The aim of the study was to investigate the regulatory effects of policosanol on hyperlipidemia, gut microbiota and metabolic status in a C57BL/6 mouse model. A total of 35 C57BL/6 mice were assigned to 3 groups, chow (n=12), high fat diet (HFD, n=12) and HFD+policosanol (n=11), then treated for 18 weeks. Policosanol supplementation significantly reduced serum triglycerides and total cholesterol, as well as the weight of brown adipose tissue (BAT) (p0.05). Combined 16S rRNA gene sequencing and untargeted metabolomic analysis demonstrated that policosanol had regulatory effects on gut microbiota and serum metabolism in mice. In obese mice, policosanol increased the proportion of Bacteroides, decreased the proportion of Firmicutes, and increased the ratio of Bacteroides to Firmicutes (p<0.05). Policosanol promoted lipolysis and thermogenesis process, including tricarboxylic acid (TCA) cycle and pyruvate cycle, correlated with the increasing level of Bacteroides, Parasutterella, and decreasing level of Lactobacillus and Candidatus_Saccharimonas. Moreover, policosanol decreased fatty acid synthase (FAS) in the iWAT of obese mice. Policosanol also increased peroxisome proliferators-activated receptor-γ (PPARγ), uncoupling Protein-1 (UCP-1), peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) and PR domain containing 16 (PRDM16) in brown adipose tissue (BAT) obese mice (p<0.05). This study presents the new insight that policosanol may inhibit the synthesis of fatty acids, and promote lipolysis, thermogenesis related gene expression and regulate gut microbiota constituents, which provides potential for policosanol as an antihyperlipidemia functional food additive and provide new evidence for whole grain food to replace refined food.

Background: Past studies suggest mixed associations between selective serotonin reuptake inhibitor (SSRI) prescription and carcinogenic risk. There is no epidemiological study reporting on the association between SSRI use and the incidence of bladder cancer. The aim of this study is to determine whether SSRI use influences the risk of bladder cancer. Methods: We conducted a nationwide retrospective cohort study by Taiwan’s National Health Insurance Research Database from January 1, 1997 to December 31, 2013. 192,392 SSRI prescribed individuals were randomly matched 1 to 1 with 191,786 individuals who had never received any SSRIs by propensity scores match. The Cox Proportional Hazard models were conducted to examine the risk of bladder cancer between individuals prescribed SSRIs and individuals not prescribed SSRIs. Results: SSRIs were associated with significant reduced risk of bladder cancer with 0.5, 1, and 2 year induction periods (adjusted hazard ratio (aHR) = 0.86, 95% CI (confidence interval) = 0.76–0.98, aHR = 0.85, 95% CI = 0.75–0.97, and aHR = 0.77, 95% CI = 0.66–0.89). When examining the effect of specific SSRI, there was significantly lower risk of bladder cancer in individuals prescribed fluoxetine (6 month induction period: aHR = 0.78, 95% CI = 0.65–0.93; 1 year induction period: aHR = 0.78, 95% CI = 0.65–0.94; 2 year induction period: aHR = 0.73, 95% CI = 0.60–0.89), paroxetine (6 month induction period: aHR = 0.78, 95% CI = 0.61–0.99; 1 year induction period: aHR = 0.79, 95% CI = 0.61–1.01; 2 year induction period: aHR = 0.72, 95% CI = 0.54–0.95), and citalopram (6 month induction period: aHR = 0.74, 95% CI = 0.53–1.03; 1 year induction period: aHR = 0.70, 95% CI = 0.50–0.99; 2 year induction period: aHR = 0.60, 95% CI = 0.41–0.88). Conclusions: Individuals prescribed fluoxetine, paroxetine, or citalopram had a reduced risk of bladder cancer in this large, cross-national database.

Background/Objectives: The potential and limitations of chatbots in medical education and clinical decision support, particularly in specialized fields like psychiatry, remain unknown. By using the Rasch model, our study aimed to evaluate the performance of various state-of-the-art chatbots on psychiatry licensing exam questions to explore their strengths and weaknesses. Methods: We assessed the performance of 22 leading chatbots, selected based on LMArena benchmark rankings, using 100 multiple-choice questions from the 2024 Taiwan psychiatry licensing examination, a nationally standardized test required for psychiatric licensure in Taiwan. Chatbot responses were scored for correctness, and we used the Rasch model to evaluate chatbot ability. Results: Chatbots released after February 2024 passed the exam, with ChatGPT-o1-preview achieving the highest score of 85. ChatGPT-o1-preview showed a statistically significant superiority in ability (p < 0.001), with a 1.92 logits improvement compared to the passing threshold. It demonstrated strengths in complex psychiatric problems and ethical understanding, yet it presented limitations in up-to-date legal updates and specialized psychiatry knowledge, such as recent amendments to the Mental Health Act, psychopharmacology, and advanced neuroimaging. Conclusions: Chatbot technology could be a valuable tool for medical education and clinical decision support in psychiatry, and as technology continues to advance, these models are likely to play an increasingly integral role in psychiatric practice.

Limited knowledge is available about the association between autistic spectrum disorder (ASD) and precocious puberty. Our study examined the association between the two medical conditions and effect modification by sex and neuropsychiatric comorbidities in a nationwide population. To compare the risk of precocious puberty between ASD and non-ASD cases, we conducted a Cox regression analysis using ASD as the exposure and time to precocious puberty as the outcome. We adjusted for sex, attention-deficit/hyperactivity disorder (ADHD), tic disorder, obsessive–compulsive disorder (OCD), anxiety disorder, intellectual disability, and epilepsy. We performed a moderation analysis to examine the potential moderating effects of sex and comorbidities. Patients with ASD were prone to have precocious puberty, with an adjusted hazard ratio (aHR) of 1.80 (95% CI: 1.61–2.01). For effect modification, sex, specifically females, moderated the association between ASD and precocious puberty, with a relative excess risk due to interaction (RERI) of 7.35 (95% CI 4.90–9.80). No significant effect modification was found for any of the comorbidities within the scope of additive effect modification. We found that patients with ASD were prone to precocious puberty, regardless of sex or comorbid neuropsychiatric disorders. Girls with ASD are at a particularly higher risk of developing precocious puberty.

Background/Objectives: The complex association between attention-deficit/hyperactivity disorder (ADHD) and methylphenidate (MPH) with precocious puberty (PP) is still unclear. This study aims to investigate the association between ADHD, MPH, and PP. Methods: This is a nationwide cohort study including a total of 3,342,077 individuals, 186,681 with ADHD and 3,155,396 without. First, we compared the risk of PP between ADHD cases and non-ADHD cases. Second, we compared the risk of PP between MPH users and non-MPH users in patients with ADHD. Results: Patients with ADHD were at a greater risk of PP (adjusted hazard ratio [aHR], 2.01 [95% CI, 1.91–2.11]). In our moderation analyses, the female gender was a positive additive effect modifier of the association between ADHD and PP, whereas tics and intellectual disability were negative effect modifiers. In patients with ADHD, MPH users had a significantly lower risk of PP (aHR, 0.63 [95% CI 0.57–0.70]), and females had a negative effect modification on the association between MPH and PP. Conclusions: Our study found that children with ADHD were at a greater risk of PP. Girls with ADHD were a group particularly vulnerable to PP. Comorbid tics or intellectual disability was associated with a lower risk of PP. Among patients with ADHD, MPH was protective against PP, especially in girls. However, these preliminary results need further validation due to the nature of them being from an electronic database study. Unmeasured confounding factors might affect the association between MPH and PP.

Psychiatric comorbidity is a prevalent phenomenon that imposes a significant burden on patients, healthcare systems, and society. However, current research on psychiatric comorbidity is often limited to single disorders or partial associations. This study aims to utilize network analysis methods to construct a psychiatric comorbidity network and explore the network structural characteristics under different network weight definitions. Based on the psychiatric outpatient data from Changhua Christian Hospital in Taiwan from January 1, 2016, to June 30, 2024, the ICD-10 diagnostic codes (F00-F99) of all patients that appeared at least three times were extracted. Three different comorbidity networks were constructed using co-occurrence counts, Jaccard index, and partial correlation coefficient estimated by the mixed graphical model (MGM) as the weights of the network edges. Network structure was analyzed using indicators such as degree centrality, modularity, and community detection. The dataset included 16,954 patients. The comorbidity frequency network showed that mood disorders (F34) and anxiety disorders (F41) had the highest weighted degree centrality. In the Jaccard coefficient network, the weighted degree centrality of developmental disorders (F8x) increased. The MGM network highlighted the central role of substance use disorders (F1x). Our findings suggested the roles and interrelationships of different disease categories in the comorbidity network. The results provide new perspectives and data support for clinical practice and future research.