K. pneumoniae strain 10. The bacterial growth was completely eradicated after 8 and 16 h incubation with TPU/ZnO and TPU/CuO nanofibers, respectively. The nanofibers SEM study reveals the adsorption of the bacterial cells on the metal oxides nanofibers surface which led to cell lysis and releasing of their content. Finally, in vitro study against Spike S-protein from SARS-CoV-2 was also evaluated to investigate the potent effectiveness of the proposed nanofibers in the virus deactivation. The results showed that the metal oxide concentration is an effective factor in the antiviral activity due to the observed pattern of increasing the antibacterial and antiviral activity by increasing the metal oxide concentration; however, TPU/ZnO nanofibers showed a potent antiviral activity in relation to TPU/CuO.Herein, in the present work two series of thermoplastic polyurethane (TPU) nanofibers were manufactured using the electrospinning techniques with ZnO and CuO nanoparticles for a potential use as an elastic functional layer in antimicrobial applications. Percentages of 0%, 2 wt%, and 4 wt% of the nanoparticles were used. The morphological characterization of the electrospun TPU and TPU/NPs composites nanofibers were observed by using scanning electron microscopy to show the average fiber diameter and it was in the range of 90–150 nm with a significant impact of the nanoparticle type. Mechanical characterization showed that TPU nanofiber membranes exhibit excellent mechanical properties with ultra-high elastic properties. Elongation at break reached up to 92.5%. The assessment of the developed nanofiber membranes for medical and personal protection applications was done against various colistin resistant bacterial strains and the results showed an increment activity by increasing the metal oxide concentration up to 83% reduction rate by using TPU/ZnO 4% nanofibers against  The global health system is struggling to provide enough protection for the public. Alongside the health care systems that are seeking all the possible ways for suitable testing and care for the potentially infectious patients ,2, the CKlebsiella pneumoniae is an opportunistic Gram-negative bacteria that can cause bacteremia, pneumonia, urinary tract infection, and wound infection in hospitalized patients  was formed and filtered out followed by washing and drying at 200 °C for 3 h. The characterization of the prepared CuO nanoparticles and ZnO one was achieved using different spectroscopic tools  were added to 5 mL of TPU solution and stirred overnight.Similar concentrations of CuO  were added to 5 mL of TPU solution and prepared for electrospinning through continuous stirring overnight and sonication.The prepared solutions were electrospun by adding each concentration into a plastic syringe with 18-gauge stainless-steel needle. High voltage power supply CZE1000R  was connected and provided positive volt of 25 kV to the needle. Feed rate of 1 mL/h was fixed by using a syringe pump NE1000 . Distance between needle tip and grounded rotating collector was adjusted to 10 cm.The morphology of the electrospun TPU and TPU composites nanofibers were observed by using scanning electron microscopy . One sample of each concentration was cut and sputtered with gold. The average fiber diameter and fiber diameter distribution were measured by using Image-J software −1 over a range of 4000–400 cm−1.A Fourier transform infrared spectrometer (FT-IR)  was operated in Attenuated Total Reflection (ATR) mode. Samples . Each sample were held and fixed between two layers of cardboard frames with 4 cm gauge length. A universal testing machine was used  to perform the test. The test was performed at a strain rate of 10 mm/min and zero initial load. The load cell used was equal to 100 N.K. pneumoniae) under test were identified and provided by the Surveillance Microbiology Department Strain Bank, at Al-Shatby Pediatric Hospital, Alexandria, Egypt.Colistin resistant strains (6 CFU/mL) were inoculated on Muller Hinton agar (MHA) plates and then different nanofibers with standard dimensions (1 cm × 1 cm) overlaid the inoculated agar. Each plate was incubated at 37 ± 2 °C for 24 h. The antibacterial activity was observed as inhibition zone halos around the nanofibers [Antibacterial activity of the prepared nanofibers was investigated using different techniques. Bacterial suspensions . A total of 100 µL of the bacterial solution was extracted at different time intervals, plated on agar plates, and incubated to calculate the number of colonies formed [Another antibacterial test was done through the assessment of the bacterial lethality curve. Different nanofibers with standard dimensions (1 cm × 1 cm) were placed inside inoculated bacterial suspension tubes  nanofiber was placed in sterilized distilled water and 1 mL of the solution was extracted every 180 s for 1 h and measured the concentration of the metal oxides by aspiration Flame atomic absorption .TPU/ZnO and TPU/CuO nanofibers antibacterial activities were assessed using electron microscope study. TPU/ZnO and TPU/CuO nanofibers (1 cm × 1 cm) were inoculated in 50 mL of the tested bacterial suspension (102 CFU/mL). After 24 h contact time in the bacterial suspension, the nanofibers were examined using scanning electron microscope (SEM) .COVID-19 Coronavirus Assay Kit  was used to screen the SARS-CoV-2 inhibition of the synthesized nanofibers. SARS-CoV-2 inhibitor screening assay is based on a colorimetric ELISA kit, which measures the binding of the Spike S protein (from SARS-CoV-2) to its human receptor ACE2. Hence, it has been used to identify the inhibitory effect of between the viral protein and its human cell receptor using different compounds. Incubated between the tested materials and Spike S was needed according to the kit manual (one hour at 37 °C) then the OD was measured at 450 nm using ELISA reader .The morphology of TPU and TPU composites nanofibrous membranes revealed that the electrospun nanofibers exhibited smooth surface and high homogeneity. The diameter of the TPU nanofibers were in the range of 90–150 nm with average fiber diameter of 116 nm along their length as shown in Adding ZnO nanoparticles with concentration of 2 wt% and 4 wt% affected the average fiber diameter significantly to be reduced to 102 and 89 nm, respectively, as shown in This decrease in fiber diameter at the concentration of 2 wt% and 4 wt% could be attributed to the excellent preparation conditions and the homogeneity of the polymer solution even after adding the nanoparticles. The nanoparticles concentration was not high enough to disturb the spinning process.−1 attributed to C=O stretching, 1529 cm−1 for N–H in-plane bending, and 1232 cm−1 that is refer to C–N stretching for the pure TPU nanofibers [−1 was assigned to O–H stretching; however, an asymmetric vibration of CH2 stretching was appeared at 2945 cm−1 Furthermore, TPU/ZnO 2 wt% and 4wt% composites showed intense peak at 1727 cm−1 with more enhancement of the wavenumber of the stare aching vibration of the C=O bond, and this could be attributed to the interaction between the C=O functional group with the metal that could increase the strength of the C=O bond [2O molecules associated with CuO nanoparticles [The manufactured nanofibers were characterized using FTIR to confirm the presence of ZnO and CuO nanoparticles into the TPU nanofibers matrix. FTIR spectra of pure TPU, TPU/ZnO and TPU/CuO nanofibers composites are shown in nofibers . FurtherC=O bond . At the articles .The mechanical properties of the electrospun TPU, TPU/ZnO, and TPU/CuO nanofiber mats were examined through the tensile testing. We noticed that TPU nanofibers exhibited a maximum strength of 17.7 MPa with strain percentage of 92.5%, which is an indication for the excellent mechanical properties for TPU polymer that can withstand high stress and ultra-high elasticity.In a comparative study  done to In another study done on TPU microfibers fabricated through laser melt electrospinning  as a potAs can be seen in K. pneumoniae strain 10. The most susceptible strain (K. pneumoniae strain 10) was chosen for further antibacterial analyses. The bacterial lethality curve indicated that the bacterial growth was completely eradicated after 8 and 16 h incubation with TPU/ZnO and TPU/CuO, respectively.The antibacterial activity of the prepared nanofibers was evaluated using various techniques due to the significant fear of the colistin resistant bacterial strains. Promising activity was noticed upon using TPU/ZnO nanofibers . The obsThis may be explained by the release of the metal ions into the solution. Hence, the metal oxides release from the nanofiber was assessed. Data revealed the Cu and Zn ions releasing into the solution were burst in the first 360 s till reaching stability state after 540 s and 720 s, respectively . This buTo determine the mechanism of action of the prepared nanofibers SEM study was used. It was found that the tested bacteria were adsorbed on the metal oxides nanofibers surface which led to bacterial cells lysis and releasing of the cell content .Few researchers started to prevent the viral infectivity through using textile materials, which are usually fibrous structures. Hence, in the present manuscript we aimed to start the journey by taking one step further using in vitro evaluation against Spike S-protein from SARS-CoV-2. Data in The manufacture of two concentrations of ZnO and CuO nanoparticles with TPU using electrospinning technique has been accomplished. The SEM morphological investigation showed the significant impact of the nanoparticle type average nanofiber diameter. Mechanical investigation showed the effect of adding nanoparticle on the mechanical properties of nanofibers membranes. The elastic properties have been affected clearly by adding the nanoparticle, where the elongation at break reduced from 92.5% in case of TPU nanofiber membrane to 81.25% in case of TPU/CuO 4 wt%. The evaluation of the manufactured nanofibers as anti-multidrug-resistant bacteria and antiviral has been achieved through assessment using various colistin resistant bacterial strains and Spike S-protein from SARS-CoV-2 deactivation of the viruses, respectively. Based on the observed trend of rising antibacterial and antiviral activity by raising the metal oxide concentration, the results revealed that the metal oxide concentration is an effective factor in antiviral activity, and TPU/ZnO displayed a potent antiviral activity in comparison to TPU/CuO. The developed nanofiber membrane showed promising potential to be used in medical and personal protection applications, especially in applications that need good mechanical and hygienic performance.


It was found that the fuel-N is constituted by 85% protein nitrogen and 15% inorganic nitrogen, and the fuel-S by 78% inorganic sulfur and 22% organic sulfur. Nitrogen oxide emissions mainly occur at the volatile combustion stage when the temperature rises to 400 °C, while the primary sulfur oxide emission appears at the char combustion stage above 400 °C. Increasing the combustion temperature and airflow cause higher NOx emissions. High moisture content in AMR can significantly reduce the NOx emission by lowering the combustion temperature and generating more reducing gases such as CO. For the SO2 emission, the combustion temperature (700 to 900 °C), airflow and AMR water content do not seem to exhibit obvious effects. The presence of CaO significantly inhibits SO2 emission, especially for the SO2 produced during the AMR char combustion because of the good control effect on the direct emission of inorganic SO2. Employing air/fuel staging technologies in combination with in-situ desulfurization by calcium oxide/salts added in the combustor with operation temperatures lower than 900 °C should be a potential technology for the clean disposal of AMRs.The antibiotic mycelial residue (AMR) generated from cephalosporin C production is a hazardous organic waste, which is usually disposed of by landfilling that causes potential secondary environmental pollution. AMR combustion can be an effective method to treat AMR. In order to develop clean combustion technologies for safe disposal and energy recovery from various AMRs, the emission characteristics of NO Antibiotic mycelial residues (AMRs) are the wastes generated from fermentative processes in antibiotics production . Around So far, most AMRs are disposed of by landfilling, which causes potential secondary environmental pollution . Other f2 emissions would be kept at a high concentration and might not be affected by steam. Ge et al. [x emissions during combustion in a fluidized bed. Jiang et al. [x/SO2 emissions with N/S species, detailed effects of combustion parameters, effectiveness of CaO desulfurization during combustion in a fixed bed, as well as effects of moisture in raw AMRs on the NOx and SO2 emissions in comparison to the external steam addition. Thus, further works need to be done to improve combustion technologies for safe disposal and energy recovery from AMRs.For the disposal of solid waste from the pharmaceutical industry, combustion is the simplest and most commonly used method around the world. By combustion, solid wastes are safely disposed of, and the energy of the organic materials can be recovered simultaneously . At prese et al.  also foug et al.  observedg et al.  investigg et al.  simulatex/SO2 emissions. Moreover, the effects of temperature, excess air, CaO addition, and water content of the fuels on the NOx and SO2 emissions were studied in the fixed bed reactor. The mechanism of NOx/SO2 emissions during AMR combustion is clarified, and the effects of NOx/SO2 control methods are evaluated in this work, which provides valuable reference data for the industrial application of AMR combustion.In this study, the N and S species in AMRs were characterized by X-ray photoelectron spectroscopy (XPS), and nitrogen and sulfur oxide emission characteristics during the AMR combustion were investigated by a TGA-MS setup and a fixed bed reactor with respect to the correlations between N/S species and NOThe AMR used in this work was supplied from a cephalosporin C production factory in Hebei Province, China. The moisture content in the raw AMR was about 70 wt.%. The ARM sample was firstly dried at 105 °C for 24 h, then pulverized to small particles, and finally sieved by a 0.2 mm diameter sieve. The results of the ultimate and proximate analyses of the dried AMR are shown in To characterize the composition of the AMR ash, the AMR was burned in an oven at 815 °C for 40 min . The remaining ash was measured by an X-ray Fluorescence Spectrometer (XRF), and the results are shown in 2, and SO2) during pyrolysis or combustion of the AMR with increasing temperature. For each test, 10 ± 0.1 mg of the AMR sample was put into an alumina crucible and was subject to a hot gas flow at 100 mL/min with 100% Ar for pyrolysis or with 79% Ar and 21% O2 for combustion. The sample was heated from room temperature to 800 °C at a heating rate of 20 °C/min; the flue gases were detected and relatively quantified by MS.A Thermalgravimetric Analyzer  was connected to a Mass Spectrometer   in order−7 Pa for 3 h, and neutralized by Ar-ion sputtering. The sample was excited by Monochromatic Al K Alpha  X-ray. All spectra were obtained at 20 eV of pass energy in the fixed transmission mode. The resolution and analysis areas were 0.05 eV and 0.8 mm2, respectively. The analysis was automatically performed at 3–5 different positions on the surface to obtain high-quality N 1s and S 2p spectra.With respect to the nitrogen and sulfur elements existing in the AMR, an XPS analyzer  was used to characterize the related functional groups . For XPSx converter (convert NO2 to NO) is connected to the outlet of the outer reactor to monitor the online concentration of O2, CO2, NOx, and SO2 [The combustion tests were conducted in a fixed bed, which can be heated by an external electric heater, as shown in  and SO2 ,17,18,192 was released from the sample. Each experimental condition was repeated at least three times to ensure reliability. For CaO additive tests, 0.25 g AMR was blended evenly with 0.025–0.050 g CaO  and then mixed with 5 g silica sand. For the water additive tests, a desired amount of water was sprayed evenly by a small injector on the bed surface before the inner reactor was inserted into the outer reactor to make the water content in the sample reach a certain level of 9.1, 18.2, or 27.3%, respectively.For the combustion tests in the fixed bed, 0.25 g AMR was blended with 5 g silica sand to maintain a certain bed height to enhance gas-solid interaction and prevent possible ash agglomeration at the plate during combustion . The welx, SO2, and CO, the gas emission rates were calculated by the following equation:x, SO2, or CO, ERi (mg/s) represents the gas emission rates, Ci (ppm) denotes the instantaneous concentration of each gas given by the flue gas analyzer, F is the total gas flow of 1.0, 1.5, 2.0, or 3.0 L/min, and Mi denotes the molar mass of the corresponding gas (NOx uses the molar mass of NO).For NO2 consumption rate, reflecting the intensity of combustion, was calculated according to Equation (2):CR (mg/s) represents the O2 consumption rates, CO (%) denotes the instantaneous concentration of O2, F is the total flow of gas, and MO indicates the molar mass of O2.The OEAi(mg), of NOx, SO2, or CO was calculated by Equation (3):t represent the combustion duration and the instantaneous time, respectively.The total emission, Xi, of N, or S, and the yield of CO, XC were calculated by the following equation:ci (%) is the mass content of N in NO . Mass denotes the mass of the added sample, 250 mg, fi (%) is the elemental content :The ratio of carbon not converted to CO, 4+-N [The N distributions over their species in the AMR feedstock are shown in 4+-N .For the S-containing species, the dominant species is inorganic sulfur  , as showThe mass loss of the AMR was registered by TGA against temperature increase, as shown in 2 emission trends against temperature present peak emission intensity between 200 and 400 °C, and leveled off above 400 °C, as shown in 2 emissions at the combustion temperatures above 400 °C suggested that the N-containing groups were not involved in the char formation (mainly by polymerization and cyclization reaction).The NO/NO2 emissions, the SO2 emission trend shows two peaks. The first peak was small at about 300 °C and should be caused by the devolatilization of the AMR; thus, it can be attributed to organic sulfur [2 emission occurred at the char combustion stage, which should be closely linked to the high content of the inorganic sulfur in the AMR [Different from NO/NOc sulfur ,31. The  the AMR ,31.2 against the combustion time, which can monitor the combustion process well. The real-time O2 consumption curve displays a unimodal distribution since combustions of both volatile and char take place continuously without staging as weight loss and simultaneously reach maximum combustion rate. The O2 consumption peak was higher at a higher combustion temperature and shifted to the left for a shorter combustion time, as seen in x emission curve also presents a unimodal distribution, and the peak and total amount of the NOx emissions increased with increasing combustion temperature from 700 to 900 °C. High temperature was in favor of the fuel-N conversion to NOx [2 consumption curve, the time of the NOx emission peak, tmax, was short and did not have an obvious shift with increasing combustion temperature as shown in 2 was consumed throughout the whole process, including both the volatile combustion and the char combustion. A similar result was observed by Zhao et al. from the algae biomass combustion experiment [x, NX, increases from 6.9% to 11.9% as the temperature increases from 700 to 900 °C.As shown in n to NOx . Compariperiment . As show2 emission, 2 consumption curves in 2 consumption and the SO2 emission, much differently from NOx emission. The oxidation of inorganic and organic sulfur overlapped in the rapid heating of the AMR samples in the fixed bed.For the SO2 consumption, the SO2 emissions started later than the O2 consumption, and tmax of the SO2 emissions was longer than the O2 consumption, as also given in Slightly different from the O2 emission and results in a shorter tmax. On the other hand, the low temperature caused a slow heating rate and slow char combustion. Nevertheless, the total SO2 emission and the conversion ratio of fuel-S to SO2 (XS) were unchanged in spite of their different combustion temperatures, as presented in The higher combustion temperature causes faster and earlier SO2 consumption, NOx, and SO2 emissions. The O2 consumption was not sensitive to airflow, but the peak shifted slightly to the left since higher excess air promotes volatile and char combustion. It should be mentioned that CO appeared at the airflow of 3 L/min due to the short residence time of the flue gas in the reactor, as given in The effect of airflow on the AMR combustion process is presented in x emissions shown in 2 consumption in response to the AMR fuel devolatilization stage. The high airflow enhanced the oxidation of fuel-N to NOx and, at the same time, inhibited the reduction of NOx to N2. As a result, XN increases from 5.4% at 1 L/min to 11.2% at 3 L/min, as shown in 2 shortage, which could significantly reduce the formation of NOx. This suggests an effective method to control the NOx emission from the AMR combustion via air staging combustion.For the NO2 emission had similar trends to the O2 consumption, as seen in 2 emission peak slightly shifted to an earlier time, which might be related to the enhancement of the char combustion by excess air as the SO2 was mainly released during the char combustion. The SO2 emission peak lagged behind the O2 consumption peak, suggesting that the char combustion and ash decomposition dominated the SO2 emission. However, the airflow hardly changed the SO2 total emission, as shown in XN was steadily kept at 23.8–25.1%, as listed in The SOXs (23.5% ± 1.5%) compared to other biomasses (5–20%) under similar combustion conditions [2 emission for AMR combustion.Most biomass fuels have a sulfur content below 0.2%, with only a few exceptions as high as 0.5–0.7% ,34. The nditions . Therefo2 consumption shown in 2 consumption in the later combustion stage of 50 to 110 s. CaO addition in the AMR also shortened the combustion time. The addition of CaO showed a positive effect on AMR combustion, especially on char combustion. This result agrees well with Yuan et al. [For the On et al. , who conx emission and tmax were similar for the different Ca/S molar ratios, as presented in 2 emissions in x started to be released at the earlier time of combustion and continued at a low level of NOx emission during the combustion time of 100 to 150 s, which increased with the Ca/S molar ratio, as seen in x emission and the XN did not change visibly, as shown in NO2 emission started at the same time but finished earlier at a higher Ca/S molar ratio. Moreover, a higher Ca/S molar ratio resulted in an earlier tmax, as shown in tmax of SO2 emissions were even shorter than the tmax of O2 consumption and approximately similar to the tmax of NOx emission (40–50 s). In other words, the organic-S in the volatiles gradually dominated the SO2 emission with the increase of Ca/S. CaO additives can effectively control the SO2 emissions from inorganic-S. On the other hand, organic-S is released with the volatiles of the sample rather than directly released in SO2 form. Thus, sulfur at the devolatilization stage is hard to capture because of the poor gas-solid interaction in a fixed bed. The addition of CaO is apt to convert the inorganic sulfur into thermally stable CaSO4 and CaS [XS decreased from 23.3 to 6.8% as the Ca/S molar ratio increased from 0 to 4, as listed in CaO plays a significant role in sulfur retention, as shown in  and CaS ,32,35. C2 consumption, following the earlier stage of devolatilization. Two aspects were involved in the CO release: (1) water vaporization makes a lower reactor temperature, thus hindering the burnout of samples, and (2) the formed water vapor increases the gas flow in the reactor and causes a shorter volatile resident time, leading to incomplete combustion. As a result, the burnout ratio decreased from 99.8% to 97.9% when the water content increased from 9.1% to 27.3%, as listed in As shown in x emission exhibited a bimodal distribution for the water-containing samples, as seen in x emission peak was much higher and should be related to the volatile decomposition stage of the AMR combustion. The second NOx emission peak contained two sub-peaks, which should be associated with the unstable combustion process for the water-containing samples, as shown by the O2 consumption curves. The occurrence of the first weak peak at 0–50 s might be explained by the devolatilization of the N-containing species, which is kept at a low level by the high concentration of CO and thus the reduction effect of CO on NOx (2NO + 2CO = N2 +2CO2) [x reaches a small peak (at 25 s), then decreases due to the reducing atmosphere, and then increases again at 40–45 s because the reducing matters are consumed by O2. In conclusion, higher water content reduces the conversion of the fuel-N to NOx, as shown in The NO2 +2CO2) ,37. Thus2 emission from combustion of the water-containing fuel sample had a similar unimodal distribution to that from the combustion of the dry AMR sample, but with a time delay due to evaporation of water in advance. The increase in water content did not seem to reduce the total amount of SO2 emissions from the AMR combustion in the present case of the fixed bed, as shown in 2 by the AMR ash in a continuous feeding fluidized bed. In the case of a fixed bed, the water was evaporated to form steam and escaped from the sample, and the chance of steam reacting with sulfur-containing species during the char combustion process was very small. In addition, compared to a continuous feeding fluidized bed, a fixed bed did not have much ash to capture SO2.As x and SO2 from AMR combustion were investigated based on an experimental study conducted in a fixed bed. Analysis of the AMR showed that the fuel-N was constituted by 85% protein nitrogen and 15% inorganic nitrogen and the fuel-S by 78% inorganic sulfur and 22% organic sulfur. The volatile combustion resulted in most NOx when the temperature rose to 400 °C, while the primary sulfur oxide emission occurred at the char combustion stage above 400 °C. Increasing the combustion temperature and airflow caused higher NOx emissions. The addition of CaO changed the distribution of NOx emission by converting part of the volatile nitrogen into char nitrogen. High moisture content in AMR significantly reduced the NOx emission by lowering the combustion temperature and generating more reducing gases such as CO. For the SO2 emission, the combustion temperature (700 to 900 °C), airflow, and AMR water content did not seem to exhibit obvious effects. The presence of CaO significantly inhibited SO2 emission, especially for the SO2 produced during the AMR char combustion. In addition, low combustion temperatures, high water contents in the fuel, as well as low airflows were unfavorable for the burnout of the AMR. Therefore, the direct combustion of AMR with in-situ NOx control must be balanced with fuel burnout. Employing air/fuel staging technologies in combination with in situ desulfurization by calcium oxide/salts, in addition to operating temperatures lower than 900 °C, could be a potential technology for the clean disposal of AMRs. In addition, a number of aspects will require further attention and dedicated experimental work. In particular, systematic studies of air/fuel staging technologies and continuous feed tests need to be carried out.The emission characteristics of NO


The association between AMU or biosecurity and AMR was explored. Significant positive associations were detected between AMU and both genotypic and phenotypic AMR for specific antimicrobial classes. Beta-lactam and colistin resistance (metagenomics sequencing); ampicillin and ciprofloxacin resistance (MIC) were associated with AMU. However, no robust AMU-AMR association was detected by analyzing qPCR targets. In addition, no evidence was found that lower biosecurity increases AMR abundance. Using multiple complementary AMR detection methods added insights into AMU-AMR associations at turkey farms.Food-producing animals are an important reservoir and potential source of transmission of antimicrobial resistance (AMR) to humans. However, research on AMR in turkey farms is limited. This study aimed to identify risk factors for AMR in turkey farms in three European countries . Between 2014 and 2016, faecal samples, antimicrobial usage (AMU), and biosecurity information were collected from 60 farms. The level of AMR in faecal samples was quantified in three ways: By measuring the abundance of AMR genes through (i) shotgun metagenomics sequencing ( Antimicrobial resistance (AMR) is a global public health concern causing a substantial health and economic burden . The typTurkeys and turkey meat are possible sources for the transmission of AMR . Within Escherichia coli (E. coli) in Great Britain . Brieflph3′-III . For eacsul2 (11 samples) and aph3′-III (20 samples) were below the limit of detection or limit of quantification. Those samples were assigned a value (in log10 copies) corresponding to the 1st percentile of the distribution when considering all values of all samples together per gene . Of those, the samples with a low abundance of 16S rRNA  were excluded from data analyses because these present very high normalized values.Among the samples passing the qPCR quality criteria (IAC and replicate consistency), those without a quantifiable 16S rRNA concentration were excluded from further analysis (14 samples). Additionally, E. coli and MIC determination was performed as previously described [w/v) with 20% glycerol in a 2-mL cryotube and thoroughly mixed. Ten samples from each farm were selected , resulting in 600 samples for E. coli isolation. Briefly, all samples were inoculated on MacConkey agar and after incubating 24 h overnight, suspected colonies were isolated and confirmed as E. coli. Isolated samples were stored individually in buffered peptone water with 20% glycerol at −80 °C. Next, MIC values by broth microdilution were determined for a fixed panel of antimicrobials by commercially-available microtitre plates . The European Committee on Antimicrobial Susceptibility Testing (EUCAST) epidemiological cut-off values were used to differentiate between wild type and non-wild-type susceptibility.Isolation of escribed . The indFirst, to examine the association between AMR and farm level factors, univariate models with AMR, and the corresponding AMU were applied, as well as with other farm-level variables selected from the questionnaires. Next, according to the association observed in univariate models, multivariable models were built.https://www.R-project.org, accessed on 28 March 2021).All statistical analyses were performed in R version 3.6.1  used trimethoprim-sulphonamide, aminoglycosides, or spectinomycin, we dichotomized these variables. From the questionnaires, the most important farm characteristics variables were selected based on expert knowledge and prior studies [The distributions of continuous variables  were explored and log studies ,19,44,45In the case of a high correlation between technical farm characteristics and biosecurity variables (Spearman ρ > 0.7), technical farm characteristics variables were selected. Variables without contrast and those with missing values were excluded. One missing value of age of turkeys in country B was replaced with the median age of the sampled birds in country B (134 days). All categorical variables were reduced to two levels to avoid convergence errors in modeling.Four technical farm characteristics variables, namely, “total number of turkeys at the farm”, “age of turkeys at sampling”, “other livestock is present at the farm”, and “season of the sampling”, as well as 19 biosecurity variables fulfilling the above criteria were considered in the following models .n = 18, E: n = 20, and H: n = 19). The abundance of AMR genes clustered at the antimicrobial class level were used as the outcome variable. Eight clusters with the reported corresponding AMU were chosen for the models. Random effects meta-analyses by country were performed as previously described [Three samples from farms for which the metagenomic data could not be matched with the questionnaire data were excluded in the risk factor analysis, resulting in 57 farms to be analyzed  and total AMU at the farm was analyzed. t to 10% .The abundances of the four genes were averaged at the farm level using the median value of the five to six samples within each farm to remove correlation in the farms , instead of adding a random effect for the farms. Random effect for both the farm and country resulted in convergence errors when modeling. Linear mixed models with random intercept for each country were applied for both univariate and multivariable analyses.p value < 0.05), and (iii) four technical farm characteristics variables because these may be related with AMU and biosecurity variables. Fixed effect variables were eliminated backward from the full models according to the p value , while keeping the corresponding AMU variable. To make the model coefficients more interpretable, all estimates and their 95% CIs were expressed as GMR values by exponentiating with base 10 coefficients  with the reported corresponding AMU and there were more than 60 resistant isolates . Nalidixic acid was not selected but ciprofloxacin was selected for quinolone resistance. This is because when using the epidemiological cut-off to define non-wild type susceptible isolates, nalidixic acid and the fluoroquinolone ciprofloxacin show the same results in proportions of non-wild type strains. Corresponding AMU variables were aminopenicillin, tetracycline, and quinolone use (fluoroquinolone and other quinolones). Penicillins were not included since E. coli is intrinsically resistant to penicillin. At first, it was intended to investigate the association between polymyxin use and colistin-resistant E. coli, but many models failed to converge in univariate analysis, which made it impossible to further investigate risk factors. Mixed effects logistic models with random intercept for farm were applied. A country random intercept was added when it improved the fit in null models.The occurrence of p value < 0.05) were added in the multivariable models. All ORs and their 95% CIs are shown in the results  and aminopenicillin and fluoroquinolone (MIC). No robust AMU-AMR association was detected by analyzing qPCR targets. No evidence was found that lower biosecurity increases AMR abundance. We showed AMR genes encoding for some antimicrobial classes were abundant in faeces despite the low prevalence of phenotypic resistance in


ZDHHC15 gene assays, and (c) blood RT-qPCR to investigate seven X-linked genes related to neurocognitive development  and XIST in comparison with 14  controls. Considering IQ 80 as the standard minimum reference, we verified that the variability in IQ scores in KS patients seemed to be associated with the XCI pattern. Seven individuals in the KS group presented a random X-inactivation (RXI) and lower average IQ than the four individuals who presented a skewed X-inactivation (SXI) pattern. The evaluation of gene expression showed higher GTPBP6 expression in KS patients with RXI than in controls (p = 0.0059). Interestingly, the expression of GTPBP6 in KS patients with SXI did not differ from that observed in controls. Therefore, our data suggest for the first time that GTPBP6 expression is negatively associated with full-scale IQ under the regulation of the type of XCI pattern. The SXI pattern may regulate GTPBP6 expression, thereby dampening the impairment in cognitive performance and playing a role in intelligence variability in individuals with KS, which warrants further mechanistic investigations.Klinefelter syndrome (KS) displays a broad dysmorphological, endocrinological, and neuropsychological clinical spectrum. We hypothesized that the neurocognitive dysfunction present in KS relies on an imbalance in X-chromosome gene expression. Thus, the X-chromosome inactivation (XCI) pattern and neurocognitive X-linked gene expression were tested and correlated with intelligence quotient (IQ) scores. We evaluated 11 KS patients by (a) IQ assessment, (b) analyzing the XCI patterns using both HUMARA and  Klinefelter Syndrome (KS) is the most frequent chromosomal disorder in males, with a prevalence of one in 650 newborn males regarding the most common KS karyotype, 47,XXY , 2019. IThe phenotypic characteristics of patients with KS vary according to the age at diagnosis and different chromosome constitutions, mainly due to mosaicisms. Before puberty, only minor changes are noted, such as slightly reduced testicular volume or elongated lower limbs . Many KSThe Wechsler adult intelligence scale (WAIS-III), used to evaluate overall intelligence, provides a detailed neurocognitive evaluation also for KS . This inGTPBP6, EIF2S3, ITM2A, HUWE1, KDM5C, GDI1, and VAMP7) process . X-inactnd VAMP7) and XISTty in KS .We aimed to assess whether the intelligence performance variability of KS patients is associated with the X inactivation pattern and expression profile of X-linked genes. Our study regarding XCI and IQ analyzed individuals presenting karyotype 47,XXY and considered them in statistical comparisons with male 46,XY and female 46,XX controls. We also describe the findings observed in individuals presenting the 49,XXXXY, and 48,XXYY karyotypes.We enrolled 27 subjects, 11 KS patients  and two polysomic individuals , from the endocrinology outpatient clinic of University’s Hospital, as indicated in The intelligence quotient (IQ) of the patients was assessed using the Brazilian version of the Wechsler Adult Intelligence Scale—WAIS-III . A trainEIF2S3, GDI1, ITM2A, GTPBP6, HUWE1, KDM5C, VAMP7  controls. Total RNA was isolated from peripheral blood using Trizol® reagent, according to the manufacturer’s instructions . One microgram of total RNA from each sample was reverse transcribed into cDNA using oligo (dT) primers. cDNA was then diluted fivefold, and 1.0 μL aliquot of cDNA was used in a 12 μL PCR reactions containing SYBR Green PCR Master Mix  and 10 μmol/L of each primer for target gene or reference gene (RPS8). PCR primers are listed in The expression level of seven X-chromosome genes C, VAMP7 , and XISRPS8 as reference gene, as described by RT-qPCR reactions were performed in triplicate, and the threshold cycle (Ct) was averaged (SD ≤ 1). The fold changes were calculated according to the comparative ΔΔCt method using AR) gene, located at Xq11.2. However, when the results were non-informative for the polymorphic region of the AR gene, another primer pair was designed to amplify the polymorphic region of the zinc finger DHHC-type containing 15 (ZDHHC15) gene, located at Xq13.3, as previously described , two patients with karyotypes 49,XXXXY (P12) and 48,XXYY (P13), and three mothers of from the patients P3, P5 and P12. The HUMARA assay was performed according to the manufacturer’s protocol as previously described . Two priescribed . Primersn repeats. The undigested samples were used as both the control of the digested reactions and to calculate the number of CAGn repeats. The XCI was considered random when the ratio presented values between 50 and 65%, indeterminate between 66 and 73%, skewed when between 74 and 90%, and extremely skewed between 90 and 100%  was calculated to evaluate the correlation between two numeric variables. GraphPad© PRISM  was used for all statistical analyses. A P-value of < 0.05 was considered statistically significant. Polysomic patients were not considered in the statistical comparison of gene expression between KS  patients and controls due to their phenotypic and molecular variability , and PIQ , are described in ZDHHC15 assays. Among the KS patients, seven (64%) demonstrated random X inactivation (RXI), while four (36%) presented skewed X inactivation (SXI)  . The meaon (SXI) .p = 0.0451). Four patients with borderline IQ levels , ranging from 70 and 77, presented RXI (The mean IQ of the seven patients with RXI was at borderline levels (Mean = 76.86), while for the four patients with SXI, it was average (Mean = 93.75), demonstrating that in this cohort, SXI is present in KS patients with higher IQ values  as expected, and the pseudoautosomal gene GTPBP6 (Xp22.33/Yp11.32) showed differential expression . Interestingly, the expression of GTPBP6 in KS patients with SXI did not differ from that observed for male and female controls , it was not statistically significant (p = 0.0787) among all KS and polysomic patients (GTPBP6 expression (Mean = 2.946) when compared with both female and male controls (p = 0.0012) (GTPBP6 expression levels that did not differ from controls (We further investigated whether patients . However 0.0012) . On the controls .Although there has been significant progress in understanding genetics concerning neurodevelopmental disorders over the last two decades, little is known about the etiopathogenesis of cognitive impairment in 47,XXY patients. Typically, each second X-chromosome is silenced in half of the cells randomly, but this pattern may be skewed when preferential inactivation of an X-chromosome occurs. Therefore, we hypothesized that the X-inactivation pattern might interfere with cognitive phenotype development in KS. To address this topic, we assessed the XCI patterns as well as the expression of seven X-linked genes associated with neurocognitive function and their correlation with intelligence performance in KS patients.Our data endorsed the variability in intelligence performance in the typical 47,XXY KS cohort, as reported previously . We alsoIntellectual disability was detected in one and a borderline level IQ in four cases. Although intellectual variability in KS has already been described , reportsIn KS, comorbidities such as an increased risk for obesity, diabetes, and seizures have been reported . A prediMany studies have attempted to establish criteria for early diagnosis of KS , with chXIST, upregulated in 46,XX subjects  gene presented differential expression among the other neurocognitive X-linked genes in KS studied group. GTPBP6 is expressed in the fetal and adult brain and has been negatively correlated with language functioning  boundaries .The datasets analyzed for this study will be found in the Unifesp Repository  Ethics Committee N° 1.774.406. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.LS assisted patients and performed all neuropsychological tests. LF, AV, JS, and IK performed molecular and bioinformatics analysis. MM and GC reviewed cytogenetics and gene expression data. LS, LF, CM, GC, and MD designed the study, performed, and reviewed statistics. GC and MD equally conceived and coordinated the study, edited, and reviewed the manuscript. All authors discussed the data and contributed to writing the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A One Health approach requires integrative research to elucidate antimicrobial resistance (AMR) in the environment and the risks it poses to human health. Research on this topic involves experts from diverse backgrounds and professions. Shortcomings exist in terms of consistent, complete, and transparent reporting in many environmental studies. Standardized reporting will improve the quality of scientific papers, enable meta-analyses and enhance the communication among different experts. In this study, we aimed to generate a consensus of reporting standards for AMR research in wastewater and related aquatic environments.Based on a risk of bias assessment of the literature in a systematic review, we proposed a set of study quality indicators. We then used a multistep modified Delphi consensus to develop the EMBRACE-WATERS statement , a checklist of recommendations for reporting in studies of AMR in wastewater and related aquatic environments.Consensus was achieved among a multidisciplinary panel of twenty-one experts in three steps. The developed EMBRACE-WATERS statement incorporates 21 items. Each item contains essential elements of high-quality reporting and is followed by an explanation of their rationale and a reporting-example. The EMBRACE-WATERS statement is primarily intended to be used by investigators to ensure transparent and comprehensive reporting of their studies. It can also guide peer-reviewers and editors in evaluation of manuscripts on AMR in the aquatic environment. This statement is not intended to be used to guide investigators on the methodology of their research.We are hopeful that this statement will improve the reporting quality of future studies of AMR in wastewater and related aquatic environments. Its uptake would generate a common language to be used among researchers from different disciplines, thus advancing the One Health approach towards understanding AMR spread across aquatic environments. Similar initiatives are needed in other areas of One Health research. •One Health research links experts from different disciplines assessing antimicrobial resistance.•Non transparent reporting in environmental studies hinders data communication.•Transparent reporting ensures research reproducibility and generalizability.•EMBRACE-WATERS lists recommendations for reporting of studies on AMR in aquatic environment.•EMBRACE-WATERS can assist authors in improving reporting quality of studies. ARGs caResearch on the environmental epidemiology of AMR in water compartments and its determinants is integral to a One Health understanding of the problem of AMR . The WorAs One Health research involves experts from different backgrounds and professions, standardized reporting is vital to improve communication among disciplines. Clarity and consistency of research papers is crucial for the generation of novel One Health evidence. A common language is needed to make the environmental literature more accessible to clinicians who deal with AMR in healthcare settings. Inappropriate reporting might involve under-reporting of crucial aspects in study's context, methods and mainly results. Selective reporting or analysis occurs when only some findings are reported, depending on the nature and direction of the results , and can introduce bias ,13,14. MWe aimed to generate a consensus reporting standard for AMR research in wastewater and related aquatic environments, resulting in the EMBRACE-WATERS  statement. We hope that implementation of these recommendations will improve the comprehensibility, transparency, comparability and reproducibility of such studies by all communities working under the One Health umbrella.2The consensus process comprised of a three-step modified Delphi method  Fig. 1)Fig. 1).F2.1In the first step, we identified quality indicators for reporting through a systematic review of the literature on AMR in urban water cycles . The sec2.2In the next step, the expert panel was expanded and questionnaire rounds were used to solicit feedback and reach consensus on the suggested reporting standards . A draftMedian scores and interquartile ranges (IQRs) were calculated for responses to each statement in each round to assess agreement and level of consensus, respectively. Responses with a median ≥ 4 and with IQR <2 (agreement and consensus) were carried to the next round; statements with a median ≥ 3 with IQR ≤2 (intermediate agreement/consensus) were revised for the subsequent round; and items with a median < 3 or IQR >2 were excluded. Descriptions were accepted if 80% of the experts agreed on their content . Irrespe33.1During the DARWIN expert group meeting held on Oct 2019, the risk of bias scoring results of the systematic review were presented  propose combining DO data and prospective modeling to guide local interventions, especially in LMIC rivers with limited data”  It has been documented in different studies that members of the genus Aeromonas can harbor genes encoding beta-lactam and plasmid mediated quinolone resistance. [… The] contribution of hospital effluents for the dissemination of these bacteria […] remains unclear. Thus, the aim [… was] to assess the role of Aeromonas spp. on the dissemination of antibiotic resistance, […] to elucidate if the hospital effluent could be considered its major reservoir or if, in contrast, Aeromonas spp. could be indicators of antibiotic resistance from non-clinical sources […]” [ces […]” .(2) “This study is globally relevant because India is the largest consumer of antibiotics for personal use in the world and β-lactams are among the most commonly used antibiotics. […] We suspect antibiotic misuse in places like India partially explains the early evolution of CRE strains, including blaNDM-1 positive isolates. Therefore, although New Delhi presents an extreme case, it is a template for understanding AMR spread in any large city without adequate wastewater management; a common scenario in the developing and emerging world” .Explanation: The background should provide information directly pertinent to the study question and justify the study in light of existing knowledge. Avoid opening with well-known, broad-sweeping, overgeneralized and oversimplified statements, such as overuse, misuse, and abuse of antibiotics, antibiotics are the root cause of the spread of AMR, etc. Instead, be focused, provide information and actual statistics related to the tackled AMR research question in the paper  were collected during 24h from the influent wastewater by a sampling device at the treatment plant that allowed continuous sampling. Another three samples were collected in the same manner from the treated urban wastewater (TW) at the exit to the Baltic Sea. Six samples from hospital wastewater (HW) were collected […] by lowering a 50mL flask every 10–15 min during 4h into the wastewater flow and pooling the aliquots”  For the surface water samples, grab samples (5 L) were taken at 50 cm downstream of the two effluent pipes… at a depth of 20 cm, in order to obtain a river sample under the direct influence of WWTP effluent”  sewage/water was diluted in 9 mL sterile saline solution (0.85% NaCl) […]. Subsequently, 0.1 mL of each suspension was placed on CCA ES medium and incubated for 24 h at 35±2 °C. […] Disks containing cefotaxime (CTX–5 mg), ceftazidime (CAZ–10 mg) […] were placed on inoculated Mueller–Hinton agar plates. After overnight incubation at 37 °C, resistance was estimated […] EUCAST (2012). Identification for ESBL production […] was confirmed by […]. Genomic DNA was extracted from 107 selected ESBL-positive […] E. coli […]. The regions of the blaCTX-M group 1, blaCTX-M group 9, blaSHV, blaTEM and blaOXA genes […] were amplified by the PCR method […]” [.hod […]” .Explanation: The microbiological methods used, whether genotypic or phenotypic, and the quality of their application affect the study's findings. The agents  and processes  studied should be reported. Microbe detection, identification and enumeration can include culture-dependent or culture independent methods, as relevant, and should be detailed. The microbiological methods used to detect or measure resistance can include phenotypic characterization , quantitative and qualitative PCR and next generation sequencing . Furthermore, if novel methods have been used, they should be validated against established reference methods and sufficient detail provided to enable reproducibility. If there are no gold standard methods available, such as in the rapidly developing field of metagenomics, it is advisable to use an ensemble of methods to ensure the results are robust and not too dependent on the particular method chosen  the sample size of the survey required 482 survey samples” . Surface water samples were collected from the receiving surface water of the Geeuw canal at two locations, 330 m south-west  and 388 m north-east  from the WWTP discharge point ” . Such dilution is common in southern Europe in the summer. […] This network provides data relevant to any location with limited wastewater dilution […]. Summer sampling was selected to assess the worst-case scenario in terms of dilution of WWTP effluents in receiving waters” [(2) “The [river] flow rate during the sampling was estimated at 0.2-0.3m waters” .Explanation: Seasonal factors can play a significant role in AMR densities in aquatic environments  COD, DNA concentration and bacteria density”  means were post-hoc adjusted and compared using least square means.”  in a previous publication by Xinzhuo Zhang et al. blaKPC-2 positive Citrobacter freundii and E. cloacae were the most abundant CRE found in hospital sewage in China. The [diversification] pattern of pan-resistant bacteria in sewage could imply the carrier rate of those organisms in the population”  to a low positive predictive value for detection of a true CPE from the growth obtained on the plate and makes quantitation extremely challenging […]. [T]he study was conducted in a region with very low CPE prevalence and may not be generalizable” [lizable” .Explanation: Rather than leaving the readers to identify and interpret the study limitations, declaring all limitations gives the authors an opportunity to explain whether these have a bearing on results and how. The limitations in study's design, data collection, analyses and results should be described and explained [In the example, the authors describe technical limitation faced during the study that made their results not generalizable.xplained . In the 4A three-step modified-Delphi consensus process was completed among a multidisciplinary panel of experts, to develop a checklist of recommendations for reporting of studies on AMR in wastewater and related aquatic environments. The items included in the EMBRACE-WATERS statement address critical points for reporting and are presented in a structured scientific paper template. The importance of clearly documenting methods, results and analyses is highlighted. Each item is followed by an example from papers on AMR in aquatic environments and an associated explanation.Reporting of clinical studies is guided by reporting recommendations . AdherenWe suggest a preferred order of item presentation. However, items can be presented differently, depending on the context. For example, the baseline AMR measure in the study settings might be better provided per location in the methods section, if multiple resistance epidemiology settings are included in the study. Previously, the COHERE statement addressed reporting of One-Health studies, but focused on the integration of the human, animal and environmental domains . The preThere are recommendations that were not included in our statement. For instance, there is no trial registry database for environmental studies; indeed, none of the studies in our systematic review were registered . Thus, wSample size calculations are not commonly used in aquatic research but are encouraged. We recommend addressing size considerations because biological systems are highly variable and hence sampling designs might have limited capacity to detect differences and quantify changes . SeveralWe did not address all types of studies evaluating AMR in aquatic environments, but focused on wastewater and related aquatic environments. Neither did we address studies evaluating risk factors for AMR, studies associating AMR in the aquatic environment with AMR in humans or studies assessing effects of intervention to reduce AMR in aquatic environments. Such studies might need to adhere to further research recommendations that may be found in the EQUATOR network . But all5In conclusion, we developed the EMBRACE-WATERS statement through a modified-Delphi consensus process among a multidisciplinary panel of experts. We hope that the present EMBRACE-WATERS checklist will assist both authors and journal reviewers to improve the reporting quality of future studies on AMR in the aquatic environment. We hope that journals will adopt these recommendations. In addition, although not primarily intended for this purpose, it can guide peer reviewers and editors in evaluation of manuscripts in this field. Results from studies following these reporting standards can be aggregated for increased statistical power, used to inform larger scale mathematical models or for discerning regional or temporal trends. We hope EMBRACE-WATERS will also make research on AMR in wastewater and related aquatic environments more relevant to the needs of the medical community in One Health initiatives, and we advocate similar initiatives in other aspects of environmental research with links to human medicine and public health.JPI-EC-AMR project DARWIN , from the Joint Transnational Call for Proposals 2016 “Transnational Research Projects on the Transmission Dynamics of Antibacterial Resistance” grant no 681055 and Newcastle University.This study was funded in part by the Conceptualization – all authors, Data curation – NHK, YS, MP; Formal analysis- NHK, MP; Funding acquisition – BFS, DG, Investigation – all authors; Methodology – NHK, MP; Project administration- BFS; Resources – BFS, DG, MP; Software – not relevant; Supervision - MP; Validation - MP; Visualization - not relevant; Writing - original draft – NHK, JK, MP; Writing - review & editing – all authors.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Medical linear accelerators (linacs) can fail in a multitude of different manners due to complex structures. An unclear identification of failure modes occurring constantly is a major obstacle to maintenance arrangements, thereby may increasing downtime. This study aims to use natural language processing techniques to deal with the unformatted maintenance logs to identify the linac failure modes and trends over time.The data used in our study are unformatted narrative maintenance logs recording linac conditions and repair actions. The latent Dirichlet allocation‐based topic modeling method was used to identify topics and keywords regarding the failure modes. The temporal analysis method was applied to examine the variation of failure modes over 20 years.Based on the output of the topic modeling, 28 topics and keywords with frequency ranking were generated automatically. The latent failure modes in topics were identified and classified into six main subsystems of linacs. Furthermore, by using the temporal analysis method, the trends of all failure modes over 20 years were illustrated. Half of the topics demonstrated variations with three different patterns, namely periodic, increasing, and decreasing.The results of our study validated the effectiveness of using the topic modeling method to automatically analyze narrative maintenance logs. With domain knowledge, failure modes of linacs can be identified and categorized quantitatively. At their core, these are particle accelerators reconfigured as medical devices. The mode of operation of the linacs uses similar physical principles as high‐energy accelerators for particle physics research. However, the medical devices operate in hospital environments that pose additional challenges for operations due to the different physical environments and availability of technical support. Operating medical linacs requires skilled personnel to repair, adjust, and otherwise maintain the proper operation of the devices. Further, medical linacs have many subsystems that all must operate faithfully for the device to function correctly. Given their complexity, linacs can fail in a multitude of different manners.It is desirable to understand the nature of medical linac failures for several reasons. First, components used in medical linacs are costly, and improved knowledge of components that fail more often can be of help in projecting service and maintenance costs for medical linacs. Second, the training of the qualified technical staff able to maintain these devices can be simplified with better knowledge of failure modes since emphasis can be placed on areas that fail more frequently. Third, a better understanding of failure modes can help medical linac operators in stocking components that are more likely to be needed in maintenance, which can help reduce repair times.There have been relatively few studies of failure modes for linacs. Wroe and colleaguesModeling of linac failure modes would be greatly simplified and improved with high quality and consistent input data for the model development. However, medical linac maintenance data are often kept in generic equipment maintenance databases whose primary purpose is to keep records of maintenance, but not to classify and analyze the failure modes. The core information in maintenance logs is recorded in narratives by maintenance personnel. In general, these logs depict the repair procedures and maintenance results, making it useful for analysts to evaluate the linac performance and identify failures. But these logs have the characteristics of colloquial, unformatted, noisy, and may contain spelling and grammatical errors, which is not suitable for general analytical tools. In particular, it is time‐consuming to analyze this type of data by humans when the scale of data is large.This research is triggered by the natural language processing (NLP) application in the transportation domain, where researchers applied topic modeling (TM), a type of unsupervised learning algorithm, into categorizing safety reports for reducing incidents’ events.The purpose of this work is to investigate the feasibility of using TM to analyze electronic medical linac maintenance logs. The main contribution of this article is to introduce TM to analyzing the unstructured maintenance logs data to find out the most frequent failure modes of linacs during daily use. Another purpose is to demonstrate the performance of different linacs over time by examining the trends of different failure modes. With a data‐driven analysis method, it is hoped that the larger pool of current medical linac maintenance logs can be used to better understand medical linac failure modes.22.1The maintenance logs used in this study were collected from several linacs of BC Cancer center, Kelowna (Canada) under the regulation designed by the Canadian Nuclear Safety Commission. The linacs were in service from April 1998 until the study date. There were nine linacs in total, four of these were replaced partway through the study period. The fifth linac was also added in 2009. These linacs were installed in five different treatment rooms, labeled A–E as shown in Table The maintenance log is a collection of narrative maintenance records of linac repair and service work completed by maintenance personnel. In our study, there are two main parts in the logs, namely ‘‘Comments’’ and ‘‘Repair Description.’’ ‘‘Comments” briefly describes the linac status and breakdown occurs on the linacs. ‘‘Repair Description’’ records the maintenance procedure, repair action, and related broken component of linacs. Some metadata were also recorded, such as the date of the maintenance service. Table As shown in Table 2.22.2.1Following the documents representation method, latent semantic indexing (LSI), Blei et al.LDA is the most commonly used algorithm to perform the TM in a collection of documents. LDA constructs a three‐layer architecture between documents, topics, and words by independent multinomial distributions. Each document is represented by several latent topics and each topic is governed by a multinomial distribution over words. In our application scenario, for each entry of the maintenance logs, the ‘‘Comments’’ and ‘‘Repair Description,’’ were combined as a document. All documents in the maintenance logs dataset comprise the corpus. LDA summarizes the documents with several topics by searching similar ‘‘bag” of words co‐occurring in all documents. The words with top frequency in each topic describe the core information of the topics. In our case, the top‐ranked words often point to some kinds of failure modes. Thus, the TM can help us find the most frequent failure modes by searching the keywords in the dataset. It should be noted that a single document is often represented by several topics. This is quite reasonable that one maintenance service usually handles multiple failures.d consists of N words, where wn is the nth word. A topic, noted as z, is a bag of semantic words and can be expressed by the z:{w,p}. The topic is indexed by k which goes from 1 to K. The process of building an LDA model was displayed in a ‘‘plate‐like’’ graphical model as shown in Figure N∼Poisson(ξ),Dir(α),Dir(β).Set prior parameters: d, choose θ∼Dir(α).For each document wn, the topic of the word belonged denoted as zn is drawn from Multinomial(θ).For each word wn itself is a variable drawn from another distribution Multinomial.The word Here, αand β are two independent symmetric Dirichlet priors; θ and ϕ are the document‐topic distribution and topic‐word distribution drawn from Dir(α)and Dir(β), respectively.To further explain the mechanism of the LDA model. Some notations and assumptions are introduced here. A document noted as z,θ, and w is given byThe joint distribution of θ can be obtained by computing the posterior distribution:By applying the Bayes’ theorem, the topic mixture 2.2.2K is the most important parameter need to be determined when training an LDA model. A too‐small K will concentrate too much information on a single topic, making it difficult to identify the specific failure mode and map it to the responsible components. Likewise, a too big Kwill make the information too scattered, leading to some meaningless topics. However, the TM is an unsupervised method, there is no ground truth to provide reference for selecting the optimal number of topics. Thus, some metrics are proposed to help to address this issue by evaluating the topic model.The number of topics K. KuhnK. Wang et al.K by a subject matter expert in the application of analyzing the aviation safety reports.Evaluation metrics can provide a good reference for finding the optimalK by computing the Kullback–Leibler divergence of topic–word distribution over singular values and is defined asCM1 is the distribution of singular values of the topic–word matrix M1, and CM2 is the distribution of the normalized document–topic matrix M2.The divergence metric proposed by Arun aims to find the optimal K can be reached by minimizing the proposed metric. Another commonly used metric is perplexity, which is an indicator of the uncertainty of a model in predicting the topic of the held‐out data. The perplexity is “equivalent to the inverse of the geometric mean per‐word likelihood” in mathematics:Several experiments showed the optimal K in some real applications. Thus, another metric called coherence was proposed to evaluate the interpretability of the model.Wk= is the list of M top words in topic k,D(w) is the number of service records that contain the word w, and D is the number of service records in which both words w and w′ occur at least once. Generally speaking, the coherence will decrease as the number of topics in a model increases and the model with a higher coherence score is more interpretable.The above two metrics are statistical evaluation methods. However, the perplexity is shown insufficient to determine the optimal K. The final optimal number of topics is selected by three subject experts by checking the model interpretability.In this article, perplexity, divergence, and coherence were used to determine a narrow range of optimal 2.3p(zj∣y), of a given year zjθj is the document–topic distribution and td=y is the year that the document was recorded.The most frequent failure modes of linacs may change as the service time increases. To examine whether there is performance degradation of specific subsystems in linacs, the occurrences of different failure modes in each year should be analyzed. As there is a replacement of linacs from different manufacturers around 2010, the analysis over time can be used to compare the performance of linacs from different manufacturers. The temporal analysis method was used to investigate the trends of different failure modes over years.N‐grams. An N‐gram is a sequence of N words. An N‐gram language model predicts the probability of a given N‐gram within any sequence of words in the language. The most commonly used are bigram and trigram models. In our work, we built two topic models using bigram and trigram, respectively.Tokenization: Token is the basic element in a topic model. This process breaks up the sentence into an individual token for the following processing and analysis.Words cleaning: Remove punctuation characters, numbers, and stop words that highly frequent occurring in most topics while contributing little to the topic building, such as preposition words and etc. In addition, any words that occurred in the whole dataset less than three times were removed.Lemmatization: Lemmatization aims to return the base or dictionary form of words so that they can be treated as a single element via TM. Another more aggressive technique called stemming is tested ineffective because it may combine distinct tokens as one and convert jargon in the linac field to some other words.Lowercase conversion: Convert words to lowercase.The NLP is able to analyze phrases or combinations of words by building a language model using 3K using the metrics mentioned in Section There are three parts in this section. First, the process of selecting the optimal 3.13.1.1K is the most significant parameter in building a good LDA model. To find the optimal K, three metrics mentioned in Section K. Figure The number of topics K. Nevertheless, the range of optimal K can be initially determined from 25 to 35 according to the above two indicators. Furthermore, from the figure, we can find the coherence value of the model with 28 topics standing out compared to others in the initial range from 25 to 35. With the help from the subject matter expert interpretation of the models with K range from 25 to 30, 28 was selected as the optimal K in the following analysis. Finally, the following analysis is based on the model with 28 topics.The value of divergence reaches a lower level, which indicates a possible good LDA model when the topic numbers were set from 25 to 50. However, the perplexity increases as the topic numbers exceed 25. Since the model with a smaller perplexity has a better prediction power for new data, these two metrics become a pair of trade‐off metrics for this problem. Thus, the third metric coherence is used to determine the final interval of the optimal 3.1.2From the output of the well‐built LDA model, 28 topics were clustered with specific words selected from the linac maintenance log dataset. However, the explicit concept and topic meanings are not generated accordingly.The most straightforward approach to find what a topic represents would be to rank words by frequency on the topic and find the common narratives among those words. Thus, the underlying failure mode and subsystem can be found. However, it would cause a problem that some words which have a high overall frequency across the corpus would show up on many topics. These words may cover words that have a relatively lower probability while contributing a lot in interpreting topics. Therefore, another statistical metric called lift was introduced to rank the top words within topics. The lift is defined as ‘‘the probability of word occurrence conditional on topic divided by the probability of word occurrence across the corpus.’’ This metric will highlight words that have a high probability within a topic locally than those across a corpus.λ(where0≤λ≤1) to adjust the weight of frequency and lift metric given a specific word. The ‘‘relevance’’ of a word to the topic k is defined as:ϕkw denote the probability of word w for topic k and pw denote the marginal probability of word w in the corpus. Given λ=1, the relevance is identical to the ranking of words in decreasing order within a topic and the relevance will equal to lift when λ=0.To use the frequency and the lift metric more flexible, the visualization and analysis package PyLDAvisλ=0.2, the words will be ranked more by their occurrence within the topic. As shown in Figure λ=0.8, the words are ranked much more by their overall probability over the corpus. From Figure λ to 0.2 and 0.8, respectively. It should be noted that the words in a topic are not purely related to one failure mode or one component. What we are seeking is to find out the dominant words in a topic and relate these words to specific failure modes.Take topic 3 as an example to demonstrate the process of determining the meaning of a specific topic. When we set All 28 topics were examined by the same process to identify dominant failure modes in each topic. However, not all topics are related to specific failure modes. Some topics are generated with words describing the general maintenance work. For example, the top‐ranked words in topic 6 are ‘‘pm day routine carried maintenance,” which point to the routine maintenance records. Under this topic, the co‐occurred verbs and nouns are ‘‘cleaned, checked” and ‘‘rim, iview, and processor,” respectively. These words indicated that the main work in routine maintenance is about cleaning and lubrication. Therefore, we categorized topics with identified failure modes into related subsystems according to Wroe's paper.3.2p(zj∣y), of a given year zj. By examining the topic trend over years, we can find out the variation of specific failure modes.The metadata “date” in service logs is used here to develop estimates of the topic distribution over years. The distribution of topics was obtained by calculating the probability The distribution of 28 topics can be divided into three patterns, namely constant, periodic, and quasi‐monotonic. About half of the 28 topics with constant patterns do not show large variation over years. Their probability fluctuates slightly around a value. In other words, most failure modes occur constantly. However, what we are interested in are those topics with a periodic pattern or a quasi‐monotonic pattern. Examining the causes resulting in the change of the failure modes can help us better understand the performance of the linac and arrange maintenance service. According to our results, two topics showed a periodic pattern, and eight topics demonstrated a quasi‐monotonic pattern, four topics with an increasing trend, and four with a decreasing trend.3.2.1As shown in Figure 3.2.2Except for the periodic pattern, as shown in Figures Topic 4: Console keyswitch failure. The linear accelerators in the period 1998–2009 did not have a keyswitch.Topic 7: Thyratron failure; De‐quing and Thyratron voltage low. The high‐voltage modulator of the two linac systems is quite different as one system used a magnetron‐driven modulator (period 1998–2009) and the other a klystron‐driven system (2011 onward). It appears from the data that failures of the thyratron with the klystron‐based systems are more frequent.Topic 8: Replaced controller printed circuit board (PCB). This component is only used in linacs after replacement, from 2011 onward.Topic 9: Mechanical calibrations: couch, gantry, collimator. There is no specific reason why mechanical failures may be more prevalent with one linac manufacturer, however it appears as though the linac manufacturer of the units from 2011 onward is more susceptible to these types of failures.Figure 3.2.3Topic 2: High‐voltage modulator components failure. In particular, magnetron failures and modulator relay failures peaked in the years before replacement of the original units. The fact that the replacement linacs did not use magnetrons caused the drop of this topic proportion.Topic 11: Couch failures and hand‐pendant failures. This appears to be more stable on the units from 2011 onward than with the original units.Topic 12: Lamps and lighting failures. On both manufacturers, these were switched from incandescent bulbs to light‐emitting diode (LED) over this time in this study, which would result in fewer failures as LED lights have a longer lifetime than incandescent bulbs.Topic 13: Orthovoltage unit failure. This unit had more kV and generator faults later in its life span.Figure 4This study sets out to apply NLP techniques to analyze narrative maintenance logs of linacs. The most significant finding to emerge from the analysis is that the TM is capable of mining failure modes topics and latent components automatically. Compared to the traditional manual inspection, with the application of the TM method, the analysis efficiency was greatly increased, where the data scale is large and the data are recorded without standardized terminology. Another main finding is that linacs from different manufacturers are prone to different failures. One unanticipated finding was that some failures may exhibit a quasi‐synchronous mode over years. For example, the MLC leaf and mechanical components of the couch demonstrate a synchronous failure occurrence. Technicians accounting for the maintenance work of linacs can have a better understanding of the main linac failures. These results further support the analysis of the cause of downtime from a quantitative view.Other than in the area of linac malfunction classification, NLP can have many other applications in understanding radiotherapy failures. For instance, the incident reporting databases used by ASTRO's RO‐ILS; NSIR‐RT in Canada; and ROSEIS for ESTRO rely on a pre‐defined taxonomy and classification scheme to classify radiotherapy incidents. Incidents that do not fit into a pre‐defined classification framework can be misclassified, which reduces the effectiveness of the incident reporting system. NLP could be useful in this area if it were applied to the comments in incident reporting systems, where common topics could be identified and used to refine the classification scheme to keep it relevant and accurate. This could help convert the unstructured data in narratives into structured data that can be automatically analyzed and used for system improvements. Such methods are used in other areas of radiotherapy and could be adapted to incident reporting.Some limitations are also revealed by this article. The dataset used in this article was collected from one cancer center with only five linacs in service. Limited by the number of the maintenance service and linacs, only 4323 entries are analyzed. Theoretically, there is no wide agreement on how much data are needed for TM. Two major factors are the length of words in each entry and the complexity of the whole corpus. More words in each entry and more entries can support analyzing a more complex corpus. Table 5This article applied NLP techniques to medical linac maintenance logs to identify frequent failure modes in daily use. Specifically, the TM method was used to identify failure modes and the temporal analysis method was used to investigate the trends of different failure modes frequency over time. The results demonstrated that TM applied here is able to identify the most frequent failure modes of linacs in a quantitative way instead of relying on the experience of maintenance personnel. In our case, the major failure modes are MLC leaf stuck, power subsystem failure, and gantry position adjustment. The temporal analysis allows analysts to discern whether a failure mode has a periodic, increasing, or decreasing trend over time. It is shown that the frequency of MLC failure and couch failure has a relatively consistent synchronization. Linacs with different structures or from different manufacturers may prone to different failure modes in daily use.Hongguang Yun: Conceived the idea of the study, designed and conducted the experiments, performed the analysis, and wrote the paper. Marco Carlone: Collected the data, conceived and validated the analysis, and wrote the paper. Zheng Liu: Conceived the idea of the study, designed the experiments, and proofed the results; All authors contributed to revising the paper.The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported


Prolactinoma has the highest incidence rate among patients with functional pituitary tumours. Although mostly benign, there is a subgroup that can be aggressive. Some clinical, radiological and pathology features have been associated with a poor prognostic. Therefore, it can be considered as a group of heterogeneous tumours. The aim of this paper is to give an overview of the molecular pathways involved in the behaviour of prolactinoma in order to improve our approach and gain deeper insight into the better understanding of tumour development and its management. This is essential for identifying patients harbouring aggressive prolactinoma and to establish personalised therapeutics options. Prolactinomas comprise 40 to 60% of all pituitary adenomas and are mostly presented in women of childbearing age as indolent microadenomas (<10 mm in diameter) . HoweverProlactinoma aggressiveness is defined as a radiologically invasive tumour which has an unusually rapid rate of tumour growth despite optimal standard therapies . BaselinIn addition, pathology proliferation markers  taken alone are not reliable markers of malignancy ,13. In fOverall, these findings reveal that prolactinomas can be considered as a group of heterogeneous tumours. Therefore, better understanding of the molecular pathways involved in tumour development is essential for identifying patients harbouring aggressive prolactinoma and establishing personalised therapeutic options.The pituitary gland has a dual embryonic origin  that confers a unique histological appearance. Both parts are made up of anatomically and functionally distinct lobes of the pituitary. The posterior lobe (neurohypophysis) consists of nervous tissue arising from the embryonic forebrain and represents an extension of the hypothalamus. The anterior lobe (adenohypophysis) , which iPars anterior or distalis: this is the largest part and is responsible for hormone secretion.Pars tuberalis: this is an upwards extension of the pars anterior and wraps the pituitary stalk, which is composed of unmyelinated axons from the hypothalamic nuclei. The hormones oxytocin and vasopressin accumulate in these axons .Pars intermedia: this is a thin epithelial layer that separates the pars anterior from the posterior lobe. Pars intermedia contains the colloidal matrix and includes the remainder of the Rathke’s pouch cleft. It has some pituitary stem cells, and secretes melanocyte-stimulating hormones and endorphins .During pituitary development, the dorsal and ventral sides generate signalling mediators which regulate transcription factors that are essential in governing cell proliferation and differentiation ,18. LactPRL secretion is mainly under inhibitory stimuli, via tuberoinfundibular dopamine (TIDA) neurons in the hypothalamus binding to Type 2 Dopamine Receptor (D2R) and GABA (gamma aminobutyric acid), the latter playing a minor role . PRL alsAs we mentioned above, prolactin is responsible for milk production and breast development, but also acts in many other less known functions such as metabolic functions , immune Circulating prolactin is low in males and nonlactating nonpregnant females, and a hypo-prolactinemic syndrome has not been described. Its overproduction could lead to galactorrhea and has an inhibitory effect on the release of gonadotropin-releasing hormone that results in infertility, menstrual cycle disturbances in women, and decreased libido and spermatogenesis in men, as well as other less studied effects that will be described below.(1)D1 and D5, also called D1-like receptors. By coupling to G stimulatory sites, adenylyl cyclase cAMP is activated, which in turn activates protein kinase A (PKA), thus enhancing transcription. These receptors are abundant in the striatum, nucleus accumbens, olfactory bulb, and substantia nigra and are essential in regulating the reward system, motor activity, memory, and learning.(2)D2, D3 and D4 (D2-like receptors). By coupling to G inhibitory sites, they inhibit adenylyl cyclase and activate K+ channels. They are expressed mainly in the striatum, as well as the external globus pallidus, the core of the nucleus accumbens, hippocampus, amygdala, and cerebral cortex.The dopamine receptors are members of the superfamily of G protein-coupled receptors. There are five types, which include D1 to D5. Each receptor has a different function . The fivThe regulation of the lactotroph cell is manly by D2R  Figure .When dopamine binds to D2R, both PRL secretion and lactotroph proliferation are inhibited. Within seconds after binding, dopamine activates K+ channels which leads to membrane hyperpolarisation and the inactivation of voltage-gated calcium channels. Consequently, a reduction of intracellular free calcium occurs, thus resulting in the inhibition of PRL release from secretory granules . Within Prolactin action has different outcomes in the endocrine, autocrine and paracrine signalling. In addition, the target cell and the different pathways of activation/inactivation determine its final action.Classically, PRL is known to activate the peripheral prolactin receptor (PRLR), promoting proliferation and inhibiting apoptosis linked to its main function of milk production and breast development. How autocrine/paracrine or endocrine PRL levels collaborate with oncogenes to foster tumourigenesis, e.g., in breast tissue, but also in other hormonally responsive cancers, is not well understood . In thisPRLR is expressed on TIDA cells (a short-loop feedback circuit) and also on lactotrophs of the pituitary gland where it may provide an autocrine loop to regulate lactotroph function . The PRLThe JAK2-STAT5 pathway is involved in processes such as immunity, cell division, cell death and tumour formation . FerrariThe mTOR pathway regulates the cell cycle and its overactivity has been associated with several cancers , as wellThe MAPK pathway is one of the best-defined pathways in cancer biology. It promotes cellular overgrowth activating proliferative genes, and, at the same time, enables cells to overcome metabolic stress by inhibiting AMPK signalling, a key sensor of cell energetic status . Long-teAs we mentioned above, dopamine not only inhibits PRL secretion but also proliferation and hypertrophy of the lactotroph cells. The evidence supporting the antimitotic activity of dopamine on lactotrophs comes from the induction of pituitary hyperplasia in D2R-deficient mice (which prevents dopamine action) ,48 and tThe major characteristics of D2R-deficient mice are chronic hyperprolactinemia and lactotroph hyperplasia which lead to adenoma development in aged females only. D2R null mice of either sex had 3- to 4-fold higher basal PRL levels, but without repercussions in spermatogenesis, and with a little impact on fertility in females. Older D2R-null female mice developed significant hyperplasia (up to 50-fold higher) and large lactotroph adenomas. However, the pituitary in age-matched males was similar or only double in size ,48. ThesDT-null mice have increased dopaminergic tone, anterior pituitary hypoplasia, dwarfism (D2R is present in somatotroph cells), and an inability to lactate . DT elimAlthough animal models are a huge source of knowledge, they are not always identical to humans. The presence of the mutated D2R gene in humans was examined by direct DNA sequencing in 79 pituitary tumours, mostly lactotroph and mixed GH/PRL-secreting tumours. No mutations were demonstrated, and all migration abnormalities detected were due to polymorphisms within the D2R gene . More reCollectively, these observations argue against the presence of mutated D2R or the loss of dopamine inhibition as primary causes of lactotroph tumours in humans.−/−) are increased 30-fold in males and 100-fold in females, and this is accompanied by a somewhat enlarged pituitary gland  AND molecular [tiab]”, “prolactinoma [tiab] AND aggressive [tiab]”, “prolactin receptor [TI] AND review [Filter]” and “dopamine receptor” [TI] AND review [Filter], “aggressive pituitary tumours [tiab]”. Articles identified by these searches and relevant references cited in those articles were reviewed. Only articles published in English were included. Review articles and book chapters are also cited to provide readers with more detail in some specific areas not addressed in this review. We largely selected those published in the past 20 years but did not exclude seminal older articles.


In recent years, the debate in the field of applications of Deep Learning to Virtual Screening has focused on the use of neural embeddings with respect to classical descriptors in order to encode both structural and physical properties of ligands and/or targets. The attention on embeddings with the increasing use of Graph Neural Networks aimed at overcoming molecular fingerprints that are short range embeddings for atomic neighborhoods. Here, we present EMBER, a novel molecular embedding made by seven molecular fingerprints arranged as different “spectra” to describe the same molecule, and we prove its effectiveness by using deep convolutional architecture that assesses ligands’ bioactivity on a data set containing twenty protein kinases with similar binding sites to CDK1. The data set itself is presented, and the architecture is explained in detail along with its training procedure. We report experimental results and an explainability analysis to assess the contribution of each fingerprint to different targets. ADMET properties, selectivity, and physicochemical properties) but generate lead molecules with favourable properties in silico.Drug discovery is a very long and expensive process that includes many stages such as drug target identification, target validation, virtual screening (VS), hit-to-lead generation, lead optimization, and so on . MoreoveThe EMBER (EMBedding multiplE molecular fingeRprints) embedding is proposed, which is made by multiple molecular fingerprints that have been generated using complementary methods to search for molecular substructures and are stacked as the spectra of a sort of “molecular image”; such an embedding aims at exploiting the ability of Convolutional Neural Networks (CNN) in learning the proper features, as they do for images;A multi classifier has been developed to prove the previous claim, which performs very well in screening ligands on twenty protein kinases presenting the closest binding sites to CDK1; moreover, our architectural design lowers the parameter numberlA curated data set made by nearly 90,000 ligands labeled as active/inactive against 20 Kinase target selected as the most similar to CDK1.An explainability analysis has been performed to assess the most relevant features for the classification task, and the results of this analysis confirm some very recent in vitro studies that outline the relevance of pharmacophore-like description fingerprints when addressing bioactivity classification for kinase inhibitorsIn particular, Virtual Screening (VS) is an often discussed topic in Chemoinformatics and Medicinal Chemistry and is widely applied in pharmaceutical research. VS consists of screening large small-molecule databases searching for bioactive molecules with respect to the target under investigation. This enables the researcher to cut the cost of experimentally testing thousands of compounds through a severe reduction in the number of candidate molecules. Research in the field of VS gained increasing importance in the last decade when Deep Learning (DL) became a mature discipline . In thisOur study is part of a wider research study aimed at screening novel compounds with anti-cancer properties.In cancer therapeutics, it is well known that protein kinases are key regulators of cell function and constitute one of the largest and most functionally diverse protein families. By adding phosphate groups to the substrate, they regulate the activity, localization, and overall function of many proteins and serve in orchestrating the activity of almost all cellular processes. Kinases are involved in signal transduction and coordination of complex functions, such as the cell cycle. Kinases have been considered one of the most promising target family in oncology. In the search of the best protein family to be focused on, the choice is usually guided by the possibility to have mutated targets that can be used for selectivity issues. Mutated kinases can become active by constitution and, thus, cause diverse cellular anomalies resulting in cancer initiation or growth. One of the most well-known mutated kinase example is BRAF, which is frequently mutated on Val-600 (p.V600E) and is a driver mutation in several cancers, including colorectal cancer, melanoma, and thyroid cancer .Between different kinases, CDK1 is a central regulator that drives cells through G2 phase and mitosis. Diril et al.  generateIn this section, some remarks are reported about both Deep Neural Networks for Virtual Screening and molecular embeddings.AtomNet, which is regarded as the first CNN for structure-based screening. Duvenaud et al. . For each kinase, a list of proteins that satisfy this dissimilarity criterion was obtained, and for each of them, the compounds considered as actives in the literature were collected. In particular, for each kinase that was dissimilar to a reference one, only compounds with IC50 values In fact, this database contains detailed information about structural kinase–ligand interactions relating to all the structures of the catalytic domains of the human protein kinases deposited in the Protein Data Bank. The Nevertheless, we wanted to further expand the number of inactive molecules by using a second approach. This second approach consists of a ligand chemical diversity search. Specifically, it was based on structurally diverse ligands compared to known active co-crystallized ligands for each protein used in this work.The ligands in sdf format for each 3D structure of the twenty proteins , we minimized the possibility of having analogue bias and artificial enrichment typical of the usage of decoys or uncurated data sets .The overall data set was built starting from two separate sets. The first one was made by 64,600 compounds that are inactive for all the targets. The second data set contains all ligands that are active on at least on one target. In the end, we merged the two data sets to obtain a final one that has a 1:100 active/inactive rate, which is referred to the less abundant class (CDK6) see .This final data set consisted of 89,373 molecules and was separated into a training set , test set , and validation set (7597 molecules), respectively.The molecules were manipulated on the Knime platform in order to generate the seven fingerprints used as the channels of our embedding, which is used as input to the network.Given the intrinsic sparsity of a molecular fingerprint, we chose to transform the 0 bits in The architecture of our classifier is a deep CNN with nine layers using Parametric Rectified Linear Units (PReLU) for feature extraction and a three-layer fully connected perceptron for actual classification. The PReLU activation function adaptively learns the parameters of the rectifiers and improves accuracy at negligible extra computational costs. A learnable parameter If multiclass, multilabel classification, which our classifier also has to assess if a ligand is active at the same time on different targets. As a consequence, the output is a vector label that is a binary vector where 1 s indicates bioactivity with respect to a particular target.The network is trained on Depthwise Separable Convolution (DSC)  with zero padding were tested. The learning rates tested were in the range of [10We introduced EMBER, a novel molecular embedding aimed at improving the effectiveness of CNNs for VS tasks. The innovation in our approach consists in representing the ligand’s structure by several molecular fingerprints stacked as the channels of the input tensor. The key idea behind EMBER is that molecular fingerprints are computed by not only using the same algorithmic process, but also by using complementary information collected from the molecular structure such that they can be regarded as the “spectra” of a sort of molecular image.We proved EMBER effectiveness by using a deep neural architecture for ligand multiclassification with respect to their bioactivity on twenty protein kinase targets. We achieved very satisfactory results with respect to the classification task; in general, we obtained a very high capacity model with a very small number of parameters.Moreover, we presented an explainability analysis by feature attribution showing that only three molecular fingerprints play an active role in classification, which are FeatMorgan, Layered, and RDKIT. Our findings confirm very recent studies that outline the relevance of functional description Fingerprints  when addressing bioactivity classification, especially for kinase inhibitors.