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Plants have evolved diverse defense mechanisms encompassing physical and chemical barriers. Cotton pigment glands are known for containing various defense metabolites, but the precise regulation of gland size to modulate defense compound levels remains enigmatic. Here, it is discovered that the VQ domain‐containing protein JAVL negatively regulates pigment gland size and the biosynthesis of defense compounds, while the MYC2‐like transcription factor GoPGF has the opposite effect. Notably, GoPGF directly activates the expression of JAVL, whereas JAVL suppresses GoPGF transcription, establishing a negative feedback loop that maintains the expression homeostasis between GoPGF and JAVL. Furthermore, it is observed that JAVL negatively regulates jasmonate levels by inhibiting the expression of jasmonate biosynthetic genes and interacting with GoPGF to attenuate its activation effects, thereby maintaining homeostatic regulation of jasmonate levels. The increased expression ratio of GoPGF to JAVL leads to enlarged pigment glands and elevated jasmonates and defense compounds, enhancing insect and pathogen resistance in cotton. These findings unveil a new mechanism for regulating gland size and secondary metabolites biosynthesis, providing innovative strategies for strengthening plant defense. [ABSTRACT FROM AUTHOR]

Objective: Demoralization has garnered increasing attention in recent years as a significant psychological distress. This study aims to identify latent classes of demoralization in lung cancer patients using Latent Class Analysis (LCA) from a person‐centered perspective and to explore the factors influencing the latent classes of demoralization. Methods: A cross‐sectional study using convenience sampling was conducted among 567 lung cancer patients in three tertiary hospitals in China. LCA was employed to classify heterogeneous classes of demoralization. Multinomial logistic regression analyses were performed to explore the associations between demographic and clinical characteristics, as well as physical symptoms, resilience, family function, and coping strategies, with class membership in the identified heterogeneous subgroups of lung cancer patients. Results: Three latent classes of demoralization were identified: the high demoralization group (Class 1, 14.8%), the moderate demoralization‐distress and helplessness group (Class 2, 37.2%), and the low demoralization group (Class 3, 48.0%). In comparison to Class 3, lung cancer patients with hypertension, higher core symptom burden, poorer resilience, dysfunctional family dynamics, and resignation coping were more likely to belong to Class 1 and Class 2. Conclusions: The demoralization patterns in lung cancer patients were varied. Targeted intervention should be developed based on the characteristics of each class, and timely attention should be paid to high‐risk patients. [ABSTRACT FROM AUTHOR]

Objective: Demoralization is a prevalent psychological problem among cancer patients and reflects a sense of subjective incompetence. This systematic review aims to identify factors influencing demoralization among cancer patients. Methods: Eleven databases were systematically searched from database inception to 31 December 2020. Google Scholar and relevant reference lists were supplementarily searched. Studies reporting demoralization measured by Demoralization Scale and its influencing factors among cancer patients were included. A qualitative synthesis was conducted owing to the heterogeneity of the study outcome. Results: A total of 49 studies involving 10,712 participants were included in this review. The results showed substantial effect size variation, but the psychological factors showed the strongest magnitude of association. Among the biological factors, the number of physical symptoms (mean r values [rs]: 0.331) was associated with increased demoralization. Among the psychological factors, negative psychological factors include hopelessness (mean rs: 0.633), desire for death (mean rs: 0.620), dignity‐related distress (mean rs: 0.595), depression (mean rs: 0.593), anxiety (mean rs: 0.589), psychological distress (mean rs: 0.465), and suicidal ideation (mean rs: 0.460) were related to increased demoralization; whereas positive psychological factors including hope (mean rs: −0.565), attachment security (mean rs: −0.530), and sense of coherence (mean rs: −0.453) were related to decreased demoralization. Among the social factors, social support (mean rs: −0.330) was negatively related to demoralization, and the demographic factors were still controversial. Quality of life was considered to be at the intersection of biopsychosocial factors and negatively associated with demoralization (mean rs: −0.599). Conclusions: Demoralization is a consequence of the interaction of physical, psychological, and social factors among cancer patients. Factors with a significant effect should not be overlooked when designing an intervention to reduce demoralization. It is necessary to distinguish demoralization from other negative psychological states and further explore positive psychological factors influencing demoralization among cancer patients. [ABSTRACT FROM AUTHOR]

Introduction and Aims. A challenge in treatment research is the necessity of adhering to protocol and regulatory strictures while maintaining flexibility to meet patients' treatment needs and to accommodate variations among protocols. Another challenge is the acquisition of large amounts of data in an occasionally hectic environment, along with the provision of seamless methods for exporting, mining and querying the data. Design and Methods. We have automated several major functions of our outpatient treatment research clinic for studies in drug abuse and dependence. Here we describe three such specialised applications: the Automated Contingency Management (ACM) system for the delivery of behavioural interventions, the transactional electronic diary (TED) system for the management of behavioural assessments and the Protocol Workflow System (PWS) for computerised workflow automation and guidance of each participant's daily clinic activities. These modules are integrated into our larger information system to enable data sharing in real time among authorised staff. Results. ACM and the TED have each permitted us to conduct research that was not previously possible. In addition, the time to data analysis at the end of each study is substantially shorter. With the implementation of the PWS, we have been able to manage a research clinic with an 80 patient capacity, having an annual average of 18 000 patient visits and 7300 urine collections with a research staff of five. Finally, automated data management has considerably enhanced our ability to monitor and summarise participant safety data for research oversight. Discussion and Conclusions. When developed in consultation with end users, automation in treatment research clinics can enable more efficient operations, better communication among staff and expansions in research methods. [Vahabzadeh M, Lin J-L, Mezghanni M, Epstein DH, Preston KL. Automation in an addiction treatment research clinic: Computerised contingency management, ecological momentary assessment and a protocol workflow system. Drug Alcohol Rev 2009;28:3–11] [ABSTRACT FROM AUTHOR]

Although treatment outcome in prize-based contingency management has been shown to depend on reinforcement schedule, the optimal schedule is still unknown. Therefore, we conducted a retrospective analysis of data from a randomized clinical trial (Ghitza et al., 2007) to determine the effects of the probability of winning a prize (low vs. high) and the size of the prize won (small, large, or jumbo) on likelihood of abstinence until the next urine-collection day for heroin and cocaine users (N=116) in methadone maintenance. Higher probability of winning, but not the size of individual prizes, was associated with a greater percentage of cocaine-negative, but not opiate-negative, urines.