Injury pattern recognition to discriminate competing causes of liver injury

A novel quantitative computer-assisted drug-induced liver injury causality assessment tool (DILI-CAT)

Background and aims

We hypothesized that a drug’s clinical signature (or phenotype) of liver injury can be assessed and used to quantitatively develop a computer-assisted DILI causality assessment-tool (DILI-CAT). Therefore, we evaluated drug-specific DILI-phenotypes for amoxicillin-clavulanate (AMX/CLA), cefazolin, cyproterone, and Polygonum multiflorum using data from published case series, to develop DILI-CAT scores for each drug.

Methods

Drug specific phenotypes were made up of the following three clinical features: (1) latency, (2) R-value, and (3) AST/ALT ratio. A point allocation system was developed with points allocated depending on the variance from the norm (or “core”) for the 3 variables in published datasets.

Results

The four drugs had significantly different phenotypes based on latency, R-value, and AST/ALT ratio. The median cyproterone latency was 150 days versus < 43 days for the other three drugs (median: 26 for AMX/CLA, 20 for cefazolin, and 20 for Polygonum multiflorum; p<0.001). The R-value for the four drugs was also significantly different among drugs (cyproterone [median 12.4] and Polygonum multiflorum [median 10.9]) from AMX/CLA [median 1.44] and cefazolin [median 1.57; p<0.001]). DILI-CAT scores effectively separated cyproterone and Polygonum multiflorum from AMX/CLA and cefazolin, respectively (p<0.001). As expected, because of phenotypic overlap, AMX/CLA and cefazolin could not be well differentiated.

Conclusions

DILI-CAT is a data-driven, diagnostic tool built to define drug-specific phenotypes for DILI adjudication. The data provide proof of principle that a drug-specific, data-driven causality assessment tool can be developed for different drugs and raise the possibility that such a process could enhance causality assessment methods.

Pattern Recognition of Grating Perimeter Intrusion Behavior in Deep Learning Method

An intrusion behavior recognition method based on deep learning is proposed in this paper in order to improve the recognition accuracy of raster perimeter intrusion behavior. The Mach–Zehnder fiber optic interferometer was used to collect the external vibration signal sensing unit, capture the external vibration signal, use the cross-correlation characteristic method to obtain the minimum frame length of the fiber vibration signal, and preprocess the intrusion signal according to the signal strength. The intrusion signals were superimposed and several sections of signals were intercepted by fixed window length; the spectrum information is obtained by Fourier transform of the intercepted stationary signals. The convolution neural network was introduced into the pattern recognition of the intrusion signals in the optical fiber perimeter defense zone, and the different characteristics of the intrusion signals were extracted, so as to realize the accurate identification of different intrusion signals. Experimental results showed that this method was highly sensitive to intrusion events, could effectively reduce the false alarm rate of intrusion signals, and could improve the accuracy and efficiency of intrusion signal recognition.

Signatures in drug-induced liver injury

PURPOSE OF REVIEW

Drug-induced liver injury (DILI) can be induced by a myriad of drugs. Assessing whether the patient has DILI and assessing which drug is the most likely culprit are challenging. There has been too little attention paid to the concept that certain drugs appear to have unique clinical features or 'phenotypes'.

RECENT FINDINGS

Several case series of DILI because of various drugs have been published, and analysis of these case series points to the fact that individual drugs have characteristic DILI signatures. These clinical phenotypes can be characterized by latency, biochemical features (R-value), as well as clinical symptoms and signs. Several drugs, including isoniazid, amoxicillin-clavulanic acid, anabolic steroids, β-interferon and others, have highly unique clinical features. Such unique properties may be able to be used to improve adjudication processes.

SUMMARY

Individual drugs have unique clinical DILI phenotypes or signatures. Furthermore, these may be able to be used to improve adjudication.