In this task, you are given a paragraph (from a research paper) and your task is to classify each sentence of the paragraph (assume n sentence) into the following categories: Background, Purpose, Method, Finding/Contribution, or Other. Return the output in this format: 1 - category of sentence 1, 2 - category of sentence 2, ..., n - category of sentence n; where each number indicates the order of the sentence. The categories can be identified using the following questions: 
 Background: - Why is this problem important? - What relevant works have been done before? - What did the previous works miss? - What are the high-level research questions? - How might this help other researchers?
 Purpose: - What specific things do the researchers want to do? - What specific knowledge do the researchers want to know? - What specific hypothesis do the researchers want to test?
 Method: - How did the researchers do it or find it out? - What are the procedures and steps of this research?
 Finding/Contribution: - What did the researchers find out? Did the proposed methods work? - Did things behave as the researchers expected?
Other: put every text fragment that does not fit into any of the categories above here. Put a sentence here if its not in English, is not part of the article, contains numbers and dates, is a caption of a figure or a table, is a formatting error, or you are not sure about it.

Although vaccines and antibiotics could kill or inhibit microbes, many infectious diseases remain difficult to treat because of acquired resistance and adverse side effects. Nano-carriers-based technology has made significant progress for a long time and is introducing a new paradigm in drug delivery. However, it still has some challenges like lack of specificity toward targeting the infectious site. Nanocarriers utilized targeting ligands on their surface called 'active target' provide the promising way to solve the problems like accelerating drug delivery to infectious areas and preventing toxicity or sideeffects. In this mini review, we demonstrate the recent studies using the active targeted strategy to kill or inhibit microbes. The four common nano-carriers (e.g. liposomes, nanoparticles, dendrimers and carbon nanotubes) delivering encapsulated drugs are introduced.
1 - background, 2 - background, 3 - background, 4 - background, 5 - background, 6 - purpose, 7 - finding

Conclusions: Normal cough produced a turbulent jet about 0.7 m towards the end of the bed from the recumbent subject. N95 mask was more effective than surgical mask in preventing expelled air leakage during coughing but there was still significant sideway leakage.
1 - finding, 2 - finding

An antigen-capture enzyme-linked immunosorbent assay (AC-ELISA) method was developed for the efficient detection of the UL24 antigen of the duck enteritis virus (DEV) using polyclonal antibodies. Ducks and rabbits were immunized, respectively, with expressed UL24 recombinant protein. The IgG antibodies against UL24 from ducks and rabbits were purified and used as the capture antibodies. The specificity of the optimized AC-ELISA was evaluated by use of DEV, duck hepatitis virus (DHV), duck hepatitis B virus (DHBV), gosling plague virus (GPV), Riemerella anatipestifer (R.A.), Escherichia coli (E. coli), Pasteurella multocida (P.M.) and Salmonella Enteritidis (S.E.). Only DEV specimens yielded a specific and strong signal. The limit of the sensitivity of this method for the detection of DEV was 46 ng/100 l. Compared with PCR and virus isolation, the rate of agreement for the detection of experimentally infected sera was 100%. A comparative test used on clinical specimens between the neutralization test and the AC-ELISA showed that the proportions of true positives and true negatives by the AC-ELISA were 0.90 and 0.67 respectively. These results indicated that the AC-ELISA approach is rapid, sensitive, and reliable for specific detection of DEV antigen.

1 - purpose, 2 - method, 3 - method, 4 - method, 5 - method, 6 - method, 7 - method, 8 - method, 9 - method, 10 - method, 11 - method, 12 - finding, 13 - finding, 14 - finding, 15 - finding, 16 - finding, 17 - finding