N-(1-Acetyl-5-benzoyl-1,4,5,6-tetra­hydro­pyrrolo­[3,4-c]pyrazol-3-yl)benzamide

In the mol­ecule of the title compound, C21H18N4O3, the fused pyrrolo­[3,4-c]pyrazole ring system is approximately planar [maximum deviation = 0.0486 (16) Å] and forms dihedral angles of 87.21 (8) and 35.46 (7)° with the phenyl rings. In the crystal, N—H⋯O and C—H⋯O hydrogen bonds and weak C—H⋯π inter­actions link the mol­ecules into chains parallel to [201]

3-(5-Chloro­naphthalene-1-sulfonamido)-2-(2-hy­droxy­eth­yl)-4,5,6,7-tetra­hydro-2H-pyrazolo­[4,3-c]pyridin-5-ium chloride

In the cation of the title compound, C18H20ClN4O3S+·Cl−, the tetra­hydro­pyridinium ring assumes a half-chair conformation. The dihedral angle between the pyrazole ring and the naphthalene ring system is 75.19 (6)°. In the crystal, ions are linked into a three-dimensional network by N—H⋯O, N—H⋯Cl and O—H⋯Cl hydrogen bonds and weak π–π stacking inter­actions with centroid–centroid distances of 3.608 (2) Å

The mechanism of NLRP3 inflammasome activation and its pharmacological inhibitors

NLRP3 (NOD-, LRR-, and pyrin domain-containing protein 3) is a cytosolic pattern recognition receptor (PRR) that recognizes multiple pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). Once activated, NLRP3 initiates the inflammasome assembly together with the adaptor ASC and the effector caspase-1, leading to caspase-1 activation and subsequent cleavage of IL-1β and IL-18. Aberrant NLRP3 inflammasome activation is linked with the pathogenesis of multiple inflammatory diseases, such as cryopyrin­associated periodic syndromes, type 2 diabetes, non-alcoholic steatohepatitis, gout, and neurodegenerative diseases. Thus, NLRP3 is an important therapeutic target, and researchers are putting a lot of effort into developing its inhibitors. The review summarizes the latest advances in the mechanism of NLRP3 inflammasome activation and its pharmacological inhibitors

tert-Butyl 3-amino-2-methyl-6,7-dihydro-2H-pyrazolo[4,3-c]pyridine-5(4H)-carboxyl­ate

In the mol­ecule of the title compound, C12H20N4O2, the dihydro­piperidine ring assumes a half-chair conformation. In the crystal, cllassical N—H⋯O and N—H⋯N inter­molecular hydrogen bonds link mol­ecules into double chains along the a axis

Relationship between adiponectin and testosterone in patients with type 2 diabetes

Introduction: This study was designed to investigate the relationship between serum adiponectin and testosterone in patients with type 2 diabetes. Materials and methods: Serum level of adiponectin and testosterone were prospectively measured in 65 patients with type 2 diabetes and in 20 healthy subjects. Testosterone was determined by the radio-immunoassay, whereas adiponectin levels were determined by enzyme-linked immunosorbent assay (ELISA). Results: The average serum testosterone did not differ between the diabetes and the control group, but the average adiponectin in the diabetes group was lower (14.6 (14.2-15.0) vs. 24.3 (24.05-24.55) ng/mL, P = 0.001). In the diabetes group, the serum adiponectin level in patients with renal dysfunction (22.3 (21.5-23.1) ng/mL) was higher than in patients with no complications (12.1 (11.45-12.75) ng/mL) and than in patients with coronary artery disease (11.2 (10.25-12.15) ng/mL) (P = 0.009). Univariate correlation analysis showed an inverse weak correlation between adiponectin and testosterone concentrations in male diabetic patients (r = -0.27, P = 0.009). There was no significant correlation between adiponectin and testosterone in female patients (r = -0.05, P = 0.167). Conclusions: We conclude that patients with type 2 diabetes have lower serum adiponectin concen-tration than healthy individuals, and that there is a weak inverse correlation between adiponectin and testosterone serum concentrations in male diabetics

N′-(4-Hydr­oxy-3-methoxy­benzyl­idene)-4-methoxy­benzohydrazide monohydrate

In the title compound, C16H16N2O4·H2O, the dihedral angle between the two aromatic rings is 19.6 (2)°. In the crystal structure, mol­ecules are linked into a three-dimensional network by inter­molecular N—H⋯O, O—H⋯N and O—H⋯O hydrogen bonds

N′-(5-Bromo-2-hydr­oxy-3-methoxy­benzyl­idene)-4-hydr­oxy-3-methoxy­benzohydrazide dihydrate

In the title compound, C16H15BrN2O5·2H2O, the dihedral angle between the two aromatic rings is 2.9 (2)° and an intra­molecular O—H⋯N hydrogen bond is observed. One of the water mol­ecule is disordered over two positions, with occupancies of 0.83 (3) and 0.17 (3). In the crystal structure, mol­ecules are linked into a three-dimensional network by inter­molecular O—H⋯O, O—H⋯(O,O), O—H⋯N and N—H⋯O hydrogen bonds. π–π inter­actions involving Br-substituted benzene rings, with a centroid–centroid distance of 3.552 (3) Å are also observed

5-tert-Butyl 1-ethyl 3-amino-1,4,5,6-tetra­hydro­pyrrolo­[3,4-c]pyrazole-1,5-dicarboxyl­ate

The asymmetric unit of the title compound, C13H20N4O4, contains two crystallographically independent mol­ecules in which the dihedral angles between the fused pyrrole and pyrazole rings are 5.06 (8) and 1.12 (8)°. In the crystal, mol­ecules are linked by inter­molecular N—H⋯O and N—H⋯N hydrogen bonds into chains parallel to the b axis

Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.Comment: 10 pages, 7 figure