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Transforming lung cancer care: Synergizing artificial intelligence and clinical expertise for precision diagnosis and treatment

  • Lung cancer is a predominant cause of global cancer-related mortality, highlighting the urgent need for enhanced diagnostic and therapeutic modalities. With the integration of artificial intelligence (AI) into clinical practice, a new horizon in lung cancer care has emerged, characterized by precision in both diagnosis and treatment. This review delves into AI's transformative role in this domain. We elucidate AI's significant contributions to imaging, pathology, and genomic diagnostics, underscoring its potential to revolutionize early detection and accurate categorization of the disease. Shifting the focus to treatment, we spotlight AI's synergistic role in tailoring patient-centric therapies, predicting therapeutic outcomes, and propelling drug research and development. By harnessing the combined prowess of AI and clinical expertise, there's potential for a seismic shift in the lung cancer care paradigm, promising more precise, individualized interventions, and ultimately, improved survival rates for patients.

    Citation: Meiling Sun, Changlei Cui. Transforming lung cancer care: Synergizing artificial intelligence and clinical expertise for precision diagnosis and treatment[J]. AIMS Bioengineering, 2023, 10(3): 331-361. doi: 10.3934/bioeng.2023020

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  • Lung cancer is a predominant cause of global cancer-related mortality, highlighting the urgent need for enhanced diagnostic and therapeutic modalities. With the integration of artificial intelligence (AI) into clinical practice, a new horizon in lung cancer care has emerged, characterized by precision in both diagnosis and treatment. This review delves into AI's transformative role in this domain. We elucidate AI's significant contributions to imaging, pathology, and genomic diagnostics, underscoring its potential to revolutionize early detection and accurate categorization of the disease. Shifting the focus to treatment, we spotlight AI's synergistic role in tailoring patient-centric therapies, predicting therapeutic outcomes, and propelling drug research and development. By harnessing the combined prowess of AI and clinical expertise, there's potential for a seismic shift in the lung cancer care paradigm, promising more precise, individualized interventions, and ultimately, improved survival rates for patients.



    Endophytes are microorganisms that live inside plant cells, roots, stems, leaves, and tissues without causing any harmful effects on the plant. They are involved in plant growth and development processes by regulating plant metabolism [1][3]. Recent research provides strong evidence that these bacteria perform several beneficial functions for their host plants, such as promoting plant growth by aiding in the acquisition of nutrients (via nitrogen fixation, phosphate solubilization, or iron chelation), preventing pathogen infections through the production of antifungal or antibacterial metabolites, outcompeting pathogens for nutrients by producing siderophores, and enhancing systemic resistance in the plant [1],[4].

    Bacteria that colonize root systems produce plant growth regulators, including auxins, cytokinins, and gibberellins. They also mobilize unavailable minerals, such as phosphorus and other essential elements, and inhibit the synthesis of ethylene through the activity of 1-aminocyclopropane-1-carboxylate (ACC) deaminase [4][6].

    Glycyrrhiza, also known as licorice, is a legume with deep roots that can withstand salt, drought, and other environmental stresses. It is a popular plant for restoring salt-affected lands [7]. With roughly 20 species, this tall perennial shrub belongs to the leguminous Fabaceae (Leguminosae) family. It is primarily native to the Mediterranean region, Central Asia, and Southwestern Asia [8]. The chemical components of G. glabra include isoliquiritin, glycyrrhizin, glycyrrhetinic acid, and isoflavones. There have been reports of several pharmacological effects associated with licorice derivatives, including expectorant, anti-demulcent, anti-ulcer, anti-cancer, anti-inflammatory, and anti-diabetic properties [9][11]. Moreover, licorice is used as animal feed and in the phytoremediation of salt-affected soils. It is well adapted to salt-affected, arid lands and desert areas. Like many plants, licorice forms associations with various soil microorganisms, including nitrogen-fixing bacteria, which can enhance the plant's nutrient uptake [12]. In addition to nitrogen-fixing bacteria, other types of bacteria may also form associations with Glycyrrhiza glabra. These can include beneficial bacteria that promote plant growth, protect against pathogens, or assist in nutrient acquisition [13]. The specific bacterial communities associated with the plant can vary based on factors such as soil type, environmental conditions, and plant health.

    Licorice can be susceptible to various pathogens, including fungi, bacteria, and viruses. For example, Phytophthora spp. cause root rot, and Fusarium spp. can cause wilt diseases that affect the vascular system, leading to plant growth inhibition [14]. Using environmentally friendly technologies to produce licorice is a significant strategy to ensure organic products. Employing plant-beneficial microorganisms is seen as an eco-friendly and alternative method of enhancing the fitness of medicinal plants [15][18]. Endophytic bacteria that live inside plants, including their roots, leaves, and stems, can be highly beneficial. Some mechanisms linked to these beneficial effects include the production of phytohormones, cell wall–degrading enzymes, hydrogen cyanide (HCN), and ACC-deaminase [19][22]. Numerous reports have documented the biological activity and diversity of endophytic bacteria associated with medicinal plants, such as Ziziphora capitata [23], Aloe vera [24], and Origanum vulgare [25]. Endophytes that colonize plant tissues are believed to play a major role in synthesizing physiologically active compounds and protecting plants from soil-transmitted diseases [26],[27]. Farhoui and coauthors [28] reported that sugar beets treated with bacterial isolates Bacillus velezensis, Bacillus amyloliquefaciens, and Bacillus subtilis exhibited a significant reduction in diseases caused by Rhizoctonia solani under greenhouse conditions. Genomic DNA extracted from each bacterial isolate revealed the presence of biosynthesis genes for lipopeptides such as iturin, surfactin, fengycin, and bacillomycin, which are known to exhibit strong antimicrobial activities. While there has been considerable research on the biological activity and phytochemical composition of licorice, studies on endophytes associated with licorice and their beneficial traits are relatively scarce. To enhance our understanding of the role of endophytes in plant growth and development, it is crucial to explore plant-microbe interactions and their physiological effects. The objectives of this study were to (1) identify culturable endophytic bacteria associated with licorice, (2) assess the plant beneficial traits of these bacterial isolates, and (3) determine the impact of bacterial inoculants on the tolerance of licorice plants to salt stress.

    Licorice (Glycyrrhiza glabra L.) was collected in June 2019 from Karakalpakstan, Uzbekistan, an area affected by salinity. The electrical conductivity of the saline soil was 7.8 dS/m. Using sterile gloves, ten separate plants, 12–15 meters apart, were collected, placed in zip-lock plastic bags, and transported to the lab for further analysis.

    Plant roots and leaves were sterilized using NaClO (10%) and ethanol (70%), then rinsed in sterile water after 3 min. The roots and leaves (10 g) were ground with a sterile mortar and mixed with a phosphate buffer solution [29]. Bacteria were isolated from these mixtures using sterile phosphate-buffered saline and a nutrient-rich medium, tryptic soy agar (TSA) (BD, Difco Laboratories, USA), supplemented with 50 µg/mL of nystatin. After spreading 100 µL of the dilutions (10–105) over TSA plates, the plates were incubated in a thermostat at 28 °C for 96 h. The sterility of the roots and leaves was verified by placing them on TSA plates [30].

    Bacteria were identified using 16S rRNA gene analysis. DNA isolation was performed by heat-treating bacterial cells according to Dashti et al. [31]. The presence of the isolated DNA was confirmed using horizontal gel electrophoresis. A portion of the 16S rRNA genes was amplified via polymerase chain reaction (PCR) using the following primers: 27F 5′-GAGTTTGATCCTGGCTCAG-3′ and 1492R 5′-GAAAGGAGGTGATCCAGCC-3′ (both from Sigma-Aldrich, St. Louis, Missouri, USA) [32]. The amplified 16S rRNA gene fragments were examined for restriction fragment length polymorphism, and bacteria with the same genotype were eliminated according to Jinneman et al. [33]. The sequencing of PCR products was performed using the ABI PRISM BigDye 3.1 Terminator Cycle Sequencing Ready Reaction Kit (Applied Biosystems). The nucleotide sequences of the 16S rRNA gene were aligned using EMBOSS Explorer (http://emboss.bioinformatics.nl/) and Chromas (v.2.6.5) software. The sequences of the isolates' 16S rRNA genes were compared with those in GenBank (http://www.ncbi.nlm.nih.gov/) using the Basic Local Alignment Search Tool (BLAST) for identification. A FASTA file containing the 16S rRNA sequences of the isolates and related strains from GenBank, obtained after multiple alignments with Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/), was used for constructing a phylogenetic tree. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (500 replicates) is shown next to the branches. The tree is drawn to scale, with branch lengths in the same units as the evolutionary distances used to infer the phylogenetic tree. These evolutionary distances were computed using the maximum composite likelihood method [34] and are expressed in units of the number of base substitutions per site. This analysis involved 36 nucleotide sequences, with all ambiguous positions removed for each sequence pair (pairwise deletion option). The final dataset comprised 1,567 positions. Evolutionary analyses were conducted using MEGA X [35].

    The nucleotide sequences of the 16S rRNA gene were registered in GenBank and received the accession numbers OQ874308 to OQ874325.

    The capability of bacterial isolates to synthesize hydrogen cyanide (HCN) was investigated using TSA media. A sterilized filter paper saturated with a 1% solution of picric acid and 2% sodium carbonate was placed in the upper lid of a Petri dish. The Petri dish was sealed with parafilm and incubated at 28 °C for 3 days. A change in the paper color from yellow to dark blue indicated HCN production [36]. The method by Schwyn and Neilands [37] was used to determine the bacterial isolates' capacity to produce siderophores. Briefly, the bacterial isolates were plated on standard blue agar with chrome azurol sulfonate (CAS) and incubated at 28 °C. After 5 days, a pink-orange zone around the bacterial colonies indicated siderophore production. Protease activity was detected by plating bacterial isolates on TSA amended with 5% skimmed milk. After 4 days of incubation at 28 °C, the appearance of a halo around the colonies indicated the presence of extracellular protease [38].

    The synthesis of β-1,3 glucanase was determined using the method described by Walsh et al. [39]. Bacterial isolates were plated on nutrient agar amended with the glucan substrate lichenan, and after 4 days of incubation, a clear zone around the colonies indicated substrate degradation. Cellulase activity was detected using the substrate carboxymethylcellulose in top-agar plates, following the method by Malleswari and Bagyanarayana [40]. The lipase activity of bacterial isolates was assessed using the Tween lipase indicator test [41]. Briefly, bacterial isolates were grown on LC agar (LB agar containing 10 mM MgSO4 and 5 mM CaCl2) with 2% Tween 80 at 28 °C. After 5 days, the degradation of Tween was indicated by a clear halo around the bacterial inoculum. Using the technique outlined by Bano and Musarrat [42], the synthesis of IAA (indole-3-acetic acid) by endophytic isolates was investigated. The bacterial isolates were grown in LC medium with tryptophan (500 µg/mL) and incubated at 28 °C. One milliliter of supernatant was transferred to a fresh tube, to which 100 µL of 10 mM orthophosphoric acid and 2 mL of reagent (1 mL of 0.5 M FeCl3 in 50 mL of 35% HClO4) were added. After 25 min, the absorbance of the developed pink color was measured at 530 nm.

    ACC-deaminase synthesis was investigated using 1-aminocyclopropane-1-carboxylic acid (ACC) as the sole nitrogen source [43]. The ability of endophytic bacterial isolates to inhibit plant pathogenic fungi Fusarium oxysporum, Fusarium culmorum, and Rhizoctonia solani was evaluated following the method described by Egamberdieva et al. [44]. Fungal strains were grown on agar plates at 28 °C for 5 days. Disks (5 mm in diameter) of fresh fungal cultures were cut out and placed in the center of a 9 cm Petri dish. Bacteria (grown on peptone agar plates) were streaked perpendicular to the fungi on the test plates. The plates were incubated at 30 °C for 7 days until the fungi had grown over control plates without bacteria. Anti-fungal activity was recorded as the width of the zone of growth inhibition between the fungus and the test bacterium.

    G. glabra seeds were surface-sterilized by immersing them for 5 min in a 70% sulfuric acid solution, followed by five rinses with sterile distilled water, and then a 3 min immersion in 70% ethanol. The bacterial strains were grown overnight in nutrient medium. One milliliter of the overnight culture was centrifuged at 13,000 × g, and the supernatant was discarded. The cells were washed with 1 mL phosphate-buffered saline (PBS) and re-suspended in PBS. The cell suspension was adjusted to an OD620 of 0.1, corresponding to a cell density of approximately 108 cells/mL. Inoculation was performed by immersing the seeds in the bacterial suspension.

    Germination tests were conducted using Petri dishes (Ø 85 mm × 15 mm) filled with 1% water agar supplemented with 50 mM NaCl. Twenty surface-sterilized licorice seeds were placed on each Petri dish, with three replications. To prevent moisture evaporation, the Petri dishes were covered with a polyethylene sheet and maintained at 28 °C in a plant growth chamber. During the six-day period, seeds were monitored, and the percentage of germination was recorded. Seeds were considered to have germinated when radicles emerged and reached a length of more than 0.5 cm. Ten days after sowing, the lengths of the seedlings were measured and recorded.

    The effect of bacterial isolates on the growth of licorice seedlings exposed to 50 mM NaCl stress was examined using six replicates in gnotobiotic sand tubes (25 mm in diameter by 200 mm in length), as described by Simons et al. [45]. Sixty grams of a sterilized mixture of washed sand and vermiculite (1:1) were soaked in 6 mL of diluted nitrogen-free Jensen nutrient solution supplemented with 50 mM NaCl. Surface-sterilized licorice seeds were allowed to germinate on 1% water agar for three days at 28 °C in the dark. One germinated seed per sterile glass tube was planted after being submerged in a bacterial suspension (108 CFU/mL) for 15 min. The seedlings were grown in a growth cabinet under a light regime of 16 h light at 22 °C and 8 h darkness at 16 °C. After 14 days, the lengths of the roots and shoots, as well as the fresh weight of the entire plant, were measured.

    Using the previously described gnotobiotic sand tubes, the colonization of licorice root tips by bacterial isolates was examined. The methods for cultivating and preparing bacterial inoculants and inoculating seeds, as described earlier, were employed. After two weeks, the seedlings were removed from the sand, and 1 cm of the root tips were excised and placed into a tube with 1 mL of PBS. The root tips were vortexed in PBS to dislodge any bacterial cells. Following a series of dilutions, homogenates were spread on agar plates at 103 and 104 dilutions. Bacterial colonies on TSA were counted after three days of incubation at 28 °C. The colony-forming units (CFU) per 1 cm of root tip were used to calculate the quantity of bacterial cells.

    Bacterial isolates were grown for 72 h in tryptic soy broth (TSB, Sigma-Aldrich), and their suspensions were adjusted to an optical density of 0.1 (OD620 = 0.1) at 620 nm, corresponding to approximately 108 cells/mL. Licorice seeds were immersed for 10 min in a bacterial suspension with a concentration of 107 colony-forming units (CFU) per milliliter. Plastic pots, 12 cm in diameter and 16 cm in depth, were filled with 500 g of soil collected from salt-affected land in the Sirdarya province of Uzbekistan. One seed per pot was sown. The experiment included two treatments—seeds that were not treated with bacteria and seeds that were inoculated with bacteria. Each treatment was replicated three times in a completely randomized block design. The plants were grown with day temperatures of 24–28 °C and night temperatures of 14–16 °C. After eight weeks, the lengths of the shoots and roots, as well as their dry weights, were assessed.

    The analysis of variance tool in Microsoft Excel 2010 was used to determine the statistical significance of the data. Student's t-test was used to perform comparisons. The least significant difference (LSD) test (P = 0.05) was used to compare means.

    A total of 55 bacterial strains were isolated from the plant tissues of G. glabra. After RFLP analysis, only 18 strains remained. These strains were identified using the BLAST (Basic Local Alignment Search Tool) and matched with corresponding strains from the NCBI GenBank. The strains were found to be 99.79%–100% identical to their closest relatives registered in GenBank®. Sequence similarities of the endophytic bacteria isolated from G. glabra are presented in Table 1. The lengths of the identified nucleotide sequences of the 16S rRNA genes of the isolates ranged from 1354 to 1492 bp, which is considered adequate for reliable identification based on 16S rRNA gene analysis using the BLAST tool. All isolated strains were assigned accession numbers, as shown in Table 1 and Figure 1. As shown in Table 1, the tissues of G. glabra harbored 18 species belonging to two phyla: Pseudomonadota (GU2, GU3, GU4, GU6, GU7, GU8, GU10, GU11, GU12, GU13, GU15, GU16, GU17, and GU18) and Bacillota (GU1, GU5, GU9, and GU14). The phylum Pseudomonadota comprised 2 classes: Gammaproteobacteria (GU3, GU4, GU7, GU8, GU10, GU11, GU12, GU13, GU15, GU16, GU17, and GU18) and Betaproteobacteria (GU2 and GU6). The phylum Bacillota was presented by a single class Bacilli with the strains given above.

    Profiling of endophytic bacteria isolated from the tissues of G. glabra demonstrated that these included 18 isolates belonging to the genera Enterobacter (4), Pantoea (3), Bacillus (2), Paenibacillus (2), Achromobacter (2), Pseudomonas (1), Escherichia (1), Klebsiella (1), Citrobacter (1), and Kosakonia (1) (Figure 1).

    Table 1.  Endophytic bacteria isolated from Glycyrrhiza glabra and their closest relatives from GenBank based on 16S rRNA gene resemblance.
    Isolated strains deposited to GenBank Closest match in GenBank
    Strain Species Query length (bp) Accession number Reference strain Accession number Identity (%)
    GU1 Paenibacillus polymyxa 1465 OQ874308 Paenibacillus polymyxa KCTC 3627 HE981792.1 99.93
    GU2 Achromobacter piechaudii 1375 OQ874309 Achromobacter piechaudii B4b52 MK737340.1 99.93
    GU3 Enterobacter hormaechei 1439 OQ874310 Enterobacter hormaechei subsp. Hoffmannii GU-HP12 OQ421693.1 99.86
    GU4 Pantoea ananatis 1464 OQ874311 Pantoea ananatis 0201935 AJ629190.1 99.8
    GU5 Paenibacillus amylolyticus 1419 OQ874312 Paenibacillus amylolyticus C2 LN827736.1 99.93
    GU6 Achromobacter xylosoxidans 1423 OQ874313 Achromobacter xylosoxidans 17SIN-B2 LC610746.1 99.86
    GU7 Pseudomonas azotoformans 1456 OQ874314 Pseudomonas azotoformans JCM 20222 LC654882.1 99.93
    GU8 Enterobacter ludwigii 1461 OQ874315 Enterobacter ludwigii 7D2C3 MN371803.1 99.79
    GU9 Bacillus velezensis 1449 OQ874316 Bacillus velezensis HAB-2 MT375545.1 99.93
    GU10 Escherichia coli 1457 OQ874317 Escherichia coli MCn2 OP727288.1 100
    GU11 Enterobacter cloacae 1464 OQ874318 Enterobacter cloacae ATCC 13047 NR_102794.2 100
    GU12 Kosakonia cowanii 1404 OQ874319 Kosakonia cowanii Gm0511 MN327620.1 99.86
    GU13 Citrobacter freundii 1465 OQ874320 Citrobacter freundii RTE-E5 LC572264.1 99.8
    GU14 Bacillus cereus 1492 OQ874321 Bacillus cereus KUBOTAB5 MK855405.1 99.87
    GU15 Enterobacter hormaechei 1354 OQ874322 Enterobacter hormaechei 0992-77 NR_042154.1 99.85
    GU16 Pantoea gaviniae 1483 OQ874323 Pantoea gaviniae LMG 25382 AB907786.1 99.87
    GU17 Klebsiella pneumoniae 1431 OQ874324 Klebsiella pneumoniae PD10 LC093514.1 99.79
    GU18 Pantoea agglomerans 1422 OQ874325 Pantoea agglomerans HTP MT635441.1 99.79

     | Show Table
    DownLoad: CSV
    Figure 1.  Phylogenetic tree of endophytic bacteria (GU1–GU18) from Glycyrrhiza glabra with the closest relatives registered in GenBank of NCBI.
    Table 2.  Beneficial traits of endophytic bacteria associated with Glycyrrhiza glabra.
    Bacterial endophytes HCN Lipase Glucanase Chitinase IAA ACC-deaminase Siderophore F. oxysporum F. culmorum R. solani
    P. polymyxa GU1 + - + + + + + + +
    A. piechaudii GU2 - - - - +
    E. hormaechei GU3 + - - + +
    P. ananatis GU4 - + - +
    P. amylolyticus GU5 + - + + + + + +
    A. xylosoxidans GU6 + - + + + +
    P. azotoformans GU7 + + - + + + + +
    E. ludwigii GU8 + + - - +
    B. velezensis GU9 + + + -
    E. coli GU10 - - + -
    E. cloacae GU11 + + - - +
    K. cowanii GU12 - + - +
    C. freundii GU13 + - + -
    B. cereus GU14 + + + - + + +
    E. hormaechei GU15 - + + - + + + +
    P. gaviniae GU16 - - - -
    K. pneumoniae GU17 - + - +
    P. agglomerans GU18 - - + + + + + +

    “+” positive to the tested activity.

     | Show Table
    DownLoad: CSV

    Table 2 provides findings about the plant-beneficial characteristics of endophytic bacteria. Bacterial isolates P. polymyxa GU1, P. amylolyticus GU5, B. cereus GU14, E. hormaechei GU15, and P. agglomerans GU18 produced IAA. Siderophore production was observed in 4 out of 18 bacterial isolates. The eight isolates P. polymyxa GU1, A. piechaudii GU2, P. amylolyticus GU5, E. ludwigii GU8, E. cloacae GU11, B. cereus GU14, E. hormaechei GU15, and P. agglomerans GU18 showed ACC deaminase production and nine of the strains showed hydrogen cyanide (HCN) production. The strains were also tested for fungal cell wall–degrading enzymes (protease, cellulase, and lipase) production. It was revealed that 11 bacterial isolates produced at least two tested enzymes (Table 2). The antifungal activity of endophytic bacterial isolates was evaluated against three plant pathogenic fungi, F. culmorum, F. oxysporum, and R. solani (Table 2). Among all tested endophytic bacteria, the five isolates P. polymyxa GU1, P. amylolyticus GU5, P. azotoformans GU7, E. hormaechei GU15, and P. agglomerans GU18 exhibited strong inhibition against three tested plant pathogenic fungi, namely F. culmorum, F. solani, and R. solani.

    We also examined the effect of bacterial inoculants on the seed germination of Glycyrrhiza glabra. The results revealed that the germination rate of non-inoculated G. glabra seeds was 57% ± 2.1%, which was lower compared to seeds inoculated with bacteria. Inoculation with bacterial isolates improved seed germination. Specifically, P. polymyxa GU1, E. hormaechei GU15, and P. agglomerans GU18 increased germination rates to 75% ± 2.4%, 70% ± 3.1%, and 70% ± 2.9%, respectively. In contrast, P. amylolyticus GU5, P. azotoformans GU7, and B. cereus GU14 exhibited lower germination rates, at 65% ± 3.2% (Figure 2).

    Figure 2.  The effect of bacterial isolates on seed germination.

    The bacterial isolates also stimulated seedling growth compared to control seeds. After 10 days incubation, the seedling length of control was 2.45 cm, whereas P. polymyxa GU1, A. xylosoxidans GU6, and P. azotoformans GU7 increased seedling length by 4.25 cm. P. amyloliticus GU5 and E. hormaechei GU15 had no effect on seedling growth and development (Table 3).

    Table 3.  The effect of bacterial inoculation on seed germination and seedling growth of Glycyrrhiza glabra.
    Bacterial isolates Seed germination (%) Seedling length (cm)
    Control (no bacterial inoculation) 57 ± 2.1 2.54 ± 0.7
    Paenibacillus polymyxa GU1 75 ± 2.4* 4.75 ± 0.9*
    Paenibacillus amylolyticus GU5 65 ± 2.9 2.6 ± 0.3
    Achromobacter xylosoxidans GU6 68 ± 2.2* 4.25 ± 0.4*
    Pseudomonas azotoformans GU7 65 ± 1.9 4.75 ± 0.9*
    Bacillus cereus GU14 65 ± 2.0 3.65 ± 0.7
    Enterobacter hormaechei GU15 70 ± 3.1* 2.95 ± 0.3
    Pantoea agglomerans GU18 70 ± 2.9* 3.35 ± 0.6*

    *Note: Asterisks indicate the level of statistical significance: p ≤ 0.05.

     | Show Table
    DownLoad: CSV

    The initial salt tolerance of G. glabra and the response of plants to salt stress (50 mM NaCl) following endophytic bacterial inoculation were evaluated. Our results demonstrated that P. polymyxa GU1, P. amylolyticus GU5, A. xylosoxidans GU6, P. azotoformans GU7, and P. agglomerans GU18 improved the fresh weight, root length, and shoot length of licorice. Specifically, fresh weight increased by 36%, root length by 52%, and shoot length by 39% compared with the uninoculated control. However, there was no significant difference compared to plants inoculated with B. cereus GU14 and E. hormaechei GU15 (Figure 3a,b). The bacterial isolates P. agglomerans GU18 and P. azotoformans GU7 exhibited the most pronounced stimulating effects.

    We have also determined the colonization of introduced bacteria in the root of licorice. Our experiment showed that CFU counts of P. azotoformans GU7 were 11.9 × 103 CFU/cm of root tip; for isolates P. polymyxa GU1 and P. agglomerans GU18, it was 8.76 and 8.10 × 103 CFU/cm of root tip, respectively. Lower colonization was observed by B. cereus GU14 and E. hormaechei GU15, being 5.55 and 4.01 × 103 CFU/cm of root tip (Figure 4).

    Figure 3.  Effect of inoculation with the bacterial isolates (P. polymyxa GU1, P. amylolyticus GU5, A. xylosoxidans GU6, P. azotoformans GU7, B. cereus GU14, E. hormaechei GU15 and P. agglomerans GU18) on the fresh weight of whole plants (a) and on shoots and roots length (b) of salt-stressed G. glabra seedlings. Columns represent means for six seedlings (N = 6) with error bars showing standard error.
    Figure 4.  Colonization ability of selected endophytic bacteria in plant root (CFU cm root), (P. polymyxa GU1, P. amylolyticus GU5, A. xylosoxidans GU6, P. azotoformans GU7, B. cereus GU14, E. hormaechei GU15, P. agglomerans GU18).

    The effect of bacterial isolates selected from previous experiments on plant growth in pots with saline soil was further investigated under greenhouse conditions. Plants were grown for 8 weeks in saline soil. The results obtained from this pot experiment were similar to those obtained from the short-term gnotobiotic experiment. After bacterial inoculation with P. polymyxa GU1, A. xylosoxidans GU6, P. azotoformans GU7, and P. agglomerans GU18, the shoot fresh weights were increased by 69%, 42%, 57%, and 55%, and roots were increased by 83%, 26%, 61%, and 66%, respectively (Table 4). In comparison with the uninoculated plant, the co-inoculation of bacterial isolates increased shoot and root dry weights by 76% (Table 4). P. polymyxa GU1, A. xylosoxidans GU6, P. azotoformans GU7, and P. agglomerans GU18 performed the best and, in comparison with uninoculated plants, the shoot and root weights increased by 71%, 51%, 74%, and 76%, respectively (Table 4, Figure 5).

    Table 4.  Effect of bacterial inoculation on shoot and root growth of Glycyrrhiza glabra.
    Plant Control GU1 GU5 GU6 GU7 GU14 GU15 GU18
    Fresh weight
    Shoot 6.38 ± 0.47 10.83 ± 0.55* 6.25 ± 0.39 9.07 ± 0.39* 10.03 ± 0.33* 7.66 ± 0.45 7.36 ± 0.50* 9.05 ± 0.43*
    Root 2.90 ± 0.24 5.60 ± 0.41* 2.80 ± 23 3.66 ± 0.39* 4.69 ± 0.25* 3.1 ± 0.23 4.01 ± 0.38* 4.83 ± 0.40*
    Dry weight
    Shoot 1.98 ± 0.11 3.21 ± 0.38* 1.84 ± 0.12 2.93 ± 0.15* 3.17 ± 0.35* 2.31 ± 0.11 2.09 ± 0.09 3.41 ± 0.17*
    Root 1.12 ± 0.19 1.91 ± 0.16* 1.06 ± 0.15 1.66 ± 0.12* 1.95 ± 0.22* 1.39 ± 0.10 1.35 ± 0.18 1.90 ± 0.12*

    *Note: Asterisks indicate the level of statistical significance: p ≤ 0.05, (P. polymyxa GU1, P. amylolyticus GU5, A. xylosoxidans GU6, P. azotoformans GU7, B. cereus GU14, E. hormaechei GU15, P. agglomerans GU18).

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    Figure 5.  Phenotype of G. glabra plants uninoculated and inoculated with P. agglomerans GU18 and grown in saline soil for eight weeks.

    To the best of our knowledge, this is the first study to examine endophytic bacteria associated with G. glabra growing in salt-affected land in Uzbekistan. Profiling of endophytic bacteria isolated from the tissues of Glycyrrhiza glabra revealed 18 isolates belonging to the following genera: Enterobacter (4), Pantoea (3), Bacillus (2), Paenibacillus (2), Achromobacter (2), Pseudomonas (1), Escherichia (1), Klebsiella (1), Citrobacter (1), and Kosakonia (1). Similar bacterial species have been reported in other medicinal plants: Bacillus cereus from Dicoma anomala [46], Paenibacillus polymyxa SK1 from Lilium lancifolium [47], Pseudomonas amylolyticus from Coix lachryma-jobi [48], and Pantoea ananatis from Iris pseudacorus [49]. We have also observed that several bacterial isolates belonging to A. piechaudii, E. hormaeche, A. xylosoxidans, E. ludwigii, E. coli, E. cloacae, K. cowanii, C. freundii, B. cereus, E. hormaechei, and K. pneumoniae are potential human pathogens. While many bacteria associated with plants are beneficial, some may pose a threat to human health [50]. Saline environments can affect the composition and diversity of microbial communities, potentially impacting the abundance of pathogenic species. Saline soils may support the growth of certain human pathogenic bacteria, leading to human diseases through direct contact with contaminated plants or indirect exposure via contaminated food or water [51].

    Furthermore, our investigation revealed that several bacterial isolates exhibited plant-beneficial traits. Previous studies have reported that bacterial isolates stimulate plant growth, enhance nutrient acquisition, and increase plant stress tolerance to abiotic stresses such as drought and salinity by synthesizing biologically active substances [52][54]. We have observed antagonistic activity of several bacterial isolates against the plant pathogenic fungi F. oxysporum, F. solani, and R. solani. These bacteria may protect plants from pathogenic fungi by producing antibiotics or competing for nutrients and niches [55],[56]. Previous studies on plant-associated bacteria from Hypericum perforatum and Chelidonium majus L. revealed higher percentages of endophytes with antifungal characteristics [57]. Evidence suggests that the physiological processes of endophytic bacteria residing within plant tissues may be influenced by the biologically active components of medicinal plants. These endophytic bacteria can mimic the biological activity and metabolite synthesis of their host plants. For example, bacteria isolated from the medicinal plants Matricaria chamomilla and Calendula officinalis demonstrated antifungal activities comparable to those of the plant extracts [57]. In another study, bacteria associated with Aloe vera exhibited antibacterial activity against human pathogenic bacteria, such as S. aureus, Streptococcus pyogenes, P. aeruginosa, and E. coli, and produced bioactive compounds with antimicrobial activities [58]. Furthermore, studies have shown that the antagonistic properties of endophytic bacteria can efficiently reduce fungal diseases without harming the host [59].

    Numerous basic mechanisms underlying the beneficial effects of endophytic bacteria have been documented in previous reports [60]. These include the synthesis of phytohormones, hydrogen cyanide (HCN), siderophores, ACC-deaminase, enzymes that degrade fungal cell walls, and phosphate solubilization. In our study, six bacterial strains produced the phytohormone auxin, ten strains synthesized hydrogen cyanide (HCN), and nine bacterial isolates produced at least two of the three tested fungal cell wall–degrading enzymes: chitinase, glucanase, and lipase. It is known that one of the primary mechanisms for suppressing plant pathogens involves bacterial production of chitinase, which degrades fungal cell walls, lipase, which breaks down certain lipids associated with fungal cell walls, and β-1,3-glucanase, which degrades cell wall carbohydrates. It has also been reported that bacteria producing hydrogen cyanide (HCN) can inhibit the growth of fungal pathogens [61]. Many studies have documented the production of phytohormones by bacterial strains associated with plants. Phytohormone-producing bacteria stimulate root and shoot growth, enhance nutrient acquisition, and improve the yield of various crop and medicinal plants [62],[63]. For instance, indole-3-acetic acid (IAA) promotes root elongation, enhances root hair formation, and facilitates better nutrient and water uptake. This enhances plant anchorage and stability, which is crucial for plants growing in stressful environments like saline soils. In our study, eight out of eighteen endophytic bacterial isolates were able to produce ACC deaminase (1-aminocyclopropane-1-carboxylate deaminase). Ethylene, a plant hormone involved in various physiological functions including stress responses, is derived from ACC. Bacteria that produce ACC deaminase can lower plant ethylene levels by breaking down ACC, thereby reducing the negative effects of ethylene on plant growth and development under stress conditions [64].

    Seven bacterial isolates improved seed germination and seedling growth and were further tested in pot experiments. Four bacterial isolates, P. polymyxa GU1, A. xylosoxidans GU6, P. azotoformans GU7, and P. agglomerans GU18, significantly increased root and shoot of licorice in saline soil. There were many reports on the positive effect of endophytic bacteria on plant growth of medicinal plants [65][67]. According to Sudarshna and Sharma [68], endophytic bacteria isolated from the Trillium govanianum with IAA-, siderophore-, and ACC deaminase-producing ability enhanced plant growth and nutrient uptake from soil. In another study, Pelargonium graveolens–associated bacteria with various plant-beneficial traits increased plant dry weight and essential oils concentration [69]. This beneficial effect largely stems from the bacteria's ability to colonize the plant's root system, which is essential for fostering positive interactions between the bacteria and the plant. Bacteria employ various mechanisms to facilitate root colonization, including chemotaxis toward root exudates and biofilm formation on root surfaces. In our study, five bacterial isolates that demonstrated the greatest potential for stimulating plant growth were also able to successfully colonize the roots of licorice.

    In this study, we identified endophytic bacteria associated with Glycyrrhiza glabra from a salt-affected region of Uzbekistan. These bacteria belong to the genera Enterobacter, Pantoea, Bacillus, Paenibacillus, Achromobacter, Pseudomonas, Escherichia, Klebsiella, Citrobacter, and Kosakonia. The bacterial isolates demonstrated the ability to produce siderophores, hydrogen cyanide (HCN), indole-3-acetic acid (IAA), and various enzymes, and exhibited antagonistic activity against F. culmorum, F. solani, and R. solani. These isolates not only enhanced root and shoot growth in licorice but also successfully colonized the rhizosphere. Our findings underscore the potential of these specific bacterial strains as effective microbial inoculants to boost licorice growth in saline soils. Using these inoculants could greatly boost agricultural productivity in saline conditions, leading to better licorice cultivation and potential economic advantages. Further research and field trials are needed to optimize inoculant formulations and confirm their effectiveness across varying environmental conditions.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.


    Acknowledgments



    All sources of funding of the study must be disclosed.

    Conflict of interest



    The authors declare there is no conflict of interest.

    [1] Xia C, Dong X, Li H, et al. (2022) Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J 135: 584-590. https://doi.org/10.1097/CM9.0000000000002108
    [2] Kanwal M, Ding XJ, Cao Y (2017) Familial risk for lung cancer. Oncol Lett 13: 535-542. https://doi.org/10.3892/ol.2016.5518
    [3] Boloker G, Wang C, Zhang J (2018) Updated statistics of lung and bronchus cancer in United States. J Thorac Dis 10: 1158. https://doi.org/10.21037/jtd.2018.03.15
    [4] Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68: 7-30. https://doi.org/10.3322/caac.21442
    [5] Planchard D, Popat ST, Kerr K, et al. (2018) Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 29: iv192-iv237. https://doi.org/10.3322/caac.21442
    [6] Wadowska K, Bil-Lula I, Trembecki Ł, et al. (2020) Genetic markers in lung cancer diagnosis: a review. Int J Mol Sci 21: 4569. https://doi.org/10.3390/ijms21134569
    [7] Pennell NA, Arcila ME, Gandara DR, et al. (2019) Biomarker testing for patients with advanced non–small cell lung cancer: real-world issues and tough choices. Am Soc Clin Oncol Educ Book 39: 531-542. https://doi.org/10.1200/EDBK_237863
    [8] Khanna P, Blais N, Gaudreau PO, et al. (2017) Immunotherapy comes of age in lung cancer. Clin Lung Cancer 18: 13-22. https://doi.org/10.1016/j.cllc.2016.06.006
    [9] Hansen RN, Zhang Y, Seal B, et al. (2020) Long-term survival trends in patients with unresectable stage iii non-small cell lung cancer receiving chemotherapy and radiation therapy: a seer cancer registry analysis. BMC cancer 20: 1-6. https://doi.org/10.1186/s12885-020-06734-3
    [10] Bradley JD, Hu C, Komaki RR, et al. (2020) Long-term results of nrg oncology rtog 0617: standard-versus high-dose chemoradiotherapy with or without cetuximab for unresectable stage iii non–small-cell lung cancer. J Clin Oncol 38: 706. https://doi.org/10.1200/JCO.19.01162
    [11] Yoon SM, Shaikh T, Hallman M (2017) Therapeutic management options for stage iii non-small cell lung cancer. World J Clin Oncol 8: 1-20. https://doi.org/10.5306/wjco.v8.i1.1
    [12] Wang Y, Liu Z, Xu J, et al. (2022) Heterogeneous network representation learning approach for ethereum identity identification. IEEE Trans Comput Social Syst 10: 890. https://10.1109/TCSS.2022.3164719
    [13] Shi Y, Li L, Yang J, et al. (2023) Center-based transfer feature learning with classifier adaptation for surface defect recognition. Mech Syst Signal Process 188: 110001. https://doi.org/10.1016/j.ymssp.2022.110001
    [14] Shi Y, Li H, Fu X, et al. (2023) Self-powered difunctional sensors based on sliding contact-electrification and tribovoltaic effects for pneumatic monitoring and controlling. Nano Energy 110: 108339. https://doi.org/10.1016/j.nanoen.2023.108339
    [15] Tian C, Xu Z, Wang L, et al. (2023) Arc fault detection using artificial intelligence: challenges and benefits. Math Biosci Eng 20: 12404-12432. https://10.3934/mbe.2023552
    [16] Liu Z, Yang D, Wang Y, et al. (2023) Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks. Appl Soft Comput 135: 110040. https://doi.org/10.1016/j.asoc.2023.110040
    [17] Wang S, Yang DM, Rong R, et al. (2019) Artificial intelligence in lung cancer pathology image analysis. Cancers 11: 1673. https://doi.org/10.3390/cancers11111673
    [18] Asuntha A, Srinivasan A (2020) Deep learning for lung cancer detection and classification. Multimed Tools Appl 79: 7731-7762. https://doi.org/10.1007/s11042-019-08394-3
    [19] Riquelme D, Akhloufi MA (2020) Deep learning for lung cancer nodules detection and classification in ct scans. Ai 1: 28-67. https://doi.org/10.3390/ai1010003
    [20] Chiu HY, Chao HS, Chen YM (2022) Application of artificial intelligence in lung cancer. Cancers 14: 1370. https://doi.org/10.3390/cancers14061370
    [21] Dlamini Z, Francies FZ, Hull R, et al. (2020) Artificial intelligence (ai) and big data in cancer and precision oncology. Comput Struct Biotechnol J 18: 2300-2311. https://doi.org/10.1016/j.csbj.2020.08.019
    [22] Mann M, Kumar C, Zeng WF, et al. (2021) Artificial intelligence for proteomics and biomarker discovery. Cell Syst 12: 759-770. https://doi.org/10.1016/j.cels.2021.06.006
    [23] Subramanian M, Wojtusciszyn A, Favre L, et al. (2020) Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med 18: 1-12. https://doi.org/10.1186/s12967-020-02658-5
    [24] Schork NJ (2019) Artificial intelligence and personalized medicine. Precis Med Cancer Ther 178: 265-283. https://doi.org/10.1007/978-3-030-16391-4_11
    [25] Magrabi F, Ammenwerth E, McNair JB, et al. (2019) Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearb Med Inform 28: 128-134. https://doi.org/10.1055/s-0039-1677903
    [26] Kim MS, Park HY, Kho BG, et al. (2020) Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board. Transl Lung Cancer Res 9: 507. https://doi.org/10.21037/tlcr.2020.04.11
    [27] Giordano C, Brennan M, Mohamed B, et al. (2021) Accessing artificial intelligence for clinical decision-making. Frontiers Digit Health 3: 645232. https://doi.org/10.3389/fdgth.2021.645232
    [28] Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, et al. (2021) Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making-a systematic review. J Dent Sci 16: 482-492. https://doi.org/10.1016/j.jds.2020.05.022
    [29] Zhao J, Lv Y (2023) Output-feedback robust tracking control of uncertain systems via adaptive learning. Int J Control Autom Syst 21: 1108-1118. https://doi.org/10.1007/s12555-021-0882-6
    [30] Qi W, Su H (2022) A cybertwin based multimodal network for ecg patterns monitoring using deep learning. IEEE Trans Industr Inform 18: 6663-6670. https://doi.org/10.1109/TII.2022.3159583
    [31] Su H, Qi W, Chen J, et al. (2022) Fuzzy approximation-based task-space control of robot manipulators with remote center of motion constraint. IEEE Trans Fuzzy Syst 30: 1564-1573. https://doi.org/10.1109/TFUZZ.2022.3157075
    [32] Kadir T, Gleeson F (2018) Lung cancer prediction using machine learning and advanced imaging techniques. Transl Lung Cancer Res 7: 304. https://doi.org/10.21037/tlcr.2018.05.15
    [33] Tuncal K, Sekeroglu B, Ozkan C (2020) Lung cancer incidence prediction using machine learning algorithms. J Adv Inform Technol Vol 11: 91-96. https://doi.org/10.12720/jait.11.2.91-96
    [34] Tu SJ, Wang CW, Pan KT, et al. (2018) Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening. Phys Med Biol 63: 065005. https://doi.org/10.1088/1361-6560/aaafab
    [35] Li Y, Lu L, Xiao M, et al. (2018) CT slice thickness and convolution kernel affect performance of a radiomic model for predicting EGFR status in non-small cell lung cancer: a preliminary study. Sci Rep 8: 17913. https://doi.org/10.1038/s41598-018-36421-0
    [36] McBee MP, Awan OA, Colucci AT, et al. (2018) Deep learning in radiology. Acad Radiol 25: 1472-1480. https://doi.org/10.1016/j.acra.2018.02.018
    [37] Yasaka K, Abe O (2018) Deep learning and artificial intelligence in radiology: current applications and future directions. PLoS Med 15: e1002707. https://doi.org/10.1371/journal.pmed.1002707
    [38] Hua KL, Hsu CH, Hidayati SC, et al. (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther 8: 2015-2022. https://doi.org/10.2147/OTT.S80733
    [39] Cengil E, Cinar A (2018) A deep learning based approach to lung cancer identification. 2018 International conference on artificial intelligence and data processing (IDAP) 2018: 1-5. https://doi.org/10.1109/IDAP.2018.8620723
    [40] Coudray N, Ocampo PS, Sakellaropoulos T, et al. (2018) Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med 24: 1559-1567. https://doi.org/10.1038/s41591-018-0177-5
    [41] Thawani R, McLane M, Beig N, et al. (2018) Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung cancer 115: 34-41. https://doi.org/10.1016/j.lungcan.2017.10.015
    [42] Su H, Qi W, Schmirander Y, et al. (2022) A human activity-aware shared control solution for medical human–robot interaction. Assem Autom 42: 388-394. https://doi.org/10.1108/AA-12-2021-0174
    [43] Su H, Qi W, Hu Y, et al. (2020) An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators. IEEE Trans Industr Inform 18: 1864-1872. https://10.1109/TII.2020.3036693
    [44] Nair JKR, Saeed UA, McDougall CC, et al. (2021) Radiogenomic models using machine learning techniques to predict EGFR mutations in non-small cell lung cancer. Can Assoc Radiol J 72: 109-119. https://doi.org/10.1177/0846537119899526
    [45] Singal G, Miller PG, Agarwala V, et al. (2019) Association of patient characteristics and tumor genomics with clinical outcomes among patients with non–small cell lung cancer using a clinicogenomic database. Jama 321: 1391-1399. https://doi.org/10.1001/jama.2019.3241
    [46] Huang S, Yang J, Shen N, et al. (2023) Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol 89: 30-37. https://doi.org/10.1016/j.semcancer.2023.01.006
    [47] Petousis P, Winter A, Speier W, et al. (2019) Using sequential decision making to improve lung cancer screening performance. Ieee Access 7: 119403-119419. https://doi.org/10.1109/ACCESS.2019.2935763
    [48] Tortora M, Cordelli E, Sicilia R, et al. (2021) Deep reinforcement learning for fractionated radiotherapy in non-small cell lung carcinoma. Artif Intell Med 119: 102137. https://doi.org/10.1016/j.artmed.2021.102137
    [49] Pei Q, Luo Y, Chen Y, et al. (2022) Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clin Chem Lab Med 60: 1974-1983. https://doi.org/10.1515/cclm-2022-0291
    [50] Wang M, Herbst RS, Boshoff C (2021) Toward personalized treatment approaches for non-small-cell lung cancer. Nat Med 27: 1345-1356. https://doi.org/10.1038/s41591-021-01450-2
    [51] Wang L (2022) Deep learning techniques to diagnose lung cancer. Cancers 14: 5569. https://doi.org/10.3390/cancers14225569
    [52] Bi WL, Hosny A, Schabath MB, et al. (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69: 127-157. https://doi.org/10.3322/caac.21552
    [53] Abid MMN, Zia T, Ghafoor M, et al. (2021) Multi-view convolutional recurrent neural networks for lung cancer nodule identification. Neurocomputing 453: 299-311. https://doi.org/10.1016/j.neucom.2020.06.144
    [54] Gu Y, Lu X, Yang L, et al. (2018) Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Comput Biol Med 103: 220-231. https://doi.org/10.1016/j.compbiomed.2018.10.011
    [55] Setio AAA, Ciompi F, Litjens G, et al. (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35: 1160-1169. https://doi.org/10.1109/TMI.2016.2536809
    [56] Xie H, Yang D, Sun N, et al. (2019) Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognit 85: 109-119. https://doi.org/10.1016/j.patcog.2018.07.031
    [57] Pezeshk A, Hamidian S, Petrick N, et al. (2018) 3-D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE J Biomed Health Inform 23: 2080-2090. https://doi.org/10.1109/JBHI.2018.2879449
    [58] Toğaçar M, Ergen B, Cömert Z (2020) Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybern Biomed Eng 40: 23-39. https://doi.org/10.1016/j.bbe.2019.11.004
    [59] Ardila D, Kiraly AP, Bharadwaj S, et al. (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 25: 954-961. https://doi.org/10.1038/s41591-019-0447-x
    [60] Teramoto A, Yamada A, Kiriyama Y, et al. (2019) Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network. Inform Med Unlocked 16: 100205. https://doi.org/10.1016/j.imu.2019.100205
    [61] Onishi Y, Teramoto A, Tsujimoto M, et al. (2019) Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks. Biomed Res Int 2019: 6051939. https://doi.org/10.1155/2019/6051939
    [62] Bharati S, Podder P, Mondal MRH (2020) Hybrid deep learning for detecting lung diseases from X-ray images. Inform Med Unlocked 20: 100391. https://doi.org/10.1016/j.imu.2020.100391
    [63] Ke Q, Zhang J, Wei W, et al. (2019) A neuro-heuristic approach for recognition of lung diseases from X-ray images. Expert Syst Appl 126: 218-232. https://doi.org/10.1016/j.eswa.2019.01.060
    [64] Gordienko Y, Gang P, Hui J, et al. (2019) Deep learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer. Advances in Computer Science for Engineering and Education 2019: 638-647. https://doi.org/10.48550/arXiv.1712.07632
    [65] Ausawalaithong W, Thirach A, Marukatat S, et al. (2018) Automatic lung cancer prediction from chest X-ray images using the deep learning approach. 2018 11th biomedical engineering international conference (BMEiCON) 2018: 1-5. https://doi.org/10.1109/BMEiCON.2018.8609997
    [66] Philip B, Jain A, Wojtowicz M, et al. (2023) Current investigative modalities for detecting and staging lung cancers: a comprehensive summary. Indian J Thorac Cardiovasc Surg 39: 42-52. https://doi.org/10.1007/s12055-022-01430-2
    [67] Bhandary A, Prabhu GA, Rajinikanth V, et al. (2020) Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images. Pattern Recogn Lett 129: 271-278. https://doi.org/10.1016/j.patrec.2019.11.013
    [68] Li X, Shen L, Xie X, et al. (2020) Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection. Artif Intell Med 103: 101744. https://doi.org/10.1016/j.artmed.2019.101744
    [69] Sim AJ, Kaza E, Singer L, et al. (2020) A review of the role of mri in diagnosis and treatment of early stage lung cancer. Clin Transl Radiat Oncol 24: 16-22. https://doi.org/10.1016/j.ctro.2020.06.002
    [70] Rustam Z, Hartini S, Pratama RY, et al. (2020) Analysis of architecture combining convolutional neural network (cnn) and kernel k-means clustering for lung cancer diagnosis. Int J Adv Sci Eng Inf Technol 10: 1200-1206. https://doi.org/10.18517/ijaseit.10.3.12113
    [71] Isaksson LJ, Raimondi S, Botta F, et al. (2020) Effects of MRI image normalization techniques in prostate cancer radiomics. Phys Med 71: 7-13. https://doi.org/10.1016/j.ejmp.2020.02.007
    [72] Rahman MM, Sazzad TMS, Ferdaus FS (2021) Automated detection of lung cancer using MRI images. 2021 3rd International Conference on Sustainable Technologies for Industry 4.0 (STI) 2021: 1-5. https://doi.org/10.1109/STI53101.2021.9732603
    [73] Wahengbam M, Sriram M (2023) MRI Lung Tumor Segmentation and Classification Using Neural Networks. International Conference on Communication, Electronics and Digital Technology 2023: 605-616. https://doi.org/10.1007/978-981-99-1699-3_42
    [74] Baxi V, Edwards R, Montalto M, et al. (2022) Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 35: 23-32. https://doi.org/10.1038/s41379-021-00919-2
    [75] Acs B, Rantalainen M, Hartman J (2020) Artificial intelligence as the next step towards precision pathology. J Intern Med 288: 62-81. https://doi.org/10.1111/joim.13030
    [76] Garg S, Garg S (2020) Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps. Proceedings of the 2020 3rd Artificial Intelligence and Cloud Computing Conference 2020: 38-45. https://doi.org/10.1145/3442536.3442543
    [77] Wang S, Chen A, Yang L, et al. (2018) Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci Rep 8: 10393. https://doi.org/10.1038/s41598-018-27707-4
    [78] Šarić M, Russo M, Stella M, et al. (2019) CNN-based method for lung cancer detection in whole slide histopathology images. 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech) 2019: 1-4. https://doi.org/10.23919/SpliTech.2019.8783041
    [79] Sha L, Osinski BL, Ho IY, et al. (2019) Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images. J Pathol Inform 10: 24. https://doi.org/10.4103/jpi.jpi_24_19
    [80] Gertych A, Swiderska-Chadaj Z, Ma Z, et al. (2019) Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci Rep 9: 1483. https://doi.org/10.1038/s41598-018-37638-9
    [81] Yu KH, Wang F, Berry GJ, et al. (2020) Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J Am Med Inform Assoc 27: 757-769. https://doi.org/10.1093/jamia/ocz230
    [82] Tiwari A, Trivedi R, Lin SY (2022) Tumor microenvironment: barrier or opportunity towards effective cancer therapy. J Biomed Sci 29: 1-27. https://doi.org/10.1186/s12929-022-00866-3
    [83] Saltz J, Gupta R, Hou L, et al. (2018) Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep 23: 181-193. https://doi.org/10.1016/j.celrep.2018.03.086
    [84] Yi F, Yang L, Wang S, et al. (2018) Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC bioinformatics 19: 1-9. https://doi.org/10.1186/s12859-018-2055-z
    [85] Wang S, Wang T, Yang L, et al. (2019) ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine 50: 103-110. https://doi.org/10.1016/j.ebiom.2019.10.033
    [86] Rączkowski Ł, Paśnik I, Kukiełka M, et al. (2022) Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer. BMC cancer 22: 1001. https://doi.org/10.1186/s12885-022-10081-w
    [87] Nooreldeen R, Bach H (2021) Current and future development in lung cancer diagnosis. Int J Mol Sci 22: 8661. https://doi.org/10.3390/ijms22168661
    [88] Wang S, Zimmermann S, Parikh K, et al. (2019) Current diagnosis and management of small-cell lung cancer. Mayo Clin Proc 94: 1599-1622. https://doi.org/10.1016/j.mayocp.2019.01.034
    [89] Li B, Zhu L, Lu C, et al. (2021) circNDUFB2 inhibits non-small cell lung cancer progression via destabilizing IGF2BPs and activating anti-tumor immunity. Nat Commun 12: 295. https://doi.org/10.1038/s41467-020-20527-z
    [90] Xu Y, Wang Q, Xie J, et al. (2021) The predictive value of clinical and molecular characteristics or immunotherapy in non-small cell lung cancer: a meta-analysis of randomized controlled trials. Front Oncol 11: 732214. https://doi.org/10.3389/fonc.2021.732214
    [91] Xiao Y, Wu J, Lin Z, et al. (2018) A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Programs Biomed 153: 1-9. https://doi.org/10.1016/j.cmpb.2017.09.005
    [92] Seijo LM, Peled N, Ajona D, et al. (2019) Biomarkers in lung cancer screening: achievements, promises, and challenges. J Thorac Oncol 14: 343-357. https://doi.org/10.1016/j.jtho.2018.11.023
    [93] Yuan F, Lu L, Zou Q (2020) Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms. Biochim Biophys Acta-Mol Basis Dis 1866: 165822. https://doi.org/10.1016/j.bbadis.2020.165822
    [94] Matsubara T, Ochiai T, Hayashida M, et al. (2019) Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles. J Bioinf Comput Biol 17: 1940007. https://doi.org/10.1142/S0219720019400079
    [95] Wiesweg M, Mairinger F, Reis H, et al. (2020) Machine learning reveals a PD-L1–independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context. Eur J Cancer 140: 76-85. https://doi.org/10.1016/j.ejca.2020.09.015
    [96] Khalifa NEM, Taha MHN, Ali DE, et al. (2020) Artificial intelligence technique for gene expression by tumor RNA-Seq data: a novel optimized deep learning approach. IEEE Access 8: 22874-22883. https://doi.org/10.1109/ACCESS.2020.2970210
    [97] Wang W, Ding M, Duan X, et al. (2019) Diagnostic value of plasma microRNAs for lung cancer using support vector machine model. J Cancer 10: 5090. https://doi.org/10.7150/jca.30528
    [98] Selvanambi R, Natarajan J, Karuppiah M, et al. (2020) Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization. Neural Comput Appl 32: 4373-4386. https://doi.org/10.1007/s00521-018-3824-3
    [99] Banaganapalli B, Mallah B, Alghamdi KS, et al. (2022) Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer. Plos one 17: e0274629. https://doi.org/10.1371/journal.pone.0274629
    [100] Tanaka I, Furukawa T, Morise M (2021) The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: Harmonization of molecular cancer biology and artificial intelligence. Cancer Cell Int 21: 1-14. https://doi.org/10.1186/s12935-021-02165-7
    [101] Choi Y, Qu J, Wu S, et al. (2020) Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts. BMC Med Genomics 13: 1-15. https://doi.org/10.1186/s12920-020-00782-1
    [102] Khan A, Lee B (2021) Gene transformer: Transformers for the gene expression-based classification of lung cancer subtypes. arXiv preprint arXiv: 2108.11833. https://doi.org/10.48550/arXiv.2108.1183
    [103] Oka M, Xu L, Suzuki T, et al. (2021) Aberrant splicing isoforms detected by full-length transcriptome sequencing as transcripts of potential neoantigens in non-small cell lung cancer. Genome Biol 22: 1-30. https://doi.org/10.1186/s13059-020-02240-8
    [104] Martínez-Ruiz C, Black JRM, Puttick C, et al. (2023) Genomic–transcriptomic evolution in lung cancer and metastasis. Nature : 1-10. https://doi.org/10.1038/s41586-023-05706-4
    [105] Hofman P, Heeke S, Alix-Panabières C, et al. (2019) Liquid biopsy in the era of immuno-oncology: is it ready for prime-time use for cancer patients?. Ann Oncol 30: 1448-1459. https://doi.org/10.1093/annonc/mdz196
    [106] Ilie M, Benzaquen J, Hofman V, et al. (2017) Immunotherapy in non-small cell lung cancer: biological principles and future opportunities. Curr Mol Med 17: 527-540. https://doi.org/10.2174/1566524018666180222114038
    [107] Pantel K, Alix-Panabières C (2019) Liquid biopsy and minimal residual disease—latest advances and implications for cure. Nat Rev Clin Oncol 16: 409-424. https://doi.org/10.1038/s41571-019-0187-3
    [108] He X, Folkman L, Borgwardt K (2018) Kernelized rank learning for personalized drug recommendation. Bioinformatics 34: 2808-2816. https://doi.org/10.1093/bioinformatics/bty132
    [109] Luo S, Xu J, Jiang Z, et al. (2020) Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing. Pharmacol Res 160: 105037. https://doi.org/10.1016/j.phrs.2020.105037
    [110] Ciccolini J, Benzekry S, Barlesi F (2020) Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC. Br J Cancer 123: 337-338. https://doi.org/10.1038/s41416-020-0918-3
    [111] Mu W, Jiang L, Zhang JY, et al. (2020) Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun 11: 5228. https://doi.org/10.1038/s41467-020-19116-x
    [112] Chang L, Wu J, Moustafa N, et al. (2021) AI-driven synthetic biology for non-small cell lung cancer drug effectiveness-cost analysis in intelligent assisted medical systems. IEEE J Biomed Health Inform 26: 5055-5066. https://doi.org/10.1109/JBHI.2021.3133455
    [113] Wang S, Yu H, Gan Y, et al. (2022) Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health 4: e309-e319. https://doi.org/10.1016/S2589-7500(22)00024-3
    [114] Khorrami M, Khunger M, Zagouras A, et al. (2019) Combination of peri-and intratumoral radiomic features on baseline CT scans predicts response to chemotherapy in lung adenocarcinoma. Radiol Artif Intell 1: 180012. https://doi.org/10.1148/ryai.2019180012
    [115] Song P, Cui X, Bai L, et al. (2019) Molecular characterization of clinical responses to PD-1/PD-L1 inhibitors in non-small cell lung cancer: Predictive value of multidimensional immunomarker detection for the efficacy of PD-1 inhibitors in Chinese patients. Thorac Cancer 10: 1303-1309. https://doi.org/10.1111/1759-7714.13078
    [116] Yu KH, Berry GJ, Rubin DL, et al. (2017) Association of omics features with histopathology patterns in lung adenocarcinoma. Cell Syst 5: 620-627. https://doi.org/10.1016/j.cels.2017.10.014
    [117] Lee TY, Huang KY, Chuang CH, et al. (2020) Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication. Comput Biol Chem 87: 107277. https://doi.org/10.1016/j.compbiolchem.2020.107277
    [118] She Y, Jin Z, Wu J, et al. (2020) Development and validation of a deep learning model for non–small cell lung cancer survival. JAMA Netw Open 3: e205842-e205842. https://doi.org/10.1001/jamanetworkopen.2020.5842
    [119] Emaminejad N, Qian W, Guan Y, et al. (2015) Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE Trans Biomed Eng 63: 1034-1043. https://doi.org/10.1109/TBME.2015.2477688
    [120] Liu WT, Wang Y, Zhang J, et al. (2018) A novel strategy of integrated microarray analysis identifies CENPA, CDK1 and CDC20 as a cluster of diagnostic biomarkers in lung adenocarcinoma. Cancer Lett 425: 43-53. https://doi.org/10.1016/j.canlet.2018.03.043
    [121] Malik V, Dutta S, Kalakoti Y, et al. (2019) Multi-omics Integration based Predictive Model for Survival Prediction of Lung Adenocarcinaoma. 2019 Grace Hopper Celebration India (GHCI) : 1-5. https://doi.org/10.1109/GHCI47972.2019.9071831
    [122] Wang X, Duan H, Li X, et al. (2020) A prognostic analysis method for non-small cell lung cancer based on the computed tomography radiomics. Phys Med Biol 65: 045006. https://doi.org/10.1088/1361-6560/ab6e51
    [123] Johnson M, Albizri A, Simsek S (2022) Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis. Ann Oper Res 308: 275--305. https://doi.org/10.1007/s10479-020-03872-6
    [124] Ekins S, Puhl AC, Zorn KM, et al. (2019) Exploiting machine learning for end-to-end drug discovery and development. Nat Mater 18: 435-441. https://doi.org/10.1038/s41563-019-0338-z
    [125] Chandak T, Mayginnes JP, Mayes H, et al. (2020) Using machine learning to improve ensemble docking for drug discovery. Proteins 88: 1263-1270. https://publons.com/publon/10.1002/prot.25899
    [126] Houssein EH, Hosney ME, Oliva D, et al. (2020) A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery. Comput Chem Eng 133: 106656. https://doi.org/10.1016/j.compchemeng.2019.106656
    [127] Zhao L, Ciallella HL, Aleksunes LM, et al. (2020) Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today 25: 1624-1638. https://doi.org/10.1016/j.drudis.2020.07.005
    [128] Zhavoronkov A, Ivanenkov YA, Aliper A, et al. (2019) Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 37: 1038-1040. https://doi.org/10.1038/s41587-019-0224-x
    [129] Bhuvaneshwari S, Sankaranarayanan K (2019) Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach. SAR QSAR Environ Res 30: 81-108. https://doi.org/10.1080/1062936X.2019.1566172
    [130] He G, Gong B, Li J, et al. (2018) An improved receptor-based pharmacophore generation algorithm guided by atomic chemical characteristics and hybridization types. Front Pharmacol 9: 1463. https://doi.org/10.3389/fphar.2018.01463
    [131] Yang H, Wierzbicki M, Du Bois DR, et al. (2018) X-ray crystallographic structure of a teixobactin derivative reveals amyloid-like assembly. J Am Chem Soc 140: 14028-14032. https://doi.org/10.1021/jacs.8b07709
    [132] Trebeschi S, Drago SG, Birkbak NJ, et al. (2019) Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol 30: 998-1004. https://doi.org/10.1093/annonc/mdz108
    [133] Wang Q, Xu J, Li Y, et al. (2018) Identification of a novel protein arginine methyltransferase 5 inhibitor in non-small cell lung cancer by structure-based virtual screening. Front Pharmacol 9: 173. https://doi.org/10.3389/fphar.2018.00173
    [134] Haredi Abdelmonsef A (2019) Computer-aided identification of lung cancer inhibitors through homology modeling and virtual screening. Egypt J Med Hum Genet 20: 1-14. https://doi.org/10.1186/s43042-019-0008-3
    [135] Shaik NA, Al-Kreathy HM, Ajabnoor GM, et al. (2019) Molecular designing, virtual screening and docking study of novel curcumin analogue as mutation (S769L and K846R) selective inhibitor for EGFR. Saudi J Biol Sci 26: 439-448. https://doi.org/10.1016/j.sjbs.2018.05.026
    [136] Udhwani T, Mukherjee S, Sharma K, et al. (2019) Design of PD-L1 inhibitors for lung cancer. Bioinformation 15: 139. https://doi.org/10.6026/97320630015139
    [137] Patel HM, Ahmad I, Pawara R, et al. (2021) In silico search of triple mutant T790M/C797S allosteric inhibitors to conquer acquired resistance problem in non-small cell lung cancer (NSCLC): a combined approach of structure-based virtual screening and molecular dynamics simulation. J Biomol Struct Dyn 39: 1491-1505. https://doi.org/10.1080/07391102.2020.1734092
    [138] Su H, Mariani A, Ovur SE, et al. (2021) Toward teaching by demonstration for robot-assisted minimally invasive surgery. IEEE Trans Autom Sci Eng 18: 484-494. https://doi.org/10.1109/TASE.2020.3045655
    [139] Qi W, Aliverti A (2019) A multimodal wearable system for continuous and real-time breathing pattern monitoring during daily activity. IEEE J Biomed Health Inf 24: 2199-2207. https://doi.org/10.1109/JBHI.2019.2963048
    [140] Khan B, Fatima H, Qureshi A, et al. (2023) Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. Biomed Mater Devices 2023: 1-8. https://doi.org/10.1007/s44174-023-00063-2
    [141] Hanif A, Zhang X, Wood S (2021) A survey on explainable artificial intelligence techniques and challenges. 2021 IEEE 25th international enterprise distributed object computing workshop (EDOCW) 2021: 81-89. https://doi.org/10.1109/EDOCW52865.2021.00036
    [142] Dicuonzo G, Donofrio F, Fusco A, et al. (2023) Healthcare system: Moving forward with artificial intelligence. Technovation 120: 102510. https://doi.org/10.1016/j.technovation.2022.102510
    [143] McLennan S, Fiske A, Tigard D, et al. (2022) Embedded ethics: a proposal for integrating ethics into the development of medical AI. BMC Med Ethics 23: 6. https://doi.org/10.1186/s12910-022-00746-3
    [144] Steffens D, Pocovi NC, Bartyn J, et al. (2023) Feasibility, reliability, and safety of remote five times sit to stand test in patients with gastrointestinal cancer. Cancers 15: 2434. https://doi.org/10.3390/cancers15092434
    [145] Askin S, Burkhalter D, Calado G, et al. (2023) Artificial Intelligence Applied to clinical trials: opportunities and challenges. Health Technol 13: 203-213. https://doi.org/10.1007/s12553-023-00738-2
    [146] Albahri AS, Duhaim AM, Fadhel MA, et al. (2023) A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Inf Fusion 96: 156-191. https://doi.org/10.1016/j.inffus.2023.03.008
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