Malignant lateral lymph nodes (LLNs) in low, locally advanced rectal cancer can cause  local recurrences ((L)LR). Accurate identification is, therefore, essential. This study explored LLN features to create an artificial intelligence prediction model, estimating the risk of (L)LR. This retrospective multicentre cohort study examined 196 patients diagnosed with rectal cancer between 2008 and 2020 from three tertiary centres in the Netherlands. Primary and restaging T2W magnetic resonance imaging and clinical features were used. Visible LLNs were segmented and used for a multi-channel convolutional neural network. A deep learning model was developed and trained for the prediction of (L)LR according to malignant LLNs. Combined imaging and clinical features resulted in AUCs of 0.78 and 0.80 for LR and LLR, respectively. The sensitivity and specificity were 85.7% and 67.6%, respectively. Class activation map explainability methods were applied and consistently identified the same high-risk regions with structural similarity indices ranging from 0.772–0.930. This model resulted in good predictive value for (L)LR rates and can form the basis of future auto-segmentation programs to assist in the identification of high-risk patients and the development of risk stratification models.  Traditionally, patients with low locally advanced rectal cancer (LARC) have been treated with the combination of neoadjuvant (chemo)radiotherapy (n(C)RT) and a total mesorectal excision (TME) procedure . The incOne major problem is that specific diagnostic criteria for suspicious LLNs are limited, and it is largely unknown which features of LLNs are predictive of LLR. The Lateral Node Consortium study found that LLNs with a primary short-axis (SA) diameter of ≥7 mm resulted in a 5-year LLR rate of 19.5% . These eConsidering these results, adequate awareness, knowledge, and treatment of malignant LLNs are essential. Presently, the main diagnostic criteria of size and location are assessed by radiologists. However, a recent study of 53 Dutch radiologists demonstrated significant inter-physician variation in the classification of the anatomical location and short-axis size of LLNs . SimilarThis study aimed to create an AI prediction model with data from three expert centres to identify malignant LLNs with a higher risk of LLR. This incorporated the identification of imaging and clinical features that are associated with a high risk of (L)LR.This multicentre retrospective cohort study included patients from three expert hospitals in the Netherlands: Amsterdam UMC, location VUmc (AUMC), the Netherlands Cancer Institute (NKI), and Catharina Hospital (CH). Patients from the NKI and CH were selected from the retrospective Lateral Node Consortium study , and patThe primary outcome was the prediction of (L)LR via AI models and was correlated with LLN features. For patients without surgery, the date of the primary MRI was used as the start of follow-up. All data were obtained from electronic health records. The study received central approval by the Institutional Review Board (IRB) of the Netherlands Cancer Institute on 2nd June 2021. Each participating centre reviewed the study protocol and provided approval. Informed consent was waived by the central IRB and by each participating centre during local ethical review and approval.Primary and restaging MRIs of all patients were evaluated during the original studies by at least one expert radiologist in each centre. The expert radiologist in each centre was a specialized abdominal radiologist with vast experience and proven knowledge within colorectal assessment. Assessment of the LLNs was based on the largest node on the primary MRI and included the short- (SA) and long-axis (LA) diameter , the presence of malignant features , and anatomical location. During re-review, radiologists used a colour atlas to help determine the anatomical location of the LLN . LymphatIf an (L)LR occurred during follow-up, the imaging was re-assessed to define the exact location of the  local recurrence.The LLNs documented during re-review were identified by the central researchers in order to establish a region of interest (RoI). In an attempt to avoid intra-observer bias and limit variation, two independent assessors evaluated the scans . If the LLN was not easily identified, a senior researcher (M.K.) was contacted for advice. The RoI was created using segmentations  made on 3. A 6 mm buffer in each direction of healthy tissue was created around the RoIs on the primary MRI. A multi-channel input image was then created consisting of the primary MRI, the restaging MRI, and the LLN RoIs. To create a standardised input size for the CNN, each RoI was resized using cubic spline interpolation to a 64 × 64 × 64 voxel volume.A deep learning model was trained using a multi-channel convolutional neural network (CNN), with the MR images as the input for the CNN. Three-dimensional images were created for the primary and restaging MRI scans by resampling the transversal, coronal, and sagittal planes to an isotropic voxel size of 1 mmThe imaging model was trained using the in-house DTOR package, which uses the PyTorch framework . The deeThe model training with the tuned hyperparameters used patients from AUMC and CH, with the NKI forming a separate and independent test dataset. The trained model was used to calculate prediction scores of the risk on an (L)LR for each LLN. Model performance was assessed by calculating the area under the curve (AUC) of the receiver operating characteristics (ROC) curves for these prediction scores. The performance of the AI prediction model was also evaluated using the precision, sensitivity, and F1 scores, where the F1 score is defined as:For the case of the F1, sensitivity, and specificity results, the optimal cut-off point was chosen separately for the imaging and clinical models according to:x(y), x(y), x and y, and the c constants are variables used to stabilise the division. Therefore, perfect agreement between two images would result in an SSIM of 1 and an MSE of 0.For the trained models, a variety of explainability methods were used in order to provide local explanations of predictions. These methods included saliency compared to multiple different class-activation-mapping (CAM) methods . This usIn total, three models were created, one based only on imaging features (Model 1), another based only on clinical features (Model 2), and a combined model with both imaging and clinical features from both sources (Model 3) . ImagingBaseline analyses were performed using SPSS Statistics, Version 26.0 . The AUn = 132), the absence of good-quality MRI scans sufficient for evaluation in the AI model (n = 60), or insufficient clinical data (n = 33) required for model development  was found for the prediction of LR using the 2D VGG model described previously. For the case of LLR, this was determined to be 0.57 (95% CI: 0.046–1.00). Using only the clinical features described previously, the AUCs from the AdaBoost classifier were determined to be 0.68 (95% CI: 0.47–0.89) and 0.73  for LR and LLR, respectively. The use of the combined model, which was trained using both T2W MR imaging and clinical features, resulted in AUC values of 0.78 (95% CI: 0.60–0.96) and 0.80 (95% CI: 0.49–1.0) for LR and LLR, respectively. The ROC curve for the test dataset is shown in Using a database-generated, primary LLN short-axis size of ≥7 mm, as determined during MRI re-review, the sensitivity and specificity for developing an LR were 32.7% and 86.6%, respectively. When using imaging data from the current deep learning model, the corresponding sensitivities and specificities for the LR and LLR rates were 71.4% and 50.7%, which improved to 85.7% and 47.9% when also examining clinical features in combination with imaging features.According to the database-generated ≥7 mm benchmark, the sensitivity and specificity for developing an LLR were 43.5% and 86.0%, respectively . When usSimilar improvements in sensitivity were seen for all versions of the deep learning model in the test centre, when only based on imaging or clinical features or a combination of both. In order to test the explainability of the models, three examples from the test set were chosen in which the model was confident of a positive recurrence, unsure of the presence of a recurrence, and was confident no recurrence was present. These are depicted in Given the increased risk of developing a locoregional recurrence due to either the insufficient treatment of or the failure to diagnose malignant lateral lymph nodes (LLNs), it is essential that malignant LLNs are adequately identified and treated appropriately. We created a deep learning model for the prediction of (L)LR rates in patients without an LLND, based on imaging features from segmented RoIs on T2W MRI images and combined these with clinical input features. This combined model was able to predict LR with an AUC of 0.78 and LLR with an AUC of 0.80, with a sensitivity of up to 85.7% for predicting LR and LLR in the test centre. However, while the sensitivity of local recurrence in the test set was high (85.7%), the corresponding specificity was found to be 47.9%. This is due to the class imbalance of the dataset and, as such, would be expected to improve with the use of larger datasets in future analyses.The impact of AI in medical research has substantially increased over the past few years and is valuable for an accurate and standardised identification of patterns in medical data, as well as the combined assessment of biomedical images with integrated features, which are often too intricate for human discernment ,27. ArtiThe current study is, to our knowledge, one of only three studies to use deep learning models for the investigation of LLNs. The other two studies used either CT scans or excluded patients who underwent neoadjuvant therapy. This means that the current study, where segmented MRI scans were incorporated for the prediction of (L)LRs in patients treated with (chemo)radiotherapy without an LLND, is very appropriate for Western cohorts. This is in contrast to traditional Japanese protocols, where neoadjuvant treatment is usually not provided. Nakanishi et al.  and KasaIn the current method, explainability images were created to identify areas of the pelvis most at risk for recurrence. These maps are aimed to aid radiologists in the identification and tracking of “at-risk” areas, to ensure thorough evaluation. This may be useful in situations where LLNs change shape or size, for example after neoadjuvant therapy, or when multiple suspicious LLNs are present. In such situations, it can be challenging to consistently ensure that the suspicious LLNs are visible on imaging series over time. This approach, to register image series to the baseline scan, would benefit the tracking of high-risk areas and provide more-accurate comparisons of changes over time and allow for this region to be closely monitored during intensive and frequent follow-up. This may be especially useful when LLNs are not well visualised on follow-up imaging series or when the pelvic anatomy has slightly changed since radiation and/or surgical treatment. Though lacking in supporting evidence, this interesting ability of the model should be explored further in the future. Our study indicates that standard saliency is potentially unsuitable for the identification of at-risk areas as residual disease is likely identified through combinations of voxels, while saliency considers the impact of individual voxels. The saturation effect of the FullGrad algorithm through the summation of bias gradients also appears to be unsuitable for the current use case.This model was able to combine a select group of multicentre patients from three expert Dutch centres with LLNs to create prediction scores for locoregional recurrence. This can in the future be used as a foundation for larger, more-detailed, machine learning models for this patient population. Considering the wide range of MRI fields used, this model appears to be rather robust. This study was also able to support the limited number of similar studies, showing that deep learning AI models can predict LR and LLRs relatively accurately based on imaging and clinical features, with a high sensitivity. Future research can apply this model to multiple and diverse validation cohorts to accumulate new information and prediction scores. This can then, in turn, aid the creation of risk stratifications, prediction models, and auto-segmentation networks. The strengths of this study lie in the future possibilities. Similar deep learning models can be created for the auto-segmentation of LLNs, which would not only decrease the chances of inter-variability between human observers, but allow for automated identification of LLNs, which could assist radiologists in their diagnostic process. Furthermore, auto-segmentation would increase the sensitivity of similar models and provide more-refined feature identification and selection of RoIs, improving the overall input channels ,31. ThisThis study had some limitations. Firstly, the current model required both primary and restaging imaging features for the development of prediction scores, which meant that many patients without restaging images had to be excluded. There was also heterogeneity present in patients between centres; however, the current model did perform the best for the test centre, which is reassuring. Another limitation was that some MRI scans differed in quality, limiting precision and accuracy during segmentation, and it Is possible that, because this study included retrospective data from different hospitals, different MRI protocols were adhered to, which might affect the obtained results. Furthermore, the current sample is rather limited, which may be reflected in the found sensitivity data. With a larger dataset, the sensitivity results could be largely improved. Even though two independent assessors evaluated the scans, in an attempt to limit intra-observer bias and variation, manual segmentation may still have been affected and auto-segmentation of LLNs with feature classification would have been ideal. Lastly, besides T2-weighted MRI, no other series, such as diffusion-weighted images, were incorporated into the model to further influence accuracy in recurrence predictability. Lastly, due to the fact that patients did not undergo an LLND, the clinical outcome and model development could not be linked to the pathological results.A deep learning model was trained to predict the local recurrence and ipsi-lateral local recurrence risk based on magnetic resonance imaging and clinical features of 196 patients with lateral lymph nodes in rectal cancer. The predictive model using T2W imaging and clinical features resulted in AUC scores of 0.78 and 0.80 for LR and LLR, respectively. Explainability methods were found to identify the high-risk regions of the input, with a high level of consistency between the different methods. Future research is needed to validate the model in external cohorts and to develop new research directions such as auto-segmentation and risk stratification.


Gut microbiome plays an indispensable role in the occurrence and progression in various diseases. The incidence of pancreatic cancer (PC) and liver metastasis (PCLM) are high, most of them are found in advanced stage. Therefore, it is particularly necessary to search for predictive biomarkers, which are helpful for early detection and treatment, and thus improve the survival rate and quality of life of PC patients.n = 44) and 50 healthy people  from March 21, 2021 and August 2, 2022. Among all PC patients, we divided them into liver metastasis group  and non-liver metastasis group . DNA was extracted and 16S ribosomal RNA (16S rRNA) gene sequencing was performed. SPSS was used for statistical analyses and all bioinformatics analyses were based on QIIME2, p < 0.05 were considered statistically significant.We retrospectively analyzed 44 pancreatic cancer patients  model, and its ability to predict PC and PCLM was verified by ROC curve.The microbial richness and diversity of group P and LM were higher than that of group N and non-LM. LEfSe analysis found that Streptococcus is a potential biomarker for early prediction of PC and PCLM, which is critical for early diagnosis of diseases.We demonstrated significant differences in intestinal microbiome composition between PC patients and healthy people, and found that  According to the GLOBOCAN’s statistics in 2020 , there w14 types of bacteria in the human digestive tract, mainly distributed in the colon and rectum  and non-liver metastasis  to find the key intestinal microorganisms and related metabolic pathways that distinguish PC and PCLM.We retrospectively analyzed 44 untreated PC patients  and 50 matched healthy volunteers  between March 21, 2021 to August 2, 2022. All patients were selected through preset inclusion and exclusion criteria, the inclusion criteria were as follows: (1) all cases were confirmed as pancreatic cancer at the cancer center of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology; (2) the patients had not received anti-tumor treatment, except for surgery, and stool samples were collected 3 weeks after the surgery; (3) no antibiotics or other drugs that may affect the intestinal flora were taken before the samples collection; (4) no intestinal invasive operations, such as gastrointestinal endoscopy and enema were performed; (5) without bile duct obstruction; and (6) the patients knew the contents of the study and had signed the informed consent. Patients who do not meet the inclusion criteria will be excluded. Among the PC patients, we divided them into liver metastasis group , tumor sites, pathological types, whether metastasis, metastasis sites, whether surgery, lines of treatment, Eastern Cooperative Oncology Group (ECOG) performance and baseline bilirubin value were available for review. This retrospective study was approved by the Ethical Committees of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (No. 2014-041).Fecal samples of the PC patients were collected by the trained medical staff before anti-tumor treatment, put each sample in a 50 mL sterile specimen collection box, then immediately transfer to a −80°C refrigerator for storage. After all samples were collected, they were sent for examination. The samples’ total microbiome DNA was isolated with Omega Mag-Bind Soil DNA kit , and the concentration and purity of the DNA were determined . The V3-t-test. All tests were performed by two-sided, and p < 0.05 were considered statistically significant. The 16S rRNA sequencing data were analyzed based on QIIME2 software. Principal coordinates analysis (PCoA)  were used for statistical analysis. Graphpad prism 8.0  was used to draw charts. The patient characteristics were compared by Chi-squared test and Student’s s (PCoA) , Linear s (PCoA) , Kruskals (PCoA)  and othe2. The tumors mainly located in the whole (40.91%) and head (40.91%) of the pancreas. The majority pathological classification was adenocarcinoma, accounting for 81.82%. Thirty-two (72.73%) PC patients suffered from metastasis, including twenty-seven  liver metastasis (LM), eighteen  distant lymph node metastasis, nine  peritoneal metastasis and four  lung metastasis, only one quarter patients underwent surgery before treatment.A total of 44 GC patients between March 21, 2021 and August 2, 2022 were included in our retrospective study. There was no statistical difference in baseline information between the two groups, the baseline characteristics of 44 PC patients were summarized in 4 species, which can better represent the microbial flora information of each group, as shown in The rarefaction curve and species accumulation curve indicated that the sample sequencing depth was sufficient and the annotated species were rich, with more than 6*10p < 0.001, p = 0.37) , indicating that there were composition differences between two groups  . Accordio groups ,C. Axis Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria in both groups rank in the top four  was conducted to estimate the effect size LEfSe) of each differential flora. The LDA threshold effect value was set to 4, and the ectively ,E.fSe of eaNext, the 16S rRNA sequencing data were predicted by the phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2) software. We used Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databaseStreptococcus1, Streptococcus2, Streptococcus3, Streptococcus4, and Streptococcus5. All of them were enriched in P group by LEfSe analysis, further analysis of PC prediction ability of the above species by ROC curve showed that the area under the curves (AUC) of Streptococcus1 was 0.927 (p < 0.001), indicated that the increase of its number can predict the occurrence of PC  analysis. The heat map showed the abundance distribution of these species in each sample . Table 3ce of PC . In othen = 27) and non-liver metastasis group , respectively. There was no significant difference in clinicopathological features between the two groups  , we divided them into liver metastasis group  , indicatWith the proposal of the Human Microbiome Project (HMP) and the extensive development of high-throughput sequencing, metagenomics, biochip technology and biological information analysis, the relationship between gut microbes and health has received unprecedented attention. Intestinal flora is an indispensable part of the body and plays an important role in human physiological health. It also plays a key role in the occurrence and development of pancreatic cancer (PC) . PC is oStreptococcus content was significantly increased. Through LEfSe, RF analysis and verified by ROC curve, it was found that it had important discrimination ability in the PC group and could specifically predict PC and PCLM. Streptococcus belongs to p__Firmicutes, c__Bacilli, o__Lactobacillales, and f__Streptococcaceae, is a common pyogenic Gram-positive coccus, which widely exists in human gastrointestinal tract and nasopharynx, mainly causing pyogenic inflammation, hypersensitivity diseases and so on. Nowadays, its role in cancer occurrence and progression is gradually known , a major low molecular weight thiol in mycobacteria, is an important cellular antioxidant. At present, a large number of literatures have reported that MSH is a promising antimicrobial target. As MSH only exists in actinomycetes, it is a good microbial target. However, as this study only explores PC and PCLM preliminarily, there is no in-depth mechanism exploration, and further in vitro and in vivo experiments are needed to prove it.Our study found that the intestinal microbial richness of PC patients was higher, and the ly known . In the and GCLM , and obsin vivo animal experiments and clinical studies are needed to confirm the specific pathogens or bacteria that cause the differences; and (5) this study does not include the exploration of mechanism, we are looking forward to further exploration of mechanism by scholars based on our research findings in the future. Despite the above defects, this study can still explain the role of Streptococcus in PC and PCLM to a great extent.In addition, there are some limitations in our study: (1) this study is limited to a single center with a small sample size, which needs to be external verified by large sample and multi-center trials; (2) we could not avoid the impact of diet on intestinal microorganisms, we hope that randomized controlled trials can be conducted in the future to eliminate uncontrollable factors; (3) as the patients are still in the treatment stage and have not reached the follow-up time, we have not analyzed the treatment-related information in this study. Our research group has established a complete specimen bank of intestinal microbes of PC patients before and after treatment. We will conduct in-depth research in the future, hoping to reveal the potential synergy between tumor treatment and microbial microbes; (4) our study can only annotate microbial species at species levels, further Streptococcus in these two groups has increased significantly, which can specifically predict PC and PCLM and serve as a predictive microbiota marker.To sum up, the intestinal microbial structure characteristics of PC and PCLM patients have changed, and the number of https://www.ncbi.nlm.nih.gov/sra/.The data presented in the study can be found in online repositories. The names of the repository and accession number can be found below: NCBI SRA, PRJNA977486, The studies involving human participants were reviewed and approved by the Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. The patients/participants provided their written informed consent to participate in this study.JL and HL: conception and design and administrative support. YM: provision of study materials or patients. JY: collection and assembly of data. JY and YM: data analysis and interpretation. All authors contributed to the article and approved the submitted version.This work was supported by National Natural Science Foundation of China (No. 82103385), Chinese Society of Clinical Oncology Research Foundation (No. Y-tongshu2021/ms-0107), Beijing Bethune Public Welfare Foundation (No. BJ-GYQZHX2021006), and Hubei Xiaoping Chen Science and Technology Development Foundation Jingrui Development Fund (No. CXPJJH122006-1003).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. 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Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors’ applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications. E-noses, through mimicking the olfactory system of humans, can detect various smells, which makes them suitable for an extensive range of environmental analysis fields, applications in the food industry, medical practice for disease diagnosis, etc. ,2,3,4,5.2) upon exposure to gases at high temperatures (150–500 °C). The sensing material is placed on a substrate with integrated electrodes  and a vapor resistor .Typically, MOX sensors are resistive sensors made of metal oxide semiconductors which have been regarded as a suitable candidate for the development of low-cost and highly efficient sensors due to their simple physical basis in the measurement of the resistance as well as high sensitivity to different gases . The MOXThe MOX sensors were first introduced as resistive chemical gas sensors in 1952 by Brattain and Bardeen, who showed that some semiconductor materials change their resistance depending on the surrounding atmosphere . ConductAlthough MOX sensors are intrinsically non-specific, their sensitivity can be improved by some techniques, among which multivariate predictive models , applica2, a response time of 5–10 s, and a power rating of 15–30 mW. The response time and energy consumption rate can be decreased by signal processing techniques [The MOX sensors are smaller, faster (response time of 10 to 20 s), and more durable than the above-mentioned AGS sensors; however, MOXs suffer from susceptibility to humidity, which can be resolved by modulation of the working temperature [chniques ,22 as wechniques .Controlling and monitoring the emission of combustion-related greenhouse gases has become a top priority of many industrial processes for the reduction in energy consumption, improvement of product quality, and protection of the environment ,24. TherThe low power requirement, simplicity, lightweight, the possibility of merging with fixed measurement systems, and portability are among the major advantages of MOX sensors . Thus, fThis article investigates the MOX sensors and e-nose sensor arrays applications in the environmental monitoring and presents the achievements in the field over the last 5 years. The review includes several sections dedicated to the description of different principles of operation of metal oxides as gas sensing materials, which are followed by the description of the selected applications of different types of MOXs for evaluation of the pollutant gases, air quality assessment, and water and wastewater pollution evaluation.2, TiO2, WO3, Ga2O3, In2O3 or Fe2O3. Out of the aforementioned materials, granular tin dioxide is the most widely studied and used; it has a developed surface on which the adsorption/desorption phenomenon of the studied gases occurs [Work on MOX semiconductor metal oxide sensors has been ongoing since the mid-20th century. The gas-sensitive properties of germanium oxide were described in 1953 . The firs occurs ,48. Depes occurs .2 or H2O [MOX sensors allow measuring gas concentrations through the phenomenon of changing the electrical conductivity of a semiconductor receptor–transducer element. In MOX sensors, the measuring element is usually a tin dioxide sinter. Depending on the purpose of the sensor, the semiconductor sinter contains various dopants in the form of precious metals. The change in electrical conductivity in the semiconductor layer is the result of a chemisorption process, i.e., the formation of chemical bonds between gas molecules and the semiconductor. This leads to a change in the concentration of current carriers on the surface of the semiconductor and thus a change in electrical conductivity ,50. The 2 or H2O ,51.gW is formed between the grains of the semiconductor, inhibiting the flow of electrons. The energy barrier is a range of energy equal to the work of exit of electrons from a semiconductor grain, which is defined in the electron volt unit. When a semiconductor is in a pure air atmosphere, the depleted layer increases, and thus, the energy barrier between grains is increased as well (Bk—Boltzmann constant (eV·K−1), T—temperature (K).An energy barrier  as well . The ene2− ionic forms appear, while at temperatures above 400 K, the more stable adsorbed O− and O2− electron-rich ionic forms of oxygen begin to appear, which are responsible for the useful properties of oxide sensors [Depending on the temperature of the semiconductor substrate, different patterns of oxygen binding on the semiconductor layer can be distinguished. At low temperature, weak physical adsorption by van der Waals forces dominates, with a binding strength of less than 0.1 eV. Between the adsorbed gas molecules and the semiconductor surface, there is no exchange of electric charge and the resulting change in the concentration of carriers in the gas-sensitive layer, so low-temperature adsorption does not cause a change in conductivity . Below 4 sensors . This reDevelopment of new gas-sensitive materials;Modification of the sensor design;Uniform temperature distribution of the receptor element;Use of doping substances;Incorporation of a catalytic filter.Sensitivity and selectivity are important parameters in the selection of sensors for the array. Improvement of these sensor parameters is performed by ,48:Devel2, the best sensitivity is obtained when the effective Debey length of the LD is close to the grain radii of the semiconductor; for this reason, the use of polycrystalline and nanocrystalline structures is preferred. The use of catalytic dopants in the gas-sensitive material has a very good effect on improving selectivity. The most common are precious metals such as gold, palladium, platinum and silver [In the case of SnOd silver ,58,59. Td silver ,60,61.2 paste is applied to a ceramic substrate and then fired at 970–1270 K. The substrate is a tube with an inner diameter of 1 mm or a plate with good thermal conductivity (20 W/m·K), made of Al2O3. A heating coil with a resistance of a few dozen ohms is placed inside the tube or on the bottom layer of the plate. Metallic electrodes (Au or Pt) are sputtered at the ends of the ceramic tube to allow electrical connection of the semiconductor [Knowing the resistance of the sensor in the gas atmosphere, the gas concentrations can be read using the characteristics provided by the manufacturer. In oxide sensors, a thin layer of SnOonductor ,50.Occupancy of active centers by interfering compounds;The co-reaction of gaseous pollutants;The presence of water vapor.Gas sensors operate in a variety of environmental conditions and atmospheres of varying composition. Changes in sensor characteristics can also occur due to ,50:OccupGas sensors operating in environments with high concentrations of chemical compounds are particularly prone to permanent poisoning, which is associated with irreversible changes in their characteristics. This phenomenon necessitates cyclic calibration. The studies conducted by Romain et al.  on the l2, In2O3, ZnO, WO3, CdO, and TiO2, are used as the sensing material in the semiconductor sensors. These distinct transparent conductive oxides are characterized by sufficient electrical conductivity, high reactivity, and a wide spectrum of transparency. Among the above-mentioned MOXs, TiO2 and SnO2 are the two most popular due to their low fabrication cost, mechanical and chemical stability, and high thermal resistance and adhesion to glass [Metal oxides, MOXs, are widely employed in modern sensing materials due to their tunable physical and chemical properties. The performance of the MOXs depends on their chemical and structural features including the chemical composition, morphology, structural defects, specific surface area, grain size, etc. . The varto glass . The sel2 and SnO2 properties, including the possible sensing material modifications, as well as the main characteristics, sensing properties and gas sensing applications of common MOXs materials will be provided in the following sections of this review.The more detailed description of TiO2) is a widespread, non-toxic, inexpensive, and environmentally friendly compound [2 reduction and H2 production processes. Being an n-type semiconductor, TiO2 is widely used in the development of conductometric gas sensors [2 benefits from large and reversible resistance variations along with exceptional high-temperature chemical stability; it can be found in three major phases: brookite (orthorhombic), anatase , and rutile  phases with energy bandgaps of 3 eV (rutile), 3.2 eV (anatase) and 3.13–3.40 eV (brookite), respectively [2 is used as the photoactive layer in the gas sensor in which the mechanism of chemical resistance and conductivity is based on the adsorption process or repulsion of reducing and oxidizing gases [2 nanostructures should be improved to further extend their applications: for instance, sensor signal, conductivity of TiO2 in the air, response and recovery level as well as doping [2 gas sensors find applications in biosensing, UV sensing, photocatalysis, and humidity sensing. Tin oxides (SnO and SnO2) gas sensors are utilized for carbon monoxide detection, environmental monitoring, industrial processes, and breath analysis. Some of the industrial processes where tin oxide gas sensors find application include the following. First, they are useful in combustion monitoring. Tin oxide gas sensors can be used in industrial combustion processes, such as those in power plants or manufacturing facilities, to detect the presence of carbon monoxide (CO) and ensure efficient and safe combustion. Second, there is emissions control. In industries that produce harmful gases as by-products, such as automotive manufacturing or chemical plants, tin oxide gas sensors can be utilized to monitor and control emissions, ensuring compliance with environmental regulations. Third, they are of use in the petrochemical industry. Tin oxide gas sensors play a vital role in the petrochemical industry, where they are used to detect and monitor volatile organic compounds (VOCs) and other gases that may be present in the production processes or storage facilities. A fourth area is semiconductor manufacturing. Tin oxide gas sensors find application in the semiconductor industry, where they are used to monitor and control gas concentrations during the manufacturing processes to ensure the quality and safety of semiconductor devices. Lastly, there are applications in the food and beverage industry. In the food and beverage industry, tin oxide gas sensors can be utilized for gas monitoring in storage facilities, fermentation processes, and packaging environments, helping to maintain product quality and safety.Titanium dioxide (TiOcompound  that mak sensors . As a seectively . Among tectively . TiO2 isng gases . Some pas doping . TiO2 ga2), also called cassiterite, has a rutile structure similar to other metal oxide semiconductors. Compared to SnO2, SnO is unstable at temperatures above 270 °C. In general, the gas sensors are made of thick porous SnO2 film with a higher surface-to-volume ratio. When the material is heated up, the electrons are entrapped and adsorbed by the molecules of material; moreover, the energy band bending alters the conductivity [Tin oxide (SnO) is a p-type semiconductor with a direct bandgap of 2.5–3.0 eV . Tin oxiuctivity .2, due to its low-temperature reactivity , has been widely used in a variety of applications, including biosensors, humidity sensors, UV sensors, photocatalysts, batteries, and thin film transistors. SnO2 belongs to the group of surface-sensitive materials [SnOaterials . Changes2, the chemisorbed oxygen (or adsorbed ion) and other molecules with net electric charge are the main stimuli of response, rather than the oxygen composing SnO2 lattice. The presence of these charged species can decrease or increase the SnO2 surface conductivity, and, as a consequence, vary the gas response signal intensity. Moreover, SnO2 performance can be modified by additives in order to increase the charge carrier concentration by donor atoms or enhance the gas sensitivity or catalytic activities by metallic additives [2 is the best material for CO gas sensing [2) gas sensors find applications in various industrial processes, environmental monitoring, and breath analysis. In industrial processes, they are used for combustion monitoring, ensuring efficient and safe combustion in power plants and manufacturing facilities. They also play a role in emissions control, helping to monitor and regulate the release of harmful gases in industries such as automotive manufacturing and chemical plants. They contribute to assessing and improving overall air quality in various settings, including urban areas, industrial zones, and residential environments. Furthermore, tin oxide gas sensors are employed in breath analysis applications. They can detect and measure certain gases present in human breath, providing valuable insights for medical diagnostics and the monitoring of respiratory conditions.For SnOdditives . Among o sensing . Tin oxi− ions) [Zinc oxide (ZnO) is a cost-effective, non-toxic, abundant, and chemically stable n-type semiconductor  with a wide energy bandgap and large excitation binding energy . Polar s− ions) . ZnO can− ions) . Accordi− ions) . The sha− ions) . Zinc oxIn addition, they are also useful in industrial processes and maintaining industrial hygiene. ZnO gas sensors are used in industrial settings to monitor workplace air quality and ensure the safety and well-being of workers. They can detect and measure hazardous gases generated in manufacturing processes, such as solvents, fumes, and toxic gases. As for process monitoring, ZnO gas sensors find application in various industrial processes where real-time monitoring of gas concentrations is required. This includes monitoring gas levels in chemical reactions, material off-gassing, and quality control in manufacturing processes. Lastly, they are also useful in leak detection. ZnO gas sensors are utilized in industries such as oil and gas, chemical, and refrigeration to detect and locate gas leaks. They provide early warning systems for detecting leaks of flammable, toxic, or harmful gases, helping to prevent accidents and ensure worker safety.2O3) is an n-type semiconductor with a large bandgap (3.75 eV). In2O3 exists in two crystalline lattice structures: cubic and rhombohedral with the bandgap energy of 3 eV. Cubic indium oxide has a relatively high electrical conductivity, it is non-stoichiometric, and it is widely used in microelectronics [2O3-based gas sensors strongly depend on the preparation conditions, the electronic state of the indium, and the phase composition. In2O3 has found numerous applications in various fields, such as photoelectric devices, biosensors, solar cells, gas sensors, and high-transparency coatings [Indium oxide (Inctronics . The procoatings .2O3) gas sensors are used for gas detection, environmental monitoring, industrial applications, and in the automotive industry for emission control. Here are examples of environmental monitoring and industrial applications for In2O3 gas sensors. First, there is environmental monitoring and environmental remediation. In2O3 gas sensors play a role in environmental remediation efforts by detecting and monitoring hazardous gases in contaminated soil, water, or air. They assist in identifying pollution sources and assessing the effectiveness of remediation strategies. Then, there are industrial applications such as emissions control. In2O3 gas sensors are used in industrial sectors, including power plants, chemical plants, and manufacturing facilities, for emissions control. They monitor and regulate the levels of pollutants such as nitrogen oxides (NOx), sulfur oxides (SOx), and volatile organic compounds (VOCs) to ensure compliance with environmental regulations. As for industrial processes, In2O3 gas sensors help detect and measure gases generated during manufacturing, chemical reactions, and material off-gassing, ensuring process efficiency and safety. In addition, In2O3 gas sensors are employed in industries such as oil and gas, chemical, and refrigeration for gas leak detection. They provide early warning systems for detecting leaks of flammable, toxic, or harmful gases, helping to prevent accidents and ensure worker safety. In the automotive industry, they monitor and measure gases such as nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbons (HC) in vehicle exhaust systems, enabling compliance with emission standards and improving air quality.Indium oxide  [3 (P21/n) has been mostly studied for gas measurement applications. The thermochromic properties of WO3 make it useful for smart electrochromic displays and windows [3 properties such as its reversible conductivity change, selectivity, biocompatibility, and high sensitivity make it suitable for biosensing applications [Tungsten oxide is an n-type semiconductor with a large bandgap (2.6–3.25 eV); it is one of the most used materials for MOXs gas sensors. WOhombeic) . The mon windows . It alsoications .3 films may be produced in different phase , porosities, sizes, and thicknesses through various methods such as sputtering, PLD/CVD, wet chemical method, vacuum sublimation, electrochemical, and thermal oxidation [3-based gas sensors mainly depend on the preparation method and post-annealing operation [3) gas sensors are used for gas detection, environmental monitoring, industrial processes, and in the development of smart windows for energy-efficient buildings. Here are examples of environmental monitoring and industrial applications for WO3 gas sensors. First, there is environmental monitoring and air quality monitoring in particular. WO3 gas sensors are utilized in air quality monitoring systems to detect and measure pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), volatile organic compounds (VOCs), and other harmful gases present in the ambient air. They contribute to assessing and improving overall air quality. Then, there are industrial applications and industrial processes. WO3 gas sensors are employed in various industrial processes for gas monitoring and control. They detect and measure gases generated during manufacturing, chemical reactions, combustion processes, and material off-gassing, contributing to process optimization, efficiency, and safety. WO3 gas sensors are also utilized in the development of smart windows for energy-efficient buildings. These sensors can detect changes in gas concentrations, such as carbon dioxide (CO2) levels, and trigger the windows to adjust their transparency or ventilation accordingly, optimizing energy usage and indoor comfort.The WOxidation . The perperation . Tungste2O), which is red in color and has a bandgap of 2 to 2.17 eV [CuO is a p-type semiconductor with a narrow bandgap of 1.2 eV. The copper oxide known as cupric oxide is black. Another copper oxide is the red-colored cuprous oxide  gas sensors are used for gas detection, industrial processes, environmental monitoring, and in renewable energy applications. Here are examples of industrial processes and environmental monitoring applications for copper oxide gas sensors. First, there are industrial processes and combustion processes. Copper oxide gas sensors are used in industrial combustion processes, such as in power plants or manufacturing facilities, to detect and monitor gases such as carbon monoxide (CO) and hydrocarbons. These sensors help ensure efficient and safe combustion. Copper oxide gas sensors also find application in the petrochemical industry for monitoring and controlling gas concentrations in various processes. They assist in detecting and measuring gases such as hydrogen sulfide (H2S), sulfur dioxide (SO2), and volatile organic compounds (VOCs) to maintain safe working environments. In addition, copper oxide gas sensors are also employed in renewable energy applications, such as monitoring gas emissions in solar cell manufacturing or detecting gas leaks in renewable energy storage systems. These sensors contribute to ensuring the safety and efficiency of renewable energy processes.Thermal oxidation is a very popular method in the synthesis of CuO-based sensors due to its cost-effectiveness, simplicity, and good quality; it is, however, time-consuming. CuO has been used as a gas sensor to detect a variety of reducing and oxidizing gases . Copper Sensitive response and recovery time, selectivity, the limit of detection (LoD), resolution, stability, and operating temperature are the major quality indices representing the performance of the gas sensor . The senThe LoD refers to the minimum concentration of the gas analyte that can be measured. It is a crucial parameter in determining the suitability of various gas sensors, including those that utilize MOS sensing materials, particularly for applications such as air quality monitoring. 2 detection is now possible at the sub-ppm level due to the decrease in the size of the sensing material [2-based sensors can detect humidity even at room temperature [2S, CH3OH, and C2H5OH at lower temperatures [As the reactions often occur on the surface of the sensing material, controlling the size of the semiconducting MOXs material is one of the priorities to increase the sensitivity of the sensor. In this regard, nanocrystals have shown the highest sensing signals due to their high specific surface area and higher adsorption capacity ,107. H2 material . It has perature  at higheeratures . Therefo2 and TiO2 can result in high-selectivity sensors, facilitating the detection of the target gas in a mixture.Selectivity defines the ability of the semiconducting layers to detect the target gases or a single gas in a mixture . The sel2 nanobelt for hydrogen detection and found that the response time declined from 220 to 60 s by elevating the working temperature of the sensor to 80 °C, while the recovery time showed a two-fold increase [2 nanofibers toward NO2 will be declined by temperature elevation, while the response time showed a decrement by increasing the concentration of NO2 [Long recovery time is accompanied by the slow surface reaction rates which can be accelerated by MOXs doping with metallic catalysts such as Pd and Ag . The resincrease . Landau n of NO2 . In addin of NO2 .2 sensor gap size on NO2 detection and found that the selection of the electrode gap size depends on the concentration of the target gas. They also revealed that the electrodes with large gaps are highly sensitive to high NO2 concentrations [In addition, the geometry and gap size of electrodes are the other important factors influencing MOXs-based sensors’ performance. Any change in the size of the electrode gap can alter the resistance of the device and hence the sensitivity of the sensor. Shaalan et al. (2011) investigated the impact of the SnOtrations . On the 2 and TiO2 have been synthesized with various morphologies. It was observed that the morphology can result in unique properties with a prominent role in their application. The following methods are often used for the synthesis of these compounds: electrochemical anodization [Previously, SnOdization , hydrothdization , templatdization ,121, eledization , and matdization .Long-term stability in the presence of siloxanes can decrement the accuracy and even makes it impossible to use MOXs-based sensors in platforms with a high siloxane load . The lacMultisensory approach, combining a series of interconnected sensors (sensor array) with machine learning algorithms, can be used to overcome these limitations. For gaseous phase analysis, such a system was named electronic nose, or e-nose, in similarity (even if rather relative) to the working principle of the mammalian olfaction system. Predictive models such as partial least square (PLS), support vector machine regression (SVM), artificial neural networks (ANNs), and others have been employed in the MOXs-based e-nose to identify and to estimate the intensity of the odor .The phenomenon of changes in the resistance of semiconductor sensor elements in the presence of an oxidative or reductive gas while operating at high temperatures is the basic principle of MOXs sensors operation . These sx content detection is a challenging analytical task. An atmospheric NOx is a relatively inert mixture with a high combustion temperature. Solid-phase MOXs sensors are widely employed for the detection of NO and NO2 gases on the industrial and laboratory scales, but a limited number of commercial NOx sensors can be found in the global market. In the laboratory scale, WO3, SnO2, ZnO, In2O3, and TiO2 are employed for NOx measurements due to their low synthesis costs, proper reactivity with gas molecules, and good selectivity in a combination of materials [3 and ZnO with Ag, Au, and Pt can improve the sensitivity of the NOx sensor. These oxides are naturally non-stoichiometric and have oxygen deficiency. The conductivity of these materials is examined based on the electrons provided by the additional metal. Upon exposure to gas at 200–300 °C, the amount of the electrons decreased due to the gas molecules of the intermediate reaction, which can increase the resistance of the sensor [x. These gases cause nitrogen deposition. They can also result in cardiac and respiratory diseases in humans. Toxic gases such as NOx from the vehicle’s exhaust and SOx released from the industrial plants are in the class of primary pollutants. Secondary pollutants are not directly released, as they are produced during the reaction/interaction of the primary pollutants in the air [x sensors in SCR  operation [Nitric oxide in the atmosphere is the main cause of acidic rains, leading to a high adverse impact on the environmental pollution in general and onto the respiration system of flora, fauna and humans in particular; hence, accurate outdoor NOaterials . A combie sensor . Industr the air . This se the air  and inveperation .x gas has no odor, color, or taste; that is, it is also called the “silent and invisible killer”. The presence of COx affects human health by decreasing the oxygen level of the blood, causing cardiac dysfunction.The COCombustion, power generation, and the transportation sector are the main sources of CO. It can lead to headaches and cardiac failure in humans. Its long-term exposure can result in coma.2 detection has had increasing significance in the industry today, as its high concentration in the workplace causes respiratory diseases. On the other hand, it contributes to the greenhouse effect. Current CO2 sensors are based on hard ceramic materials that require high working temperatures (200–600 °C) and high energy consumption (200–300 mW) [Fossil fuels, cement, and vehicles are the main source of carbon dioxide. This gas is the major cause of global warming and can affect the oxygen content of the blood in humans. CO–300 mW) .2 absorbs short wavelengths reflected from Earth to space, thus elevating the Earth’s temperature, altering climate patterns, and causing floods, drought, and melting of ices in the poles [2 concentrations for outdoor and indoor spaces should not exceed 350–800 and 1000 ppm, respectively [Moreover, an excess amount of carbon dioxide is harmful to our planet energy balance since COhe poles . Accordiectively .2 sensors such as solid electrolytes, semiconductors, mixed oxide capacitors, and carbonate-soluble polymers [2 gas sensor is highly useful due to its excellent sensitivity, low manufacturing cost, and proper selectivity. In recent years, NASICON (sodium (Na) Super Ionic CONductor) and LISICON (LIthium Super Ionic CONductor) are used mainly as active substances for CO2 measurement by combining the auxiliary phase of alkali metal carbonate [Researchers today report different materials and classes of COpolymers . The solarbonate . The staarbonate .x gas in atmosphere, which then becomes the main cause of acid rain that affects trees, rivers, and lakes. Exposure of the leaf to high levels of this gas (>100 ppm) destroys its tissue and also damages the edges and the area between the veins [x gas, the main problem is their insufficient stability [x remain also quite stable for a long time even in highly corrosive media [Combustion and power generation are two main sources of SOhe veins . In additability . On the ve media .2S gas is colorless but malodorous, well water-soluble, corrosive and flammable in ambient conditions. It is poisonous already at sub-ppm levels, while the higher concentrations may cause a death of living beings. The main sources of this gas are geothermal activities , landfills, organic decomposition of wastewater, and Sargasso algae [2S is one of the most dangerous gases in the workplace [2S and to protect workers from acute and chronic exposure. Humans can smell H2S at low concentrations (0.5 to 300 ppb). At higher concentrations, this gas can cause serious injuries such as nausea, loss of olfactory ability, irritation of the nose and throat, and even death [2S is also present in environments where the humidity and concentrations of auxiliary pollutants  are constantly changing. Therefore, it is necessary to have control and monitoring tools capable of operating in different concentrations and environmental conditions. Analytical methods such as gas chromatography and/or spectroscopy are commonly used to determine the concentration of H2S in the air [2S sensing by all nanomaterials and different types of gas sensors [The Hso algae . Accordiorkplace . Internaen death . H2S is  the air . Meanwhi the air . Among t the air , H2S sen sensors , and the sensors .Volatile organic compounds (VOCs) can also be detected with MOX gas sensors. Personal and home care products, building materials, as well as combustion processes  constitute the most well-known indoor sources of VOCs . In turn2 nanowire-based formaldehyde sensor in which the addition of gold and platinum nanoparticles not only improved the sensor sensitivity but also has permitted the detection of HCHO at low working temperatures. In the same period, Descamps et al. proposed a method for measuring the concentration of gaseous formaldehyde by means of a nanoporous film doped with Fluoral-P [Formaldehyde (HCHO) is an achromatic toxic VOC with a stimulant odor. Short-term exposure to HCHO causes headaches, while its prolonged exposure leads to asthma and pulmonary disorders . In 149149, Castluoral-P .The issue of the selective detection of hazardous indoor VOCs using metal oxide gas sensors relating to formaldehyde (as mentioned earlier) as well as benzene and naphthalene in ppb and sub-ppb concentrations with a varying background of ethanol was thoroughly discussed in . MOX gasOther VOCs were also detected using metal oxide sensors. A paper by Wang et al.  showed tThe application of electronic noses in the environment includes the following main categories: (i) air quality parameters analysis; (ii) water quality monitoring; (iii) process control; and (iv) verification of odor control systems efficiency .In each of the mentioned applications, an e-nose can be used as an alternative or auxiliary method for traditional analytical methods. Concerning air quality monitoring, an MOXs-based electronic nose can be an alternative to gas chromatography in assessing the presence and concentration of various pollutants . TherefoPollution monitoring is a key step in the management of air quality. The concentration of the pollutants is measured in the reference stations using accurate analytical tools comprising energy-demanding and heavy equipment. Therefore, the number of these stations is limited due to their high operation and maintenance costs. These stations are preferably located in urban areas, although they may be far from the main source of pollution. In rural, less-populated, and remote areas, there are no pollution measurement stations; hence, no pollution data are available . It has 2); sulfur dioxide (SO2); a nitrogen oxides group (NOx); and several volatile organic compounds (VOCs).Most of the studies have emphasized air pollution as the largest environmental hazard throughout the globe, which is almost intangible for most people. Based on the latest data from the World Health Organization (WHO), air pollution leads to 7 million death cases each year . Despitex compounds (one of the main air pollutants) using a perceptron ANN in an application specifically developed for smartphones has been estimated. Several tests were conducted under various relative humidities to assess the effect of humidity on the detection performance of the gas sensors, and it was demonstrated that high humidity can dramatically affect the device performance, especially in the detection of low levels of pollutants. The authors have planned that future works should be focused on the improvement of the reliability of MOX sensors in the presence of humidity to achieve proper prediction for various air pollutant contents.Recently, the application of employed an e-nose equipped with MOX sensors connected to a smartphone (by Bluetooth) to measure the air quality was reported by Arroyo et al. . The det2 in a controlled atmosphere at room temperature. The general design of these two devices was very similar, as shown in In , Sayago 2 measurement due to their high error, and they should be used in combination with other sensors. The MOX sensors, however, exhibited proper performance at higher temperatures.The sensors showed low error, especially in the temperature range of 50–100 °C, and there was even lower error at room temperature. Moreover, a combination of both sensors in a multi-linear calibration led to better results. The PLS method exhibited very good performance at 50 °C, but its error rose with the temperature elevation. Nanofiber-based sensors showed better performance at lower temperatures as compared with MOX sensors. MOX sensors, alone, showed no considerable response at temperatures below 250 °C. Regarding the obtained results, it seems that the MOX sensors, alone, are not suitable for NOIn research presented by Vuka and coworkers , the six2 and H2S in CO2-containing atmospheres with certain levels of humidity [2 and humidity and is only accurate when the mentioned parameters are known.Previously, Helli et al. have employed MOXs sensors to detect the presence and concentration of NOhumidity . It was In , Negri aWolfrum et al. have used fourteen MOXs sensors for the identification and quantification of several VOCs, such as acetone, toluene, and isopropanol even at very low concentrations (ppb) . To estaThe ability of an MOXs-based e-nose to classify malodorous air was reported by Sironi et al. . The rec2, NO2, and CO) and may be influenced by environmental interactions such as humidity, which are commonly present in the detection of chemicals. The application of these cases on temperature-modulated MOXs sensors is a challenge due to the nonlinear multivariate sensor response, cross-sensitivity to humidity and temperature, dissimilar quantitative conditions, and sensor drift. However, some researchers have estimated LOD in MOXs sensors using alternative approaches [Burgués et al. have used a pure analyte signal to estimate the limit of detection in temperature-modulated MOXs sensors . The limproaches . In [164In the research of the Lilienthal group, a cluster analysis approach based on the use of density peaks was employed for outdoor gas detection by means of e-noses . The modPreviously, various solutions have been proposed for the problem of gas detection in uncontrolled environments. For example, Vergara et al. developed a gas detection system using SVM . It was With the prospect of biological olfactory imitation, e-nose technology has its advantages and limitations. Currently, most of the reported sensors can measure and detect gas at different ppm levels. In some cases , however, high-sensitivity sensors are required. Therefore, finding an effective way to increase the sensitivity of the sensor is vital. Among the different solutions in regard to the enlargement of the surface of the sensing materials , the preHigh sensitivity, proper stability, working at room temperature, fast recovery and response are among the vital factors in the e-nose technology. The point is that strong adsorption or chemical reaction may result in proper selectivity and/or sensitivity, but it will hinder the sensor recovery . The senWhile dealing with both indoor and outdoor gas analyses, the synchronous measurement of the signal of an array of sensors at high precision is still a challenge that can be resolved by the following solutions: (i) a combination of a switcher and source-seeking method, (ii) using a timer for the simultaneous operation of several synchronous source-seeking methods, and (iii) a specifically designed measurement system. The first approach is not expensive and involves the use of a switcher, which delays shifting from one sensor to another for precise measurement of the data. The second method is synchronous, but its precision is lower than the source-seeking method in addition to being much more expensive than the first method. The third method is the most suitable approach with the least costs . The fluRegarding the above-mentioned discussion, the system should be first enhanced and calibrated depending on the type and location of the device; then, the e-nose can be utilized for the intended goal. Regarding the advantages and methods to resolve the limitations of the e-nose system, it can be employed for precise detection of the pollutants and the source of pollution in remote regions for a long time.The drinking water is easily accessible to all people in developed countries. The drinking water is, however, exposed to permanent threats due to the uncontrollable release of industrial wastes, which can result in microbial storms. In this context, frequent evaluation of the quality of water and the precise management of water resources are essential factors to ensure the supply of clean and safe water for humans  and the In many cases, online precise tools are required for the analysis of the wastewater and water, which can be realized by the use of chemical gas sensors as they can present information about the condition in a short time. They can be used in the quality control of food products, environmental monitoring, and diagnosis of human and animal diseases. Their adaptability can be also assigned to the various materials used in their structure.Wastewater systems, wastewater sludge processing plants, pumping stations, separation, and waste treatment and recovery units play a decisive role in environmental protection. They, however, pose a potential risk, the failure consequences and smell inconvenience can be reduced or eliminated by ensuring the correct performance of the system and taking preventive measures  . WastewaWastewater treatment plants can be mentioned among some of the most troublesome sources of volatile air pollutant emissions. Any wastewater treatment process, especially those involving anaerobic conditions, contributes to odorant emissions. A survey of technical personnel at 100 wastewater treatment plants in Germany indicates that the predominant sites of odorant generation are mainly wastewater pretreatment processes  and the processes associated with sludge treatment (sludge thickening and dewatering) .E/m3; biological treatment is associated with concentrations of 5–120 ouE/m3, while sludge treatment is 100–1,000,000 ouE/m3.The various processes contribute to significant levels of odor concentration. According to Kosmider et al. , the odoWastewater is a source of formation of a wide range of volatile organic compounds, so it is difficult to perform a comprehensive study of their chemical composition, and it is even more difficult to determine their synergistic effect on odor concentrations . They usE/m3. This allowed them to derive the equation cod = 38,902·cH2S0.6371, describing the aforementioned relationship with a coefficient of determination of R2 = 0.69. In contrast, in a study conducted at the University of Hertfordshire, UK, at 10 wastewater treatment plants, no statistically significant relationship was found between the concentration of hydrogen sulfide in wastewater and TON (Threshold Odor Number) in the ranges 0–4 × 103 and 0–4 × 105 ouE/m3 [Concentration levels of individual pollutant components, such as hydrogen sulfide, can provide an indicator of odor nuisance in the air . Gostelo5 ouE/m3 .The possibility of MOXs-based sensory systems for pollution evaluation in water and wastewater analysis has been demonstrated previously in several research studies . The mosOnkal-Engin et al.  evaluate3 min−1 was passed. PCA  was used to analyze the data. The purpose of conducting the measurement was first to identify the source (process) of malodorous substances and then to determine their odor concentration. As a result of the study, treatment plant processes were divided into three classes, which were related to wastewater, sludge and neutral ambient air. For odor concentrations of 100–150 ouE/m3, the accuracy of the classification was 0.95. The correlation between odor concentration and matrix response in the range of 20–80 ouE/m3 was also determined with an accuracy greater than 0.9.In another study, three measurement stations with EOS25, EOS28 and EOS35 analyzers were deployed near a wastewater treatment plant, while each station consisted of six MOX sensors . The senAttempts were also made to determine the relationship of volatile air pollutants with wastewater parameters such as COD, suspended solids, turbidity. However, the correlation coefficient proved to be statistically insignificant (R = 0.52–0.67) . On the Systems with a sensor array are a very good tool for continuous control of the wastewater treatment process. Such a station that detects and alerts to the presence of abnormal contaminants has been installed at Cranfield University’s municipal wastewater treatment plant ,98,99. TA system for the classification of stages of wastewater treatment processes and full-scale WWTP (particular devices) was designed and presented by Łagód and coworkers ,104. The2 at a level of 0.992. As the final step, ridge regression was used to predict water quality indicators and odor concentration with the RMSE being less than 0.9476 [A full-scale WWTP featuring an electronic nose system to characterize water quality parameters and odor concentration of wastewater emitted from different treatment devices was also developed and tested . The appn 0.9476 .2 = 0.83–0.95) and air temperature (R2 = 0.86–0.91). In contrast, the results from the MOX sensors were more stable, as evidenced by a lower coefficient of determination to relative humidity (R2 = 0.64–0.89) and air temperature (R2 = 0.76–0.86).According to a study by Nake et al. , for mea3 (0.978), N-NO2 (0.0.869), N-NO3 (0.958), and VOC (0.98), while the worst results were reached for pH (0.554) [Basing on readouts from a matrix of eight sensors by Figaro analyzing the headspace air from a laboratory sequence bath reactor with activated sludge, Guz at al. ,100 pres (0.554) .In , the useAn interesting approach to detect pollutants in the water samples using a wireless manual e-nose was adopted by Lozano et al. . Two yeaIn , TonacciIn a work of Baby et al., the e-nose was employed to investigate the possibility of pollutants and pesticides detection in surface water . An e-noAnother application of e-nose technology for water quality monitoring is the detection of microorganisms. In , Bastos In another research study reported by Sohn et al., the quality of the contaminated waters was assessed. The study aimed to use an e-nose for the assessment of the volatile solid substances’ concentration in contaminated waters through the sample headspace analysis . HoweverThanks to recent technological progress, researchers have found novel solutions in the field of potable water quality monitoring. For instance, the emergence of the Internet of Things (IoT) has been shown to be a prominent tool in a research of Climent et al., who employed IoT and developed a portable low-cost e-nose named as the multisensory olfactory system (MOOSU4) for bottled water quality control. The possibility of further extending the developed system for seawater assessment was also shown . The volAs hydrocarbons are the major source of marine contaminations, some new approaches have been developed in this field. For example, in , the AlpThe marine and shore ecosystems are under the continuous threats of human activities  which can result in serious and irreversible consequences for these regions and their wildlife. Various innovative funding bodies and solutions, including national and international projects, research teams, and scientific articles have been presented in the past decades . These sThere are numerous e-noses available on the market today, with a wide range of applications in different markets and industries, including environmental engineering. Moreover, they represent a wide range of sensor types, some of which also include MOX sensors. As e-noses are used increasingly often and the demand for them is growing, the companies producing commercial e-noses are constantly developing new prototypes that use other technologies and sensors but also have modules for increasingly advanced statistical data analysis. The above-mentioned facts also contribute to the use of commercial e-noses as part of scientific research.The paper  investigThe PEN3 e-nose  also consists of 10 MOX sensors, according to the manufacturer’s recommendations, and it can be used for odor testing in wastewater treatment plants and composting plants, filter inspection, leaks, exhaust emissions, and fire warning, among other applications. In the work , the PENThe paper  used a FThe KAMINA electronic nose with an MOX sensor microarray was used in the work  to recog2 MOX sensors and amperometric gas sensors (AGSs). Measurements from the two electronic noses enabled to determine the presence of mold indoors but not to classify the observations into the corresponding mold species present.Schreiber also used commercial KAMINA and MOSES II e-noses in his doctoral thesis . They weIn the present work, the applications of MOXs-based gas sensors and sensor arrays for environmental monitoring purpose have been overviewed. The environmental pollution is of particular importance in today’s society such that the full attention of national and international environmental institutions as well as researchers are focused on finding solutions to respond to the urgent need for environmental protection. Pollutant gases emissions in some industrial activities cannot be completely avoided despite a wide range of reducing agents. This can disturb and damage human health and the environment. Identifying and controlling the environmental effects of pollutants as a result of the activities of industrial plants is a key element for preventing and reducing industrial pollution and minimizing its impact on humans, which has become mandatory in many countries. In addition, water pollution is constantly intensified by human activities, which can lead to irreversible consequences for humans and even animals. Therefore, useful solutions are offered, as timely measures can prevent the further spread of pollution and even monitor the environment. In this regard, the MOXs gas sensors and sensor arrays, commonly called e-noses, are a very convenient tool for monitoring the environment, and their application involves many degrees of freedom, for example in sampling, training and data processing methods. However, the most important aspect limiting the use of gas sensors and electronic noses for environmental applications is the lack of specific regulations for their standardization. The definition and standardization of the features and performance of e-noses tools and their application methods are prerequisites of their publication.


Common variable immunodeficiency disorders (CVIDs), which are primary immunodeficiencies characterized by the failure of primary antibody production, typically present with recurrent bacterial infections, decreased antibody levels, autoimmune features, and rare atypical manifestations that can complicate diagnosis and management. Although most cases are sporadic, approximately 10% of the patients may have a family history of immunodeficiency. Genetic causes involving genes related to B-cell development and survival have been identified in only a small percentage of cases.NFKB2 gene. Based on this diagnosis and re-evaluation of other family members, the father and brother were diagnosed with this rare immune and preneoplastic syndrome. All CVID-affected family members presented with mycosis fungoides as their only symptom, which is, to the best of our knowledge, the first case to be reported.We present the case of a family with two brothers who presented with mycosis fungoides as an exclusive symptom of a common variable immunodeficiency disorder (CVID). Whole-exome sequencing of the index patient revealed a pathogenic variant of the This case highlights the importance of high-throughput sequencing techniques for the proper diagnosis and treatment of hereditary hematological disorders. Several nonsense  are a group of primary immunodeficiencies associated with impaired B-cell differentiation and defective primary antibody production . CVID isnonsense , 8 and lNFKB2 is a widely studied transcription factor with key roles in the functioning of the immune system , the patient would be excluded from a CVID diagnosis and would then be reclassified as having a CVID-like disorder caused by a specific PID (primary immunodeficiency disorder)/inborn error of immunity  exclusively presents with infections, whereas the second, less frequent group has a variety of noninfectious inflammatory, autoimmune, and/or lymphoproliferative presentations, which are also associated with systemic immune activation , 19. In Mycosis fungoides (MF) is a primary cutaneous T-cell lymphoma associated with the clonal proliferation of epidermotropic T-lymphocytes residing in the skin. MF accounts for more than half of all cutaneous lymphomas , and itsNFKB2 gene. Based on this diagnosis and re-evaluation of other family members, the father and brother were also diagnosed with this rare immune and neoplastic syndrome. All CVID-affected family members presented with mycosis fungoides as their only symptom, which is, to the best of our knowledge, the first case series to be reported.Here, we present the case of a family with two brothers who presented with mycosis fungoides as an exclusive symptom of CVID. Diagnosis was made via exome sequencing which revealed pathogenic variants in the 22.1A 20 year-old, male patient has been referred to our hematological service from the dermatological unit with a diagnosis of folliculotropic mycosis fungoides. He has no comorbidities and no history of infections or autoimmune diseases. He has had a long history of generalized skin lesions and erythema since the age of nine treated with topical corticosteroids. Since 2 years ago, he began to notice the appearence of new lesions in different sites. At the time of referral to our service, he presented with generalized hairless skin, erythematous lesions in the armpit, in the left cheek and in the right thigh , and negative for CD30. Thus, the diagnosis of mycosis fungoides with large cell transformation was confirmed.Peripheral blood study shows no Sézary cells by cytomorphology or flow cytometry.The PET scan showed the area of uptake in the above mentioned skin lesions (SUV between 2 to 3.5), hypermetabolic consolidation in the lower pulmonary lobe (SUV 2.5), and pleural thickness with an SUV of 2.7.In conclusion, mycosis fungoides with large-cell transformation was verified with visceral involvement (pulmonary) without Sézary syndrome.Treatment included local dermatological treatment and two initial cycles of chemotherapy with cyclophosphamide, doxorubicin, etoposide, vincristine, and prednisone (CHOEP), after one and a half month, we changed the therapeutic approach to gemcitabine monotherapy.Clinical progression during treatment, with enlargement of the skin lesions and known pulmonary consolidation, was confirmed. Third-line treatment was performed based on photobeam and subsequently pembrolizumab (2 mg/kg IV) every 3 weeks. Now, one year later, he is receiving the 5th cycle of pembrolizumab with a partial response, and we plan to perform haploidentical allogeneic stem cell transplantation with his mother.2.2The brother had a history of few cutaneous lesions over the years, mostly in both legs, erythematous, not pruriginous (he does not remember the exact timeline of events), he never consulted because of them  on both siblings to obtain a specific molecular diagnosis.2.3The father has a history of cutaneous lesions, with the same features as described in the second son. He did not want any follow up but he agreed to a cutaneous biopsy of one of the lesions. The diagnosis of mycosis fungoides also was made. He dropped out of follow up.33.1We performed exome sequencing as part of a local pilot program aimed at promoting the use of high-throughput sequencing for the diagnosis of rare genetic diseases, with a special focus on hematological diseases. Informed consent to participate in the study and its publication was obtained from the participants and their families. In this case, we performed WES (100X estimated depth of sequencing) in both affected siblings according to the guidelines shown in After variant calling, we obtained 312,842 and 301,845 variants for both patients , of which 119 and 120 were nonsynonymous or located in canonical splice sites with a low population frequency (less than 0.5%), respectively. Of these, 62 were present in both siblings, 57 were missense, four frameshift variants and one stop gain (variants are listed in NFKB2 gene (NM_001261403:exon21:c.C2557T:p. R853X) and lies towards the end of the protein’s C-terminus  is in the NFKB2 function , 8 mg/dL for IgM , 29 mg/dL for IgA , 450 mg/dL for the brother’s IgG, 20 mg/dL for IgM, and 73 mg/dL for IgA.The inmunogloblin concentration of the brother was: IgA 73 mg/dL, IgG 450 mg/dL, IgM 20 mg/dLPrimary immunodeficiency screening and orientation tube (PIDOT) cytometry studies were performed in both siblings and tested positive for decreased immunoglobulin and memory B cell levels  does not.Father and both sons have a novel stop-gain variant in heterozygosity in the in silico predictors . The variant lies towards the end of the gene (at position 853); it has no in-silico prediction of activating the nonsense-mediated-decay (NMD) mechanism, but it compromises the last 48 amino acids of the protein, which are key for proper function. Previous studies have shown that NFKB2 protein that was produced from the R853* mutation was able to localize to the nucleus and activate transcription, suggesting that it was functional (no NMD). However, the authors also found that the protein was degraded more rapidly than wild-type NFKB2 protein  at two conserved C-terminal Serines  (Activation of  Ser870)  by a kinNFKB2, thus supporting the diagnosis , a phenotype described in association with LOF variants in iagnosis .NFKB2 variant might act as a first “driver-hit” that might need a second-hit to determine lymphoma development favored by the immunodeficiency.To the best of our knowledge, hereditary MF as the sole manifestation of CVID is rare. It might have an additional genetic component (not only the NFKB2 variant) detectable in the patient’s genome. Hence, we analyzed other genomic variants present in both siblings with low frequencies and potential pathogenic impacts. We found a heterozygous frameshift variant in the ECM1 gene with a population frequency of 0,000015, that falls within the first 45-48% of the protein (depending on the transcript) and is predicted to activate an NMD mechanism and be damaging. Although variants of this gene are related to a dermatological disorder with generalized thickening of the skin and lipoid proteinosis of Urbach and Wiethe (OMIM:247100), it seems unlikely that this variant has an impact on the phenotype. Both brothers were heterozygous for this autosomal recessive disorder and did not have any elements suggestive of this phenotype. Hence, we did not speculate the role of this variant in ECM1 in this case. However, we do not exclude the possibility that the https://www.omim.org/entry/615577). We propose to depart from the “umbrella diagnosis” of CVID when a specific cause is found  for the publication of any potentially identifiable images or data included in this article. Informed consent to participate in the study and its publication was obtained from the participants and their families.MNS, SG: treated and followed the patient and family. Wrote the manuscript. CS, MB: bioinformatic analysis of whole exome data. Reviewed the manuscript. MBo, SR: followed the patients in the hematological service. AB, JG-L, DL, NT: cytometry analysis. Reviewed the manuscript. VR: Interpretation of variants. Wrote original draft. LS: Bioinformatic analysis, interpretation of variants and funding acquisition. Wrote original draft. All authors contributed to the article and approved the submitted version.


To evaluate the feasibility of using mpMRI image features predicted by AI algorithms in the prediction of clinically significant prostate cancer (csPCa).This study analyzed patients who underwent prostate mpMRI and radical prostatectomy (RP) at the Affiliated Hospital of Jiaxing University between November 2017 and December 2022. The clinical data collected included age, serum prostate-specific antigen (PSA), and biopsy pathology. The reference standard was the prostatectomy pathology, and a Gleason Score (GS) of 3 + 3 = 6 was considered non-clinically significant prostate cancer (non-csPCa), while a GS ≥ 3 + 4 was considered csPCa. A pre-trained AI algorithm was used to extract the lesion on mpMRI, and the image features of the lesion and the prostate gland were analyzed. Two logistic regression models were developed to predict csPCa: an MR model and a combined model. The MR model used age, PSA, PSA density (PSAD), and the AI-predicted MR image features as predictor variables. The combined model used biopsy pathology and the aforementioned variables as predictor variables. The model’s effectiveness was evaluated by comparing it to biopsy pathology using the area under the curve (AUC) of receiver operation characteristic (ROC) analysis.prostate) varied significantly across different ISUP grade groups of RP pathology (P < 0.001). Other clinical variables and image features did not vary significantly across different ISUP grade groups (P > 0.05). The MR model included PSAD, the ratio of ADC value between the lesion and the prostate outside the lesion (ADClesion/prostate), the signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate), and the ratio of DWIlesion/prostate to ADClesion/prostate. The combined model included biopsy pathology, ADClesion/prostate, mean signal intensity of the lesion on DWI (DWIlesion), DWI signal intensity of the prostate outside the lesion (DWIprostate), and signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate). The AUC of the MR model  was not significantly different from that of biopsy pathology . The AUC of the combined model  was higher than that of the biopsy pathology (P = 0.042) and MR model (P = 0.031).A total of 315 eligible patients were enrolled with an average age of 70.8 ± 5.9. Based on RP pathology, 18 had non-csPCa, and 297 had csPCa. PSA, PSAD, biopsy pathology, and ADC value of the prostate outside the lesion (ADCThe aggressiveness of prostate cancer can be effectively predicted using AI-extracted image features from mpMRI images, similar to biopsy pathology. The prediction accuracy was improved by combining the AI-extracted mpMRI image features with biopsy pathology, surpassing the performance of biopsy pathology alone. Prostate cancer (PCa) is the most common malignant tumor among men in Europe and America, and the second leading cause of cancer-related deaths. Early detection of PCa is crucial for proper diagnosis and treatment. The 2017 European Association of Urology Prostate Cancer Guidelines  for continuous variables that did not follow a normal distribution. Categorical variables were reported as frequencies (percentage %).lesion was found to be applicable in this particular scenario. On the other hand, if the variances were not homogeneous, the corrected t test was applied to compare the features between the non-csPCa and csPCa groups, and the Kruskal‒Wallis test was used to compare the features among the ISUP groups of post-operation pathology. In this case, ADCprostate and ADCprostate/lesion were deemed applicable. For continuous variables that did not conform to a normal distribution, the Mann‒Whitney test was employed to compare the features between the non-csPCa and csPCa groups, and the Kruskal‒Wallis test was utilized to compare the features among the ISUP groups of post-operation pathology. The following variables in this study fell into this category: age, PSA, PSAD, prostate volume, lesion volume, DWIlesion, DWIprostate, DWIlesion/prostate, DWIlesion/ADClesion, and DWIlesion/prostate/ADClesion/prostate.The Shapiro‒Wilk test was employed to assess the normality of continuous variables. If the continuous variables followed a normal distribution, additional testing was conducted to examine the homogeneity of variances using an F test. If the variances were found to be homogeneous, the t test was utilized to compare features between the non-csPCa and csPCa groups, while one-way ANOVA was used to compare features among the ISUP groups of post-operation pathology. ADCR2) values of the multivariable regression models. The DeLong test was used to compare the AUCs of the biopsy pathology, MR model, and combined model. A P value less than 0.05 was considered statistically significant.The Nagelkerke test was used to obtain the coefficient of determination  patients underwent MRI examination after biopsy, with a median interval of 15  days. On the other hand, 273 (86.7%) patients underwent MRI examination before biopsy, with a median interval of 6  days. Of the 315 patients, 59 (18.7%) were diagnosed with non-csPCa by biopsy pathology, and 256 (81.3%) were diagnosed with csPCa. However, based on RP pathology, only 18 (5.7%) patients were diagnosed with non-csPCa, while 297 (94.3%) were diagnosed with csPCa.3. The median volume of the prostate gland was 35.6  cm3. In terms of biopsy pathology, the median number of biopsy cores was 12 , and the median percentage of biopsy cores positive for cancer was 30% . Among the 256 patients diagnosed with csPCa by biopsy pathology, the majority had a Gleason score of 7 , followed by Gleason score 6  and Gleason score 8–10 .Table prostate . However, no significant differences were observed in the other clinical and image features . In the comparison between the csPCa and non-csPCa groups . There were no significant differences observed in the other clinical and imaging features between the csPCa and non-csPCa groups .Statistically significant differences were observed among the five RP ISUP groups Table  in termsps Table , signifilesion/prostate, DWIlesion/ADClesion, and DWIlesion/prostate/ADClesion/prostate. In the combined model, biopsy pathology, ADClesion/prostate, DWIlesion, DWIprostate, and DWIlesion/prostate were included.Table R2 of 0.219, indicating that it explains 21.9% of the variability in the outcome. On the other hand, the combined model had an R2 of 0.411, indicating that it explains 41.1% of the variability in the outcome. These results suggest that both the MR model and the combined model can be useful in predicting csPCa. The combined model, which includes biopsy pathology and imaging features, had a higher R2 value and, thus, may provide more accurate predictions.Figure n = 59), the transitional zone , and lesions located across both the TZ and PZ (n = 180), in addition to the analysis of the entire cohort (n = 315). The evaluation metrics, including AUC, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, are presented in Table The predictive performance of the biopsy pathology, MR model, and combined model were evaluated using ROC analysis, and the results are displayed in Fig. pz), TZ (AUCtz), and PZ + TZ (AUCpz+tz) groups . The specific comparison of AUC values can be found in Table Within each method, we observed that there were no statistically significant differences in the AUC values when comparing the lesions located in the PZ (AUCP = 0.884). However, the AUC of the combined model  was significantly higher than that of the biopsy ISUP (P = 0.042) and the MR model (P = 0.031). The results of DCA, presented in Fig. In terms of overall performance, the AUC of the biopsy pathology was 0.820 , and the MR model had an AUC of 0.830 , with no significant difference observed between the two methods  with systematic biopsy (SB) as the first-line biopsy method in patients diagnosed with PCa with an abnormal MRI . ProstatProstate MRI and related MRI-directed biopsies have been shown to be at least as diagnostically effective as systematic biopsies alone in diagnosing significant cancers. Studies have proven that the concordance between biopsy and prostatectomy grading was highest in combined biopsy (CB) but still with misdiagnosis of csPCa in 25% of men . Thus, wlesion) and a higher contrast ratio between the DWI signal intensity of the lesion and the background signal intensity (DWIlesion/prostate) indicate a more prominent display of the lesion on the DWI image. Similarly, a lower ADC value (ADClesion) and a lower contrast ratio between the ADC value of the lesion and the background ADC value (ADClesion/prostate) result in a more distinct display of the lesion on the ADC map. These types of image features have been widely used in previous studies and have demonstrated their value in indicating tumor invasiveness. Previous studies have found that there is a negative correlation between DWI signal intensities, ADC values and Gleason score, indicating that higher DWI signal intensities and lower ADC values are associated with higher Gleason scores and more aggressive prostate cancer [In the PI-RADS system, DWI and ADC image features are utilized for the detection of csPCa, including the typical observations of significantly high DWI signal and significantly low ADC values. In this study, we transformed these descriptive features into computed image feature values. For example, a higher DWI signal intensity  placement, differences in the b-values used for calculation, and differences in the software used for calculation . FurtherOur study has some limitations. The first limitation of this study is that the data were collected from a single institution and were not obtained prospectively. This limits the generalizability of the study findings to other settings and populations, and the retrospective nature of the data collection can introduce bias and confounding factors. Thus, caution should be exercised when interpreting the results of this study, and further studies are needed to validate the findings in larger and more diverse populations. The second limitation of this study is that only patients who underwent radical prostatectomy were enrolled, as RP pathology was required for the analysis. However, in the broader clinical context, many patients may not be candidates for RP due to various reasons, such as advanced prostate cancer with no chance for curative surgery. Therefore, the generalizability of the study’s conclusions should be further assessed in patient populations that do not undergo RP. Future studies could include patients who undergo alternative treatments or active surveillance to assess the performance of the AI model in these populations. The study has a limitation in that only a limited number of clinical variables were included, which were age, PSA, and mpMRI. Other important clinical and imaging data, such as digital rectal examination, ultrasound, PET-CT, and prostate health index (PHI), were not taken into account. Therefore, incorporating a broader range of relevant data in future studies is necessary to enhance the precision and dependability of the prediction model.In summary, AI-extracted image features from mpMRI images can accurately predict the aggressiveness of prostate cancer, similar to biopsy pathology. The accuracy of this prediction can be further improved by combining the AI-extracted mpMRI image features with biopsy pathology, which outperforms biopsy pathology alone. After further evaluation, this prediction model can be used for the re-evaluation of biopsy pathology and active surveillance of prostate cancer.