Potential risk quantification from multiple biological factors via the inverse problem algorithm as an artificial intelligence tool in clinical diagnosis.

BACKGROUND: The inverse problem algorithm (IPA) uses mathematical calculations to estimate the expectation value of a specific index according to patient risk factor groups. The contributions of particular risk factors or their cross-interactions can be evaluated and ranked by their importance. OBJECTIVE: This paper quantified the potential risks from multiple biological factors by integrated case studies in clinical diagnosis via the IPA technique. Acting as artificial intelligence field component, this technique constructs a quantified expectation value from multiple patients' biological index series, e.g., the optimal trigger timing for CTA, a particular drug in blood concentration data, the risk for patients with clinical syndromes. METHODS: Common biological indices such as age, body surface area, mean artery pressure, and others are treated as risk factors upon their normalization to the range from -1.0 to +1.0, with a non-dimensional zero point 0.0 corresponding to the average risk factor index. The patients' quantified indices are re-arranged into a large data matrix. Next, the inverse and column matrices of the compromised numerical solution are constructed. RESULTS: This paper discusses quasi-Newton and Rosenbrock analyses performed via the STATISTICA program to solve the above inverse problem, yielding the specific expectation value in the form of a multiple-term nonlinear semi-empirical equation. The extensive background, including six previous publications of these authors' team on IPA, was comprehensively re-addressed and scrutinized, focusing on limitations, stumbling blocks, and validity range of the IPA approach as applied to various tasks of preventive medicine. Other key contributions of this study are detailed estimations of the effect of risk factors' coupling/cross-interactions on the IPA computations and the convergence rate of the derived semi-empirical equation viz. the final constant term. CONCLUSION: The main findings and practical recommendations are considered useful for preventive medicine tasks concerning potential risks of patients with various clinical syndromes.

LipL41, a hemin binding protein from Leptospira santarosai serovar Shermani.

Leptospirosis is one of the most widespread zoonotic diseases in the world. It is caused by the pathogen Leptospira that results in multiple-organ failure, in particular of the kidney. Outer membrane lipoprotein is the suspected virulence factor of Leptospira. In Leptospira spp LipL41 is one major lipoprotein and is highly conserved. Previous study suggests that LipL41 bears hemin-binding ability and might play a possible role in iron regulation and storage. However, the characterization of hemin-binding ability of LipL41 is still unclear. Here the hemin-binding ability of LipL41 was examined, yielding a K d = 0.59 ± 0.14 µM. Two possible heme regulatory motifs (HRMs), C[P/S], were found in LipL41 at (140)Cys-Ser and (220)Cys-Pro. The mutation study indicates that Cys140 and Cys220 might be cooperatively involved in hemin binding. A supramolecular assembly of LipL41 was determined by transmission electron microscopy. The LipL41 oligomer consists of 36 molecules and folds as a double-layered particle. At the C-terminus of LipL41, there are two tetratricopeptide repeats (TPRs), which might be involved in the protein-protein interaction of the supramolecular assembly.