest due to their environmentally friendly nature. For the benefit of the market economy and environment, biodegradable materials should play a more critical role in packaging materials, which currently account for more than 50% of plastic products. However, various challenges remain for biodegradable polymers for practical packaging applications. Particularly pertaining to the poor oxygen/moisture barrier issues, which greatly limit the application of current biodegradable polymers in food packaging. In this review, various strategies for barrier property improvement are summarized, such as chain architecture and crystallinity tailoring, melt blending, multi-layer co-extrusion, surface coating, and nanotechnology. These strategies have also been considered effective ways for overcoming the poor oxygen or water vapor barrier properties of representative biodegradable polymers in mainstream research.

Effect of low back pain on the kinetics and kinematics of the lumbar spine - a combined in vivo and in silico investigation.

Lifting is a significant risk factor for low back pain (LBP). Different biomechanical factors including spinal loads, kinematics, and muscle electromyography (EMG) activities have previously been investigated during lifting activities in LBP patients and asymptomatic individuals to identify their association with LBP. However, the findings were contradictory and inconclusive. Accurate and subject-specific prediction of spinal loads is crucial for understanding, diagnosing, planning tailored treatments, and preventing recurrent pain in LBP patients. Therefore, the present study aimed to estimate the L5-S1 compressive and resultant shear loads in 19 healthy and 17 non-specific chronic LBP individuals during various static load-holding tasks (holding a 10 kg box at hip, chest, and head height) using full-body and personalized musculoskeletal models driven by subject-specific in vivo kinematic/kinetic, EMG, and physiological cross-sectional areas (PCSAs) data. These biomechanical characteristics were concurrently analyzed to identify potential differences between the two groups. Statistical analyses showed that LBP had almost no significant effect on the range of motion (trunk, lumbar, pelvis), PCSA, and EMG. There were no significant differences (p > 0.05) in the predicted L5-S1 loads. However, as the task became more demanding, by elevating the hand-load from hip to head, LBP patients experienced significant increases in both compressive (33 %, p = 0.00) and shear (25 %, p = 0.02) loads, while asymptomatic individuals showed significant increases only in compressive loads (30 %, p = 0.01). This suggests that engaging in more challenging activities could potentially magnify the effect of LBP on the biomechanical factors and increase their discrimination capacity between LBP and asymptomatic individuals.

Choroid plexus volume enlargement in first-episode antipsychotic-naïve schizophrenia.

Investigation of the choroid plexus in schizophrenia has seen growing interest due to its role in the interaction between neuroinflammation and brain dysfunction. Most previous studies included treated and long-term ill patients, while antipsychotics and illness course might both affect the choroid plexus. Here, we recruited first-episode antipsychotic-naïve schizophrenia patients, performed high-resolution structural brain scan and manually extracted choroid plexus volume. Choroid plexus volume was compared between patients and healthy controls after controlling for age, sex and intracranial volume. Age and sex effects were examined on choroid plexus volume in patient and healthy control groups respectively. In patients, we also examined the correlation of choroid plexus volume with volume measures of cortical and subcortical gray matter, white matter, lateral ventricular as well as symptom severity and cognitive function. Schizophrenia patients showed significantly enlarged choroid plexus volume compared with healthy controls. Choroid plexus volume was positively correlated with age in only patient group and we found significantly larger choroid plexus volumes in males than females in both patient and healthy control groups, while the sex effects did not differ between groups. Choroid plexus volume was only found correlated with lateral ventricular volume among the brain volume measures. No significant correlation between choroid plexus volume and clinical ratings or cognitive performance was observed. Without potential confounding effects of pharmacotherapy or illness course, our findings indicated the enlargement of choroid plexus in schizophrenia might be an enduring trait for schizophrenia.

Inter-software and inter-threshold reliability of quantitative paraspinal muscle segmentation.

PURPOSE: Changes in the cross-sectional area (CSA) and functional cross-sectional area (FCSA) of the lumbar multifidus (MF) and erector spinae muscles (ES) are factors that can contribute to low back pain. For the assessment of muscle CSA and composition there are various software and threshold methods used for tissue segmentation in quantitative analysis. However, there is currently no gold standard for software as well as muscle segmentation. This study aims to analyze the measurement error between different image processing software and different threshold methods for muscle segmentation. METHODS: Magnetic resonance images (MRI) of 60 patients were evaluated. Muscle CSA and FCSA measurements were acquired from axial T2-weighted MRI of the MF and ES at L4/L5 and L5/S1. CSA, FCSA, and FCSA/CSA ratio were measured independently by two observers. The MRI images were measured using two different software programs (ImageJ and Amira) and with two threshold methods (Circle/Overlap method) for each software to evaluate FCSA and FCSA/CSA ratio. RESULTS: Inter-software comparisons revealed high inter-rater reliability. However, poor inter-rater reliability were obtained with different threshold methods. CSA, FCSA, and FCSA/CSA showed excellent inter-software agreement of 0.75-0.99 regardless of the threshold segmentation method. The inter-rater reliability between the two observers ranged between 0.75 and 0.99. Comparison of the two segmentation methods revealed agreement between 0.19 and 0.84. FCSA and FCSA/CSA measured via the Overlap method were significantly higher than those measured via the Circle method (P < 0.01). CONCLUSION: The present study showed a high degree of reliability with very good agreement between the two software programs. However, study results based on different threshold methods should not be directly compared.

Prediction of gap balancing based on 2-D radiography in total knee arthroplasty for knee osteoarthritis patients.

BACKGROUND: To investigate the influence of osteophytes on postoperative gap balancing, and to work out a predictive model of the relationship between osteophyte size and gap gaining in primary total knee replacement. METHODS: One hundred and ten patients were enrolled in the study. Pre- and postoperative radiographs were collected and analyzed. They were assigned to the training dataset and test dataset randomly at a ratio of 9:1 by using the statistical package R (version 4.0.5). Size and marginal distances of osteophytes, planned bone cut planes, predicted bone cuts and joint gaps were labeled on the preoperative standing anteroposterior and lateral views, while actual bone cuts and joint gaps were recorded on the postoperative plain films, respectively. Statistical analysis was performed. RESULTS: Actual joint gaps were significantly related to the distances of medial and lateral predictive bone cutting lines, bone cut thickness on tibial side and posterior condylar, as well as size and marginal distances of osteophytes (P < 0.05). A predictive equation was generated, with a root mean square error (RMSE) of 3.4761 in validation. A 2-D planning system with adjustable input parameters and dim predictive outputs on joint gap was developed. The equation is [Formula: see text] CONCLUSION: Postoperative joint gap can be predicted on the basis of preoperative measurements on 2-D plain films. Larger sample size may help improve the effectiveness and accuracy of the predictive equation.

Dual-Hand Motion Capture by Using Biological Inspiration for Bionic Bimanual Robot Teleoperation.

Bionic bimanual robot teleoperation can transfer the grasping and manipulation skills of human dual hands to the bionic bimanual robots to realize natural and flexible manipulation. The motion capture of dual hands plays an important role in the teleoperation. The motion information of dual hands can be captured through the hand detection, localization, and pose estimation and mapped to the bionic bimanual robots to realize the teleoperation. However, although the motion capture technology has achieved great achievements in recent years, visual dual-hand motion capture is still a great challenge. So, this work proposed a dual-hand detection method and a 3-dimensional (3D) hand pose estimation method based on body and hand biological inspiration to achieve convenient and accurate monocular dual-hand motion capture and bionic bimanual robot teleoperation. First, a dual-hand detection method based on body structure constraints is proposed, which uses a parallel structure to combine hand and body relationship features. Second, a 3D hand pose estimation method with bone-constraint loss from single RGB images is proposed. Then, a bionic bimanual robot teleoperation method is designed by using the proposed hand detection and pose estimation methods. Experiment results on public hand datasets show that the performances of the proposed hand detection and 3D hand pose estimation outperform state-of-the-art methods. Experiment results on a bionic bimanual robot teleoperation platform shows the effectiveness of the proposed teleoperation method.