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Taiwan is among the countries with the highest global prevalence of chronic renal disease. However, when advised to undergo dialysis therapy, patients with end-stage renal disease often hesitate. Attitudes toward medication and Taiwanese cultures are the main reasons for this delay, and delay conditioning requires urgent dialysis. This study aimed to explore the experience of urgent dialysis patients with end-stage renal disease. This study used a purposive sampling strategy with semi-structured interviews leading to in-depth interviews. Patients were recruited from the nephrology ward and hemodialysis center of a northern Taiwanese hospital. All participants were aged over 20 years with end-stage renal disease. Although advised by doctors to undergo dialysis, these patients delayed their treatment and later suffered severe complications. After emergency hospitalization, the patients' condition improved. Data were analyzed by content analysis. Interviews with five participants suffering from end-stage renal disease identified six themes: \"experiencing a sudden jolt,\" \"silent organ brings the most pain,\" \"feeling angry: why me?,\" \"facing a dilemma,\" \"taking risks,\" and \"facing consequences.\" These patients delayed their treatment and later suffered severe complications, even though doctors advised them to undergo dialysis. Health professionals play an important role in communication and coordination, assisting patients in coping with their situation. The analysis of the reasons for the delay in undergoing dialysis, therefore, should help health professionals provide proper guidance and care to patients who are faced with the decision to accept dialysis treatment.

Partial label learning aims to learn from training examples each associated with a set of candidate labels, among which only one label is valid for the training example. The basic strategy to learn from partial label examples is disambiguation, i.e. by trying to recover the ground-truth labeling information from the candidate label set. As one of the popular machine learning paradigms, maximum margin techniques have been employed to solve the partial label learning problem. Existing attempts perform disambiguation by optimizing the margin between the maximum modeling output from candidate labels and that from non-candidate ones. Nonetheless, this formulation ignores considering the margin between the ground-truth label and other candidate labels. In this paper, a new maximum margin formulation for partial label learning is proposed which directly optimizes the margin between the ground-truth label and all other labels. Specifically, the predictive model is learned via an alternating optimization procedure which coordinates the task of ground-truth label identification and margin maximization iteratively. Extensive experiments on artificial as well as real-world datasets show that the proposed approach is highly competitive to other well-established partial label learning approaches.

Objective(s): To examine the mechanisms underlying changes in ovarian function after total hysterectomy, identify relevant risk factors, and summarize clinical management strategies for such changes. Mechanism: The pathogenesis of impaired ovarian function post-total hysterectomy involves three key pathways: (1) reduced ovarian blood supply due to uterine artery ligation; (2) neuroendocrine imbalance caused by abnormal gonadotropin levels; (3) oxidative stress and fibrosis induced by chronic inflammation. Findings in Brief: Total hysterectomy is associated with diminished ovarian reserve, including a 20–30% decrease in anti-Müllerian hormone (AMH), elevated serum follicle-stimulating hormone (FSH) levels, and an approximate 3–4-year acceleration of menopause. Risk factors include the surgical approach (e.g., laparoscopic electrocoagulation decreases AMH by 40% vs. 20% with open surgery), unilateral ovarian preservation (increases the risk of menopause by 2.93-fold compared to bilateral preservation), and age <40 years (increases the risk of postoperative ovarian failure). Conclusions: Personalized clinical management, including preoperative assessment of AMH levels and ovarian blood flow, preference for ovarian and uterine artery-preserving techniques (e.g., STHMUV, uterine blood supply-preserving hysterectomy technique), and postoperative hormone/pelvic floor function monitoring may mitigate damage to ovarian function. To optimize long-term outcomes, future research should focus on vasoprotective strategies and precision interventions guided by biomarkers.

Background Naturally existing and human-produced heavy metals are released into the environment and cannot be completely decomposed by microorganisms, but they continue to accumulate in water and sediments, causing organisms to be exposed to heavy metals. Results This study designs and proposes heavy metal hazard decision trees for aquatic products, which are divided into seven categories including pelagic fishes, inshore fishes, other fishes, crustaceans, shellfish, cephalopods, and algae. Based on these classifications, representative fresh and processed seafood products are at the root of the heavy metal hazard decision trees. This study uses 2,107 cases of eating 556 cooked fresh or processed seafood product samples. The constructions of the proposed decision trees consist of 12 heavy metals, which include inorganic arsenic (iAs), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), strontium (Sr), thallium (Tl), and zinc (Zn). The heavy metal concentrations in cooked fresh and processed seafood product samples are subjected to a food safety risk assessment. Conclusions The results indicate the relationships among the seven categories of aquatic products, the relationships among 12 heavy metals in aquatic products, and the relationships among potential human health risks. Finally, the proposed heavy metal hazard decision trees for aquatic products can be used as a reference model for researchers and engineers.

Summary Global food security is one of the utmost essential challenges in the 21st century in providing enough food for the growing population while coping with the already stressed environment. High temperature (HT) is one of the main factors affecting plant growth, development and reproduction and causes male sterility in plants. In male reproductive tissues, metabolic changes induced by HT involve carbohydrates, lipids, hormones, epigenetics and reactive oxygen species, leading to male sterility and ultimately reducing yield. Understanding the mechanism and genes involved in these pathways during the HT stress response will provide a new path to improve crops by using molecular breeding and biotechnological approaches. Moreover, this review provides insight into male sterility and integrates this with suggested strategies to enhance crop tolerance under HT stress conditions at the reproductive stage.

Understanding the particle size distribution in the air and patterns of environmental contamination of SARS-CoV-2 is essential for infection prevention policies. Here we screen surface and air samples from hospital rooms of COVID-19 patients for SARS-CoV-2 RNA. Environmental sampling is conducted in three airborne infection isolation rooms (AIIRs) in the ICU and 27 AIIRs in the general ward. 245 surface samples are collected. 56.7% of rooms have at least one environmental surface contaminated. High touch surface contamination is shown in ten (66.7%) out of 15 patients in the first week of illness, and three (20%) beyond the first week of illness ( p  = 0.01, χ 2 test). Air sampling is performed in three of the 27 AIIRs in the general ward, and detects SARS-CoV-2 PCR-positive particles of sizes >4 µm and 1–4 µm in two rooms, despite these rooms having 12 air changes per hour. This warrants further study of the airborne transmission potential of SARS-CoV-2. Here, the authors sample air and surfaces in hospital rooms of COVID-19 patients, detect SARS-CoV-2 RNA in air samples of two of three tested airborne infection isolation rooms, and find surface contamination in 66.7% of tested rooms during the first week of illness and 20% beyond the first week of illness.

Purpose The purpose of this paper is to apply the self-concept theory and conservation of resources theory to develop a model that explains how both corporate social responsibility (CSR) and ethical leadership influence turnover intention through work engagement and burnout. Design/methodology/approach A survey of employees from banking industry in Taiwan and the research hypotheses were empirically tested by two-step structural equation modeling (SEM) and regression analysis. Findings The empirical findings indicate that CSR and ethical leadership are both related to work engagement positively and burnout negatively. Turnover intention is affected by work engagement negatively and burnout positively. While the relationship between CSR and work engagement is positively moderated by ethical leadership, the relationship between burnout and turnover intention is negatively moderated by self-efficacy. Research limitations/implications This study confirms that both CSR and ethical leadership play critical roles for influencing turnover intention through the mediation of work engagement and burnout. The moderating effects of ethical leadership and self-efficacy are also presented in this study. Practical implications The authors’ findings bring some suggestions for managers who want to prevent high turnover intention from spreading all over their organization. Specifically, CSR and ethical leadership should be taken into account when managers develop their strategies to reduce turnover intention. Originality/value This study analyzes how turnover intention takes shape from ethical perspectives and through which work-related state of mind (such as burnout, work engagement) can turnover intention be eventually affected.

Background Neuropathic pain is still a challenge for clinical treatment as a result of the comprehensive pathogenesis. Although emerging evidence demonstrates the pivotal role of glial cells in regulating neuropathic pain, the role of Schwann cells and their underlying mechanisms still need to be uncovered. Pannexin 1 (Panx 1), an important membrane channel for the release of ATP and inflammatory cytokines, as well as its activation in central glial cells, contributes to pain development. Here, we hypothesized that Schwann cell Panx 1 participates in the regulation of neuroinflammation and contributes to neuropathic pain. Methods A mouse model of chronic constriction injury (CCI) in CD1 adult mice or P0-Cre transgenic mice, and in vitro cultured Schwann cells were used. Intrasciatic injection with Panx 1 blockers or the desired virus was used to knock down the expression of Panx 1. Mechanical and thermal sensitivity was assessed using Von Frey and a hot plate assay. The expression of Panx 1 was measured using qPCR, western blotting, and immunofluorescence. The production of cytokines was monitored through qPCR and enzyme-linked immunosorbent assay (ELISA). Panx1 channel activity was detected by ethidium bromide (EB) uptake. Results CCI induced persistent neuroinflammatory responses and upregulation of Panx 1 in Schwann cells. Intrasciatic injection of Panx 1 blockers, carbenoxolone (CBX), probenecid, and Panx 1 mimetic peptide ( 10 Panx) effectively reduced mechanical and heat hyperalgesia. Probenecid treatment of CCI-induced mice significantly reduced Panx 1 expression in Schwann cells, but not in dorsal root ganglion (DRG). In addition, Panx 1 knockdown in Schwann cells with Panx 1 shRNA-AAV in P0-Cre mice significantly reduced CCI-induced neuropathic pain. To determine whether Schwann cell Panx 1 participates in the regulation of neuroinflammation and contributes to neuropathic pain, we evaluated its effect in LPS-treated Schwann cells. We found that inhibition of Panx 1 via CBX and Panx 1-siRNA effectively attenuated the production of selective cytokines, as well as its mechanism of action being dependent on both Panx 1 channel activity and its expression. Conclusion In this study, we found that CCI-related neuroinflammation correlates with Panx 1 activation in Schwann cells, indicating that inhibition of Panx 1 channels in Schwann cells reduces neuropathic pain through the suppression of neuroinflammatory responses.

Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is associated with not only multiple instances but also a candidate label set that contains one ground-truth label and some false positive labels. Specifically, at least one instance pertains to the ground-truth label while no instance belongs to the false positive labels. In this paper, we formalize such problems as multi-instance partial-label learning (MIPL). Existing multi-instance learning algorithms and partial-label learning algorithms are suboptimal for solving MIPL problems since the former fails to disambiguate a candidate label set, and the latter cannot handle a multi-instance bag. To address these issues, a tailored algorithm named M ipl G p , i.e., multi-instance partial-label learning with Gaussian processes, is proposed. M ipl G p first assigns each instance with a candidate label set in an augmented label space, then transforms the candidate label set into a logarithmic space to yield the disambiguated and continuous labels via an exclusive disambiguation strategy, and last induces a model based on the Gaussian processes. Experimental results on various datasets validate that M ipl G p is superior to well-established multi-instance learning and partial-label learning algorithms for solving MIPL problems.

Data on travelers returning from areas with many cases of Covid-19 may be useful in estimating incidence. The authors report follow-up data on 94 persons who boarded an evacuation flight from Wuhan, China, to Singapore on January 30, 2020.