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Journal of Dairy Science Jun 2024Despite considerable research efforts, lipase catalysis in a fluid milk system with aqueous multi-component mixtures containing multiple microphases, remains...
Despite considerable research efforts, lipase catalysis in a fluid milk system with aqueous multi-component mixtures containing multiple microphases, remains challenging. Pickering interfacial biocatalysis (PIB) platforms are typically fabricated with organic solvents/lipids and water. Whether a PIB with excellent catalytic performance can be constructed in complex milk mixtures remains unknown. Here, we challenged PIB with skim milk, and a small amount of flaxseed oil, and phytosterols as a model system for transesterification and lipolysis to enhance quality and flavor. The amino-modified mesoporous silica spheres (MSS-N) were employed as an emulsifier and carrier of lipase AYS (AYS@MSS-N). The conversion of phytosterol esters reached 75.5% at 1.5 h and prepared phytosterol ester-fortified milk with a content of 1.0 g/100 mL. The relative conversion rate remained above 70% after 6 cycles. In addition, the fortified milk showed an intensified and favorable effect on sensory traits through volatile flavor composition analysis. The findings provide a versatile alternative for PIB applications in complex environments, i.e., milk, which might inspire a new bioprocess strategy for dairy products.
PubMed: 38945261
DOI: 10.3168/jds.2024-25037 -
Journal of Minimally Invasive Gynecology Jun 2024To reinterpret the surgical anatomy of paracolpium in radical hysterectomy and to explore its implications for the surgery.
OBJECTIVE
To reinterpret the surgical anatomy of paracolpium in radical hysterectomy and to explore its implications for the surgery.
SETTING
The term "paracolpium," first defined by Fothergill in 1907, is essential in radical hysterectomy. However, several challenges remain unresolved. These include: (1) inconsistent terminology in relation to its defined attributes; (2) the lack of consensus on anatomical landmarks; (3) unclear associations with the cardinal and sacral ligaments; and (4) the critical implications and requirements of paracolpium resection in radical hysterectomy practices.
PARTICIPANTS
A patient in her 60s diagnosed with stage IB2 cervical cancer was enrolled in a clinical trial and assigned to the laparoscopic surgery group. A step-by-step, narrated video demonstration.
INTERVENTIONS
During the procedure, post-excision of the uterosacral, cardinal, and vesicovaginal ligaments, we identified a ligament-like structure situated between the middle third of the vagina and the pelvic wall. We have termed this structure the "paracolpium ligament." A detailed anatomical description was performed, outlining its crucial attachments.
PubMed: 38945252
DOI: 10.1016/j.jmig.2024.06.012 -
World Neurosurgery Jun 2024This study aimed to pinpoint independent predictors influencing overall survival (OS) and cancer-specific survival (CSS) in elderly patients with small cell lung cancer...
OBJECTIVE
This study aimed to pinpoint independent predictors influencing overall survival (OS) and cancer-specific survival (CSS) in elderly patients with small cell lung cancer (SCLC) brain metastasis (BM), and to create and validate nomograms for OS and CSS prediction.
METHODS
Data from elderly SCLC BM patients were extracted out of the SEER database, including 1200 patients identified from 2010 and 2015 who were randomly allocated into a training set and an internal validation set at a proportion of 7:3, and 666 patients diagnosed between 2018 and 2020 as a temporal external validation set. Independent predictors for OS and CSS were determined through univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis sequentially. Nomograms for OS and CSS were constructed, and validated by the internal and temporal external validation sets.
RESULTS
Age, N stage, chemotherapy, and liver metastasis were determined as independent predictors of OS and CSS, while radiotherapy and surgery were not. Nomograms were constructed based on these independent predictors. The results of the receiver operator characteristic (ROC) curves, the areas under the curve (AUC) and calibration curve demonstrated that the nomograms exhibited commendable discriminative ability and calibration. Moreover, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) also suggested that the nomograms possessed superior clinical usefulness and predictive capability relative to the TNM system.
CONCLUSIONS
Prognostic nomograms for elderly patients with SCLC BM have been developed, demonstrating good performance in terms of accuracy, reliability, and practicality.
PubMed: 38945206
DOI: 10.1016/j.wneu.2024.06.137 -
Current Problems in Cardiology Jun 2024Differentiating Takotsubo cardiomyopathy (TTC) from acute coronary syndrome involving the left anterior descending coronary artery (LAD-ACS) is difficult due to left... (Review)
Review
Simplified echocardiographic assessment of regional left ventricular wall motion pattern in patients with takotsubo and acute coronary syndrome: The Randomized Blinded Two-chamber Apical Kinesis Observation (TAKO) Study.
BACKGROUND
Differentiating Takotsubo cardiomyopathy (TTC) from acute coronary syndrome involving the left anterior descending coronary artery (LAD-ACS) is difficult due to left ventricular apical wall motion abnormality pattern in both and typically requires an invasive coronary angiography (ICA) study for diagnostic confirmation.
OBJECTIVES
To identify differences in the regional wall motion abnormality (RWMA) pattern using a comprehensive comparative analysis of the transthoracic echocardiographic (TTE) findings in patients with TTC versus LAD-ACS.
METHODS
This was a retrospective, randomized, blinded comparison study including a derivation cohort of 105 patients with TTC (N=52) or LAD-ACS (N=53) with concomitant TTE and ICA identified from our institutional database. A comprehensive echocardiographic wall motion analysis was performed (unblinded) to search for subtle differences in RWMA patterns by marking the exact locations of the end-systolic hinge points (HP) - defined as the intersection between the normal and abnormal regional myocardial thickening - in all apical views. The HP location relative to mitral annulus in each apical view was compared for symmetry and the apical 2-chamber (A2C) view was identified as having the most consistent, quantitative difference between TTC and LAD-ACS. This A2C quantitative model was then prospectively studied in a randomized, blinded, validation cohort of 30 subjects with either TTC or LAD-ACS by eight echocardiographic readers with all levels of clinical experience.
RESULTS
In the unblinded derivation cohort, the A2C view showed that the ratio (1.02) and the absolute distance between the anterior HP (3.57 cm) and the inferior HP (3.53 cm) in TTC was significantly different than the ratio (0.761) and the absolute differences between the AHP (4.5 cm) and the IHP (5.93 cm) in LAD-ACS. An AHP: IHP of 0.96 for men and 0.84 for women was able to correctly categorize 84.8% of male and 91.7% of female patients. When applied to the validation cohort, the model showed fairly accurate results with a 74% prediction rate in diagnosing TTC in female patients.
CONCLUSION
We propose a relatively simple 2-D TTE diagnostic tool emphasizing subtle differences in the RWMA pattern in the A2C view alone as a semi-quantitative imaging parameter to help differentiate TTC from LAD-ACS.
PubMed: 38945184
DOI: 10.1016/j.cpcardiol.2024.102731 -
Archives of Suicide Research : Official... Jun 2024Nearly 50,000 Americans die each year from suicide, despite suicide death being a rare event in the context of health risk assessment and modeling. Prior research has...
OBJECTIVE
Nearly 50,000 Americans die each year from suicide, despite suicide death being a rare event in the context of health risk assessment and modeling. Prior research has underscored the need for contextualizing suicide risk models in terms of their potential uses and generalizability. This sensitivity analysis makes use of the Maryland Suicide Data Warehouse (MSDW) and illustrates how results inform clinical decision support.
METHOD
A cohort of 1 million living control patients were extracted from the MSDW in addition to 1,667 patients who had died by suicide between the years 2016 and 2019 according to the Maryland Office of the Medical Examiner (OCME). Data were extracted and aggregated as part of a 4-year retrospective design. Binary logistic and two penalized regression models were deployed in a repeated fivefold cross-validation. Model performances were evaluated using sensitivity, positive predictive value (PPV), and F1, and model coefficients were ranked according to coefficient size.
RESULTS
Several features were significantly associated with patients having died by suicide, including male sex, depressive and anxiety disorder diagnoses, social needs, and prior suicidal ideation and suicide attempt. Cross-validated binary logistic regression outperformed either ridge or LASSO (least absolute shrinkage and selection operator) models but generally achieved low-to-moderate PPV and sensitivity across most thresholds and a peak F1 of 0.323.
CONCLUSIONS
Suicide death prediction is constrained by the context of use, which determines the best balance of precision and recall. Predictive models must be evaluated close to the level of intervention. They may not hold up to different needs at different levels of care.
PubMed: 38945167
DOI: 10.1080/13811118.2024.2363227 -
Journal of Mental Health (Abingdon,... Jun 2024Personal recovery is operationalized in the CHIME framework (connectedness, hope, identity, meaning in life, and empowerment) of recovery processes. CHIME was initially...
BACKGROUND
Personal recovery is operationalized in the CHIME framework (connectedness, hope, identity, meaning in life, and empowerment) of recovery processes. CHIME was initially developed through analysis of experiences of people mainly with psychosis, but it might also be valid for investigating recovery in mood-related, autism and other diagnoses.
AIMS
To examine whether personal recovery is transdiagnostic by studying narrative experiences in several diagnostic groups.
METHODS
Thirty recovery narratives, retrieved from "Psychiatry Story Bank" (PSB) in the Netherlands, were analyzed by three coders using CHIME as a deductive framework. New codes were assigned using an inductive approach and member checks were performed after consensus was reached.
RESULTS
All five CHIME dimensions were richly reported in the narratives, independent of diagnosis. Seven new domains were identified, such as "acknowledgement by diagnosis" and "gaining self-insight". These new domains were evaluated to fit well as subdomains within the original CHIME framework. On average, 54.2% of all narrative content was classified as experienced difficulties.
CONCLUSIONS
Recovery stories from different diagnostic perspectives fit well into the CHIME framework, implying that personal recovery is a transdiagnostic concept. Difficulties should not be ignored in the context of personal recovery based on its substantial presence in the recovery narratives.
PubMed: 38945156
DOI: 10.1080/09638237.2024.2361225 -
Clinical Neurology and Neurosurgery Jun 2024This study aimed to identify clinical and surgical features associated with poor long-term postoperative outcomes in patients diagnosed with Type I Chiari Malformation...
Predictors of poor functional outcomes in adults with type I Chiari Malformation: Clinical and surgical factors assessed with the Chicago Chiari Outcome Scale over long-term follow-up.
OBJECTIVE
This study aimed to identify clinical and surgical features associated with poor long-term postoperative outcomes in patients diagnosed with Type I Chiari Malformation (CMI) treated with posterior fossa decompression with duroplasty (PFDD), with or without tonsillar coagulation.
METHODS
This retrospective, single-center study included 107 adult patients with CMI surgically treated between 2010 and 2021. The surgical technique involved a midline suboccipital craniectomy, C1 laminectomy, durotomy, arachnoid dissection, duroplasty, and tonsillar coagulation until 2014, after which tonsillar coagulation was discontinued. Postoperative outcomes were assessed using the Chicago Chiari Outcome Scale (CCOS) at a median follow-up of 35 months. Clinical, surgical, and neuroimaging data were analyzed using the Wilcoxon signed-rank test, Cox regression analysis, and Kaplan-Meier survival curves to identify predictors of poor functional outcomes.
RESULTS
Of the 107 patients (mean age 43.9 years, SD 13), 81 (75.5 %) showed functional improvement, 25 (23.4 %) remained unchanged, and 1 (0.9 %) experienced worsened outcomes. Cephalalgia, bilateral motor weakness, and bilateral paresthesia were the most frequent initial symptoms. Tonsillar coagulation was performed in 31 cases (28.9 %) but was clinically associated with higher rates of unfavorable outcomes. The Wilcoxon signed-rank test indicated that long-term follow-up CCOS was significantly higher than postoperative CCOS (Z = -7.678, p < 0.000). Multivariate Cox analysis identified preoperative bilateral motor weakness (HR 6.1, 95 % CI 1.9-18.9; p = 0.002), hydrocephalus (HR 3.01, 95 % CI 1.3-6.9; p = 0.008), and unilateral motor weakness (HR 2.99, 95 % CI 1.1-8.2; p = 0.033) as significant predictors of poor outcomes on a long-term follow-up.
CONCLUSION
This study highlights the high rate of functional improvement in CMI patients following PFDD. Preoperative motor weakness and hydrocephalus were significant predictors of poor long-term outcomes. Tonsillar coagulation did not demonstrate a clear clinical benefit and may be associated with worse outcomes. Our findings suggest that careful preoperative assessment and selection of surgical techniques are crucial for optimizing patient outcomes.
PubMed: 38945118
DOI: 10.1016/j.clineuro.2024.108392 -
Neural Networks : the Official Journal... Jun 2024In practical engineering, obtaining labeled high-quality fault samples poses challenges. Conventional fault diagnosis methods based on deep learning struggle to discern...
In practical engineering, obtaining labeled high-quality fault samples poses challenges. Conventional fault diagnosis methods based on deep learning struggle to discern the underlying causes of mechanical faults from a fine-grained perspective, due to the scarcity of annotated data. To tackle those issue, we propose a novel semi-supervised Gaussian Mixed Variational Autoencoder method, SeGMVAE, aimed at acquiring unsupervised representations that can be transferred across fine-grained fault diagnostic tasks, enabling the identification of previously unseen faults using only the small number of labeled samples. Initially, Gaussian mixtures are introduced as a multimodal prior distribution for the Variational Autoencoder. This distribution is dynamically optimized for each task through an expectation-maximization (EM) algorithm, constructing a latent representation of the bridging task and unlabeled samples. Subsequently, a set variational posterior approach is presented to encode each task sample into the latent space, facilitating meta-learning. Finally, semi-supervised EM integrates the posterior of labeled data by acquiring task-specific parameters for diagnosing unseen faults. Results from two experiments demonstrate that SeGMVAE excels in identifying new fine-grained faults and exhibits outstanding performance in cross-domain fault diagnosis across different machines. Our code is available at https://github.com/zhiqan/SeGMVAE.
PubMed: 38945116
DOI: 10.1016/j.neunet.2024.106482 -
Multiple Sclerosis and Related Disorders Jun 2024Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS) patients,...
OBJECTIVE
Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS) patients, compared to healthy control (HC) individuals. To date, a number of studies have applied machine learning to OCT thickness measurements, aiming to enable accurate and automated diagnosis of the disease. However, there have much less emphasis on other less common retinal imaging modalities, like infrared scanning laser ophthalmoscopy (IR-SLO), for classifying MS. IR-SLO uses laser light to capture high-resolution fundus images, often performed in conjunction with OCT to lock B-scans at a fixed position.
METHODS
We incorporated two independent datasets of IR-SLO images from the Isfahan and Johns Hopkins centers, consisting of 164 MS and 150 HC images. A subject-wise data splitting approach was employed to ensure that there was no leakage between training and test datasets. Several state-of-the-art convolutional neural networks (CNNs), including VGG-16, VGG-19, ResNet-50, and InceptionV3, and a CNN with a custom architecture were employed. In the next step, we designed a convolutional autoencoder (CAE) to extract semantic features subsequently given as inputs to four conventional ML classifiers, including support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and multi-layer perceptron (MLP).
RESULTS
The custom CNN (85 % accuracy, 85 % sensitivity, 87 % specificity, 93 % area under the receiver operating characteristics [AUROC], and 94 % area under the precision-recall curve [AUPRC]) outperformed state-of-the-art models (84 % accuracy, 83 % sensitivity, 87 % specificity, 92 % AUROC, and 94 % AUPRC); however, utilizing a combination of the CAE and MLP yields even superior results (88 % accuracy, 86 % sensitivity, 91 % specificity, 94 % AUROC, and 95 % AUPRC).
CONCLUSIONS
We utilized IR-SLO images to differentiate between MS and HC eyes, with promising results achieved using a combination of CAE and MLP. Future multi-center studies involving more heterogenous data are necessary to assess the feasibility of integrating IR-SLO images into routine clinical practice.
PubMed: 38945032
DOI: 10.1016/j.msard.2024.105743 -
Ultrasonics Jun 2024Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material,...
Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50 % during localization and 24 % during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100 % prediction accuracy at all levels of damage characterization.
PubMed: 38945018
DOI: 10.1016/j.ultras.2024.107390