Irbesartan (IBS) is a tetrazole derivative and antihypertensive drug that has two interconvertible struc-tures, 1H-and 2H-tautomers. The difference between them lies in the protonation of the tetrazole ring. In the solid-state, both tautomers can be isolated as crystal forms A (1H-tautomer) and B (2H-tautomer). Studies have reported that IBS is a polymorphic system and its forms A and B are related monotropically. These reports indicate form B as the most stable and less soluble form. Therefore, the goal of this contribution is to demonstrate through a complete solid-state characterization, thermodynamic study and dissolution properties that the IBS forms are desmotropes that are not related monotropically. However, the intention is also to call attention to the importance of conducting strict chemical and in solid-state quality controls on the IBS raw materials. Hence, powder X-ray diffraction (PXRD) and Raman spectroscopy (RS) at ambient and non-ambient conditions, differential scanning calorimetry (DSC), hot stage microscopy (HSM), Fourier transform infrared (FT-IR) and scanning electron microscopy (SEM) techniques were applied. Furthermore, intrinsic dissolution rate (IDR) and structural stability studies at 98％relative humidity (RH), 25 ?C and 40 ?C were conducted as well. The results show that in fact, form A is approximately four-fold more soluble than form B. In addition, both IBS forms are stable at ambient conditions. Nevertheless, structural and/or chemical instability was observed in form B at 40 ?C and 98％RH. IBS has been confirmed as a desmotropic system rather than a polymorphic one. Consequently, forms A and B are not related monotropically.

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia (FES), 125 with MDD, and 237 demographically-matched healthy controls (HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with a one-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD. Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.
Adult ; Algorithms ; Depressive Disorder, Major ; classification ; diagnosis ; Endophenotypes ; analysis ; Female ; Humans ; Machine Learning ; Male ; Markov Chains ; Neuropsychological Tests ; Schizophrenia ; classification ; diagnosis ; Young Adult