Psychological pain and sociodemographic factors classified suicide attempt and non-suicidal self-injury in adolescents.

This study aimed to utilize machine learning to explore the psychological similarities and differences between suicide attempt (SA) and non-suicidal self-injury (NSSI), with a particular focus on the role of psychological pain. A total of 2385 middle school students were recruited using cluster sampling. The random forest algorithm was used with 25 predictors to develop classification models of SA and NSSI, respectively, and to estimate the importance scores of each predictor. Based on these scores and related theories, shared risk factors (control feature set) and distinct risk factors (distinction feature set) were selected and tested to distinguish between NSSI and SA. The machine learning algorithm exhibited fair to good performance in classifying SA history [Area Under Curves (AUCs): 0.65-0.87] and poor performance in classifying NSSI history (AUC: 0.61-0.68). The distinction feature set comprised pain avoidance, family togetherness, and deviant peer affiliation, while the control feature set included pain arousal, painful feelings, and crisis events. The distinction feature set slightly but stably outperformed the control feature set in classifying SA from NSSI. The three-dimensional psychological pain model, especially pain avoidance, might play a dominant role in understanding the similarities and differences between SA and NSSI.

Identifying suicide attempter in major depressive disorder through machine learning: the importance of pain avoidance, event-related potential features of affective processing.

How to achieve a high-precision suicide attempt classifier based on the three-dimensional psychological pain model is a valuable issue in suicide research. The aim of the present study is to explore the importance of pain avoidance and its related neural features in suicide attempt classification models among patients with major depressive disorder. By recursive feature elimination with cross-validation and support-vector-machine algorithms, scores from the measurements and the task-based EEG signals were chosen to achieve a suicide attempt classification model. In the multimodal suicide attempt classifier with an accuracy of 83.91% and an area under the curve of 0.90, pain avoidance ranked as the top one in the optimal feature set. Theta (reward positive feedback minus neutral positive feedback) was the shared neural representation ranking as the top one of event-related potential features in pain avoidance and suicide attempt classifiers. In conclusion, the suicide attempt classifier based on pain avoidance and its related affective processing neural features has excellent accuracy among patients with major depressive disorder. Pain avoidance is a stable and strong indicator for identifying suicide risks in both traditional analyses and machine-learning approaches. A novel methodology is needed to clarify the relationship between cognitive and affective processing evoked by punishment stimuli and pain avoidance.

Creep Monitoring of Submersible Observation Windows Using Mueller Matrix Imaging.

Safety of the observation window is one of the core concerns for manned submersibles. When subjected to underwater static pressure, extrusion and creep deformation always occur in the observation window, which can pose a threat to both safety and optical performance. To assess the deformation, real-time and non-contact monitoring methods are necessary. In this study, a conceptual setup based on the waveplate rotation and dual-DoFP (division of focal-plane polarimeter) polarization camera is built for the observation window's creep monitoring by measuring the Mueller matrix images of the samples under different pressures and durations. Then, a series of characteristic parameters, such as t1, R, r, R', are extracted from the Muller matrix images by Mueller matrix transformation (MMT), Mueller matrix polar decomposition (MMPD), correlation analysis and phase unwrapping method. The results demonstrate that these parameters can effectively describe the observation window's creep at different pressure levels which are simulated by finite element analysis. Additionally, more characterization parameters, such as ψ, A and D, are given from the Mueller matrix images and discussed to illustrate the method's potential for further applications and investigations. Ultimately, future devices based on this method could serve as a valuable tool for real-time and non-contact creep monitoring of the submersible observation windows.

Sparse deconvolution for background noise suppression with total variation regularization in light field microscopy.

In this Letter, we present a method aiming at background noise removal in the 3D reconstruction of light field microscopy (LFM). Sparsity and Hessian regularization are taken as two prior knowledges to process the original light field image before 3D deconvolution. Due to the noise suppression function of total variation (TV) regularization, we add the TV regularization term to the 3D Richardson-Lucy (RL) deconvolution. By comparing the light field reconstruction results of our method with another state-of-the-art method that is also based on RL deconvolution, the proposed method shows improved performance in terms of removing background noise and detail enhancement. This method will be beneficial to the application of LFM in biological high-quality imaging.

Risk factors of preoperative myocardial injury in patients with gastrointestinal tumors.

BACKGROUND: Recent studies indicated that the prognosis of patients with gastrointestinal tumors is frequently influenced by its complications, notably myocardial injury. The main object is to investigate the occurrence and risk factors of myocardial injury in patients with gastrointestinal tumor. METHODS: 1126 patients who received gastrointestinal tumor related surgery from May 2018 to June 2020 in the Sixth Affiliated Hospital of Sun Yat-sen University were retrospectively collected and divided into the non-myocardial injury group and the myocardial injury group (high-sensitive cardiac troponin I (hs-cTnI) ≥ 0.028 ng/ml). The occurrence and risk factors of myocardial injury in patients with gastrointestinal tumor are analyzed. The influence of myocardial injury on the ICU detention time in gastrointestinal tumor patients is also studied. RESULTS: In total, 78 (6.93%) patients developed myocardial injuries. Compared with patients in the non-myocardial injury group, patients in the myocardial injury group have a higher prevalence of cardiovascular risk factors (including advanced age and higher smoking ratio), a higher prevalence of comorbidities (such as previous coronary artery disease, hypertension, atrium fibrillation and diabetes), and a higher rate of premedication (such as anticoagulation, ß-blocker, Angiotensin-converting enzyme inhibitor/Angiotensin II receptor blocker, and diuretic) (all with P-value < 0.05). In addition, patients in the myocardial injury group also presented with a higher revised cardiac risk index (Lee index), higher neutrophil granulocyte ratio, lower hemoglobin, and higher likelihood of impaired cardiac structure and function (all with P-value < 0.05). There was a trend of statistical significance in the ICU detention time between the myocardial injury group and the non-myocardial injury group (1[1,3] vs. 2[1,10], P = 0.064). In this study, there were 7 patients presented with clinical symptoms in the myocardial injury group (chest discomfort in 4 cases, non-compressive precordial chest pain in 1 case, dyspnea in 2 cases). In the multivariate analysis, advanced age, increased Lee index score, increased neutrophil granulocyte ratio, decreased left ventricular ejection fraction (LVEF), increased interventricular septum were independent risk factors for myocardial injury. CONCLUSION: In conclusion, advanced age, increased Lee index, increased neutrophil granulocyte ratio, decreased left ventricular ejection fraction, and increased ventricular septum were independent risk factors for preoperative myocardial injury in patients with gastrointestinal tumors. The proportion of clinical symptoms in gastrointestinal tumor patients with myocardial injury was low, indicating the necessity to closely monitor the cardiac status of individuals with gastrointestinal tumors.

Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells.

We propose a polarization-based probabilistic discriminative model for deriving a set of new sigmoid-transformed polarimetry feature parameters, which not only enables accurate and quantitative characterization of cancer cells at pixel level, but also accomplish the task with a simple and stable model. By taking advantages of polarization imaging techniques, these parameters enable a low-magnification and wide-field imaging system to separate the types of cells into more specific categories that previously were distinctive under high magnification. Instead of blindly choosing the model, the L0 regularization method is used to obtain the simplified and stable polarimetry feature parameter. We demonstrate the model viability by using the pathological tissues of breast cancer and liver cancer, in each of which there are two derived parameters that can characterize the cells and cancer cells respectively with satisfactory accuracy and sensitivity. The stability of the final model opens the possibility for physical interpretation and analysis. This technique may bypass the typically labor-intensive and subjective tumor evaluating system, and could be used as a blueprint for an objective and automated procedure for cancer cell screening.

Polarization imaging-based radiomics approach for the staging of liver fibrosis.

Mueller matrix imaging contains abundant biological microstructure information and has shown promising potential in clinical applications. Compared with the ordinary unpolarized light microscopy that relies on the spatial resolution to reveal detailed histological features, Mueller matrix imaging encodes rich information on the microstructures even at low-resolution and wide-field conditions. Accurate staging of liver fibrosis is essential for the therapeutic diagnosis and prognosis of chronic liver diseases. In the clinic, pathologists commonly use semiquantitative numerical scoring systems to determine the stages of liver fibrosis based on the visualization of stained characteristic morphological changes, which require skilled staining technicians and well-trained pathologists. A polarization imaging based quantitative diagnostic method can help to reduce the time-consuming multiple staining processes and provide quantitative information to facilitate the accurate staging of liver fibrosis. In this study, we report a polarization imaging based radiomics approach to provide quantitative diagnostic features for the staging of liver fibrosis. Comparisons between polarization image features under a 4× objective lens with H&E image features under 4×, 10×, 20×, and 40× objective lenses were performed to highlight the superiority of the high dimensional polarization image features in the characterization of the histological microstructures of liver fibrosis tissues at low-resolution and wide-field conditions.

A Polarization-Imaging-Based Machine Learning Framework for Quantitative Pathological Diagnosis of Cervical Precancerous Lesions.

Polarization images encode high resolution microstructural information even at low resolution. We propose a framework combining polarization imaging and traditional microscopy imaging, constructing a dual-modality machine learning framework that is not only accurate but also generalizable and interpretable. We demonstrate the viability of our proposed framework using the cervical intraepithelial neoplasia grading task, providing a polarimetry feature parameter to quantitatively characterize microstructural variations with lesion progression in hematoxylin-eosin-stained pathological sections of cervical precancerous tissues. By taking advantages of polarization imaging techniques and machine learning methods, the model enables interpretable and quantitative diagnosis of cervical precancerous lesion cases with improved sensitivity and accuracy in a low-resolution and wide-field system. The proposed framework applies routine image-analysis technology to identify the macro-structure and segment the target region in H&E-stained pathological images, and then employs emerging polarization method to extract the micro-structure information of the target region, which intends to expand the boundary of the current image-heavy digital pathology, bringing new possibilities for quantitative medical diagnosis.

Deriving Polarimetry Feature Parameters to Characterize Microstructural Features in Histological Sections of Breast Tissues.

OBJECTIVE: Mueller matrix polarimetry technique has been regarded as a powerful tool for probing the microstructural information of tissues. The multiplying of cells and remodeling of collagen fibers in breast carcinoma tissues have been reported to be related to patient survival and prognosis, and they give rise to observable patterns in hematoxylin and eosin (H&E) sections of typical breast tissues (TBTs) that the pathologist can label as three distinctive pathological features (DPFs)-cell nuclei, aligned collagen, and disorganized collagen. The aim of this paper is to propose a pixel-based extraction approach of polarimetry feature parameters (PFPs) using a linear discriminant analysis (LDA) classifier. These parameters provide quantitative characterization of the three DPFs in four types of TBTs. METHODS: The LDA-based training method learns to find the most simplified linear combination from polarimetry basis parameters (PBPs) constrained under the accuracy remains constant to characterize the specific microstructural feature quantitatively in TBTs. RESULTS: We present results from a cohort of 32 clinical patients with analysis of 224 regions-of-interest. The characterization accuracy for PFPs ranges from 0.82 to 0.91. CONCLUSION: This work demonstrates the ability of PFPs to quantitatively characterize the DPFs in the H&E pathological sections of TBTs. SIGNIFICANCE: This technique paves the way for automatic and quantitative evaluation of specific microstructural features in histopathological digitalization and computer-aided diagnosis.

Characterization of anisotropy of the porous anodic alumina by the Mueller matrix imaging method.

Porous anodic alumina (PAA) is a photonic crystal with a hexagonal porous structure. To learn more about the effects brought by pores on the anisotropy of the PAA, we use the orientation sensitive Mueller matrix imaging (MMI) method to study it. We fabricated the PAA samples with uniform pores and two different pore diameters. By the MMI experiments with these samples, we found that the birefringence is the major anisotropy of the PAA and that there are many small areas with different orientations that formed spontaneously in the process of production on the surface of the PAA. By the MMI experiments at different orientations of the sample with two different pore diameters, we found that the pores affect the birefringence of the sample and the effect increases with the increased inclination of the sample. To further analyze the PAA, we present a symmetrical rotation measurement method according to the Mueller matrix of the retarder. With this method, we can calculate the average refractive index (RI) of birefringence and the orientation of the optical axis of uniaxial crystal. The results also show the effect of the pores on the anisotropy of PAA.