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Review Topical Sections

Neurosurgery and artificial intelligence

  • Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.

    Citation: Mohammad Mofatteh. Neurosurgery and artificial intelligence[J]. AIMS Neuroscience, 2021, 8(4): 477-495. doi: 10.3934/Neuroscience.2021025

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  • Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.


    Abbreviations

    AI:

    artificial intelligence; 

    BCI:

    brain-computer interface; 

    CAD:

    computer-assisted diagnosis; 

    CT:

    computerised tomography; 

    DL:

    deep learning; 

    ML:

    machine learning; 

    MRI:

    magnetic resonance imaging; 

    TBI:

    traumatic brain injury; 

    TLE:

    temporal lobe epilepsy

    Gastroenteritis, typhoid fever and a host of other gastro-intestinal tract (GIT) infections caused by bacteria of the Enterobacteriaceae are currently a significant cause of morbidity and mortality among children and adults in developing countries [1]. According to a 2004 World Health Organization (WHO) report, there is an annual incidence of 22 million cases of typhoid fever with over 216 thousand deaths per year [1]. Transmission of infection occurs mainly through consumption of contaminated food and water [2].

    Enterobacteriaceae comprise a family of Gram-negative rod shaped bacteria. Some members exist as free-living organisms in the environment while some members are normal flora of the gastrointestinal tract of humans and animals [3]. Many are significant human and animal pathogens causing opportunistic infections following incorrect handling of food animals and fecal contamination of food and water [3],[4].

    Antibiotics are natural, synthetic or semi-synthetic chemicals which in low concentration either inhibit the growth of or kill bacteria and are used to treat and prevent infections in humans and animals [5]. Quinolones are a class of synthetic, broad spectrum and bacteriocidal antibiotics that are active against both Gram-positive and Gram-negative bacteria including Enterobacteriacea and other intestinal pathogens and are used in human and veterinary medicine [6],[7]. The first quinolone in use was nalidixic acid. Fluoroquinolones (FQs) are quinolones with a fluorine atom at the C-6 position and include pefloxacin, ciprofloxacin, ofloxacin, norfloxacin, iomefloxacin, moxifloxacin and gemifloxacin. Fluoroquinolones are the drugs of choice for treatment of invasive gastrointestinal infections in adults worldwide. Quinolone exhibit their bacteriocidal activity by inhibiting DNA synthesis through inhibition of the enzyme topoisomerase IV and DNA gyrase and by causing breakage of bacterial chromosomes [7],[8].

    Resistance to quinolones is being increasingly reported among humans and animals [9]. Antibiotic resistance occurs when bacteria are able to survive bacteriocidal or bacteriostatic effects of antibiotics it was once susceptible to [10]. The implication of quinolone resistance is that diseases caused by members of Enterobacteriacea become difficult to treat which in turn affects the economic and social life of those infected, resulting in high morbidity and mortality [11],[12].

    Quinolones when used according to the manufacturer's directions should not result in resistance. However, factors contributing to quinolone resistance include: (i) It's misuse and overuse as a result of its effectiveness, less stringent regimen and inexpensiveness, over the counter availability and use of fake drugs [13] (ii) The use of quinolones at sub-therapeutic doses for purposes other than treatment of sick animals in livestock and aquaculture production [14] and (iii) Environmental pollution resulting from the discharge of untreated pond water containing antibiotic residue, antibiotic resistant bacteria and/or genes into the environment and the use of livestock manure for soil fertilization which may introduce antibiotic resistant bacteria into the environment from where resistance spreads to other bacteria [15].

    Quinolone resistant bacteria may be transmitted to humans following direct exposure of humans to infected animals, their waste products and body fluids (such as blood, urines, feces, milk, saliva and semen) and occupational exposure of animal and food handlers. Antibiotic resistant bacteria and antibiotic resistant genes can also be transmitted to humans indirectly via contact with or consumption of contaminated food products (such as meat, eggs, milk and dairy products) containing antibiotic resistant bacteria or antibiotic resistance genes which has a far reaching effect than the direct transmission [16].

    Bacteria acquire resistance to quinolones in two ways: chromosomal mutation in the genes gyrA and parC which encode the quinolone targets DNA gyrase and topoisomerase and acquisition of plasmid containing genes for quinolone resistance. Chromosomal mutation confers high–level of resistance to quinolones and it is transmitted vertically while plasmid mediated quinolone resistance (PMQR) confers low–level of resistance, it is transmitted horizontally among distantly related organisms making their spread much faster than that of chromosomal mutation [17].

    Three major mechanisms are involved in PMQR: (a) limiting quinolone inhibition by qnr protein protection of drug targets. The following plasmid genes: qnrA, qnrB, qnrC,qnrD, qnrS and qnrVC code for proteins of the pentapeptide repeat family that protects DNA gyrase and topoisomerase IV from quinolone inhibition [18], (b) acetylation of the quinolone molecule by the variant aminoglucosideacetyltransferase AAC(6′)–lb–cr and (c) the quinolone specific efflux pump QepA [19].

    Nigeria is not left out in the global, ravaging emergence of plasmid-mediated fluoroquinolone resistance (PMQR) that causes cases of treatment failure. Self-medication and abuse of antibiotics are common in Nigeria. Antibiotics are freely hawked in the open market while non-medically qualified persons make prescriptions. Quinolones also are used indiscriminately in livestock production and aquaculture. All these practices predispose quinolones to bacteria resistance. Therefore there is need for the knowledge of quinolone resistance in major towns in Delta State. This will assist the relevant agencies in formulating policies that will arrest the trend of antibiotic abuse and minimize the development of resistance.

    The aim of this study was to survey for, and determine plasmid-mediated quinolone resistance determinants in enteric bacterial isolates of animal and human origin in Delta State, Nigeria.

    In this study, samples were obtained from two major sources namely, human (diarrhoeal stool of patients attending public hospitals, and those attending private hospital) and animal (poultry litter and fish pond water). A total of 720 samples were collected from three cities in Delta State. A total of 240 samples was collected from each city and comprise 60 stool samples from patients attending private hospitals, 60 stool samples from patients attending public hospitals, 60 fish pond water samples and 60 poultry litter samples. In each city, 20 mL of fish pond water was aseptically collected from 30 fish farms in sterile bijou bottles, dipped below the surface of the water, and transported to the laboratory in ice packs. 10 g each of poultry litter was aseptically collected in sterile bijou bottles containing peptone water from 30 poultry farms. 10g each of diarrhea stool samples were collected from 30 patients attending private and 30 from patients attending public hospitals and inoculated into sterile bijou bottle containing peptone water and selenite F broth. All samples were collected in duplicates and transported in ice packs to the Lahor research laboratory, Benin City, for analysis.

    Table 1.  Sample sources.
    Location Source of sample Sample type Number of samples [N = 720]
    Human Samples
    Warri Public Hospital (PBH) Diarrhoeal stool 60
    Private Hospital (PRH) Diarrhoeal stool 60
    Sapele Public Hospital (PBH) Diarrhoeal stool 60
    Private Hospital (PRH) Diarrhoeal stool 60
    Ughelli Public Hospital (PBH) Diarrhoeal stool 60
    Private Hospital (PRH) Diarrhoeal stool 60

    Animal Samples
    Warri Fish Farms Fish Pond Water (FPW) 60
    Poultry Farms Poultry Litter (PDR) 60
    Sapele Fish Farms Fish Pond Water (FPW) 60
    Poultry Farms Poultry Litter (PDR) 60
    Ughelli Fish Farms Fish Pond Water (FPW) 60
    Poultry Farms Poultry Litter (PDR) 60

     | Show Table
    DownLoad: CSV

    A Loop full from bijou bottles of all samples was streaked in triplicates onto prepared plates of Nutrient Agar (NA), MaCconkey Agar (MA) and Deoxycholate Citrate Agar (DCA) and incubated at 37 °C for 24 hours. Emerging colonies were subcultured for purification and subsequently identified based on morphology, gram reaction and biochemical characteristics, using the enterotube, according to guidelines of the Manual of Clinical Microbiology [20].

    Isolated colonies from pure culture plates were sub-cultured into peptone water, and incubated for 12 hours. Turbidity was then adjusted by dilution with sterile peptone water until visually comparable with a MacFarland's 0.5 standard. The MacFarland's 0.5 standard was prepared by adding Barium Chloride (BaCl) with Tetraoxosulphate VI acid (H2SO4). Standardized bacteria culture was referred to as bacteria stock solution, and was used for subsequent experiments.

    All isolates were tested for susceptibility to the FQ antibiotics. This was carried out according to the standard disc diffusion technique as described by Clinical Laboratory Standard Institute (CLSI, 2014). Paper discs (AbtekBiologicals) containing the fluoroquinolones (FQs):Nalidixic acid NA-30 µg, Ciprofloxacin CPX-20 µg, Pefloxacin PEF-20 µg and Ofloxacin OFL-20 µg, were taken out of the refrigerator and allowed to equilibrate at room temperature. A sterile cotton swab was dipped into the respective bacterial stock solution, and excess fluid removed by pressing the swab against the wall of the tube. The entire surface of a Mueller-Hinton (MH) agar plate was then swabbed with the bacterial suspension, and allowed to dry for 15 min. Antibiotic discs were then layered aseptically, on the MH agar surface ensuring no air space forming between the disc and the plate.

    Plates were then incubated at 37 °C overnight and the zones of inhibition (ZI) recorded after about 12 hours. Interpretation of results was based on guidelines of the Clinical and Laboratory Standards [21].

    The isolates showing resistance to FQ were subjected to plasmid curing experiments. Sodium dodecyl sulphate (SDS) was used as curing agent [22]. Graded concentrations (10 µg/mL to 1000 µg/mL) of curing agent was prepared, and loop full of standardized culture was seeded. Sub-lethal concentrations were tested for their ability to cure the bacteria of their resistance plasmids. Bacteria that lost their FQ resistance sequel to curing experiment were regarded as Plasmid-mediated FQ-resistant (PMQR) isolates. Isolates which on the other hand, retained their resistance were regarded as Chromosome-mediated FQ-resistant isolates. All PMQR isolates were subsequently subjected to plasmid extraction.

    This procedure was adopted from the Qiagen kit for rapid isolation of DNA. Ten colonies of pure culture of bacteria were emulsified in 1 mL Tris EDTA (TE) buffer in an Eppendrof tube. The tubes were spun at 5000 rpm for 5 minutes and the pellets were re-suspended in 100 µL TE-buffer. The tubes were boiled for 10 min at 100 °C. Then they were spun at 5000 rpm for 5 min (Eppendrof tubes were placed once). The supernatants were transferred to new clean Eppendrof tubes and the samples were stored at 2 °C until use.

    A 1.5% agarose (Bio-Rad, USA) in 0.75x Tris borate EDTA (TBE) buffer was prepared. (The agarose was dissolved by boiling the solution in microwave oven).A0.5 µg/mL Ethidium Bromide (EtBr) [Sigma-Aldrich LP, USA] was then added for staining the DNA molecules. The agarose-EtBr solution was poured into the gel tray of the electrophoresis apparatus containing the combs and allowed to set for 20 minutes. 5 µL of each DNA extract was loaded into the gel wells and 5 µL of 1 KB plus DNA molecular size marker (Invitrogen, USA) was loaded into one of the wells.

    The electrophoresis was run at 120V for approximately 1 hour, 15 minutes and the gel was visualized on GelDoc system (BioRad, USA) and stored on disks as TIFF files.

    All DNA extracts were subjected to a multiplex PCR for detection of the qnr genes. In multiplex PCR more than one target sequence are amplified by including more than one pair of primers in the reaction [23]. Multiplex PCR was used here due to the considerable savings of time and effort within the laboratory without compromising test utility.

    Screening for the presence of qnrA, qnrB, and qnrS genes was carried out by modification of previously described PCR protocol [24]. The amplification was carried out using JumpStart™ REDTaq® ReadyMix™ PCR Reaction Mix and using specific primers for qnrA, to give a 516-bp product, for qnrB, to give a 469-bp product, and for qnrS, to give a 417-bp product [24]. The conditions were altered to 95 °C for 5 minutes then 30 cycles of 95 °C for 15 seconds, 55 °C for 15 seconds, and 72 °C for 40 seconds, then 72 °C for 4 minutes.

    Screening for the presence of aac(6′)-Ib gene was carried out by modification of previously described PCR protocol [25]. The amplification was embarked upon using JumpStart™ REDTaq® ReadyMix™ PCR Reaction Mix and using specific primers for aac(6′)-Ibto give a 482-bp product [25]. The PCR conditions were altered to 95 °C for 5 minutes then 30 cycles of 95 °C for 15 seconds, 58 °C for 15seconds, and 72 °C for 40 seconds, then 72 °C for 4 minutes.

    Screening for the presence of the qep A gene was carried out as described by Yamane et al., 2008 with reagents as previously described, to give a 199 bp product [26] Base sequence of all primer sets used for this study is described below.

    Table 2.  Base sequence of all primer.
    Primer Name DNA Sequence (5′–3′) Target site Amplicon size (bp)
    aac(6′)-Ib-F ttgcgatgctctatgagtggcta aac(6′)-Ib 482
    aac(6′)-Ib-R ctcgaatgcctggcgtgttt
    qnrA-F multiplex atttctcacgccaggattg qnrA 516
    qnrA-R multiplex gatcggcaaaggttaggtca
    qnrB-F multiplex gatcgtgaaagccagaaagg qnrB 469
    qnrB-R multiplex acgatgcctggtagttgtcc
    qnrS-F multiplex acgacattcgtcaactgcaa qnrS 417
    qnrS-R multiplex taaattggcaccctgtaggc
    qepA-F gcaggtccagcagcgggtag qepA 199
    qepA-R cttcctgcccgagtatcgtg

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    DownLoad: CSV

    Comparison of zones of inhibition of the various fluoroquinolones was determined using the one-way Analysis of Variance, while comparison between zones of inhibition of FQs on isolates from human and animal sources was done using the student's t-test. Proportions of occurrences and charts were done by descriptive statistics, all using the spss 16.0 package.

    Five different enteric bacterial species, Escherichia coli, Salmonella spp., Shigella spp., Klebsiella spp. and Aeromonas spp. were isolated in varying proportions. The overall population of the isolates stood at 1,964 with the prevalence rate shown in Table 3. E.coli alone, accounted for about one-third of the entire isolates from each of the sources with a range of 28.2–44.7%. Klebsiella spp. was the least encountered among all isolates, ranging from 6.8% to 12.3%. Generally, the population of isolates varied from one source to another, with stool from patients attending private hospitals having the least (396), and fish pond water having the highest number of isolates (563). The isolates were recovered more from the animal sources than the human sources.

    Table 3.  The number of enteric bacterial isolates from sampling sites.
    Species Number of isolates from:
    All samples
    Public Hospital (PBH) Private Hospital (PRH) Poultry Droppings (PDR) Fish-pond Water (FPW)
    E.coli 168 (33.6) 177 (44.7) 165 (32.7) 159 (28.2) 669 (34.1)
    Salmonella spp. 126 (25.2) 87 (22.0) 177 (35.0) 98 (17.4) 488 (24.8)
    Shigella spp. 131 (26.2) 76 (19.2) 46 (9.1) 68 (12.1) 321 (16.3)
    Klebsiella spp. 44 (8.8) 27 (6.8) 61 (12.1) 69 (12.3) 201 (10.2)
    Aeromonas spp. 31 (6.2) 29 (7.3) 56 (11.1) 169 (30.0) 285 (14.5)
    Total isolates 500 396 505 563 1964

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    For Escherichia coli, the percentage of FQ resistance was higher with isolates from patients attending public hospital (73.2), than from private hospitals (37.9). Also, the percentage FQ resistance was higher with isolates from poultry litter (61.2) than for isolates from fish pond water (38.4). This trend was equally observed with all isolates except Klebsiella spp. with higher FQ resistance observed for isolates from patients attending private hospital than those from public hospitals (Figure 1). Generally however, percentage FQ resistance tended to be higher with isolates from human sources, than animal sources.

    Figure 1.  Relative proportion of fluoroquinolone resistance among isolates from human and animal sources.

    Statistical analysis by ANOVA shows that there was significant differences (F: 170.944–613.302; P: 0.000) in the zones of inhibition (Table 4) however, Post-hoc test reveals that the differences exist in the resistance pattern between Nalidixic acid and Ofloxacin for isolates obtained from Public hospitals, Private hospital and Fish pond water. Only for E.coli isolates from poultry litter, did resistance pattern show significant difference for every single FQ tested

    A similar observation was recorded for Salmonella spp. There was a significant difference in the resistance pattern of isolates from all sources to the FQs. However, for isolates from poultry litter, no significant difference existed in the resistance pattern to Ciprofloxacin and Pefloxacin (Table 4). Isolates of Shigella spp. from Public and Private Hospitals, as well as those from Poultry droppings and Fish pond water were significantly different in their zones of inhibition to all FQs tested (Table 4). Furthermore, Klebsiella spp., from human sources was significantly different to all tested FQs but isolates from animal sources displayed similar resistance pattern to Ciprofloxacin and Pefloxacin (Table 4). However, there was generally a significant difference in zones of inhibition of the tested FQs.

    For isolates of Aeromonas spp. from Public hospital, they were significantly different (P < 0.05) in their resistance to the tested FQs. Similar observation was recorded for isolates from Fish pond water and Poultry droppings. However, resistance to Ciprofloxacin and Pefloxacin were not significantly different among isolates obtained from patients attending private hospital (Table 4).

    Generally, the observation was that resistance to the various FQs by all bacterial isolates were significantly different irrespective of their sources, but there tended to be a similarity in the resistance pattern to Ciprofloxacin and Pefloxacin. While resistance to Nalidixic acid and Ofloxacin was always significantly different, resistance pattern to Ciprofloxacin and Pefloxacin were often similar.

    One major aim of this study was to compare resistance of isolates from animals, with those from humans. All comparisons for susceptibility testing were performed using the t test with the SPSS version 16.0 at a 5% probability level (Table 5).

    Table 4.  Susceptibility of isolates to the Fluoroquinolones.
    Sources Zones of inhibition (mean ± SD) mm
    F value Sig.
    Nalidixic acid Ciprofloxacin Pefloxacin Ofloxacin
    Escherichia coli
    (PBH) 4.2 ± 0.59a 8.4 ± 0.94b 8.9 ± 1.40b 13.1 ± 2.70c 613.302 0.000
    (PRH) 4.1 ± 0.64a 8.8 ± 1.39b 9.0 ± 1.51b 11.5 ± 2.50c 231.306 0.000
    (PDR) 4.4 ± 0.70a 8.0 ± 1.14b 8.8 ± 1.42c 12.1 ± 2.81d 341.371 0.000
    (FPW) 4.3 ± 0.73a 8.2 ± 1.68b 8.6 ± 2.26b 12.7 ± 2.87c 170.944 0.000
    Salmonella sp.
    (PBH) 4.2 ± 0.77a 8.2 ± 1.69b 8.8 ± 1.96b 11.8 ± 2.36c 262.940 0.000
    (PRH) 4.2 ± 0.51a 8.3 ± 1.56b 9.0 ± 1.90b 11.8 ± 3.42c 94.018 0.000
    (PDR) 3.9 ± 0.76a 8.2 ± 1.42b 8.9 ± 1.84c 13.4 ± 2.92d 271.532 0.000
    (FPW) 3.5 ± 0.88a 8.6 ± 1.90b 8.7 ± 1.61b 12.7 ± 2.65c 144.776 0.000
    Shigella sp.
    (PBH) 3.9 ± 0.84a 8.0 ± 1.22b 9.4 ± 2.12c 12.2 ± 2.32d 300.895 0.000
    (PRH) 4.5 ± 0.62a 7.5 ± 0.79b 9.3 ± 2.18c 12.2 ± 2.38d 137.107 0.000
    (PDR) 2.5 ± 0.43a 6.3 ± 1.73b 8.4 ± 1.54c 11.7 ± 3.90d 82.472 0.000
    (FPW) 2.7 ± 0.45a 5.9 ± 0.37b 9.2 ± 2.17c 13.6 ± 2.51d 136.899 0.000
    Klebsiella sp.
    (PBH) 3.2 ± 0.79a 5.2 ± 0.96b 9.5 ± 1.92c 13.0 ± 2.84d 163.729 0.000
    (PRH) 3.5 ± 0.61a 5.4 ± 0.79b 9.6 ± 2.21c 11.2 ± 3.71d 49.489 0.000
    (PDR) 3.5 ± 0.70a 8.2 ± 1.41b 8.5 ± 1.55b 12.5 ± 3.42c 158.643 0.000
    (FPW) 3.4 ± 0.66a 7.6 ± 1.33b 8.4 ± 1.75b 13.3 ± 2.99c 220.856 0.000
    Aeromonas sp.
    (PBH) 3.0 ± 0.69a 7.8 ± 1.25b 10.2 ±2.12c 13.5 ± 3.00d 105.466 0.000
    (PRH) 3.5 ± 0.52a 9.4 ± 1.64b 9.7 ± 2.48b 13.0 ± 2.37c 46.767 0.000
    (PDR) 3.3 ± 0.61a 5.2 ± 0.93b 8.8 ± 1.72c 13.5 ± 2.82d 205.346 0.000
    (FPW) 3.3 ± 0.65a 5.3 ± 0.95b 7.8 ± 1.48c 13.2 ± 3.21d 362.324 0.000

    (Means with similar alphabets on same row are not significantly different)

     | Show Table
    DownLoad: CSV

    The zones of inhibition of the FQs were compared for E.coli obtained from human and animal sources. It was observed that there was no significant difference in the zones of inhibition exerted by OFL and PEF but the mean zones of inhibition of NA and CPX on E.coli isolates were significantly different for isolates from human and animal sources (Table 5).

    For Salmonella isolates, there was a significant difference in the zones of inhibition by NA and OFL in respect of isolates from human and animal sources (Table 5). However, no significant difference was observed in the mean zones of inhibition by CPX and PEF on isolates from human and animal sources. Furthermore, the mean zones of inhibition by NA, PEF and CPX on Shigella sp. isolates from human and animal sources, were significantly different, but no significant difference existed in the resistance to OFL by Shigella isolates from human and animal sources (Table 5).

    Table 5.  Comparison of FQ resistance of isolates from human and animal sources.
    Fluoroquinolone Zones of inhibition (mean±SD) mm
    P value
    Human Animal
    E.coli
    Nalidixic Acid 4.20 ± 0.61 4.39 ± 0.71 0.007
    Ciprofloxacin 8.56 ± 1.13 8.05 ± 1.37 0.000
    Pefloxacin 8.90 ± 1.45 8.70 ± 1.78 0.250
    Ofloxacin 12.50 ± 2.72 12.32 ± 2.84 0.707
    Salmonella sp
    Nalidixic Acid 4.21 ± 0.69 3.76 ± 0.82 0.000
    Ciprofloxacin 8.25 ± 1.65 8.36 ± 1.61 0.502
    Pefloxacin 8.86 ± 1.94 8.84 ± 1.76 0.975
    Ofloxacin 11.80 ± 2.74 13.16 ± 2.84 0.000
    Shigella sp.
    Nalidixic Acid 4.13 ± 0.82 2.59 ± 0.44 0.000
    Ciprofloxacin 7.87 ± 1.12 6.14 ± 1.39 0.000
    Pefloxacin 9.38 ± 2.13 8.70 ± 1.82 0.041
    Ofloxacin 12.17 ± 2.33 12.40 ± 3.53 0.682
    Klebsiella sp.
    Nalidixic Acid 3.33 ± 0.74 3.41 ± 0.68 0.504
    Ciprofloxacin 5.29 ± 0.89 7.91 ± 1.39 0.000
    Pefloxacin 9.52 ± 2.02 8.43 ± 1.64 0.002
    Ofloxacin 12.31 ± 3.31 12.91 ± 3.22 0.309
    Aeromonas sp.
    Nalidixic Acid 3.20 ± 0.66 3.31 ± 0.63 0.436
    Ciprofloxacin 8.31 ± 1.58 5.27 ± 0.94 0.000
    Pefloxacin 10.03 ± 2.22 8.11 ± 1.61 0.000
    Ofloxacin 13.36 ± 2.77 13.31 ± 3.08 0.928

     | Show Table
    DownLoad: CSV

    Resistance to CPX and PEF were significantly different for human and animal isolates while resistance to NA and OFL were not, for Klebsiella isolated from human and animal sources (Table 5). Resistance by Aeromonas from human and animal sources to CPX and PEF, were significantly different but resistance to NA and OFL were not, for isolates from human and animal sources (Table 5).

    Generally, the CPX and PEF resistance pattern exhibited by isolates from human and animal sources tended to be always similar except for E.coli. Furthermore, resistance of human and animal isolates to OFL were always not significantly different except for Salmonella isolates from human and animal sources that were significantly different with human isolates exhibiting significantly higher resistance.

    The proportion of plasmid mediated quinolone resistance among all isolates of E.coli is presented in Table 6. Isolates from animal sources had a higher occurrence of plasmid mediated resistance than isolates from human sources. Furthermore, the proportion of PMQR E.coli among FQ resistant E.coli equally showed animal isolates having a higher proportion in comparison with isolates from human sources (Table 6). This observation was generally similar among all enteric bacteria isolates of Salmonella spp. (Table 7), Shigella spp. (Table 8), Klebsiella spp. (Table 9) and Aeromonas spp. (Table 10). Generally, all bacterial isolates from animal sources showed higher proportion of plasmid-mediated quinolone resistance than isolates from human sources.

    Table 6.  Proportion of Plasmid Mediated Quinolone Resistance in all E.coli isolates from human and animal sources and Proportion of Plasmid Mediated Quinolone Resistance in Quinolone resistant E.coli isolates from human and animal sources.
    Sources Total number of isolates/FQr Number of PMQR isolates PMQR Proportion (%)
    Per total isolates/Per FQr
    Animal Poultry Droppings 165/101 5 3.03 4.95
    Fish Pond Water 159/61 2 1.26 3.28
    TOTAL 324/162 7 2.16 4.32
    Human Public Hospital 168/123 1 0.60 0.81
    Private Hospital 177/67 1 0.56 1.49
    TOTAL 345/190 2 0.58 1.05

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    Table 7.  Proportion of Plasmid Mediated Quinolone Resistance in all Salmonella isolates from human and animal sources and Proportion of Plasmid Mediated Quinolone Resistance in Quinolone resistant Salmonella isolates from human and animal sources.
    Sources Total number of isolates/FQr Number of PMQR isolates PMQR Proportion (%)
    Per total isolates/Per FQr
    Animal Poultry Droppings 177/65 4 2.26 6.15
    Fish Pond Water 98/36 3 3.06 8.33
    TOTAL 275/101 7 2.55 6.93
    Human Public Hospital 126/87 0 0.00 0.00
    Private Hospital 87/43 0 0.00 0.00
    TOTAL 213/130 0 0.00 0.00

     | Show Table
    DownLoad: CSV
    Table 8.  Proportion of Plasmid Mediated Quinolone Resistance in all Shigella isolates from human and animal sources and Proportion of Plasmid Mediated Quinolone Resistance in Quinolone resistant Shigella isolates from human and animal sources.
    Sources Total number of isolates/FQr Number of PMQR isolates PMQR Proportion (%)
    Per total isolates / Per FQr
    Animal Poultry Droppings 46/29 1 2.17 3.45
    Fish Pond Water 68/18 2 2.94 11.1
    TOTAL 114/47 3 2.63 6.38
    Human Public Hospital 131/77 0 0.00 0.00
    Private Hospital 76/38 0 0.00 0.00
    TOTAL 207/115 0 0.00 0.00

     | Show Table
    DownLoad: CSV
    Table 9.  Proportion of Plasmid Mediated Quinolone Resistance in all Klebsiella isolates from human and animal sources. and Proportion of Plasmid Mediated Quinolone Resistance in Quinolone resistant Klebsiella isolates from human and animal sources.
    Sources Total number of isolates/FQr Number of PMQR isolates PMQR Proportion (%)
    Per total isolates/Per FQr
    Animal Poultry Droppings 61/48 4 6.56 8.33
    Fish Pond Water 69/47 1 1.45 2.13
    TOTAL 130/95 5 3.85 5.26
    Human Public Hospital 44/28 1 2.27 3.57
    Private Hospital 27/19 1 3.70 5.26
    TOTAL 71/47 2 2.82 4.26

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    Table 10.  Proportion of Plasmid Mediated Quinolone Resistance in all Aeromonas sp. isolates from human and animal sources. and Proportion of Plasmid Mediated Quinolone Resistance in Quinolone resistant Aeromonas isolates from human and animal sources.
    Sources Total number of isolates/FQr Number of PMQR isolates PMQR Proportion (%)
    Per total isolates/Per FQr
    Animal Poultry Droppings 56/31 2 3.57 6.45
    Fish Pond Water 169/68 1 0.59 1.47
    TOTAL 225/99 3 1.33 3.03
    Human Public Hospital 31/21 0 0.00 0.00
    Private Hospital 29/11 0 0.00 0.00
    TOTAL 60/32 0 0.00 0.00

     | Show Table
    DownLoad: CSV

    With the exception of Salmonella isolates from animal sources, which had the highest proportion of plasmid mediated quinolone resistance among FQ resistant isolates (Table 7), Klebsiella spp. generally had the highest occurrences of plasmid mediated quinolone resistance from human sources (Table 9). Generally, there were no plasmid resistant isolates of Salmonella (Table 5), Shigella (Table 8) and Aeromonas spp (Tables 10) from human sources.

    While the aac(6′)-Ib-cr gene was not detected in the present study, the qnrS gene had the highest occurrence, both among the FQ-resistant and PMQR (Table 11, Figures 2 and 3) isolates. The only qepA gene detected was among isolates of Salmonella spp., and it was obtained from poultry sample.

    Table 11.  PCR detection and proportion of the plasmid-mediated quinolone resistance genes in fluoroquinolone resistant isolates and PCR detection and proportion of the plasmid-mediated quinolone resistance genes in plasmid-mediated fluoroquinolone resistant isolates.
    Quinolone resistant/PMQR isolates Number of isolates with:
    qnrA QnrB qnrS qepA aac(6′)-Ib-cr
    Aeromonas sp. (n = 131/3) 2 2 0 0 0
    E.coli (n = 352/9) 4 3 3 0 0
    Klebsiella sp. (n = 142/7) 2 3 0 0 0
    Salmonella sp. (n = 231/7) 2 5 3 1 0
    Shigella sp. (n = 162/3) 1 2 1 0 0
    Total (N = 1018/29) 11 15 7 1 0

     | Show Table
    DownLoad: CSV
    Figure 2.  Plasmid bands and their approximate molecular weights of Plasmid-Mediated Resistant Isolates.
    Figure 3.  Plasmid bands and their approximate molecular weights of Plasmid-Mediated Resistant Isolates. Qnr A: Lanes 1, 4, 6, 7, 10, 11, 12, 13, 14, 15, 17; Qnr B: Lanes 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19; Qnr S: Lanes 2, 4, 5, 7, 8, 18, 19; Qep A: Lane 9.

    The results of the study revealed that E. coli was the dominant species among the enteric organisms isolated from both animal and human sources. This observation agrees with previous studies and is quite significant especially for fish pond water samples. The presence of E.coli is indicative of fecal pollution [27], due to the fact that the gastro-intestinal tract is the natural habitat of the organism. Hence, this suggests a possible contamination of the fish pond with human or animal excreta. This was the case with a good number of these fish ponds surveyed, that were integrated with poultry. It is of public health concern because, if products of aquaculture from such ponds are not properly boiled before consumption, could pose health risk to the consumer [28].

    In poultry litter, the greatest prevalence was however Salmonella sp. This again, could cross-contaminate fish pond water when used in pond fertilization and even egg shells, resulting in infection when proper sanitary conditions are not observed. From previous researches, Salmonella is more frequently isolated from chicken litter or fecal samples as compared to other pathogens investigated [29],[30].

    The observation in this study was that for all isolates, the percentage resistance was higher among isolates from public hospital than private hospitals. Individuals visiting public hospitals tend to be those of the low income category, who equally engage in self-medication more than the higher income category. Self-medication is one major form of antibiotic misuse which is a cause of resistance emergence. Also, the percentage FQ resistance was higher among isolates from poultry litter, than isolates from fish pond water. From market surveys conducted during this study, antibiotics sold and used in aquaculture, is largely tetracycline but those sold and used in poultry, were quite varied and more numerous. The FQs are well represented in almost every additive administered to poultry. This indiscriminate prophylactic use of the FQs in poultry may be responsible for the high resistance among isolates from poultry. This observation is an emerging concern [27].

    A comparison of resistance trend among isolates from human and from animals, were also conducted. Percentage FQ resistance tends to be higher among human isolates than animal isolates. This came as a surprise, because of the initial observation that misuse and abuse is higher in animal husbandry. This research has however proved otherwise. Individuals still indulge in antibiotic misuse and abuse, more than the prophylactic use of antibiotics in animal farming. There is paucity of information on such comparative study. With increasing antibiotic abuse, and non-adherence to prescription regimen, we tend to perpetually place the bacteria steps ahead of man in the resistance development tussle.

    A further comparison of the resistance patterns to the various test FQs was undertaken. Resistance to all FQs was significantly different. A similar resistance pattern was however recorded for Ciprofloxacin and Pefloxacin. These drugs are commonly marketed with the trade names Ciprotab/ Ciprocin and Peflotab respectively. They are quite affordable and accessible and as such highly prone to abuse. On the other hand, Ofloxacin, with trade name Tarivid, is costlier less affordable and accessible hence its limited abuse. This may explain the greater susceptibility of the organisms to Ofloxacin when compared to other FQs. Previous studies has focused more on Ciprofloxacin [31],[32],[33], as this seems to be the most common of all FQs.

    The involvement of plasmids has further compounded the widespread occurrence of drug resistance [34]. The FQs have suffered same fate, as reports from other nations confirm emergence of Plasmid Mediated Quinolone Resistance (PMQR) among bacteria [24],[34]. In the present study, PMQR was equally recorded among isolates from the Nigerian Niger-delta. Among the FQs studied, resistance to Ofloxacin by isolates was largely chromosome mediated. The PMQR phenomenon was more observed with Ciprofloxacin and Pefloxacin, with resistance being lost sequel to plasmid curing in line with previous studies [35],[36].

    In line with the objective of this study, comparison of PMQR among isolates from animal and human sources was determined. As observed earlier, FQ resistance was more among human isolates than animal isolates. In the case of plasmid involvement with FQ resistance however, there was a reversal in the trend as PMQR was higher among animal isolates than human isolates as observed in a previous study [37]. The possible explanation for this observation lies in the mode of spread and dissemination of resistance genes. While chromosomally mediated resistance is disseminated basically vertically from parents to offspring, plasmid-mediated resistance is disseminated both vertically and horizontally from one bacterium to another, by conjugation, transformation and transformation. These mechanisms tend to be enhanced by the microbial pool prevalent in animal settings.

    Implication of this observation is that, FQ resistance will spread faster among humans if organisms harboring FQ resistance plasmids are transferred by zoonotic cross-contamination, to man. The relative ease with which antibiotic resistance borne on plasmids are spread, [24],[34] indicates that FQ resistance will spread rapidly within the animal pool and eventually cross migrate to man. Humans are hence at a risk of acquiring FQ resistance either directly, or indirectly even in the absence of antibiotic abuse.

    In this study, the aac(6′)-Ib-cr determinant was not detected at all. This is in contrast with what has been observed in other studies; the prevalence of aac(6`)-Ib-cr was 11.3% (62/549) among ciprofloxacin-and/or tobramycin-resistant E. coli and Klebsiella spp. clinical isolates from Canada [38] and 9.9% (36/365) among Extended Spectrum Beta-lactamase (ESBL)-producing E. coli and K. pnumoniae isolates from six provinces in China [39] while it was 51% among ciprofloxacin-resistant clinical isolates of E. coli isolated from Shanghai, China [34]. This gene, a variant of the aminoglycoside acetyltransferase, is responsible for modification of the FQ and its subsequent inactivation [33],[40]. Usually, it confers resistance on Ciprofloxacin only, since only Ciprofloxacin among the other FQs are the only compounds with an un-substituted piperazinyl group [40]. This may account for the observation in this study that resistance to Ciprofloxacin alone did not occur. All FQ resistance was jointly associated with at-least two of the quinolones under study.

    On the other hand, the occurrence of the quinolone efflux pump protein qepA was very low in this study. Experimentally, the qepA protein does not alter the MICs of ampicillin, erythromycin, kanamycin or tetracycline, but it does decrease susceptibility to norfloxacin, enrofloxacin and ciprofloxacin by up to 64-fold [41]. This may be attributed to the fact that the qep determinant is only emerging when compared to other determinants. Indeed previous studies also showed a low prevalence of this gene where it occurs [26],[42],[43]. There is mounting information about the epidemiology of the newly discovered PMQR pump qepA. In the present study, only one (0.10%) qepA was identified among the FQ-resistant population and, that was amongst Salmonella sp. A survey conducted in Japan found qepA in 2 (0.3%) of 751 E. coli isolates collected from 140 hospitals between 2002 and 2006 [26]. A second large survey was done by PCR in France. A single E. coli isolate among 121 (0.8%) ESBL-positive Enterobacteriaceae strains isolated in 2007 was positive for a variant named qepA2 [44]. In a study of pig farms in China, qepA was found in 28 of 48 (58.3%) aminoglycoside resistant methyl transferase, rmtB-positive E.coli isolates [45]. A follow-up study from the same region in China tested for qepA among ceftaxidime-resistant isolates of Enterobacteriaceae. qepA was found in 16 of 101 (15.8%) isolates, including, for the first time, K. pneumoniae and E. cloacae [46]. Few recently published studies indicated a broad distribution of the gene. A survey of 461 isolates of Enterobacteriacea in South Korea found qepA in one isolate from 2005 [47]. qepA has also been found in the United Kingdom. Two additional studies screened isolates from Seoul, South Korea, for qepA. Four clonally unrelated strains of 621 (0.6%) E. coli bloodstream isolates were found to be positive in one study [48], and two E. aerogenes isolates of 223 (0.9%) E. cloacae, E. aerogenes, C. freundii, and Serratia marcescens isolates with reduced susceptibility to quinolones were qepA positive in the second survey [49]. qepA was not found in a large survey of non-Typhi Salmonella enterica isolates collected in the United States from 1996 to 2006 [50].

    Although the findings indicated a low prevalence, the previous studies cited showed that this determinant was found transferable in most cases. This suggests that its presence in the Niger-delta could quickly be amplified due to vertical and horizontal dissemination.

    This study also revealed the low occurrence of the qnr determinants. A similar low occurrence of qnr was also reported in Denmark where only 1.63% (2/122) of nalidixic acid-resistant E. coli isolates was qnr-positive [51]. Low prevalence of qnr genes has also been reported from France and Canada. In France, the prevalence of qnr genes was 1.6% (2/125) among ESBL producing E. coli and Klebsiella spp. isolates [44],[52] while in Canada only about 1% (5/550) of ciprofloxacin and/or tobramycin resistant E. coli and Klebsiella spp. isolates were qnr-positive [38]. Nevertheless, higher prevalence has also been detected in other parts of the world such as Spain (5%) [53], China (8%) [39] and the United States (15%) [24]. Furthermore, investigations in China showed that qnr, aac(6′)-Ib-cr, qepA, and oqxAB genes were detected in 5.7%, 4.9%, 2.6%, and 20.2% of 1,022 FQ-resistant Escherichia coli isolates from humans, animals, and the environment, respectively.

    However, these comparisons should be taken with caution since different criteria for the selection of the bacterial isolates were used in these studies. In the present study, the criteria used for selecting these strains included resistance to nalidixic acid, as well as resistance or reduced susceptibility to any, or all of the FQs. These criteria were chosen since qnrA1, the first PMQR determinant to be discovered, increased the MIC of nalidixic acid to clinically resistant levels [54] and because a ciprofloxacin MIC of ≥ 0.125 µg/mL is the minimum expected for Enterobacteriaceae containing a qnr gene [34]. However, it is noteworthy that several qnr-positive isolates have increasingly been detected in nalidixic acid-susceptible isolates [51],[55],[56] and so the true prevalence of qnr determinants could be underestimated in this study.

    The prevalence of the qnr genes were more in E.coli and Salmonella sp. The lower prevalence of qnr genes in Klebsiella spp. isolates than in E. coli isolates is unlike what was observed in other studies conducted in France [52], USA [34], Spain [53], and China [39]. In France, for example, qnr was detected in 0.63% (3/472) and 7% (5/70) among the E. coli and Klebsiella spp. isolates, respectively. qnrS and qnrB were detected in six and two isolates, respectively, while qnrA was not found in any of the isolates.

    The dominance of qnrB as the present investigation showed is similar to other studies from Europe [47],[52]. In contrast, qnrA gene was dominant in a selected collection of blood culture isolates of Enterobacteriaceae resistant to both ciprofloxacin and cefotaxime in UK [57]. Furthermore, qnrA1 was the most prevalent qnr gene in a Spanish study [53] where qnrA1 was detected in 14 of 305 ESBL-producing enterobacterial isolates whereas only one qnrS and no qnrB were detected. The implication of results obtained in this study is that, the global phenomenon of PMQR epidemic is becoming a local reality in Nigeria.

    Nigeria is not left out in the global, ravaging emergence of plasmid-mediated fluoroquinolone resistance (PMQR) that precipitates cases of treatment failure. This menace is further compounded by the fact that such resistance has a potential of rapid spread among the bacterial pool. The study revealed a high level of FQ resistance, which was quite significant in samples from human and animal sources. However, a markedly higher Plasmid-mediated FQ resistance was observed among isolates from animal sources than from human sources. Furthermore, these cases of plasmid-mediated resistance were higher among poultry litter isolates than fish pond water isolates. In this study, the quinolone resistance determinants- qnrA, qnrB and qnrS, were more implicated in conferring the plasmid-mediated FQ resistance and they were found on plasmids.



    Conflict of interest



    All authors declare no conflicts of interest.

    [1] Wise J (2020) Life as a neurosurgeon. BMJ 368: m395. doi: 10.1136/bmj.m395
    [2] Kaptigau WM, Rosenfeld JV, Kevau I, et al. (2016) The establishment and development of neurosurgery services in Papua New Guinea. World J Surg 40: 251-257. doi: 10.1007/s00268-015-3268-1
    [3] Rolston JD, Zygourakis CC, Han SJ, et al. (2014) Medical errors in neurosurgery. Surg Neurol Int 5: S435-S440. doi: 10.4103/2152-7806.142777
    [4] Kwoh YS, Hou J, Jonckheere EA, et al. (1988) A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans Biomed Eng 35: 153-160. doi: 10.1109/10.1354
    [5] Aziz T, Roy H (2021) Deep Brain Stimulation. Oxford Research Encyclopedia of Psychology Oxford University Press.
    [6] Panesar SS, Kliot M, Parrish R, et al. (2020) Promises and perils of artificial intelligence in neurosurgery. Neurosurgery 87: 33-44. doi: 10.1093/neuros/nyz471
    [7] Bohl MA, Oppenlander ME, Spetzler R (2016) A prospective cohort evaluation of a robotic, auto-navigating operating microscope. Cureus 8: e662-e662.
    [8] Lanfranco AR, Castellanos AE, Desai JP, et al. (2004) Robotic surgery: a current perspective. Ann Surg 239: 14-21. doi: 10.1097/01.sla.0000103020.19595.7d
    [9] Shimizu S, Kuroda H, Mochizuki T, et al. (2020) Ergonomics-based positioning of the operating handle of surgical microscopes. Neurol Med-Chir 60: 313-316. doi: 10.2176/nmc.rc.2020-0018
    [10] Van Bavel J (2013) The world population explosion: causes, backgrounds and pro-jections for the future. Facts Views Vision Obgyn 5: 281-291.
    [11] Vaupel JW (2010) Biodemography of human ageing. Nature 464: 536-542. doi: 10.1038/nature08984
    [12] You D, Hug L, Ejdemyr S, et al. (2015) Global, regional, and national levels and trends in under-5 mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Inter-agency Group for Child Mortality Estimation. Lancet 386: 2275-2286. doi: 10.1016/S0140-6736(15)00120-8
    [13] Aluttis C, Bishaw T, Frank MW (2014) The workforce for health in a globalized context-global shortages and international migration. Global Health Action 7: 23611-23611. doi: 10.3402/gha.v7.23611
    [14] Senders JT, Arnaout O, Karhade AV, et al. (2018) Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery 83: 181-192. doi: 10.1093/neuros/nyx384
    [15] Sullivan R, Alatise OI, Anderson BO, et al. (2015) Global cancer surgery: delivering safe, affordable, and timely cancer surgery. Lancet Oncol 16: 1193-1224. doi: 10.1016/S1470-2045(15)00223-5
    [16] Michael CD, Abbas R, Graham F, et al. (2019) Global neurosurgery: the current capacity and deficit in the provision of essential neurosurgical care. Executive summary of the global neurosurgery initiative at the program in global surgery and social change. J Neurosurg 130: 1055-1064. doi: 10.3171/2017.11.JNS171500
    [17] Swagoto M, Maria P, Abbas R, et al. (2019) The global neurosurgical workforce: a mixed-methods assessment of density and growth. J Neurosurg 130: 1142-1148. doi: 10.3171/2018.10.JNS171723
    [18] Kato Y, Liew BS, Sufianov AA, et al. (2020) Review of global neurosurgery education: horizon of neurosurgery in the developing countries. Chin Neurosurg J 6: 19. doi: 10.1186/s41016-020-00194-1
    [19] Solomou G, Murphy S, Bandyopadhyay S, et al. (2020) Neurosurgery specialty training in the UK: What you need to know to be shortlisted for an interview. Ann Med Surg 57: 287-290. doi: 10.1016/j.amsu.2020.07.047
    [20] Mooney MA, Yoon S, Cole T, et al. (2019) Cost transparency in neurosurgery: a single-institution analysis of patient out-of-pocket spending in 13673 consecutive neurosurgery cases. Neurosurgery 84: 1280-1289. doi: 10.1093/neuros/nyy185
    [21] Yoon JS, Tang OY, Lawton MT (2019) Volume–cost relationship in neurosurgery: analysis of 12,129,029 admissions from the national inpatient sample. World Neurosurg 129: e791-e802. doi: 10.1016/j.wneu.2019.06.034
    [22] Obermeyer Z, Emanuel EJ (2016) Predicting the future–Big data, Machine learning, and Clinical medicine. N Engl J Med 375: 1216-1219. doi: 10.1056/NEJMp1606181
    [23] Cruz JA, Wishart DS (2007) Applications of machine learning in cancer prediction and prognosis. Cancer Inform 2: 59-77.
    [24] Marcus HJ, Williams S, Hughes-Hallett A, et al. (2017) Predicting surgical outcome in patients with glioblastoma multiforme using pre-operative magnetic resonance imaging: development and preliminary validation of a grading system. Neurosurg Rev 40: 621-631. doi: 10.1007/s10143-017-0817-0
    [25] Rudie JD, Rauschecker AM, Bryan RN, et al. (2019) Emerging applications of artificial intelligence in neuro-oncology. Radiology 290: 607-618. doi: 10.1148/radiol.2018181928
    [26] Deo RC (2015) Machine learning in medicine. Circulation 132: 1920-1930. doi: 10.1161/CIRCULATIONAHA.115.001593
    [27] Lane T (2018) A short history of robotic surgery. Ann R Coll Surge Engl 100: 5-7. doi: 10.1308/rcsann.supp1.5
    [28] Sheetz KH, Claflin J, Dimick JB (2020) Trends in the adoption of robotic surgery for common surgical procedures. JAMA Network Open 3: e1918911-e1918911. doi: 10.1001/jamanetworkopen.2019.18911
    [29] Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25: 44-56. doi: 10.1038/s41591-018-0300-7
    [30] Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349: 255. doi: 10.1126/science.aaa8415
    [31] Senders JT, Staples PC, Karhade AV, et al. (2018) Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg 109: 476-486. doi: 10.1016/j.wneu.2017.09.149
    [32] LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521: 436-444. doi: 10.1038/nature14539
    [33] Alvin MD, Lubelski D, Alam R, et al. (2018) Spine surgeon treatment variability: the impact on costs. Global Spine J 8: 498-506. doi: 10.1177/2192568217739610
    [34] Daniels AH, Ames CP, Smith JS, et al. (2014) Variability in spine surgery procedures performed during orthopaedic and neurological surgery residency training: an analysis of ACGME case log data. J Bone Joint Surg Am 96: e196. doi: 10.2106/JBJS.M.01562
    [35] Deyo RA, Mirza SK (2006) Trends and variations in the use of spine surgery. Clin Orthop Relat Res 443: 139-146. doi: 10.1097/01.blo.0000198726.62514.75
    [36] Mroz TE, Lubelski D, Williams SK, et al. (2014) Differences in the surgical treatment of recurrent lumbar disc herniation among spine surgeons in the United States. Spine J 14: 2334-2343. doi: 10.1016/j.spinee.2014.01.037
    [37] Rasouli JJ, Shao J, Neifert S, et al. (2020) Artificial intelligence and robotics in spine surgery. Global Spine J 11: 556-564. doi: 10.1177/2192568220915718
    [38] Arvind V, Kim JS, Oermann EK, et al. (2018) Predicting surgical complications in adult patients undergoing anterior cervical discectomy and fusion using nachine learning. Neurospine 15: 329-337. doi: 10.14245/ns.1836248.124
    [39] Galbusera F, Casaroli G, Bassani T (2019) Artificial intelligence and machine learning in spine research. JOR Spine 2: e1044. doi: 10.1002/jsp2.1044
    [40] Kim JS, Merrill RK, Arvind V, et al. (2018) Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine 43: 853-860. doi: 10.1097/BRS.0000000000002442
    [41] Emblem KE, Nedregaard B, Hald JK, et al. (2009) Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering. J Magne Reson Imaging 30: 1-10. doi: 10.1002/jmri.21815
    [42] Emblem KE, Pinho MC, Zöllner FG, et al. (2014) A generic support vector machine model for preoperative glioma survival associations. Radiology 275: 228-234. doi: 10.1148/radiol.14140770
    [43] Brady AP (2017) Error and discrepancy in radiology: inevitable or avoidable? Insights Imaging 8: 171-182. doi: 10.1007/s13244-016-0534-1
    [44] Siarkowski M, Lin K, Li SS, et al. (2020) Meta-analysis of interventions to reduce door to needle times in acute ischaemic stroke patients. BMJ Open Qual 9: e000915. doi: 10.1136/bmjoq-2020-000915
    [45] Mun SK, Wong KH, Lo S-CB, et al. (2021) Artificial Intelligence for the Future Radiology Diagnostic Service. Front Mol Biosci 7: 614258. doi: 10.3389/fmolb.2020.614258
    [46] Furlan Anthony J (2006) Time is brain. Stroke 37: 2863-2864. doi: 10.1161/01.STR.0000251852.07152.63
    [47] Fonarow Gregg C, Smith Eric E, Saver Jeffrey L, et al. (2011) Improving door-to-needle times in acute ischemic stroke. Stroke 42: 2983-2989. doi: 10.1161/STROKEAHA.111.621342
    [48] Man S, Xian Y, Holmes DN, et al. (2020) Association between thrombolytic door-to-needle time and 1-year mortality and readmission in patients with acute ischemic stroke. JAMA 323: 2170-2184. doi: 10.1001/jama.2020.5697
    [49] Nagaratnam K, Harston G, Flossmann E, et al. (2020) Innovative use of artificial intelligence and digital communication in acute stroke pathway in response to COVID-19. Future Healthcare J 7: 169-173. doi: 10.7861/fhj.2020-0034
    [50] Yamashita K, Yoshiura T, Arimura H, et al. (2008) Performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images. Am J Neuroradiol 29: 1153-1158. doi: 10.3174/ajnr.A1037
    [51] Kassahun Y, Perrone R, De Momi E, et al. (2014) Automatic classification of epilepsy types using ontology-based and genetics-based machine learning. Artif Intell Med 61: 79-88. doi: 10.1016/j.artmed.2014.03.001
    [52] Bidiwala S, Pittman T (2004) Neural network classification of pediatric posterior fossa tumors using clinical and imaging data. Pediatr Neurosurg 40: 8-15. doi: 10.1159/000076571
    [53] Zhang B, Chang K, Ramkissoon S, et al. (2017) Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol 19: 109-117. doi: 10.1093/neuonc/now121
    [54] Titano JJ, Badgeley M, Schefflein J, et al. (2018) Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med 24: 1337-1341. doi: 10.1038/s41591-018-0147-y
    [55] Ueda D, Yamamoto A, Nishimori M, et al. (2018) Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms. Radiology 290: 187-194. doi: 10.1148/radiol.2018180901
    [56] Dolz J, Betrouni N, Quidet M, et al. (2016) Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study. Comput Med Imag Grap 52: 8-18. doi: 10.1016/j.compmedimag.2016.03.003
    [57] Cohen KB, Glass B, Greiner HM, et al. (2016) Methodological issues in predicting pediatric epilepsy surgery candidates through natural language processing and machine learning. Biomed Inform Insights 8: 11-18. doi: 10.4137/BII.S38308
    [58] Dumont TM, Rughani AI, Tranmer BI (2011) Prediction of symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network: feasibility and comparison with logistic regression models. World Neurosurgery 75: 57-63. doi: 10.1016/j.wneu.2010.07.007
    [59] Nielsen A, Hansen Mikkel B, Tietze A, et al. (2018) Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke 49: 1394-1401. doi: 10.1161/STROKEAHA.117.019740
    [60] Lüders H, Acharya J, Baumgartner C, et al. (1998) Semiological seizure classification. Epilepsia 39: 1006-1013. doi: 10.1111/j.1528-1157.1998.tb01452.x
    [61] Emblem KE, Nedregaard B, Nome T, et al. (2008) Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 247: 808-817. doi: 10.1148/radiol.2473070571
    [62] Lev MH, Ozsunar Y, Henson JW, et al. (2004) Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendroglimoas. Am J Neuroradiol 25: 214-221.
    [63] Marcus AP, Marcus HJ, Camp SJ, et al. (2020) Improved prediction of surgical resectability in patients with glioblastoma using an artificial neural network. Sci Rep 10: 5143. doi: 10.1038/s41598-020-62160-2
    [64] Young R, Babb J, Law M, et al. (2007) Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas. J Magn Reson Imaging 26: 1053-1063. doi: 10.1002/jmri.21064
    [65] Duffau H, Capelle L (2004) Preferential brain locations of low-grade gliomas. Cancer 100: 2622-2626. doi: 10.1002/cncr.20297
    [66] Clarke LP, Velthuizen RP, Clark M, et al. (1998) MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation. Magn Reson Imaging 16: 271-279. doi: 10.1016/S0730-725X(97)00302-0
    [67] Pfirrmann CWA, Metzdorf A, Zanetti M, et al. (2001) Magnetic resonance classification of lumbar intervertebral disc degeneration. Spine 26: 1873-1878. doi: 10.1097/00007632-200109010-00011
    [68] Toyoda H, Takahashi S, Hoshino M, et al. (2017) Characterizing the course of back pain after osteoporotic vertebral fracture: a hierarchical cluster analysis of a prospective cohort study. Arch Osteoporos 12: 82. doi: 10.1007/s11657-017-0377-5
    [69] Jeffrey EA, Craig M, Zhiyue JW, et al. (1997) Prediction of posterior fossa tumor type in children by means of magnetic resonance image properties, spectroscopy, and neural networks. J Neurosurg 86: 755-761. doi: 10.3171/jns.1997.86.5.0755
    [70] Kitajima M, Hirai T, Katsuragawa S, et al. (2009) Differentiation of common large sellar-suprasellar masses: effect of artificial neural network on radiologists' diagnosis performance. Acad Radiol 16: 313-320. doi: 10.1016/j.acra.2008.09.015
    [71] Christy PS, Tervonen O, Scheithauer BW, et al. (1995) Use of a neural network and a multiple regression model to predict histologic grade of astrocytoma from MRI appearances. Neuroradiology 37: 89-93. doi: 10.1007/BF00588619
    [72] Juntu J, Sijbers J, De Backer S, et al. (2010) Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images. J Magn Reson Imaging 31: 680-689. doi: 10.1002/jmri.22095
    [73] Zhao Z-X, Lan K, Xiao JH, et al. (2010) A new method to classify pathologic grades of astrocytomas based on magnetic resonance imaging appearances. Neurology India 58: 685-690. doi: 10.4103/0028-3886.72161
    [74] Sinha M, Kennedy CS, Ramundo, et al. (2001) Artificial neural network predicts CT scan abnormalities in pediatric patients with closed head injury. J Trauma 50: 308-312. doi: 10.1097/00005373-200102000-00018
    [75] Chiang S, Levin HS, Haneef Z (2015) Computer-automated focus lateralization of temporal lobe epilepsy using fMRI. J Magn Reson Imaging 41: 1689-1694. doi: 10.1002/jmri.24696
    [76] Berg AT, Vickrey BG, Langfitt JT, et al. (2003) The multicenter study of epilepsy surgery: recruitment and selection for surgery. Epilepsia 44: 1425-1433. doi: 10.1046/j.1528-1157.2003.24203.x
    [77] Tankus A, Yeshurun Y, Fried I (2009) An automatic measure for classifying clusters of suspected spikes into single cells versus multiunits. J Neural Eng 6: 056001. doi: 10.1088/1741-2560/6/5/056001
    [78] Anand IR, Travis MD, Zhenyu L, et al. (2010) Use of an artificial neural network to predict head injury outcome. J Neurosurg 113: 585-590. doi: 10.3171/2009.11.JNS09857
    [79] Chang K, Bai HX, Zhou H, et al. (2018) Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR Imaging. Clin Cancer Res 24: 1073-1081. doi: 10.1158/1078-0432.CCR-17-2236
    [80] Yu J, Shi Z, Lian Y, et al. (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 27: 3509-3522. doi: 10.1007/s00330-016-4653-3
    [81] Hollon TC, Pandian B, Adapa AR, et al. (2020) Near real-time intraoperative brain tumor diagnosis using stimulated raman histology and deep neural networks. Nat Med 26: 52-58. doi: 10.1038/s41591-019-0715-9
    [82] Gal AA, Cagle PT (2005) The 100-year anniversary of the description of the frozen section procedure. JAMA 294: 3135-3137. doi: 10.1001/jama.294.24.3135
    [83] Novis D, Zarbo R (1997) Interinstitutional comparison of frozen section turnaround time. A college of American Pathologists Q-Probes study of 32868 frozen sections in 700 hospitals. Arch Pathol Lab Med 121: 559-567.
    [84] Mosa ASM, Yoo I, Sheets L (2012) A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 12: 67. doi: 10.1186/1472-6947-12-67
    [85] Bardram JE, Bossen C (2005) Mobility Work: The spatial dimension of collaboration at a hospital. CSCW 14: 131-160.
    [86] Hector EJ (2016) Pediatric neurosurgery telemedicine clinics: a model to provide care to geographically underserved areas of the United States and its territories. J Neurosurg Pediatr 18: 753-757. doi: 10.3171/2016.6.PEDS16202
    [87] Reider-Demer M, Raja P, Martin N, et al. (2018) Prospective and retrospective study of videoconference telemedicine follow-up after elective neurosurgery: results of a pilot program. Neurosurg Rev 41: 497-501. doi: 10.1007/s10143-017-0878-0
    [88] Susan RS (2018) Editorial. Telemedicine for elective neurosurgical routine follow-up care: a promising patient-centered and cost-effective alternative to in-person clinic visits. Neurosurg Focus 44: E18.
    [89] Semple JL, Armstrong KA (2017) Mobile applications for postoperative monitoring after discharge. CMAJ 189: E22-E24. doi: 10.1503/cmaj.160195
    [90] Layard Horsfall H, Palmisciano P, Khan DZ, et al. (2021) Attitudes of the surgical team toward artificial intelligence in neurosurgery: international 2-stage cross-sectional survey. World Neurosurg 146: e724-e730. doi: 10.1016/j.wneu.2020.10.171
    [91] Tsermoulas G, Zisakis A, Flint G, et al. (2020) Challenges to neurosurgery during the coronavirus disease 2019 (COVID-19) pandemic. World Neurosurg 139: 519-525. doi: 10.1016/j.wneu.2020.05.108
    [92] Zemmar A, Lozano AM, Nelson BJ (2020) The rise of robots in surgical environments during COVID-19. Nat Mach Intell 2: 566-572. doi: 10.1038/s42256-020-00238-2
    [93] Michael CD, Ronnie EB, Abbas R, et al. (2018) Pediatric neurosurgical workforce, access to care, equipment and training needs worldwide. Neurosurg Focus 45: E13.
    [94] Mofatteh M (2021) Risk factors associated with stress, anxiety, and depression among university undergraduate students. AIMS Public Health 8: 36-65. doi: 10.3934/publichealth.2021004
    [95] Stein SC (2018) Cost-effectiveness research in neurosurgery: we can and we must. Neurosurgery 83: 871-878. doi: 10.1093/neuros/nyx583
    [96] Sejnowski TJ (2020) The unreasonable effectiveness of deep learning in artificial intelligence. Proc Natl Acad Sci 117: 30033-30038. doi: 10.1073/pnas.1907373117
    [97] Bell C, Shenoy P, Chalodhorn R, et al. (2008) Control of a humanoid robot by a noninvasive brain-computer interface in humans. J Neural Eng 5: 214-220. doi: 10.1088/1741-2560/5/2/012
    [98] Zhang X, Ma Z, Zheng H, et al. (2020) The combination of brain-computer interfaces and artificial intelligence: applications and challenges. Ann Transl Med 8: 712. doi: 10.21037/atm.2019.11.109
    [99] National Spinal Cord Injury Statistical Center (2021)  Facts and Figures at a Glance Birmingham, AL: University of Alabama at Birmingham.
    [100] Li M, Cui Y, Hao D, et al. (2015) An adaptive feature extraction method in BCI-based rehabilitation. J Intell Fuzzy Syst 28: 525-535. doi: 10.3233/IFS-141329
    [101] Bouton CE, Shaikhouni A, Annetta NV, et al. (2016) Restoring cortical control of functional movement in a human with quadriplegia. Nature 533: 247-250. doi: 10.1038/nature17435
    [102] Flesher SN, Downey JE, Weiss JM, et al. (2021) A brain-computer interface that evokes tactile sensations improves robotic arm control. Science 372: 831. doi: 10.1126/science.abd0380
    [103] Bauer R, Gharabaghi A (2015) Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation. Front Neurosci 9: 36. doi: 10.3389/fnins.2015.00036
    [104] Palmisciano P, Jamjoom AAB, Taylor D, et al. (2020) Attitudes of patients and their relatives toward artificial intelligence in neurosurgery. World Neurosurg 138: e627-e633. doi: 10.1016/j.wneu.2020.03.029
    [105] Leite M, Leal A, Figueiredo P (2013) Transfer function between EEG and BOLD signals of epileptic activity. Front Neurology 4: 1. doi: 10.3389/fneur.2013.00001
    [106] Abdolmaleki P, Mihara F, Masuda K, et al. (1997) Neural networks analysis of astrocytic gliomas from MRI appearances. Cancer Lett 118: 69-78. doi: 10.1016/S0304-3835(97)00233-4
    [107] Chari A, Budhdeo S, Sparks R, et al. (2021) Brain-machine interfaces: The role of the neurosurgeon. World Neurosurg 146: 140-147. doi: 10.1016/j.wneu.2020.11.028
    [108] Bonaci T, Calo R, Chizeck HJ (2015) App stores for the brain: privacy and security in brain-computer interfaces. IEEE Technol Soc Mag 34: 32-39. doi: 10.1109/MTS.2015.2425551
    [109] Collins JW, Marcus HJ, Ghazi A, et al. (2021) Ethical implications of AI in robotic surgical training: A Delphi consensus statement. Eur Urol Focus .
    [110] Groiss SJ, Wojtecki L, Südmeyer M, et al. (2009) Deep brain stimulation in Parkinson's disease. Ther Adv Neurol Disord 2: 20-28. doi: 10.1177/1756285609339382
    [111] Mofatteh M (2020) mRNA localization and local translation in neurons. AIMS Neurosci 7: 299-310. doi: 10.3934/Neuroscience.2020016
    [112] Mofatteh M (2021) Neurodegeneration and axonal mRNA transportation. Am J Neurodegener Dis 10: 1-12.
    [113] Pinto dos Santos D, Giese D, Brodehl S, et al. (2019) Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol 29: 1640-1646. doi: 10.1007/s00330-018-5601-1
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