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ObjectivesComposite diagnostic criteria alone are likely to create and introduce biases into diagnoses that subsequently have poor relationships with input symptoms. This study aims to understand the relationships between the diagnoses and the input symptoms, as well as the magnitudes of biases created by diagnostic criteria and introduced into the diagnoses of mental illnesses with large disease burdens (major depressive episodes, dysthymic disorder, and manic episodes).SettingsGeneral psychiatric care.ParticipantsWithout real-world data available to the public, 100 000 subjects were simulated and the input symptoms were assigned based on the assumed prevalence rates (0.05, 0.1, 0.3, 0.5 and 0.7) and correlations between symptoms (0, 0.1, 0.4, 0.7 and 0.9). The input symptoms were extracted from the diagnostic criteria. The diagnostic criteria were transformed into mathematical equations to demonstrate the sources of biases and convert the input symptoms into diagnoses.Primary and secondary outcomesThe relationships between the input symptoms and diagnoses were interpreted using forward stepwise linear regressions. Biases due to data censoring or categorisation introduced into the intermediate variables, and the three diagnoses were measured.ResultsThe prevalence rates of the diagnoses were lower than those of the input symptoms and proportional to the assumed prevalence rates and the correlations between the input symptoms. Certain input or bias variables consistently explained the diagnoses better than the others. Except for 0 correlations and 0.7 prevalence rates of the input symptoms for the diagnosis of dysthymic disorder, the input symptoms could not fully explain the diagnoses.ConclusionsThere are biases created due to composite diagnostic criteria and introduced into the diagnoses. The design of the diagnostic criteria determines the prevalence of the diagnoses and the relationships between the input symptoms, the diagnoses, and the biases. The importance of the input symptoms has been distorted largely by the diagnostic criteria.

Syndromes are defined with signs or symptoms that occur together and represent conditions. We use a data-driven approach to identify the deadliest and most death-averse frailty syndromes based on frailty symptoms. A list of 72 frailty symptoms was retrieved based on three frailty indices. We used data from the Health and Retirement Study (HRS), a longitudinal study following Americans aged 50 years and over. Principal component (PC)-based syndromes were derived based on a principal component analysis of the symptoms. Equal-weight 4-item syndromes were the sum of any four symptoms. Discrete-time survival analysis was conducted to compare the predictive power of derived syndromes on mortality. Deadly syndromes were those that significantly predicted mortality with positive regression coefficients and death-averse ones with negative coefficients. There were 2,797 of 5,041 PCbased and 964,774 of 971,635 equal-weight 4-item syndromes significantly associated with mortality. The input symptoms with the largest regression coefficients could be summed with three other input variables with small regression coefficients to constitute the leading deadliest and the most deathaverse 4-item equal-weight syndromes. In addition to chance alone, input symptoms' variances and the regression coefficients or p values regarding mortality prediction are associated with the identification of significant syndromes.Conceptually, frailty syndromes are similar to composite measures or indices that are sums of multiple input variables with equal or unequal weights 20 . Given how differently they are measured and the distinctive theories that inspired them, it is surprising that most frailty syndromes are significantly associated with major health outcomes, especially mortality. One reason is that significant health outcomes may be more likely to be published 21 . Alternatively, there are numerous candidate syndromes to screen, test, and publish. Recent findings in index mining suggest that syndromes can be searched systematically using large data sets and pre-specified rules 20 . For example, there are 72 frailty symptoms identified to form frailty syndromes in the Health and Retirement Study (HRS), and a large number of possible combinations are available 20,22 . Facing this large number of candidate syndromes, there are no well-established criteria to select clinically meaningful syndromes regardless of its statistical significance 18 . The underlying causes associated with new statistically significant frailty syndromes with important outcomes have not been identified. It is necessary to identify the factors contributing to statistical significances of frailty syndromes before assessing the importance of statistical significances in frailty syndromes. Then, a set of criteria for the selection of clinical meaningful syndromes could be developed. This study aims to identify the factors related to the statistical significances of newly generated syndromes, taking frailty syndromes as an example. The characteristics of the newly identified f...

Yen-po cheng 4 , Yi-chun Lai 5 & Wei-chih chen 6* composite diagnostic criteria are common in frailty research. We worry distinct populations may be linked to each other due to complicated criteria. We aim to investigate whether distinct populations might be considered similar based on frailty diagnostic criteria. the functional Domains Model for frailty diagnosis included four domains: physical, nutritive, cognitive and sensory functioning. Health and Retirement Study participants with two or more deficiencies in the domains were diagnosed frail. the survival distributions were analyzed using discrete-time survival analysis. the distributions of the demographic characteristics and survival across the groups diagnosed with frailty were significantly different (p < 0.05). A deficiency in cognitive functioning was associated with the worst survival pattern compared with a deficiency in the other domains (adjusted p < 0.05). The associations of the domains with mortality were cumulative without interactions. Cognitive functioning had the largest effect size for mortality prediction (Odds ratios, OR = 2.37), larger than that of frailty status (OR = 1.92). The frailty diagnostic criteria may take distinct populations as equal and potentially assign irrelevant interventions to individuals without corresponding conditions. We think it necessary to review the adequacy of composite diagnostic criteria in frailty diagnosis.

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor with poor prognosis, largely due to resistance to current radiotherapy and Temozolomide chemotherapy. The constitutive activation of Signal Transducer and Activator of Transcription 3 (STAT3) is evidenced as a pivotal driver of GBM pathogenesis and therapy resistance, and hence, is a promising GBM drug target. 5-acetyloxy-6,7,8,4′-tetramethoxyflavone (5-AcTMF) is an acetylated derivative of Tangeretin which is known to exert anticancer effects on breast, colon, lung, and multiple myeloma; however, its effect on GBM remains elusive. Herein, we reported that 5-AcTMF suppressed the viability and clonogenicity along with inducing apoptosis in multiple human GBM cell lines. Mechanistic analyses further revealed that 5-AcTMF lowered the levels of Tyrosine 705-phosphorylated STAT3 (p-STAT3), a canonical marker of STAT3 activation, but also dampened p-STAT3 upregulation elicited by Interleukin-6. Notably, ectopic expression of dominant-active STAT3 impeded 5-AcTMF-induced suppression of viability and clonogenicity plus apoptosis induction in GBM cells, confirming the prerequisite of STAT3 blockage for the inhibitory action of 5-AcTMF on GBM cell survival and growth. Additionally, 5-AcTMF impaired the activation of STAT3 upstream kinase JAK2 but also downregulated antiapoptotic BCL-2 and BCL-xL in a STAT3-dependent manner. Moreover, the overexpression of either BCL-2 or BCL-xL abrogated 5-AcTMF-mediated viability reduction and apoptosis induction in GBM cells. Collectively, we, for the first time, revealed the anticancer effect of 5-AcTMF on GBM cells, which was executed via thwarting the JAK2-STAT3-BCL-2/BCL-xL signaling axis. Our findings further implicate the therapeutic potential of 5-AcTMF for GBM treatment.