System of predicting sensitivity of <i>Klebsiella </i>against MeropeneM and method

Abstract

A system of predicting sensitivity of Klebsiella against meropenem includes comprises a computer readable storage medium on which is stored a computer program. An Exp (k) power value calculation method is implemented when the computer program is executed by a processor. The Exp(k) power value calculation method comprises-includes following computing steps: S1: k value is calculated according to formula I, $k=-1.44+0.708\left(\frac{C1-0.252}{2.567}\right)-0.66\left(\frac{C2-0.981}{5.69}\right)+0.088\left(\frac{C3-0.952}{0.652}\right)+3.048\left(\frac{C4-0.469}{1.095}\right)-0.46\left(\frac{C5-0.296}{0.67}\right)$
S2: Exp(k) power value with natural constant e as base and k as exponent is calculated.

Claims

1. A method of predicting sensitivity of Klebsiella against meropenem, comprising: obtaining a microbial sample from a patient with pneumonia; isolating a candidate Klebsiella strain from the microbial sample; obtaining copy numbers of a plurality of genes in the candidate Klebsiella strain; calculating a k value according to the formula $k=-1.44+0.708\left(\frac{C1-0.223}{0.757}\right)-0.66\left(\frac{C2-0.951}{0.09}\right)+0.088\left(\frac{C3-0.952}{0.103}\right)+3.048\left(\frac{C4-0.469}{1.09}\right)-0.46\left(\frac{C5-0.196}{0.67}\right),$ wherein, in the formula: C1 is the number of mphA gene copies in the candidate Klebsiella strain, C2 is the number of marA gene copies in the candidate Klebsiella strain, C3 is the number of Klebsiella pneumoniae KpnE gene copies in the candidate Klebsiella strain, C4 is the number of KPC-1 gene copies in the candidate Klebsiella strain, and C5 is the number of floR gene copies in the candidate Klebsiella strain; calculating an Exp(k) power value with e=2.718281828459045 as base and k as exponent, wherein an Exp(k) power value of <1 corresponds to a prediction that the candidate Klebsiella strain is sensitive to meropenem, and an Exp(k) power value of 1 corresponds to a prediction that the candidate Klebsiella strain is resistant against meropenem; predicting that the candidate Klebsiella strain is sensitive to meropenem; and administering meropenem to the patient in a medication treatment to treat the pneumonia based on the prediction in response to the prediction that the candidate Klebsiella strain is sensitive to meropenem.

2. The method of predicting sensitivity of Klebsiella against meropenem according to claim 1, wherein the number of mphA, marA, Klebsiella pneumoniae KpnE, KPC-1, and floR gene copies in the candidate Klebsiella strain are obtained through a second-generation high-throughput sequencing method.

3. The method of predicting sensitivity of Klebsiella against meropenem according to claim 2, wherein the number of gene copies in the candidate Klebsiella strain=depth of gene contigs/depth of genome contigs.

4. The method of predicting sensitivity of Klebsiella against meropenem according to claim 3, wherein: said genome contigs is the longest consensus sequence of those present in a plurality of contigs assembled from sequencing results by SPAdes version 3.13.0 software; said depth of genome contigs is calculated by SPAdes version 3.13.0 software; and said depth of gene contigs refers to a sum of depths of each contig which has said gene copies and said gene is located on.

5. The method of predicting sensitivity of Klebsiella against meropenem according to claim 4, wherein each contig which has said gene copies is annotated by BLAT version 36 software and DIAMOND version 2.0.4.142 software through CARD database alignment between said gene IDs and protein sequence.

6. The method of predicting sensitivity of Klebsiella against meropenem according to claim 4, wherein depths of each contig which has said gene copies and said gene is located on are calculated through SPAdes version 3.13.0 software.

Description

DESCRIPTION OF DRAWINGS

(1) FIG. 1 is a schematic diagram of the structure of the drug resistance prediction system provided by some examples of the present invention (in dashed boxes) and its workflow diagram.

(2) FIG. 2 is a schematic diagram of the structure of the drug resistance prediction system provided by other examples of the present invention (in dashed boxes) and its workflow diagram.

EMBODIMENTS

(3) In order to facilitate the understanding of the present invention, a more comprehensive description of the present invention will be provided in the embodiments below. Unless otherwise defined, all technical and scientific terms used in this article have the same meanings as those commonly understood by a person skilled in the art of the present invention. The terms used in the specification of the present invention in this article are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

(4) The reagents used in the following embodiments are commercially available unless otherwise specified.

(5) Sources of Biomaterials

(6) The 160 samples used in the experimental example of the present invention are pure cultures of Klebsiella strains isolated from clinical blood culture, from Beijing Union Medical College Hospital of the Chinese Academy of Medical Sciences.

(7) All tested strains (strains) were identified as Klebsiella (scientific name: Genus Klebsiella, Latin name: Klebsiella trevisan, systematic classification level: Genus) by mass spectrometry MALDI-TOF MS.

(8) On the Illumina Novaseq NGS sequencing platform, these strains include 122 Klebsiella pneumoniae strains, 18 Klebsiella aerogenes strains, 7 Klebsiella oxytoca strains, 6 Klebsiella quasipneumoniae strains, 4 Klebsiella variicola strains, and 3 Klebsiella michiganensis strains, all of them are reported strains of the Klebsiella genus.

(9) The above strains or strains can be obtained from common cases of Klebsiella pneumoniae pneumonia or from the applicant's laboratory. The applicant promises to distribute strains to the public within 20 years from the application date of the present invention for verifying the technical effects of the present invention.

Examples Group 1. System of Resistance Predicting System

(10) This group of examples provides a system of predicting sensitivity of Klebsiella against MeropeneM. All examples of this group possesses the following common features: as shown in FIG. 1 and FIG. 2, the system of predicting sensitivity of Klebsiella against MeropeneM comprising: computing unit; said computing unit comprises: computer readable storage medium, which is stored with computer program, characterized in that, an Exp (k) power value calculation method is implemented when said computer program is executed by processor; said Exp(k) power value calculation method comprises following computing steps: S1: k value is calculated according to formula I:

(11) $\begin{array}{cc}k=-1.44+0.708\left(\frac{C1-0.223}{0.757}\right)-0.66\left(\frac{C2-0.951}{0.09}\right)+0.088\left(\frac{C3-0.952}{0.103}\right)+3.048\left(\frac{C4-0.469}{1.09}\right)-0.46\left(\frac{C5-0.196}{0.67}\right)& \mathrm{Formula}I\end{array}$ S2: Exp(k) power value with natural constant e as base and k as exponent is calculated; in formula I, C1 is the number of mphA gene copies in a candidate Klebsiella strain, C2 is the number of marA gene copies in a candidate Klebsiella strain, C3 is the number of Klebsiella pneumoniae KpnE gene copies in a candidate Klebsiella strain, C4 is the number of KPC-1 gene copies in a candidate Klebsiella strain, C5 is the number of floR gene copies in a candidate Klebsiella strain.

(12) In some examples of this invention, said natural constant e=2.718281828459045.

(13) In more specific examples, above genes are all known genes reported in the art, specifically as follows: mphA gene is mphA gene recorded in Molecular Characterization of a Multidrug-Resistant Klebsiella pneumoniae Strain R46 Isolated from a Rabbit.

(14) marA gene is marA gene recorded in Correlation of the expression of acrB and the regulatory genes marA, soxS and ramA with antimicrobial resistance in clinical isolates of Klebsiella pneumoniae endemic to New York City.

(15) Klebsiella pneumoniae KpnE gene is Klebsiella pneumoniae KpnE gene recorded in An unequivocal superbug: PDR Klebsiella pneumoniae with an arsenal of resistance and virulence factor genes.

(16) KPC-1 gene is KPC-1 gene recorded in Novel Carbapenem-Hydrolyzing b-Lactamase, KPC-1, from a Carbapenem-Resistant Strain of Klebsiella pneumoniae. floR gene is floR gene recorded in Spread of the florfenicol resistance floR gene among clinical Klebsiella pneumoniae isolates in China.

(17) In further examples, as shown in FIG. 1 and FIG. 2, the system of predicting sensitivity of Klebsiella against MeropeneM, also comprising: result output unit; said result output unit outputs sensitive result or resistant result; said sensitive result S refers to that the candidate Klebsiella strain is sensitive to MeropeneM, and said resistant result R refers to that the candidate Klebsiella strain is resistant against MeropeneM. the result output unit outputs resistant result R when Exp(k) power value <1; the result output unit outputs sensitive result S when Exp(k) power value 1;

(18) Preferably, the result output unit and the computing unit are communicated through data-path;

(19) Preferably, Exp(k) power value calculated by the computing unit is transmitted to the result output unit.

(20) In more further example, as shown in FIG. 1, the system of predicting sensitivity of Klebsiella against MeropeneM, also comprising: experiment unit and data input unit; the experiment unit and data input unit are communicated through data-path; the experiment unit outputs experiment results which are transmitted to the data input unit and transformed to independent variables; said data input unit and computing unit are communicated through data-path; independent variables are transmitted to the computing unit through data-path.

(21) In more specific example, said data-path is data transmission carrier well-known to a person skilled in the arts of computer and electronics. The data path can be selected from wired or wireless form, for example, it can be a wired path, line, wireless path, WiFi connection, wireless channel, etc.

(22) Preferably, said independent variables include: values of C1, C2, C3, C4, C5;

(23) Preferably, said experiment results comprise: the number of mphA, marA, Klebsiella pneumoniae KpnE, KPC-1, floR gene copies in the candidate Klebsiella strain.

(24) The copy number of known genes in known strains can be routinely obtained by a person skilled in the arts of molecular biology and bioinformatics through conventional techniques such as sequencing and bioinformatics analysis. The mphA, marA, Klebsiella pneumoniae KpnE, KPC-1, and floR genes involved in the experiment results output by the experiment unit of the prediction system of the present invention are all reported genes in the art, and their gene information and primary structural sequences can be queried through the NCBI website or other known bioinformatics databases. By conducting whole genome sequencing of the candidate Klebsiella strain, the numbers of each of the aforementioned genes copies in the strain can be obtained.

(25) In other specific examples, the number of mphA, marA, Klebsiella pneumoniae KpnE, KPC-1, floR gene copies in the candidate Klebsiella strain are obtained through a second-generation high-throughput sequencing method.

(26) In more specific examples, the number of gene copies in the candidate Klebsiella strain=depth of gene contigs/depth of genome contigs; preferably, said genome contigs is a longest contigs segment assembled from sequencing results by SPAdes v3.13.0 software; said depth of genome contigs is a depth of genome contigs calculated by SPAdes v3.13.0 software; said depth of gene contigs refers to a sum of depth of each contig which has said gene copies and said gene is located on; preferably, each contig which has said gene copies is annotated by blat(v.36) software and diamond (v2.0.4.142) software through CARD database alignment between said gene IDs and protein sequence; preferably, depth of each contig which has said gene copies and said gene is located on are calculated through SPAdes v3.13.0 software.

(27) The second-generation high-throughput sequencing method has a conventional technical meaning that is well-known to a person skilled in the art, while obtaining gene copy numbers using the second-generation high-throughput sequencing method is a conventional technical means that is well-known to a person skilled in the art.

(28) In some specific embodiments, the specific method for calculating the gene copy number is as follows:

(29) Sequencing of strains is conducted by using second-generation high-throughput sequencing methods. The average sequencing depth is about 1 50, and the approximate sequencing amount for the Klebsiella genus is about 1G. Using the depth of contigs obtained and calculated by SPAdes (v3.13.0) assembly software during the assembly process as the standard, the longest contigs segment is defined as the genome segment. Prokka software (1.14.6) is used to conduct gene predicting on contigs, and all gene IDs and protein sequences on contigs are obtained. blat(v.36) software and Diamond (v2.0.4.142) software are respectively used to compare the IDs and protein sequences in the CARD database, sequences with a similarity greater than 90% are positive sequences, and annotation results for all resistance genes are obtained. The number of all gene copies on contigs are calculated using formula II as follows:

(30) Formula II: The Number of all Gene Copies on Said Contigs-Depth of Said Contigs/Depth of Genome Contigs

(31) If a gene has two or more genome copies on different or the same contigs, the final number of gene copies is equal to the sum of all calculated number of the gene copies. Examples of calculation methods are as follows: assuming that there is only one copy of the KPC-1 gene on all contigs, the number of the KPC-1 gene copies is: the number of KPC-1 gene copies=depth of contigs which KPC-1 gene is located on/depth of genome contigs.

(32) Assuming that the KPC-1 gene has 2 copies on one contigs and no copies on other contigs, the number of the KPC-1 gene copies is: the number of KPC-1 gene copies=2 depth of contigs which KPC-1 gene is located on/depth of genome contigs.

(33) Assuming that the KPC-1 gene has one copy on one contig1 and contig2, but no copies on other contigs, the number of the KPC-1 gene copies is: the number of KPC-1 gene copies=depth of contig 1 which KPC-1 gene is located on/depth of genome contigs+ depth of contig 2 which KPC-1 gene is located on/depth of genome contigs.

(34) In more specific embodiments, the result output unit, experiment unit, and data input unit are all set as computer readable storage media on which computer programs are stored.

(35) In some embodiments, when the computer program on the computer readable storage medium of the result output unit is executed by the processor, a method of comparing the Exp (k) power value with 1 is implemented and the result is output;

(36) That method of comparing the Exp (k) power value with 1 is implemented and the result is output refers to: the result output unit outputs resistant result R when Exp(k) power value <1; the result output unit outputs sensitive result S when Exp(k) power value 1;

(37) In other embodiments, a method for calculating the number of gene copies is implemented when the computer program on the computer readable storage medium of the experiment unit is executed by the processor;

(38) The method for calculating the number of gene copies is a conventional technical means well known to a person skilled in the art, and the specific steps are as follows: S1: Take the maximum from depth of the genome contigs calculated by SPAdes v3.13.0 assembly software to obtain the genome contigs; S2: Use BLAT (v.36) software and Diamond (v2.0.4.142) software to compare the CDs and protein sequences of a certain gene in the CARD database, and obtain each contigs with that gene copies by annotation; S3: SPAdes v3.13.0 assembly software calculates the depth of the gene on each contigs has the gene copies; S4: Calculate the sum of the depths of the gene on each contigs has the gene copies to obtain the depth of the contigs where the gene is located; S5: Calculate the number of the gene copies according to the following formula, the number of copies=depth of gene contigs/depth of genome contigs.

(39) In some embodiments, the computer program on the computer readable storage medium of the data input unit is executed by the processor to achieve dimensionless processing of the number of gene copies.

(40) The dimensionless processing involves removing data dimensions or data units from the number of gene copies to obtain dimensionless values. Generally speaking, the data dimension or unit of the number of gene copies is: copy, number, or copies.

(41) In other embodiments, as shown in FIG. 2, the system of predicting sensitivity of Klebsiella against meropenem may not require a data input unit. The experiment unit is directly connected to the computing unit through a data-path, allowing the number of gene copies (experiment results) or independent variable data calculated by the experiment unit to be directly input into the computing unit for Exp (k) power value calculation.

Examples Group 2. The Method of Predicting Klebsiella Resistant Against MeropeneM of this Invention

(42) This group examples provides a method of predicting sensitivity of Klebsiella against meropenem. All examples of this group possess the following common features: S1: k value is calculated according to formula I:

(43) $\begin{array}{cc}k=-1.44+0.708\left(\frac{C1-0.223}{0.757}\right)-0.66\left(\frac{C2-0.951}{0.09}\right)+0.088\left(\frac{C3-0.952}{0.103}\right)+3.048\left(\frac{C4-0.469}{1.09}\right)-0.46\left(\frac{C5-0.196}{0.67}\right)& \mathrm{Formula}I\end{array}$ S2: Exp(k) power value with natural constant e as base and k as exponent is calculated; in formula I, C1 is the number of mphA gene copies in a candidate Klebsiella strain, C2 is the number of marA gene copies in a candidate Klebsiella strain, C3 is the number of Klebsiella pneumoniae KpnE gene copies in a candidate Klebsiella strain, C4 is the number of KPC-1 gene copies in a candidate Klebsiella strain, C5 is the number of floR gene copies in a candidate Klebsiella strain; a predicting result corresponding to Exp(k) power value<1 is the candidate Klebsiella strain sensitive to MeropeneM, and a predicting result corresponding to Exp(k) power value1 is the candidate Klebsiella strain resistant against MeropeneM.

(44) In above formula I, e, as a mathematical constant, is the base of a natural logarithmic function, also known as a natural constant, natural base, or Euler number. It is an infinite non recurring decimal, which has the conventional technical meaning commonly understood by a ordinary technical person skilled in the art of mathematics. Its value is approximately: e=2.71828182845904523536.

(45) In some examples of this invention, the value of said natural constant e is 2.718281828459045;

(46) In some specific examples, the number of mphA, marA, Klebsiella pneumoniae KpnE, KPC-1, floR gene copies in the candidate Klebsiella strain are obtained through a second-generation high-throughput sequencing method.

(47) In more specific examples, the number of gene copies in the candidate Klebsiella strain=depth of gene contigs/depth of genome contigs; preferably, said genome contigs is a longest contigs segment assembled from sequencing results by SPAdes v3.13.0 software; said depth of genome contigs is a depth of genome contigs calculated by SPAdes v3.13.0 software; said depth of gene contigs refers to a sum of depths of each contig which has said gene copies and said gene is located on; preferably, each contig which has said gene copies is obtained by annotation with blat(v.36) software and diamond (v2.0.4.142) software through CARD database alignment between said gene IDs and protein sequence; preferably, depths of each contigs which has said gene copies and said gene is located on are calculated through SPAdes v3.13.0 software.

(48) The second-generation high-throughput sequencing method has a conventional technical meaning that is well-known to a person skilled in the art, while obtaining the number of gene copies by using the second-generation high-throughput sequencing method is a conventional technical means that is well-known to a person skilled in the art.

(49) In some specific embodiments, the specific method for calculating the number of gene copies is as follows:

(50) Sequencing of strains Is conducted by using second-generation high-throughput sequencing methods. The average sequencing depth is about 150, and the approximate sequencing amount for the Klebsiella genus is about 1G. Using the depth of contigs obtained and calculated by SPAdes (v3.13.0) assembly software during the assembly process as the standard, the longest contigs segment is defined as the genome segment. Prokka software (1.14.6) is used to conduct gene predicting on contigs, and all gene IDs and protein sequences on contigs are obtained. blat(v.36) software and Diamond (v2.0.4.142) software are respectively used to compare the gene IDs and protein sequences in the CARD database, sequences with a similarity greater than 90% are positive sequences, and annotation results for all resistance genes are obtained. The number of all gene copies on contigs are calculated using formula II as follows:

(51) Formula II: The Number of all Gene Copies on Said Contigs=Depth of Said Contigs/Depth of Genome Contigs

(52) If a gene has two or more genome copies on different or the same contigs, the final number of gene copies is equal to the sum of all calculated number of the gene copies.

(53) Examples of calculation methods are as follows: assuming that there is only one copy of the KPC-1 gene on all contigs, the number of the KPC-1 gene copies is: the number of KPC-1 gene copies=depth of contigs which KPC-1 gene is located on/depth of genome contigs.

(54) Assuming that the KPC-1 gene has 2 copies on one contigs and no copies on other contigs, the number of the KPC-1 gene copies is: the number of KPC-1 gene copies=2 depth of contigs which KPC-1 gene is located on/depth of genome contigs.

(55) Assuming that the KPC-1 gene has one copy on one contig1 and contig2, but no copies on other contigs, the number of the KPC-1 gene copies is: the number of KPC-1 gene copies-depth of contig 1 which KPC-1 gene is located on/depth of genome contigs+depth of contig 2 which KPC-1 gene is located on/depth of genome contigs.

Experimental Example. Performance Evaluation on Predicting System and Predicting Method of this Invention

(56) The prediction system of the present invention was evaluated using 160 clinical samples, and the comparison between the broth micro dilution classification results and the system prediction results of 160 clinical samples is shown in Table 1. In the table below, S represents sensitivity and R represents resistance.

(57) TABLE-US-00001 TABLE 1 Sample result of Result of Broth micro NO. Exp(k) predicting system dilution method s1 0.005025126 R R s2 7.695652174 S S s3 18.60784314 S S s4 18.60784314 S S s5 22.25581395 S S s6 22.25581395 S S s7 22.25581395 S S s8 23.3902439 S S s9 22.25581395 S S s10 10.11111111 S S s11 18.23076923 S S s12 22.25581395 S S s13 24.64102564 S S s14 23.3902439 S S s15 8.433962264 S S s16 19.83333333 S S s17 22.25581395 S S s18 30.25 S S s19 22.25581395 S S s20 22.80952381 S S s21 8.259259259 S S s22 22.25581395 S S s23 0.10864745 R R s24 16.24137931 S S s25 0.023541453 R R s26 0.027749229 R R s27 0.886792453 R R s28 22.25581395 S S s29 22.25581395 S S s30 18.60784314 S S s31 22.25581395 S S s32 19.40816327 S S s33 0.015228426 R R s34 16.24137931 S S s35 14.15151515 S S s36 22.25581395 S S s37 0.092896175 R R s38 22.25581395 S S s39 22.25581395 S S s40 22.80952381 S S s41 11.5 S S s42 23.3902439 S S s43 22.25581395 S S s44 21.72727273 S S s45 22.25581395 S S s46 22.25581395 S S s47 22.25581395 S S s48 22.25581395 S S s49 0.098901099 R R s50 22.25581395 S S s51 22.25581395 S S s52 13.28571429 S S s53 22.25581395 S S s54 18.60784314 S S s55 65.66666667 S S s56 22.25581395 S S s57 0.011122346 R R s58 0.078748652 R R s59 22.25581395 S S s60 12.33333333 S S s61 22.25581395 S S s62 15.66666667 S S s63 16.85714286 S S s64 22.25581395 S S s65 22.25581395 S S s66 22.25581395 S S s67 9.309278351 S S s68 10.62790698 S S s69 16.24137931 S S s70 10.11111111 S S s71 22.25581395 S S s72 0.022494888 R R s73 22.25581395 S S s74 22.25581395 S S s75 22.25581395 S S s76 21.72727273 S S s77 22.25581395 S S s78 20.73913043 S S s79 0.026694045 R R s80 22.25581395 S S s81 22.25581395 S S s82 22.25581395 S S s83 18.60784314 S S s84 4.076142132 S S s85 0.338688086 R R s86 22.25581395 S S s87 14.15151515 S S s88 13.92537313 S S s89 110.1111111 S S s90 4.952380952 S S s91 18.60784314 S S s92 22.25581395 S S s93 22.25581395 S S s94 22.25581395 S S s95 18.60784314 S S s96 22.25581395 S S s97 22.25581395 S S s98 22.25581395 S S s99 0.051524711 R R s100 0.03950104 R R s101 22.80952381 S S s102 22.25581395 S S s103 5.451612903 S S s104 22.25581395 S S s105 22.80952381 S S s106 22.80952381 S S s107 22.25581395 S S s108 7.771929825 S S s109 11.5 S S s110 0 R R s111 22.25581395 S S s112 22.25581395 S S s113 11.19512195 S S s114 23.3902439 S S s115 21.22222222 S S s116 22.25581395 S S s117 22.25581395 S S s118 17.86792453 S S s119 9.526315789 S S s120 22.25581395 S S s121 36.03703704 S S s122 22.80952381 S S s123 22.25581395 S S s124 22.25581395 S S s125 7.928571429 S S s126 0.02145046 R R s127 22.25581395 S S s128 15.39344262 S S s129 22.25581395 S S s130 0.007049345 R R s131 14.15151515 S S s132 22.25581395 S S s133 0.100110011 R R s134 2.448275862 S R s135 30.25 S S s136 23.3902439 S S s137 0.034126163 R R s138 0.009081736 R R s139 0.123595506 R R s140 10.36363636 S S s141 0.019367992 R R s142 32.33333333 S S s143 0.044932079 R R s144 0.005025126 R R s145 12.51351351 S S s146 22.80952381 S S s147 49 S S s148 49 S S s149 51.63157895 S S s150 13.92537313 S S s151 22.25581395 S S s152 65.66666667 S S s153 0.006036217 R R s154 23.3902439 S S s155 11.65822785 S S s156 0.016260163 R R s157 0.033057851 R R s158 0.024590164 R R s159 0.016260163 R R s160 9.752688172 S S

(58) The confusion matrix generated by the test result data is shown in Table 2:

(59) TABLE-US-00002 TABLE 2 predicting result confusion matrix R S real result R 30 1 S 0 129

(60) Assuming TP (True Positive) represents the number of true positive cases, FP (False Positive) represents the number of false positive cases, FN (False Negative) represents the number of false negative cases, and TN (Ture Negative) represents the number of true negative cases. Precision refers to the proportion of positive samples in the positive case determined by the classifier. The recall rate refers to the proportion of predicted positive cases to the total positive cases. Accuracy refers to the proportion of correct judgments made by the classifier on the entire sample. F1 score is the harmonic mean of accuracy and recall, with a maximum of 1 and a minimum of 0. The calculation results of each indicator are as follows:

(61) $\mathrm{precision}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}=\frac{30}{30+0}=1\mathrm{recall}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}=\frac{30}{30+1}=0.968\mathrm{accuracy}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{FP}+\mathrm{TN}+\mathrm{FN}}=\frac{30+129}{30+1+129+0}=0.994F1=\frac{2\mathrm{precision}\mathrm{recall}}{\mathrm{precision}+\mathrm{recall}}=0.984$

(62) The above examples only express the embodiments of the present invention, and their description is more specific and detailed, but they cannot be understood as a limitation on the scope of the invention patent. It should be pointed out that for an ordinary technical person skilled in the art, several deformations and improvements can be made without departing from the concept of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention patent should be based on the claims.