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Abstract Background Medical dispute is a global public health issue, which has been garnering increasing attention. In this study, we used machine learning (ML) method to establish a dispute prediction model and explored the clinical-application efficiency of this model in effectively reducing the occurrence of medical disputes. Methods Retrospective study of All disputes filed by Gansu Medical Mediation Committee from 2019 to 2021 and patients with the same hospital level as that of the dispute group and hospitalization year were randomly selected as the control group in 1:1 ratio. SPSS software was used for univariate feature selection of the 14 factors that may cause disputes, and factors with statistical differences were selected. The data were divided into training and test sets in a 7:3 ratio. Six ML models were selected, and Python was used to establish a dispute prediction model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, specificity, accuracy, precision, average precision (AP), and F1 score were used to characterize the fitting and accuracy of the models, while decision curve analysis (DCA) was used to evaluate their clinical utility. Results A total of 1189 patients in the dispute and control groups were extracted. Following 11 influencing factors were selected: the inpatient department, doctor title, patient age, patient gender, patient occupation, payment method, hospitalization days, hospitalization times, discharge method, blood transfusion volume, and hospitalization espenses. Compared to other models, the AUC (0.945, 95% CI 0.913–0.981), Sensitivity (0.887), Accuracy (0.887), AP (0.834), and F1 score (0.880) of the random forest model were higher than those of other models, while the DCA curve indicated its high clinical benefits. Conclusions Inpatient department, hospitalization expenses, and discharge type are the primary influencing factors of dispute. Random forest exhibited high dispute prediction and clinical-application value and is expected to be promoted for offline dispute prediction.

Robotic surgery, also known as robotic-assisted surgery (RAS), has rapidly evolved during the last decade. RAS systems are developed to assist surgeons to perform complex minimally invasive surgeries, and necessitate augmented interfaces for precise execution of these image-guided procedures. Extended Reality (XR) technologies, augmenting the real-world perception via integrating digital contents, show promise in enhancing RAS efficacy in various studies. Despite multiple reviews on technological and medical aspects, the crucial elements of human-robot interaction (HRI) and user experience (UX) remain underexplored. This review fills this gap by elucidating HRI dynamics within XR-aided RAS systems, emphasizing their impact on UX and overall surgical outcomes. By synthesizing existing literature, this systematic review study identifies challenges and opportunities, paving the way for improved XR-enhanced robotic surgery, ultimately enhancing patient care and surgical performance.

Abstract Background The 11th revision of the World Health Organization’s International Classification of Diseases (ICD-11) includes a new disorder, complex posttraumatic stress disorder (CPTSD), the diagnostic applicability of which has not been discussed sufficiently in Chinese culture. The network approach to psychopathology enables investigation of the structure of disorders at the symptom level, which allows for analysis of direct symptom interactions. The main objectives of the present study were to explore CPTSD symptom structure and identify key symptoms in CPTSD among young adults in China. Methods The present study collected a large, stratified sample of Beijing university students (1368), ranging from 18 to 25 years old, the majority of whom (65.4%) were female. CPTSD symptoms were assessed using the International Trauma Questionnaire (ITQ). A regularized partial correlation network and Bayesian network were applied to estimate the network structure and the upstream symptoms of CPTSD, respectively. Results The regularized partial correlation network showed that the high central symptoms were feelings of failure and hypervigilance, while the bridge symptom between posttraumatic stress disorder (PTSD) and disturbance in self-organization (DSO) domains was long-term upset. The Bayesian network showed that external avoidance and hypervigilance symptoms were upstream in CPTSD symptoms. Conclusions Hypervigilance is a central symptom that can be predictive of other symptoms of CPTSD. While feeling of failure is also a highly central symptom, it may be influenced by other symptoms. In the diagnosis and intervention of CPTSD, more attention should be given to hypervigilance symptoms.

Purpose: Investigate the oxylipin profiles in the aqueous humor of primary open-angle glaucoma (POAG) patients. Methods: Aqueous humor samples were collected from 17 POAG patients and 15 cataract subjects and subjected to a liquid chromatography/mass spectrometry (LC-MS) analysis to detect the oxylipins. The prediction potential of the differential abundant oxylipins was assessed by the receiver operating characteristic (ROC) curves. Pathway and correlation analyses on the oxylipins and clinical and biochemical parameters were also conducted. Results: The LC-MS analysis detected a total of 76 oxylipins, of which 29 oxylipins reached the detection limit. The multivariate analysis identified five differential abundant oxylipins, 15-keto-prostaglandin F2 alpha (15-kPGF2α), Leukotriene B4 (LTB4), 12,13-Epoxyoctadecenoic acid (12,13-Epome), 15-Hydroxyeicosatetraenoic acid (15-HETE) and 11-Hydroxyeicosatetraenoic acid (11-HETE). The five oxylipins are enriched in the arachidonic acid metabolism and linoleic acid metabolism pathways. Pearson correlation analysis showed that 11-HETE was positively correlated with intraocular pressure and central corneal thickness and negatively with cup/disk area ratio in the POAG patients. In addition, 15-kPGF2α was moderately and positively correlated with the mean deviation (MD) of visual field defect, and LTB4 was moderately and negatively correlated with macular thickness. Conclusions: This study revealed the oxylipin profile in the aqueous humor of POAG patients. Oxylipins involved in the arachidonic acid metabolism pathway could play a role in POAG, and anti-inflammatory therapies could be potential treatment strategies for POAG.

Learning English as a second language (ESL) has garnered significant attention from researchers and practitioners over the past few decades, with numerous ESL learning outcomes examined in the literature. However, self-sustained learning (SSL), a crucial factor in promoting students’ sustained learning and development within a sustainable society, has long been overlooked. To deepen the understanding of SSL, especially in the context of ESL in China, this study examined the direct and indirect effects of intercultural communicative skills, language mindset, and positive L2 self on sustained English learning among 1238 Chinese college students through descriptive statistics and a Structural Equation Modeling (SEM) mediation analysis. The results indicated that Chinese college students exhibited a strong language mindset and positive L2 self. They also scored high in intercultural communicative skills and long-term self-sustained English learning. SEM analysis showed that, after controlling for students’ demographic characteristics, both intercultural communicative skills and language mindset positively predicted positive L2 self and self-sustained English learning. Moreover, intercultural communicative skills and language mindset had significant and positive indirect effects on self-sustained English learning through positive L2 self, underscoring the significant mediating role of positive L2 self in the relationships between intercultural communicative skills, language mindset, and self-sustained English learning. These findings suggest that, to promote self-sustained learning among English learners, instructors should enhance students’ intercultural communicative skills, foster a growth language mindset, and cultivate positive language learning beliefs.

Sulfur hexafluoride (SF6) is a typical fluorine gas with excellent insulation and arc extinguishing properties that has been widely used in large-scale power equipment. The detection of SF6 gas in high-power electrical equipment is a necessary measure to ensure the reliability and safety of power grid operation. A failure of SF6 insulated electrical equipment, such as discharging or overheating conditions, can cause SF6 gas decomposition, resulting in various decomposition products. The decomposed gases inside the equipment decrease the insulating properties and are toxic. The leakage of SF6 can also decrease the insulating properties. Therefore, it is crucial to monitor the leakage of SF6 decomposed gases from electrical equipment. Quantitative testing of decomposition products allows us to assess the insulation state of the equipment, identify internal faults, and maintain the equipment. This review comprehensively introduces the decomposition formation mechanism of SF6 gas and the current detection technology of decomposition products from the aspects of principle and structure, materials, test effect, and practicability. Finally, the development trends of SF6 and decomposition gas detection technology for the reliability and safety of power grid operation are prospected.

Abstract This paper investigates a novel wireless information surveillance scheme assisted by reconfigurable intelligent surface (RIS) beamforming and artificial noise jamming cooperation, aiming at monitoring the information sent by an access point (AP) to a suspicious illegal user (SIU). It is assumed that the AP adopt the fixed maximum ratio transmission (MRT) precoding scheme, which is not affected by the information monitoring party. The goal of this paper is to maximize the effective information monitoring rate by jointly optimizing the RIS phase shifts, the receive beamforming vector of the legitimate receiver (LR), and the transmit beamforming vector along with jamming power of the jamming antenna (JA). The resultant optimization problem is non‐convex, and its optimization variables are highly coupled in the objective function and constraints. To tackle this difficulty, the optimization variables are optimized under the alternate optimization (AO) framework. Especially, the intractable RIS phase shifts are optimized by using Riemannian manifold optimization (RMO) algorithm under the penalty dual decomposition (PDD) framework and the semidefinite relaxation (SDR) technique, respectively. Numerical results verify the effectiveness of the proposed algorithms, and also demonstrate the superiority of the designed wireless information surveillance scheme over other benchmark schemes.

ABSTRACT Fecal microbiota transplantation (FMT) targeting gut microbiota has recently been applied to the treatment of ulcerative colitis (UC). However, preliminary trials showed that only a subset of patients responded to FMT, and the heterogeneity in donor gut microbiota probably played important roles in patients’ responses, implying the significance of matching an appropriate donor to a specified patient. We developed a strategy to build a donor-recipient matching model to guide rational donor selection for UC in FMT. We collected and uniformly reanalyzed 656 fecal 16S rRNA gene sequencing samples (350 from UC patients and 306 from healthy subjects) from 9 studies. Significantly lower α-diversity indexes were observed in UC patients by random effects model. Thirty-four bacterial genera and 34 predicted pathways were identified with significant odds ratios and classification potentials for UC patients. Based on six bacterial indicators, including richness, overall distance, genera, and pathways (beneficial and harmful), the analytic hierarchy process-based donor-recipient matching model was set to rank and select appropriate donors for patients with UC. Finally, the model showed favorable classification powers (>70%) for FMT effectiveness in two previous clinical trials. This study revealed the dysbiosis of fecal bacterial diversity, composition, and predicted pathways of patients with UC by meta-analysis and hereby developed a donor-recipient matching strategy to guide donor selection for UC in FMT. This strategy can also be applied to other diseases associated with gut microbiota. IMPORTANCE Modulation of gut microbiota by FMT from donors has been applied to the treatment of UC and yielded variable effectiveness in clinical trials. One possibility is that this variable effectiveness was related to donor selection, as a patient’s response to FMT may rely on the capability of the used donor’s microbiota to restore the specific gut disturbances of the patient. However, the biggest issues on the practical level are what should be considered in the selection process and how to set up such a donor-recipient matching model. In this study, we presented a bacterial profile-based donor-recipient matching strategy to guide donor selection for UC in FMT by first meta-analysis of 656 fecal 16S rRNA gene sequencing samples from 9 studies to identify significant indicators and then setting up the model by an analytic hierarchy process. The applicability and accuracy of this model were verified in the data sets from two previous FMT clinical studies. Our data indicate that the donor-recipient matching model built in this study enables researchers to rationally select donors for UC patients in FMT clinical practice, although it needs more samples and prospective trials for validation. The strategy adopted in this study to leverage existing data sets to build donor-recipient matching models for precision FMT is feasible for other diseases associated with gut microbiota.

Evidence suggests that sex differences commonly occur in trauma-related disorders. The current study aims to explore sex differences in complex posttraumatic stress disorder (CPTSD) symptom networks among Chinese young adults with childhood trauma. The current study utilized a representative sample of college students in Beijing and included 1416 participants (409 men and 907 women) who had childhood trauma experience. CPTSD symptoms were evaluated using the International Trauma Questionnaire. Regularized partial correlation network analysis and Bayesian network analysis were used to estimate the network structure and possible causality of CPTSD symptoms for both sexes. Male and female CPTSD symptom networks had differences in strength centrality and bridge centrality. Nightmares and feelings of failure had the highest strength centrality, and long-term upset and nightmares had the highest bridge centrality for men. Hypervigilance and feelings of failure had the highest strength centrality, and long-term upset and exaggerated startle response had the highest bridge centrality for women. The current study provides the first evidence of sex differences in the CPTSD symptom network among Chinese young adults with childhood trauma. Young men and women differed in highly central symptoms, which may speak to sex specificity in the main manifestations of CPTSD symptoms.

In this paper, a criterion for sea surface zoning based on the fractal characteristics of disturbances is demonstrated. To improve radar detection performance in heavy-tailed sea clutter, a multitude of sea clutter models and corresponding optimum and suboptimum detectors have been designed in recent years. However, there are cases where these models and detectors become insufficient to describe the coexistence of noise and sea clutter. The commonly used signal-to-clutter ratio (SCR) can hardly serve as an indicator revealing which kind of disturbances dominate in a certain area since it is difficult to decide the level of SCR at which sea clutter or noise exceeds the other. Therefore, it is necessary that a set of rules reflecting essential differences between sea clutter and noise are proposed to tell areas where sea clutter dominates, areas where sea clutter and noise coexist and areas where noise dominates. Analyzing fractal characteristics of disturbances, we consider the Hurst exponent H as a feature distinguishing sea clutter and noise from each other. A modified Sigmoid function is employed to model the variation in H with range bins, and the derivative of the function helps to formulate a set of rules for sea surface zoning.