Information in pathology reports is critical for cancer care. Natural language processing (NLP) systems used to extract information from pathology reports are often narrow in scope or require extensive tuning. Consequently, there is growing interest in automated deep learning approaches. A powerful new NLP algorithm, bidirectional encoder representations from transformers (BERT), was published in late 2018. BERT set new performance standards on tasks as diverse as question answering, named entity recognition, speech recognition, and more.The aim of this study is to develop a BERT-based system to automatically extract detailed tumor site and histology information from free-text oncological pathology reports.What organ contains the tumor? and What is the kind of tumor or carcinoma? This involved supervised training on 8197 pathology reports, each with ground truth answers to these 2 questions determined by certified tumor registrars. The data set included 214 tumor sites and 193 histologies. The tumor site and histology phrases extracted by the Q&A model were used to predict International Classification of Diseases for Oncology, Third Edition (ICD-O-3), site and histology codes. This involved fine-tuning two additional BERT models: one to predict site codes and another to predict histology codes. Our final system includes a network of 3 BERT-based models. We call this CancerBERT network (caBERTnet). We evaluated caBERTnet using a sequestered test data set of 2050 pathology reports with ground truth answers determined by certified tumor registrars.We pursued three specific aims: extract accurate tumor site and histology descriptions from free-text pathology reports, accommodate the diverse terminology used to indicate the same pathology, and provide accurate standardized tumor site and histology codes for use by downstream applications. We first trained a base language model to comprehend the technical language in pathology reports. This involved unsupervised learning on a training corpus of 275,605 electronic pathology reports from 164,531 unique patients that included 121 million words. Next, we trained a question-and-answer (Q&A) model that connects a Q&A layer to the base pathology language model to answer pathology questions. Our Q&A system was designed to search for the answers to two predefined questions in each pathology report: caBERTnet’s accuracies for predicting group-level site and histology codes were 93.53% (1895/2026) and 97.6% (1993/2042), respectively. The top 5 accuracies for predicting fine-grained ICD-O-3 site and histology codes with 5 or more samples each in the training data set were 92.95% (1794/1930) and 96.01% (1853/1930), respectively.We have developed an NLP system that outperforms existing algorithms at predicting ICD-O-3 codes across an extensive range of tumor sites and histologies. Our new system could help reduce treatment delays, increase enrollment in clinical trials of new therapies, and improve patient outcomes. Much of the information in electronic medical records (EMRs) required for the practice of clinical oncology and cancer research is contained in unstructured text. As much as 80% of EMR data can be found in narrative text notes and scanned documents , rangingNatural language processing (NLP) has been used to extract information from medical text for several decades -6, and aMore recently, there has been growing interest in more highly automated deep learning approaches for clinical NLP ,22. In lBERT’s proficiency at question answering prompted us to construct a question-and-answer (Q&A) system to extract clinical data from pathology reports. This concept has long been compelling—Q&A systems for medical data extraction have been pursued for >40 years . Such a This work describes three primary contributions:CancerBERT, or caBERT for short.A new BERT language model for comprehension of pathology reports in oncology. We call this A new Q&A caBERT-based system, tolerant to varied terminologies, word orders, and spelling mistakes, to extract tumor site and histology descriptions from free-text pathology reports.A new caBERT network (caBERTnet) to predict International Classification of Diseases for Oncology, version 3.2 (ICD-O-3.2), codes from the extracted descriptions. This system can handle up to 332 organ sites and 1143 tumor histologies. On an unseen test data set with 214 sites and 193 histologies it achieved overall accuracies that are equal to, or above, those of existing systems, while also expanding on the number of site and histology classes captured by these systems. Although the results in practice still require human validation, they provide a means of early abstraction from unstructured pathology text over a very broad set of sites and histologies and in addition can provide an initial signal to assist expert cancer registrars in their case diagnosis–abstraction workflow.Information on our software development tools is provided in unsupervised training on a large corpus of pathology text. For this we constructed a pathology language–model training data set, described in more detail in the next section (Pathology Language Model).Constructing this system required us to first train a base language model to comprehend the technical language in pathology reports . This inFragment of text from a pathology report generated at Moffittclinical history: not given. preoperative diagnosis: right lower lobe, squamous cell carcinoma. specimen types: a: right station 7 fs b: station # 10 c: right lung fs d: station 4r e: additional station 10 f: additional station 7 final diagnosis: a. lymph node right station 7 biopsy: anthracotic lymph node 1 with lymphoid hyperplasia negative for malignancy. b. lymph node station 10 biopsy: anthracotic lymph node 1 negative for malignancy. c. lung right pneumonectomy: moderate to poorly differentiated squamous cell carcinoma with extensive necrosis see key pathological findings. bronchial and vascular resection margins negative for malignancy. three of 10 hilar lymph nodes with metastatic squamous cell carcinoma. d. lymph nodes station 4r biopsies: anthracotic lymph nodes 5 negative for malignancy. e. lymph node additional station 10 biopsy: minute lymph node 1 negative for malignancy. f. lymph node station 7 biopsy: anthracotic lymph nodes 4 negative for malignancy. key pathological findings tumor type: squamous cell carcinoma with extensive necrosis. histological grade: moderate to poorly differentiated. tumor location: right lung involving right lower lobe. right upper and middle lobes free of tumor.Next, it required us to train a Q&A model that appends a Q&A layer onto the pathology language model to answer pathology questions. Our Q&A system was designed to search for the answers to two predefined questions in each pathology report:What organ contains the tumor?What is the kind of tumor or carcinoma?What organ contains the tumor? with C343: lower lobe, lung, and would respond to the question What is the kind of tumor or carcinoma? with 8070/3: squamous cell carcinoma, nos (not otherwise specified).For example, when presented with the report shown in supervised training on a set of pathology reports, each with ground truth answers to these 2 questions determined by human experts. To do this, we constructed a second fine-tuning training data set, described in more detail in the Pathology Q&A Model section. Finally, we evaluated our system using a sequestered fine-tuning testing data set, described in more detail in the Pathology Q&A Model section.This involved Training a base language model to comprehend pathology reports leveraged prior work by several groups . Lee et Alsentzer et al  created mask token. The language model is then trained to predict the masked words. The word-masking process is performed automatically at the beginning of each training run.Transfer learning was accomplished by performing masked-language modeling . BrieflyOur language-model training corpus included electronic pathology reports of solid tumors produced by pathologists at Moffitt between 1986 and 2020. The year 1986 was the earliest date on pathology reports cataloged in our enterprise data warehouse. The data set was restricted to solid tumors for two reasons: first, to focus the problem domain for this proof-of-concept study, and, second, Moffitt hematologic pathology reports follow a quasi-structured format, reducing the need for extraction of data from unstructured text.This data set contained both Health Level Seven International messages and plain-text pathology reports. These were minimally processed to extract and clean the text relevant to pathology , version 1.1 . SQuAD cFor the second stage of training we used the large-scale Biomedical Semantic Indexing and Question Answering (BioASQ) data set . In partNext, we constructed a Q&A fine-tuning data set in SQuAD format based on Moffitt pathology reports. We obtained ground truth answers to our 2 questions from data abstracted by Moffitt certified tumor registrars (CTRs). CTRs undergo an extensive training and internship program to become proficient at extracting quantitative and categorical data from unstructured pathology reports. They are widely employed by cancer centers and other organizations to extract data for clinical and research applications and for reporting to state and national agencies. The MCR deploys state-of-the art quality assurance procedures: its benchmark for quality is 90% and its target accuracy is 95% .Accommodate Diverse Terminology). Our search produced 16782 reports that met these inclusion criteria  the relative frequencies of the 10 most common tumor sites and histologies in this collection matched the relative frequencies in Moffitt’s patient population as a whole and (2) all MCR-assigned tumor sites and histologies reported for Moffitt patients after 2006 were represented in the data set. The final curated collection contained 10,247 reports . We willAccurate ICD-O-3 Codes section. The testing subset was used to evaluate the impact of Q&A training at the end of each training stage  of the reports were used to create the Moffitt fine-tuning training data set and 20.01%  were used to create the Moffitt fine-tuning testing data set. Each data set was saved in SQuAD format using a custom-written Python program. The training data set was used for the final stage of Q&A training and also for ICD-O-3 code predictions, described in more detail in the Question-answering accuracy was evaluated using two metrics: exact match and F1 score. Exact match is true if the caBERT-extracted phrase is an identical word-for-word match with the MCR phrase and false otherwise. We calculated the average number of true results across all test samples. The F1 score is a measure of the degree of overlap among the words in the caBERT-extracted phrase and the MCR phrase. This varies from 0 (no words in common) to 1 . Each exact match corresponded to an F1 score of 1.0. We calculated the average F1 score across all test samples and expressed it as a percentage.ClinicalBERT–Bio+Discharge Summary BERT) with a new randomly initialized Q&A layer. This allowed us to determine the impact of developing a pathology-tuned BERT model on extraction accuracy over the baseline accuracy of using ClinicalBERT alone  program Site/Histology Validation List [To address this issue, we used data from several canonical sources. Our primary source was the ICD-O-3 standards , which wion List , along wion List .To provide a little more detail, the specific sources we used to construct our acceptable phrase tables were as follows. For histology, we used the ICD-O-3.2 morphology table (version 15112019)  and suppThe aforementioned sources have the benefit of being subject to an international standard and are useful in designating preferred terms for each histology and site code. However, we should note that there do exist slight discrepancies between the World Health Organization–maintained ICD-O-3 coding standards and the North American Association of Central Cancer Registries coding guidelines, which are followed in the SEER materials. For simplicity, we chose to base our model on the ICD-O-3 standards, but this caveat may prove relevant for any future cancer registry applications of the model.Although these sources provided us with preferred and alternative terminologies, they did not encompass the full range of language used for every label in our pathology reports, which often included things such as permutations of word orderings as well as acronyms and other typographical differences with the canonical terms. Of note, Moffitt CTRs routinely record a short description of the histology and site for every labeled pathology report in a text-based field. For each histology and site code, we appended these additional phrases to the list of synonyms of the preferred canonical terminology.Using the sources described earlier, we created two hierarchical tree structures as illustrated in Table S2 in To search each pathology report for appropriate spans of text, we used the trees to construct a dictionary with keys provided by the specific site and histology codes and values provided by the associated acceptable phrase table. Using this dictionary, for each pathology report we implemented a simple search for an exact match from the list of preferred terms and synonyms for the labeled ground truth histology and site code, giving preference for the preferred term, followed by each synonym ordered by length (with the longest matching synonym given preference over the others).itertools module available as part of the Python Standard Library.Even with the diverse terminology within the acceptable phrase table for each code, not every pathology report contained an exact match within the list of allowed terms. For pathology reports that did not contain an exact match, we further refined the search by allowing for matches that only overlapped with a subset of the word tokens within each phrase, again giving preference to the longest synonyms and also using a set of stop terms to avoid overly general terminology. To capture potential word-ordering differences, we allowed these word token subsets to be constructed in an arbitrarily permuted order, which was made efficient by using the Using the aforementioned procedure, of the 10,247 pathology reports in the Moffitt fine-tuning training and testing data sets, we were able to find appropriate textual answers within 10,096 (98.53%) reports for primary site  and within 10,218 (99.72%) reports for histology .The tumor site and histology phrases extracted by the Q&A model  were useExtract Tumor Site and Histology Descriptions section). Training samples were screened to ensure that each contained ground truth site and histology codes and at least one site or histology phrase provided by the MCR. Missing site and histology phrases were filled using SEER preferred terms. These were identified by performing a lookup into the ICD-O-3 table using the site or histology code in the training sample.Classifier training parameters are described in more detail in Table S1 in lung lower lobe and squamous cell carcinoma, then the combined phrase would be site: lung lower lobe. histology: squamous cell carcinoma. The combined phrase was used to train both the caBERT site classification model and the caBERT histology classification model. The use of a combined phrase allowed caBERTnet to leverage any correlation between site and histology to improve its performance. For example, astrocytomas are brain tumors. When caBERTnet encountered a previously unseen pathology report during the test phase with the combined phrase site: frontal. histology: anaplastic astrocytoma, it correctly predicted a brain site of C711, frontal lobe and a histology of 9401/3 astrocytoma anaplastic NOS (not otherwise specified).After screening, the ground truth phrases were labeled and concatenated to form a single combined phrase. For example, if the MCR phrases were Extract Tumor Site and Histology Descriptions section. For each test sample, the MCR-generated site and histology phrases were used to create a ground truth combined phrase. Next, the site and histology phrases extracted by the Q&A stage of caBERTnet were used to create a predicted combined phrase. The predicted phrase was tokenized to prepare it for input into each classification model. Ground truth site and histology codes from the MCR were enumerated, as described earlier, and stored as true labels. Subsequently, the trained site and histology classification models were used to classify the tokenized predicted combined phrases for each test sample. The outputs from this classification, logits, were converted into probabilities, sorted, and converted back into ICD-O-3 codes as described earlier, labeled as predicted codes, and saved for further performance analysis.After training of the site and histology ICD-O-3 code classification models was complete, caBERTnet performance was evaluated using the sequestered Moffitt fine-tuning test data set described in the caBERTnet performance was evaluated in 3 different ways. First, the top 5 accuracies were determined. This metric  is commonly used to evaluate classification algorithms . BrieflySecond, we examined the effect of culling or removing infrequently occurring codes. Our hypothesis was that the caBERT site and histology code classifiers suffer when they do not have enough training data to learn from. Therefore, to examine the effect of training sample size, we iteratively eliminated site and histology codes from the full 2050-sample test data set when the number of examples with a particular code in the training set  fell below a specified threshold. We varied that threshold from 0 samples (no culling) to 35 samples in increments of 5 samples. At each culling threshold we recalculated the top 5 performance of the site and histology classifiers.group code for each site and histology code. Group codes occur higher up in the ontological tree and, as the name implies, encompass a group or range of related tumor sites or histologies. For example, the site codes C341 upper lobe, lung and C349 lung, NOS have the same group code: C34 bronchus and lung. The histology codes 8070/3 squamous cell carcinoma, NOS and 8051/0 verrucous carcinoma both have the same group code: 805-808 squamous cell neoplasms. The ICD-O-3 ontology includes 82 group-level site codes covering the 332 fine-grained site codes. It includes 49 group-level histology codes covering the 1143 fine-grained histology codes.Third, we calculated the overall accuracy of predicting the correct Accommodate Diverse Terminology section. For each fine-grained code, the search started at that code’s location in the tree and proceeded upward. Finally, we calculated the overall accuracy of prediction within each group code for both site and histology predictions.Group codes are useful for search and summary applications. The group codes for both the predicted and ground truth fine-grained codes were determined by searching in the tree data structures described in the The accuracy of the both the ClinicalBERT and caBERT Q&A models when tested on the Moffitt fine-tuning testing data set improved at each Q&A training stage  to 91.05% , without culling . The accWe also computed the accuracy of the more coarse–grained group-level code predictions by mapping each top 1 code prediction to its corresponding group in the ICD-O-3 ontology. The accuracy of predicting group-level site codes was 93.53% (1895/2026) overall . The tenThe accuracy of predicting group-level histology codes was 97.6% (1993/2026) overall . The tenThis work describes 3 primary contributions. First, we created caBERT: a BERT-based language model for comprehension of cancer pathology reports. We are aware of only 1 other attempt to create a pathology- and oncology-specific BERT language model . That stSecond, we created a new Q&A system to extract tumor site and histology descriptions from free-text pathology reports. This is the first functional Q&A system for extracting information from pathology reports that we are aware of. The Q&A format has 2 important benefits. First, it provides a user-friendly interface to the information extraction system. Second, incorporation of additional questions into our system is straightforward. With appropriate ground truth–labeled training data, this should allow us to extract additional data fields from free-text pathology reports.Third, we created a new caBERT network, caBERTnet, to predict fine-grained ICD-O-3 site and histology codes using the answers extracted through the initial Q&A component. There has been considerable prior work using NLP methods to predict ICD-O codes from pathology reports ,12,16,67Much of the prior work has focused on a single anatomical site or histology. For example, Coden et al  describeprimary cancer sites, 6 cancer reasons, and 3 metastatic disease states. In all, 11 of their cancer sites corresponded to ICD-O-3 group-level site classifications . The others were broader groupings . Of their cancer reasons, 4 corresponded to ICD-O-3 group-level histology classifications . The remaining two cancer reasons were very broad groupings: carcinoma and blastoma. They achieved accuracies on the full test set of 96.7% and 98.5% for cancer site and cancer reason, respectively. However, they did not predict ICD-O-3 group or fine-grained codes.The BERT system from Ma et al , mentionNguyen et al  developeAlawad et al  developeQiu et al  also devhedging in pathology reports [Our study included a greater diversity of cancer cases than previous studies (214 site codes and 193 histology codes), while obtaining similar or better accuracy scores. Many of the site and histology codes in our training data set included ≤5 samples, whereas prior studies reported ≥10 training samples per code. Culling codes from the test set with ≤5 samples in the training set reduced the size of our test data set by 6.39% . However, this increased our top-1 accuracy on the test data to 73.84%  and 85.29%  for site and histology, respectively. Our system also ranks and reports the top 5 predictions for ICD-O-3 site and histology codes. This has useful clinical applications: often there is a degree of uncertainty or Other group , along with two of the site group codes with the lowest accuracies: C49 Connective, Subcutaneous, and Other Soft Tissues  and C16 Stomach .The overall accuracy for predicting site group codes was 93.53% (1895/2026) . NeverthOther site category included 27 group codes. Together, these group codes contained 61 fine-grained codes with at least one sample pathology report each in the training data set, as determined by the MCR. The mean and median number of reports in the training data set for each fine-grained code in the Other category were 6.3 (SD 6.3) and 4 (IQR 5), respectively. Consequently, caBERTnet accuracy on these rare sites was likely limited by the availability of training data.The C49 Connective, Subcutaneous, and Other Soft Tissues. We manually reviewed 50% (7/14) of these cases, all of which were labeled by the MCR as soft tissue of the limb, shoulder, and hip or pelvis . In 14% (1/7) of these cases, the information required to determine the correct site was not present in the pathology report text. In these situations, CTRs would use additional information in the patient record. However, this information was not available to caBERTnet. In the remaining 86% (6/7) of the cases, the pathology report described characteristics of a lesion that had metastasized from the limb, shoulder, hip, or pelvis to another location. The MCR recorded the originating organ as the tumor site, whereas caBERTnet predicted the metastasis site.caBERTnet failed to predict the MCR site code for 14 test cases in the group C16 Stomach. All these cases were labeled by the MCR as C160 Cardia, NOS, and by caBERTnet as lesions of the lower third of the esophagus  or as overlapping lesions of the esophagus . The MCR labels are due to a rule in the American Joint Committee on Cancer Staging Manual, Eighth Edition [caBERTnet failed to predict the MCR site code for 13 test cases in the group  Edition . On pageCancers involving the Esophagogastric Junction (EGJ) that have their epicenter within the proximal 2 cm of the cardia (Siewert types I/II) are to be staged as esophageal cancers. Cancers whose epicenter is more than 2 cm distal from the EGJ, even if the EGJ is involved, will be staged using the stomach cancer TNM  and stage groupings (see Chapter 17).The pathology reports on these cases did not mention the spatial location of the tumor sample in relation to the EGJ. Consequently, measurement of the tumor location in pretreatment imaging was required to determine the correct tumor site code.801-804 Epithelial Neoplasms, NOS (77.8%). caBERTnet failed to predict the MCR histology code for 5 of these cases. In 80% (4/5) of these cases, the pathology report was based on histology at the metastatic site of disease. The MCR coded these as the histology of the originating tumor, whereas caBERTnet predicted the histology at the metastatic site. In 20% (1/5) of the cases, the information required to determine the correct histology code was not present in the pathology report and required the CTR to conduct a review of the patient medical record.The overall accuracy for predicting histology group codes was 97.7% . Only onspindle cell carcinoma. The latter included the following statements: “the differential diagnosis includes sarcomatoid carcinoma and inflammatory myofibroblastic tumor...the histomorphologic and immunoprofile support the diagnosis of sarcomatoid carcinoma.” The MCR coded the histology as 8032/3 spindle cell carcinoma, NOS, based on the intraoperative statements, whereas caBERTnet predicted 8033/3 pseudosarcomatous carcinoma. The phrase sarcomatoid carcinoma is an alternative form of the ICD-O-3 preferred phrase pseudosarcomatous carcinoma. Although caBERTnet’s prediction did not agree with that of the MCR, downstream applications may still value automatic prediction and codification of the final diagnosis.The last case was quite interesting because the pathology report included an initial intraoperative diagnosis that disagreed with the final diagnosis. The former indicated a histological type of There are multiple potential applications of caBERTnet at Moffitt. For example, there is a delay of several months between initial pathology report dictation and CTR abstraction because the CTRs typically wait for enough time to have elapsed for the first course treatment to have been administered to minimize the number of times they have to review the medical record. caBERTnet can be used to extract information from pathology reports in a timelier way, thus facilitating the use of the data for clinical pathway reporting and screening for clinical trials. Furthermore, CTRs only abstract the subset of pathology reports associated with the cancer diagnosis and first-course treatment. caBERTnet could be used to extract information from pathology reports associated with subsequent biopsies and surgeries that would never be manually curated by the CTRs. To facilitate these use cases, we plan to extract tumor site and histology information close to real time and link these values to other patient data stored in our analytics platform. These data can be incorporated into real-time dashboards and data sets for a wide range of decision support and research applications.We do not believe that caBERTnet will replace CTRs at cancer clinics. Many complex, difficult, and rare cases require intuition and information outside of the pathology report to determine the correct coding. These cases are beyond the scope of an NLP tool. However, caBERTnet may help simplify and accelerate MCR workflows. For example, caBERTnet could preprocess pathology reports to identify the top 5 site and histology ICD-O-3 codes and their corresponding phrases. The phrases could then be highlighted within the report body. In addition, two pull-down menus could be prepopulated with top 5 code predictions: one for site and the other for histology. The CTR could then quickly choose a code from either pull-down menu. If the correct code was not among the top 5, then the CTR would resort to their current workflow, entering this information by hand.Although there are immediate applications of the caBERTnet model within our internal workflows, there are a number of aspects of our modeling approach that limit the application of caBERTnet to other use cases. In this section, we provide an overview of several important limitations that we believe should be considered before caBERTnet implementation.The most critical limitations correspond to issues related to our data curation and preprocessing approaches. In the curation of our Moffitt fine-tuning data set, we restricted the available reports to only those containing a single primary tumor diagnosis. Although this was partly imposed by the nature of the SQuAD Q&A task (which expects a single answer to each question asked of each input), it nonetheless limits the generalizability of our model to reports containing multiple (or zero) positive diagnoses. We are exploring methods of mitigating these issues within the current setup by adjusting the likelihood thresholds for output predictions that could be used to screen out reports with no diagnosis.Another limitation inherent to a Q&A system is the necessity of knowing the precise span of text corresponding to the answer to each question asked of each report. Owing to the sheer number of reports in our fine-tuning data set, it was infeasible to manually curate answer labels. To circumvent this issue, we chose to create our own automated system to determine the answer text in each report. Any such automated preprocessing necessarily leaves a fingerprint on downstream tasks. The drawbacks of our approach in particular relate to the restriction of answers from a predetermined list of possibilities for each site and histology. Although these phrase sets were diverse , this process nevertheless limits the allowed terminology and is necessarily incomplete.In addition, a single pathology report may reference several tissue samples . Although we limited our automated answer-search preprocessing algorithm to only find terms associated with the known diagnosis label for each report, it is possible that the answer found in the report text by the preprocessing corresponds to a false answer from a different sample in the report; this is particularly relevant for cases with multiple samples from related sites . When the 2 samples are in the same site (or histology) group, this issue is avoided by outputting the group code; however, this does not help when the 2 samples belong to different groups altogether.We plan to continue development of caBERTnet. Of particular interest is extending the system with additional questions and MCR-derived ground truth labels to train it to extract additional tumor characteristics. These include grade, size, involvement of lymph nodes, primary or metastatic status, presence or absence of molecular markers, and others. caBERTnet could also be customized to extract information on hematological malignancies.A caBERTnet-assisted MCR abstraction tool could also be used for active  or humanThe system’s accuracy on rare sites and histologies could be improved with additional training data. A potential option may be to collaborate with other academic cancer centers; distributing caBERTnet for training on local pathology reports at other sites based on ground truth labeling of training and test data sets from highly standardized registry data or the application of federated multitask learning  that disOur new NLP system, caBERTnet, is built around a network of 3 cooperating BERT instances. On a sequestered test data set, it produced top 5 accuracies of 92.95% (1794/1930) and 96.01% (1853/1930) for fine-grained ICD-O-3 site and histology codes, respectively. This level of accuracy is on par with existing systems in the literature, while also being accurate over a broader range of site and histology groups.Pathology report abstraction systems such as caBERTnet cannot be expected to achieve performance on par with CTRs who abstract data ultimately incorporated into population-based cancer registries , given t


Background and Objectives: Barefoot peak plantar pressures (PPPs) are elevated in diabetes patients with neuropathic foot ulcer (DFU) history; however, there is limited reported evidence for a causative link between high barefoot PPP and DFU risk. We aimed to determine, using a simple mat-based methodology, the site-specific, barefoot PPP critical threshold that will identify a plantar site with a previous DFU. Materials and Methods: In a cross-sectional study, barefoot, site-specific PPPs were measured with normal gait for patients with DFU history (n = 21) and healthy controls (n = 12), using a validated carbon footprint system. For each participant, PPP was recorded at twelve distinct plantar sites , midfoot and heel), per right and left foot, resulting in the analysis of n = 504 distinct plantar sites in the diabetes group, and n = 288 sites in the control group. Receiver operator characteristic curve analysis determined the optimal critical threshold for sites with DFU history. Results: Median PPPs for the groups were: diabetes sites with DFU history (n = 32) = 5.0 (3.25–7.5) kg/cm2, diabetes sites without DFU history (n = 472) = 3.25 (2.0–5.0) kg/cm2, control sites (n = 288) = 2.0 (2.0–3.25) kg/cm2; (p < 0.0001). Diabetes sites with elevated PPP (>6 kg/cm2) were six times more likely to have had DFU than diabetes sites with PPP ≤ 6 kg/cm2 , p < 0.0001). PPP > 4.1 kg/cm2 was determined as the optimal critical threshold for identifying DFU at a specific plantar site, with sensitivity/specificity = 100%/79% at midfoot; 80%/65% at 5th metatarsal head; 73%/62% at combined midfoot/metatarsal head areas. Conclusions: We have demonstrated, for the first time, a strong, site-specific relationship between elevated barefoot PPP and previous DFU. We have determined a critical, highly-sensitive, barefoot PPP threshold value of >4.1 kg/cm2, which may be easily used to identify sites of previous DFU occurrence and, therefore, increased risk of re-ulceration. This site-specific approach may have implications for how high PPPs should be investigated in future trials. The aetiology of plantar foot ulceration for people with diabetes is underpinned by the presence of peripheral neuropathy, leading to a loss of protective sensation, deformities in the bony architecture of the foot and soft tissue changes, culminating in elevated weight-bearing plantar pressures and tissue breakdown ,2. The cAlthough current guidelines advise diabetes patients with neuropathy to use adequately off-loading therapeutic footwear on a daily basis to reduce elevated plantar pressures and DFU risk , the lifThere are, to date, no DFU risk reports of the distribution of barefoot PPP at distinct plantar sites, the site-specific relationship between barefoot PPP with DFU or barefoot PPP critical thresholds above which DFU develop. Maintaining PPP below a threshold of 200 kPa has been recommended as the target in-shoe pressure threshold in footwear prescription for patients with prior ulcers . This isPlantar DFU location is discrete and site-specific, occurring predominantly at forefoot sites—often at the great toe and metatarsal heads (MTHs)—with mid-foot sites commonly affected in Charcot patients ,18,19. WIn a cross-sectional analysis of a cohort of ‘high-risk’ people with diabetes with a previous history of plantar DFU, we aimed to: (1) determine the prevalence and distribution of individual plantar sites with critically high barefoot PPP; (2) determine whether previously ulcerated sites remain under high PPP (and therefore explain the high-risk of DFU recurrence); (3) determine a site-specific, critical threshold of barefoot PPP for DFU history, in order to help the footcare team identify and target occasional ‘high-risk’ plantar areas.This study was a cross-sectional cohort study of a sub-cohort of 21 people with diabetes with a previous history of plantar DFU, participating in a randomised, proof-of-concept trial of an intelligent insole system for DFU prevention . ParticiThe study was approved by local research ethics committees on 24 October 2013 and other relevant governance bodies in the UK .n = 21), who had been previously referred by their general practitioner to a secondary care, multi-disciplinary outpatient diabetes foot clinic in central Manchester, UK, for their foot ulcer assessment and management, were recruited to the study. Inclusion criteria were age ≥ 18 years, Type 1 or Type 2 diabetes, history of previous ulceration on the weight-bearing surfaces of the foot, presence of diabetic polyneuropathy as defined by any loss of sensation, ability to walk independently for 30 steps. Main exclusion criteria included: active foot ulceration, severe vascular disease, lower limb amputation above the level of the ankle, BMI > 40 kg/m2 [n = 12) who had never received a clinical diagnosis of diabetes, and who were staff at Manchester Royal Infirmary, were also consented and recruited into the study.Participants with diabetes  classified patients with neuropathy. Callus severity was scored at 12 distinct plantar sites per foot, as previously described .n = 12 healthy, age-matched controls without diabetes underwent the same procedure.Barefoot peak plantar pressures during normal gait were measured using the simple, validated carbon footprint system, PressureStat™ , formerly known as Podotrack  . At the Following data collection, three researchers trained in PressureStat™ assessment  independp < 0.001) and the diabetes group ; therefore, one analyser’s results alone was randomly selected for all further data analyses. Focal plantar pressure of ≥ 6 kg/cm2  was used as the initial standard definition for critically ‘high’ pressure during barefoot walking [The intraclass correlation coefficient (ICC) for inter-observer reliability for PPP recordings was high for both the control group . All data were checked for normality of distribution by examining histograms and comparison of mean/median values. Baseline data were compared between groups of participants with diabetes versus healthy controls using an independent Student’s For the inter-observer reliability analysis, a two-way mixed-effects consistency multiple-observers/measurements ICC model was used.n = 288), (b) all diabetes sites without DFU history (n = 472), and (c) all diabetes sites with DFU history (n = 32) were compared between the groups using a Kruskal-Wallis one-way ANOVA.The frequency distribution of PPP recordings for (a) all control sites  control sites, (b) diabetes sites without DFU history and (c) diabetes sites with DFU history were compared between groups using a one-way ANOVA and subsequent post-hoc Scheffé test.Logistic regression analysis models were constructed to assess the predictive value of PPP cut-off values for identifying DFU history for diabetes plantar sites only (odds ratios equate to relative risks when outcomes are < 10%). Receiver operator characteristic (ROC) curves were used to generate performance thresholds for PPP and examine the sensitivity/specificity of these thresholds at different plantar sites. All individual plantar sites were considered to be independent of sites on the same or other foot for each participant, based on evidence from prior research  and the n = 21) and the age-matched healthy controls (n = 12) are in n = 12, (38%)) and the hallux ), with other sites being at the toes 2–5 ), midfoot ) and heel ). The mean duration since the previous ulcers had healed was 5.5 ± 3.9 years.The baseline characteristics of those with diabetes (2) was 41, i.e., just 8.1% of all plantar sites assessed. The total number of sites with PPP ≥9 kg/cm2 (‘very high’ PPP) was 5/504, i.e., <1% of all plantar sites assessed. In the control group, the total number of plantar sites assessed for PPP was 288 (12 plantar sites × 2 feet × 12 patients = 288). The number of sites with PPP ≥6 kg/cm2 (‘high’ PPP) was 0/288 (0%).In the diabetes cohort, PPP was assessed at 504 individual plantar sites (12 plantar sites × 2 feet × 21 patients = 504). The total number of sites with ‘high’ PPP (≥6 kg/cmn = 19) had zero ‘high’ PPP sites (≥6 kg/cm2), 36% (n = 15) had 1 high PPP site, and 19% (n = 8) had two or more high PPP sites. There was an average of just 1 high PPP site per foot.Analysis of the 42 individual feet in the diabetes cohort showed that 45% feet (n = 42) were highest at the 5th MTH (4.6 ± 2.5 kg/cm2), 2nd MTH (4.3 ± 2.2 kg/cm2), great toe (4.3 ± 1.6 kg/cm2), 1st MTH (4.2 ± 2.0 kg/cm2) and 4th MTH (4.1 ± 1.5 kg/cm2). All other plantar sites had mean PPP < 4 kg/cm2(data not shown). Mean PPP at the great toe was significantly higher in feet with general toe deformities (claw/hammer toes) , than feet without toe deformities .Mean PPP levels at specific plantar areas in all individual diabetes feet (n = 792) recorded at specific plantar sites for all (n = 33) study participants, split into 3 sub-groups for analysis: (A) healthy control sites (n = 288); (B) diabetes sites without DFU history (n = 472); (C) diabetes sites with DFU history (n = 32). Median (IQR) PPP for the groups were: healthy control sites = 2 kg/cm2 (2–3.25); diabetes sites without DFU history = 3.25 kg/cm2 (2–5); diabetes sites with DFU history = 5 kg/cm2 (3.25–7.5), and all 3 groups were significantly different (p < 0.0001). Curves for control sites and diabetes sites without DFU history were normally distributed/moderately skewed ; whereas the curve for diabetes sites with DFU history was moderately/highly skewed towards the right (skewness = 0.97).n = 288), diabetes sites without DFU history (n = 472) and diabetes sites with DFU history (n = 32), is given in p < 0.0001). Furthermore, specific, individual sites with DFU history  had significantly higher PPP  than their equivalent sites without DFU history . In contrast, there was no difference at the great toe for PPP when comparing DFU history sites vs. no DFU history sites; furthermore, across all conditions, PPPs were generally lower at the hallux than other plantar sites. All healthy control sites had lower mean PPP compared to all diabetes group sites without a history of DFU (p < 0.0001). Similarly, the control group mean PPP was consistently lower at each individual site between these two groups: at the great toe, 1st–5th MTHs, midfoot and heel (p < 0.05).A comparison of mean PPP levels between the three groups of plantar sites  PPP for identifying DFU history. The model was DFU site history vs. no DFU site history, using a PPP threshold of ≥ 6 kg/cm2 at that site (n = 504 sites included). The odds ratio for this model was 6.4 , p < 0.0001, indicating that any plantar sites with PPP ≥ 6 kg/cm2 were six times more likely to have a history of DFU than sites with PPP below this threshold. This ‘site-specific’ model showed a much stronger and predictive OR compared to an alternative ‘whole foot’ model, i.e., DFU site history vs. no DFU site history, with high PPP threshold of (≥ 6 kg/cm2) at any plantar site on the foot (n = 504). In the latter ‘whole foot’ model, the OR showed no association between DFU site history and high whole-foot PPP (OR = 1.4 (0.67–2.93), p = 0.37).For all diabetes sites (with and without a DFU history), a logistic regression analysis model was constructed to assess the predictive value of site-specific ‘high’ (≥ 6 kg/cmn = 504 sites); (B) great toe only (n = 42 sites); (C) combined metatarsal and mid-foot sites (n = 252 sites); (D) mid-foot only (n = 42 sites). The area under the ROC curves (A), (C) and (D) were large and significantly different from a value of 0.5 (2 (402 kPa) was the most reliable prediction method of DFU history at a specific plantar site and was significantly better than the established > 6 kg/cm2 threshold . At midfoot alone (p = 0.026) sensitivity was 80% and specificity was 65%. Conversely, however, the ROC curve for PPP at the great toe site was not significantly different from 0.5 and had no diagnostic ability.e of 0.5 , and curhreshold . The 4.1tar site A, with aoot only C, the diot alone D, sensit2 (>402 kPa), with optimal sensitivity for predicting these ‘high-risk’ plantar sites. The methodology for this, using a single-use carbon footprint mat, could be adopted very easily into the clinical setting.This study has demonstrated, for the first time, a strong, site-specific relationship between ‘elevated’ barefoot PPP and DFU history for ‘high-risk’ patients. Furthermore, we found surprisingly low numbers of discrete areas of high pressure across the plantar surface of ‘high-risk’ feet , despite the general current assumption that ‘high-risk’ feet are at risk of re-ulceration across the plantar surface because of generally higher PPPs. We have also determined a critical barefoot peak plantar pressure threshold value of >4.1 kg/cm2 ) were six times more likely to have a history of DFU than sites with barefoot PPP below this threshold; (b) however, there was no relationship between DFU sites and ‘high’ barefoot PPP (>6 kg/cm2) recorded more generally across the plantar surface of the foot; (c) ‘high’ barefoot PPP (>6 kg/cm2) sites were infrequent across the whole plantar surface, averaging just one ‘high’ PPP site per foot; (d) ROC curve coordinates showed that PPP critical threshold of 4.1 kg/cm2 (402 kPa) was the most reliable prediction method of DFU history at any plantar site, and significantly better than the established >6 kg/cm2 threshold; (e) sensitivity (the ability to correctly identify the DFU site) of the 4.1 kg/cm2 threshold was highest at the metatarsal heads and midfoot sites (73%) and peaked at 100% at midfoot alone . All of this evidence indicates that there is a very strong relationship between barefoot PPP and DFU history occurring at discrete plantar sites per foot. These sites are very easily identifiable in the clinical setting using a single-use carbon footprint mat, with a highly sensitive threshold for capturing the DFU risk at the metatarsal heads and midfoot regions, enabling a simple, cost-effective, site-specific, targeted approach for off-loading foot management.Our evidence was (a) plantar sites with established ‘high’ barefoot PPP ) as ‘high-risk’ for barefoot walking [2 ) [The very strong association of DFU history when PPP is above the 4.1 kg/cmpressure . Althougpressure , we showkg/cm2)) .2 range for PressureStat™. Of note, their model examining ‘all DFU recurrences per foot’ showed that barefoot PPP at ‘plantar foot distal to the heel’ was not independently significant in the multivariate model; conversely, PPP was independently significant in their site-specific model. This is important data, highlighting a site-specific relationship between PPP and DFU; unfortunately, information regarding actual locations on the plantar foot was not included in their study. Elsewhere, mean PPP at the ‘forefoot’ during barefoot walking in patients with previous DFU (mean = 566 kPa (range =107–1192 kPa) [2) in diabetic patients with neuropathy has previously been considered as a ‘region of interest’ [2 provides optimal sensitivity and specificity for identifying risk of DFU site validate this proposed cut-off value.The barefoot PPP range for our cohort is highly comparable to barefoot PPP ranges observed in similar populations ,16,29. WThe above studies, in which in-shoe plantar pressures are reduced compared to equivalent barefoot pressures (up to a 4-fold reduction) ,16,29, sThe number of discrete areas of high pressure across the plantar surface of ‘high-risk’ feet were scarce , despite the general current assumption that ‘high-risk’ feet are at risk of re-ulceration across the plantar surface because of generally higher PPPs. This novel finding has been enabled by the ability to capture an instant ‘snapshot’ plantar image of all barefoot PPPs using the carbon mat PressureStat™ system.2) for identifying new DFU. The model indicated that any plantar sites with PPP > 9 kg/cm2 were 60-fold more likely to develop a new DFU than sites with PPP below this critical threshold (p < 0.0001); however, because of the low number of events, the effect estimate was very large, and the model was considered unstable. Predictive analyses of this kind may prove to be successful with a larger participant sample size.The PressureStat™ system used for our analyses is an inexpensive, validated, highly sensitive, quick and simple alternative device for measuring barefoot PPP foot pressures in the clinical setting , contrasWe recommend the use of this inexpensive (approximately $2 per footprint), robust screening tool in the annual review clinic, ideally at the point when patients’ shoes and socks have already been removed for foot examination and neuropathy screening, to capture evidence of high-risk barefoot plantar pressures. The carbon footprint provides an immediate visual image for the patient and physician to view and discuss together, acting as an educational tool for the patient and enabling the physician to immediately target specific, problem plantar areas for off-loading therapy. Furthermore, as we have shown for the first time, areas of high pressure on the ‘at-risk’ foot are very scarce; we suggest that this may have implications for how high PPPs should be investigated in future clinical trials.2 to identify specific plantar sites of previous DFU occurrence, using an inexpensive, simple carbon footprint screening tool. We can recommend the use of this threshold value for ‘high-risk’ patients in daily clinical practice  to identify which individual plantar sites are at higher risk of DFU recurrence, enabling immediate, targeted DFU prevention strategies.To conclude, we have determined and validated a robust, highly sensitive, critical plantar pressure threshold value of >4.1 kg/cm


The highest sucrose and fat levels were detected in the SC1 treatment compared with the other treatments (p < 0.05). The lowest fat and sugar amounts were observed in the SC2, SC3, and SC4 treatments (p < 0.05). The highest viscosity, overrun, and hardness values (p < 0.05) were detected in the control ice cream. Total aerobic mesophilic bacterial counts were not significantly recorded between different ice cream treatments (p < 0.05). The sensory scores were not significantly affected by sweeteners and bulk agents in the different treatments. The highest calorific value was calculated in the SC1 samples (p < 0.05). On the other hand, the lowest calorific value was calculated in SC2, followed by the SC3 and SC4 treatments. In scanning electron microscopy (SEM), the gel exhibited a homogeneous structure with a fine network within the SC2, SC3, and SC4 treatments, as it contained a cohesive structure with small-sized pores.Ice cream is a popular dessert product across the world. Structure, body, taste, and odor properties are created by adding non-milk ingredients and milk ingredients. The main aim of the study is to decrease the caloric value of ice cream by using sugar and fat replacements. Ice cream treatments were investigated based on microstructural, chemical, physical, microbiological, sensory, and calorific values. Four different ice creams were used (control ice cream (SC1), ice cream with stevia (SC2), ice cream with sucralose (SC3), and ice cream with sorbitol (SC4)). The chemical properties in all treatments of ice cream were significantly recorded ( Ice creams are a rich source of fat, carbohydrates, and protein, which reflects its energy value. The energy value of standard ice cream is almost 200 kcal/100 g. The consumer awareness of functional and healthier dairy has led to the development of new methods to produce ice cream . Ice creSugar gives a sweet and pure taste to ice cream and thus has a crucial regulating effect on the overall taste sensation. To produce ice cream, which is sufficiently low in calories, as well as to provide a refreshing frozen dessert for obese and diabetic individuals, it is necessary to reduce or remove the sugar content. Once fat levels are between 3% and 15% and sucrose levels between 9% and 28%, the sugar and fat are to be replaced by intense sweeteners and bulking agents. Hence, there are some expected problems . This can usually be achieved using low-calorie bulking agents . However, the addition of bulking agents creates somewhat smooth products because they lower the freezing point to a much lower degree than sucrose does. Bulking agents impart creaminess and smoothness improves texture, and thus they provide a mouthfeel and protection against temperature fluctuation to please customers .Stevia rebaudiana) is a natural sweetener and is about 250–300 times sweeter than sucrose, depending on the dispersion matrix , a refractive-index detector (RID-10A), an LC-16ADVP binary pump, a DCou-14A, a guard column (Sc-LcShodex), and an acetonitrile–water mix ; 2 mL/min flow rate). 20 µL of the extract was injected in the column temperature at 40 °C. Sugars  were identified by comparing their sugar standards with retention times.The sugar patterns were observed according to Arslaner et al. . A five-10 CFU g−1), and in bacteria were enumerated from 30 to 300 colonies, and yeasts and molds were counted from 15 to 150 colonies of samples.The total aerobic mesophilic bacterial count was carried out in a plate count agar (Oxoid) incubated at 30 °C for 48 h, and at 5 °C for 10 days for total psychrotrophic bacteria . ColiforThe freezing point was determined according to Marshall et al. , applyin2) over a glass funnel fitted on a conical flask at ambient temperature. The melted ice cream was weighed after 30 min.The weight of melted ice cream expressed as a percentage of the initial weight of ice cream was measured according to Arndt and Wehling . Ice creTM, Tokyo, Japan) equipped with a load cell (250 Ibf) and connected to a computer programmed with Texture ProTM texture analysis software . A flat rod probe (49.95 mm in diameter) was used to uniaxially compress the ice cream samples to 50% of their original height. The hardness was adjusted to a test speed of 60 mm/s, with a trigger force of 1 N, deformation of 25%, and a holding time of 2 s between cycles at −20 °C. The hardness of ice cream samples was determined by a Universal Testing Machine . qPCR datasets were analyzed using PROC GLM of SAS [ij is the dependent variable, µ is the overall mean, Ti is the effect of treatment , and eij is the residual standard error.Results were subjected to ANOVA and statistical variances were analyzed by Duncan’s test. Variances were considered significant at M of SAS . The appp < 0.05) effect on the change of total solids, fat, protein, and ash values of all treatments. The total solid changed between 27.98 ± 1.03% and 31.95 ± 1.54% (w/w) in all treatments. Protein content changed between 3.53 ± 0.19% and 3.97 ± 0.15% (w/w) in all groups. Fat value was 6.08 ± 0.10% (w/w) in the SC1 sample and was not detected in the other treatments. Ash content varied from 0.86 ± 0.01% to 1.01 ± 0.00% (w/w) in all samples. Titratable acidity values were not significantly varied between all ice cream treatments. The highest levels of total solids, fat, protein, and ash were detected in the control samples (SC1). The replacement of sucrose with sweeteners and bulking agents influenced the chemical properties of the ice cream mixtures.The chemical parameters of the ice cream treatments are shown in p < 0.05) effect on the change of glucose, fructose, sucrose, and maltose values in all groups. Glucose ranged from 0.20 ± 0.13% to 0.73 ± 0.10% in all samples. The highest glucose value was observed in the SC1 treatment compared to the other treatments. In the SC1 treatment, fructose and sucrose levels were 0.20 ± 0.07% and 14.97 ± 01.02%, respectively, while these values were not observed in the SC2, SC3, and SC4 treatments. The highest level of sucrose (14.97%) was obtained in the SC1 treatment, while sucrose not detected in the other treatments. The addition of sweeteners and bulking agents had no significant (p > 0.05) effect on the change of galactose and lactose contents in all treatments. Lactose values changed between 3.60 ± 0.21% and 3.76 ± 0.19% in all treatments. Generally, the replacement of sucrose with sweeteners caused a significant reduction in the sugars and calories in the ice cream treatments.p < 0.05) by sucrose replacement using sweeteners and bulking agents. Overrun changed between 57.95 ± 1.60% to 59.82 ± 1.56% in all samples. The overrun level in the SC1 treatment was higher than in the other treatments.The overrun of ice cream is an important property since it has a direct relationship with the yield and profit. It affects the body, texture, and palatability of the ice cream. It is the incorporated amount of air in the mixtures of ice cream, producing foam which is stabilized by surface active agents. The overrun of ice cream samples is shown in p < 0.05) between all treatments. The viscosity levels ranged from 89.94 ± 4.56 to 92.57 ± 4.23 cP in all treatments. The highest levels of viscosity were recorded in the SC1 treatment, while the lowest levels were detected in the SC2, SC3, and SC4 treatments.Viscosity had been considered an important property of ice cream mixtures, and up to a certain extent, it appears essential for whipping ability and retention of air. The viscosity value of mixtures is affected by fat, protein, stabilizer, bulking agent, and the quality of materials used. p < 0.05) between all treatments. Levels of freezing point were affected by the replacement of sucrose with sweeteners and bulking agents.The freezing point values of the ice cream mix samples are presented in p < 0.05) between all treatments. The highest hardness value was detected in the SC1 treatment compared with the other treatments.The hardness of the products at the temperature at which it has the optimum consistency for dipping or scooping is an important consideration. Hardness is affected by many factors: total solids, overrun, principal melting point, amount, and type of stabilizer, etc. Therefore, it is desirable to have the overruns and melting points of all ice creams nearly the same. Melting and freezing points decreased as the level of water-soluble ingredients increased. The ice cream formulator must carefully choose the amounts and type of substances because they affect the melting and freezing points of the mixes. p < 0.05) between all treatments. At zero time, melting percentage varied from 3.34 ± 1.24 g 100 g−1 to 4.21 ± 1.17 g 100 g−1 in all treatments. At 30 min, melting percentage changed between 20.56 ± 1.18 g 100 g−1 and 22.19 ± 1.08 g 100 g−1 in all samples. A lower melting percentage was detected in the SC1 treatment.Melting percentage is expressed as the loss of weight-tested sample compared to its initial weight. Additionally, melting percentage is a good indicator of the structure of the product. p > 0.05). The count of total aerobic mesophilic bacterial ranged from 4.10 ± 0.41 log10 CFU g−1 to 4.30 ± 0.54 log10 CFU g−1 for all treatments. The count of total psychrotrophic bacteria in all treatments was < 1 log10 CFU g−1. On the other hand, coliform bacteria, yeast, and mold counts were not detected in any of the ice cream treatments.Total aerobic mesophilic and psychrotrophic bacteria counts are presented in p > 0.05) in sensory scores for all ice cream treatments. The structure and consistency scores ranged from 9.80 ± 0.12 to 9.90 ± 0.18 in all samples. The taste and odor scores varied from 9.85 ± 0.10 to 9.90 ± 0.16 in all treatments. The melt in mouth scores varied from 9.70 ± 0.12 to 9.80 ± 0.18 in all treatments. Total acceptability scores ranged from 9.80 ± 0.07 to 9.90 ± 0.13 in all samples.The evaluation results for sensory characteristics  are presented in −1, 8.79 kcal g−1, and 4.27 kcal g−1, respectively (p < 0.05) in caloric values for all ice cream treatments. Total caloric values ranged from 68.35 kcal 100 g−1 to 148.60 kcal 100 g−1 in all treatments. The lowest caloric value was obtained in the SC2 treatment , followed by the SC3 treatment . The highest caloric value was observed in the SC1 treatment .The caloric value of ice cream was calculated by taking the caloric value of carbohydrates, fat, and protein as 3.87 kcal gectively . There wThe microstructures of ice cream samples are represented in The main aim of the current study was to evaluate a novel fat- and sucrose-free ice cream using different sweeteners and bulking agents as alternatives for sucrose and fat, and to compare their potential influences on the chemical, physical, microbiological, and sensory properties, as well as the calorific values. The physical, textural, and sensory characteristics of ice cream are important quality properties because they play an important role in consumers’ acceptance of these products. Ice cream is a frozen dairy product that includes healthy and nutritious values. Ice cream is a rich source of sugar, protein, and fat. Sucrose levels in ice cream products range between 9% and 30% and fat content ranges from 2% to 15% of the total components. Due to the high prevalence of Type 2 diabetes and obesity among children and adolescents, people are now more aware of their health status, and hence conscious of their diet. This health-conscious decision poses a formidable challenge to ice cream production. The replacement of sucrose and fat with sweeteners and bulking agents in the preparation of ice cream can address the issues of current customers who center on normal and healthfully adjusted food sources . Mainly,With increased consumer attentiveness for efficient and improved dairy and foods, various new technologies have come to the fore to produce such products. Ice cream is one of the most served and loved frozen dairy products, but it is high in sugar levels (15%) and fat (6%); therefore, formulating its sugar- and fat-free version will serve as a good cause for decreasing the extra caloric intake and make it healthier. Stevia, sucralose, and sorbitol contained in sugar-free dairy products can aid in calorie reduction, weight loss, and diet control. Low-sugar dairy is important in dietary management as it allows the slow movement of glucose into blood, resulting in a very low rise in blood—glucose, obesity, and insulin levels .Significant differences between the ice cream treatments in their chemical characteristics are due to the variance of the total solids, fat, ash, and protein levels in the mixtures. In a similar study, significant differences in the compositional properties were observed by Alizadeh et al.  when sucThe viscosity values saw progressive increases in the SC1 samples compared with the other treatments. This increase is due to the increase of the total solids, fat, sucrose, ash, and protein ratios in the mixtures. Alizadeh et al.  detected10 CFU g−1. Coliform, yeast, and mold counts were not detected in any of the ice cream treatments. This may be due to the sufficient heat treatment of milk during manufacture and high sanitation conditions during the making of the ice cream. The authors of [Aerobic mesophilic bacterial count for the ice cream showed no significant difference between the treatments. These results are similar to those of Arslaner et al. , who stuthors of  found ththors of  for yoguThe scores of taste and odor, structure and consistency, and total acceptability were not affected by the replacement of sugar with sweeteners and bulking agents for ice cream. This result confirms those of Verma , who repIce cream products are an excellent source of dairy and calories. The highest caloric value was observed in the SC1 samples. This increase is due to the increased fat, sucrose, and protein ratios in the SC1 mixture. On the other hand, the lowest caloric value was obtained in SC2, followed by the SC3 and SC4 treatments. The reduction in caloric value was 55.07% for the SC2 treatment, 54.64% for SC3 treatment, and 47.03% for SC4 compared with the SC1 treatment (100%). This result confirms those of Pinto and Dharaiya , who detIn general, it was found that replacing sugar and fat in ice cream with sweeteners and bulking agents led to a reduction in energy value due to the decrease in the amount added of sweeteners and the decrease in the energy value produced from them. This leads to the production of healthy products for the consumer and nutritional value at the same time.The changes in microstructure between ice cream with sweeteners and bulk agents or control samples could arise from variances in particle properties (protein and bulking agents). Bulking agents  impart smoothness and creaminess, improve texture, and structure, and protect against temperature fluctuations to please customers . These dThe effect of sweeteners  and bulking agents on the chemical, physical, microbiological, and sensory properties and calorific values of ice cream was studied. Four different treatments were studied: the ice cream control (SC1), ice cream with stevia (SC2), ice cream with sucralose (SC3), and ice cream with sorbitol (SC4). The chemical analyses were significantly recorded between all treatments of ice cream. The viscosity, overrun, and hardness values were significantly affected by sweeteners and bulking agents in the ice cream samples. The sensory scores were not significantly affected by sweeteners and bulking agents in the ice cream samples. The reduction in calorific value was 55.07% for the SC2 treatment, 54.64% for the SC3 treatment, and 47.03% for SC4 compared with the SC1 treatment (100%). In SEM micrographs of SC2, SC3, and SC4 treatments, the gel exhibited various structures with a fine network, and it contained a number of very small-sized pores. The current study suggests and concludes that ice cream with sweeteners  and bulking agents can be promising for marketing. As a result, the ice cream market trend is moving towards a sugar- and fat-free ice cream formulation and with excellent texture, structure, and sensory attributes to gain consumer satisfaction. The replacement of sucrose and fat with sweeteners and bulking agents in the preparation of ice cream can address the issues of current customers who center on normal and healthfully adjusted food sources.


Our results indicate that during the same period, glacier melting accounted for 63%, 28%, and 92% of the total freshening occurred in the Atlantic, Indian, and Pacific sectors of the SOc, respectively. This suggests that the ice shelf basal melt in West Antarctica and the Antarctic Peninsula plays a dominant role in the freshening of the surrounding seas.With the accelerating mass loss of Antarctic ice sheets, the freshening of the Southern Ocean coastal oceans  is gradually intensifying, which will reduce the formation of bottom water and weaken the meridional overturning circulation, thus having a significant negative impact on the ocean’s role in regulating global climate. Due to the extreme environment of the Southern Ocean and the limitations of observational techniques, our understanding of the glacier-derived freshening of SOc is still vague. We developed a method that first provided us with an expansive understanding of glacier-derived freshening progress over the SOc. Applying this method to the observational data in the SOc from 1926 to 2016, revealed that the rate of glacier-derived freshwater input reached a maximum of 268 ± 134 Gt year With ongoing global warming, the Antarctic ice sheet is losing its mass at a remarkable rate2. Continuous freshwater input derived from glacier melting would lead to ocean freshening and sea-level rise, which significantly influences the global climate system and long-term climate change4. In recent years, many studies have demonstrated that the basal melt of the Antarctic ice shelf could explain more than half of the Antarctic ice sheet mass loss7. Because of the emission of anthropogenic materials and the resulting positive trend of the Southern Annular Mode (SAM), the poleward shift of westerly winds and large-scale cyclonic eddies are bringing more and warmer modified Circumpolar Deep Water (mCDW) into Antarctic ice cavities10, accelerating the basal melt of the Antarctic ice shelf and freshwater export to the ocean11. Several studies have found that Antarctic Bottom Water (AABW) has become warmer and fresher, and the formation of AABW has been reduced, which may eventually weaken the meridional overturning circulation of the global ocean16. Therefore, clarifying the impact of glacier melting on the progress of ocean freshening is important for understanding future climate change.The Antarctic ice sheet accounts for ~ 70% of the freshwater on Earth, equivalent to ~ 60 m of the global sea-level height17. Satellite remote sensing techniques are widely used to monitor Antarctic ice sheet mass balance and sea-level change. The basal melt rate of the Antarctic ice shelf can be directly estimated by subtracting the surface ice discharge from the total mass change of the Antarctic ice sheet. However, this method cannot estimate the effect of glacial melting on ocean freshening. Furthermore, only satellite data from 1979 onward are considered reliable because of the introduction of the multifrequency passive microwave technique1. The second method is numerical simulation based on the ocean-sea ice-ice shelf coupled model18. Numerical simulations can elucidate the physical processes between the ocean and the ice shelf, providing a comprehensive understanding of the current state of Antarctic ice shelf melting based on ice-ocean interactions. However, it is difficult for models to completely reconstruct the complex processes around Antarctica without sufficient physical and chemical parameter observations. The third type of method is in-situ observation. Long-term changes in seawater salinity can describe overall freshening which includes all freshwater sources21. Setting end-members  to characterise each water component is a common way to distinguish different sources of freshwater input18. However, it is difficult to apply this method on a wide-scale because the end-members of water always change spatiotemporally.At present, the correct and expansive estimation of glacier-derived freshening remains bottlenecked due to the severe weather conditions of the Southern Ocean (SO) and limitations of observational techniques. Studies on Antarctic glacier melting and freshening occurring around Antarctica have been primarily based on the following methods. The first method is satellite-based observations25. On the one hand, high-accuracy observations of basic hydrographic parameters such as seawater temperature (T), salinity (S), dissolved oxygen (DO), and pressure (Pr) have been conducted for nearly 100 years over the SO with a relatively higher spatiotemporal resolution. Hence, the parameterization constructed by these basic hydrographic parameters enables us to obtain more available data on various chemicals in the SO24. On the other hand, if T, S, DO, and Pr are used in the parameterization function, a relationship reflecting the physical (e.g. water mass transportation) and biological  processes can be obtained that relate to the chemical concentration in a specific region. This functional relationship does not change unless it is influenced by any external process. This suggests that for a parameterization of chemical A, the predicted concentration of A (Apre) contains a component of the ocean internal processes (Ain) and a component of the average external process (Aex) within the spatiotemporal range of the observed dataset used  that allows us to estimate the glacier-derived freshening without setting any end-member so long as basic ocean hydrographic data  are available. Applying this method to the Southern Ocean coastal oceans  using ocean hydrographic data set from 1926 to 2016 were collected from the Global Ocean Data Analysis Project version 2 (GLODAPv2) and Southern Ocean Atlas (SOA)Processes such as glacier melting, sea ice melting, and precipitation release large amounts of freshwater into the SO Fig. . We trea2) = 0.98.(Number of data points (n) = 46,753; coefficient of determination (R−1)Root-mean-square error (RMSE) = 6.08 µmol kg2 = 0.95; RMSE = 4.84 µmol kg−1),, as shown in Eq.  over SOc. To obtain the decadal change in freshening, we divided the dataset into seven periods with approximately 10-year intervals . Regarding spatial division, we divided the SOc into the Atlantic, Indian, and Pacific sectors which are mainly controlled by the ice shelves of the Antarctic Peninsula, East Antarctica, and West Antarctica, respectively g values were generally near 0 during all periods  in the 2000s. In both the Indian and Atlantic sectors, we also found Fg reached 4.0 ± 1.5‰ at the surface in the TIS, the SGT, and the Eastern Weddell Sea (EWS). At the Cape Darnley of the Indian sector, which is an important region for sea ice and bottom water formations in East Antarctica30, the mass balance of the Amery Ice Shelf (AIS) has recently attracted extensive concern  and AIS (2006) in Fig. g in the AIS in 1961 with that in 2006, we identified a positive trend in Fg (− 6 ± 2.2‰ to − 1 ± 0.4‰) from 1961 to 2006  in the SOc using the salinity trend divided by the average salinity of the research region . If we assume that the melting of ice floe has no significant variation on a decadal time-scale, we can consider that the difference between Rg and Rall  contribute significantly to the warm mCDW intrusion and the ice sheet mass loss in the ABS34. ABS is located near the eastern limb of the Ross Gyre and is adjacent to the main stem of the Antarctic Circumpolar Current (ACC). This geographic location is very conducive to the intrusion of mCDW into Antarctic ice cavities28. The Amundsen Sea Low (low-pressure centre located over the southern Pacific) can also drive the transportation of warm air to West Antarctica, which causes melting of the surface ice sheet and thereby contributes to freshening35.The mass balance of the Antarctic ice sheet is controlled by a combination of several processes36. However, our estimate and several previous studies showed that there were freshening signals in specific regions such as TIS, AIS, and SGT , we can obtain more spatiotemporally complete basic hydrographic data in the SO. Applying our parameterization method to the Bio-Argo-float dataset27. The quality of data on chemicals such as carbon species and nutrients after 2000 was controlled using certified reference materials43. Therefore, high-accuracy data for these chemicals began to be obtained mainly after 2000. Basic hydrographic data  used to estimate the time series of freshening over the SOc were sourced from GLODAPv2_2019 from 1979 to 2016 and SOA from 1926 to 1984 27. Information on the cruises from which we obtained the data is shown in Supplementary Tables open and 2059 data points for DICcoastal from 2000 to 2017  were sourced from GLODAPv2_2019 of the SO (south of 30° S) from 2000 to 201744. Several constraints were set for the raw data  was used to investigate the presence of multicollinearity between each parameter. Standardised regression coefficients (β) were used to compare the contribution of each parameter to DIC , we selected one independent cruise for each of the three sectors  that were not used in the construction of parameterization as the testing data set  as follows:open indicates the MAD of DICopen, and n is the data amount of each independent testing dataset. MADopen in the Pacific, Indian, and Atlantic sectors were 3.24, 2.48, and 5.06 µmol kg−1, respectively (Supplementary Table open (6.08 µmol kg−1), implying that DICopen has sufficient reliability. In contrast, for the parameterization of SOc , the sparseness of observational data makes it difficult to find additional independent testing datasets for accuracy validation. To check the reliability of DICcoastal, we used the “k-fold cross-validation”46. The k-fold cross-validation uses part of the available data to construct the parameterization (training dataset) and uses the remaining part to test it (testing dataset). Here, we divided the observational data set into 10 roughly equal-sized groups by longitude (i.e. k = 10), using one group as the testing dataset and the remaining nine groups as the training data set. We then exchanged other groups as the testing dataset and the remaining nine groups as the training dataset. We repeated the above process nine times. The MAD of DICcoastal  is similar to that in Eq.  contains a term for the ocean internal processes (Ain) and a term of the average external process (Aex) within the spatiotemporal range of the observed dataset used  and the physical components (Cphy), which can be represented by the parameters . The DICex includes the evaporation and precipitation components (Cep), sea ice  components (Csi), and air-sea exchange components (Cair) in both the open SO and the SOc. It is worth noting that in the SOc, there is a unique external DIC component derived from the Antarctic glacier (Cg).As discussed in the main text, for the parameterization of chemical A, the predicted value of A (Aused Eq. . Therefoused Eq. –12.7\docg) based on the above parameterizations and processes shown in Fig. int), and the other is the external freshwater added into the SOc (with DIC concentration of DICfw). The relationship between these water components can be expressed by the following conservation equations:int is the fraction of the initial seawater. Ffw is the fraction of freshwater added to SOc. DICfw was assumed to be equal to zero.We quantified the fraction of glacier-derived freshwater in the SOc (Fint by substituting the parameters of the SOc into the open ocean parameterization (DICopen).Assuming that the initial seawater in the SOc comes entirely from the open SO, this allows us to calculate DICin is completely controlled by the parameters , so when we substitute the parameters of the SOc into DICopen, DICin becomes DICin_c, whereas DICex remains as DICex_o because this term is binding to DICopen. Combining Eq. .We found that FWe attempted to use various oceanic chemicals, including DIC, nitrate, and phosphate, as indicators of freshwater input. The essential advantage of DIC compared with other chemicals is that it maintains a relatively good linear relationship with hydrographic parameters, even within the surface mixed layer. Especially in the open ocean, the concentration of nutrients in the surface mixed layer is almost zero, which makes it difficult to construct parameterizations. Therefore, DIC was chosen as the freshwater indicator.g in each sector of the SOc shown in Fig. 47. The area of each grid was also considered when calculating the average, since the area of the grid varies with latitude. The average Fg of the SOc is shown as the area weight average of the three sectors.The average FWe changed the fraction of freshwater into volume by multiplying the fraction by the seawater volume. The seawater volumes used here were calculated by multiplying the average depth of all data profiles by the ocean surface area of the SOc or the three sectors. The ocean surface areas are listed in Table all indicates the rate of overall freshening in the SOc; dSobs/dt indicates the observed salinity trend, which is controlled by evaporation and precipitation, sea ice, and glaciers, and Save is the average salinity over our research region (Save = 34.3).To quantify the impact of glacier melting on the SOc, we calculated the rate of overall freshening in the SOc according to the following steps: It is impossible to estimate the fraction of freshwater at a given moment through the salinity. Thus, we simply estimated the rate of freshening based on the rate of salinity change.g) is calculated as follow:The rate of glacier-derived freshening (RSupplementary Information.


A machine learning model, Multivariate Adaptive Regression Spline (MARS), was utilized to determine the relative importance of climatic, topographic, forest structure and human modification variables on fire dynamics across wet and dry seasons, both in El Niño and non-El Niño years. The findings of this study make clear that declining precipitation and increased temperatures have strong impact on fire dynamics  for El Niño years. El Niño years also saw greater fire sizes and speeds as compared to non-El Niño years. Dense and relatively undisturbed forests were found to have the lowest fire activity and increased human impact on a landscape was associated with exacerbated fire dynamics, especially in the El Niño years. Additionally, the presence of grass-dominated ecosystems such as grasslands also acted as a driver of fire in both El Niño and non-El Niño years. Hence, from a conservation perspective, increased interventions during the El Niño periods should be considered.In the past two decades, Amazon rainforest countries  have experienced a substantial increase in fire frequency due to the changes in the patterns of different anthropogenic and climatic drivers. This study examines how both fire dynamics and bioclimatic factors varied based on the season (wet season and dry season) El Niño years across the different countries and ecosystems within the Amazon rainforest. Data from publicly available databases on forest fires  and bioclimatic, topographic and anthropogenic variables were employed in the analysis. Linear mixed-effect models discovered that year type (El Niño  We assessed how the spatial temporal variation of these fire characteristics is influenced by bioclimatic and anthropogenic drivers and the interactions of these with the ecosystem type and structure across seasons and with El Niño/non-El Niño years. The main aims of the research are to identify: (1) If and how fire characteristics  and their bioclimatic drivers  varied between dry and wet seasons of the Amazon biome from 2003–2016; (2) if the variations in fire and bioclimatic driver dynamics were more pronounced in the El Niño years; (3) interactions between climatic, topographic, forest structure and human modification variables and fire dynamics during the wet and dry seasons of the El Niño and non-El Niño years (from 2003–2016); and (4) the relative importance of both the anthropogenic and natural drivers in influencing the different fire attributes between the dry and wet seasons of El Niño and non-El Niño years.The study region boundary is defined by the biogeographical limit of the greater Amazon ecosystem which includes ecosystems known as Amazonian biome within the seven countries—Brazil, Bolivia, Colombia, Ecuador, Guyana, Peru and Venezuela whose borders fall within this biogeographical limit , see FigIn terms of ecosystems, Amazon biome mostly comprises dense moist tropical forests and other smaller vegetation types including savannas, grasslands and swamps . Owing tIn this research, we used the Global Fire Atlas (GFA), a global dataset of fire dynamics (from 2003–2016). GFA is a dataset that monitors the daily dynamics of individual fires across the globe. The dataset is derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The MCD64A1 MODIS, which underpins the global fire atlas dataset, has been extensively used for fire mapping and monitoring in the tropics, including the Amazon rainforest . MCD64A12  forests , (2) shrublands (including open and closed shrublands), (3) woody savannas, and (4) savannas and grasslands. The fire perimeters from GFA were also compared to the fire perimeter estimates from the Monitoring Trends in Burn Severity (MTBS) project from 2003–2015. The two thresholds were used to select a subset of MTBS and GFA perimeters to assess the accuracy of estimated fire duration. Fires were first matched based on perimeters, with a maximum tolerance of a threefold difference in length between perimeters. MTBS perimeters was selected with VIIRS active-fire detections for at least 25% of the 500 m Fire Atlas grid cells. Over the 14-year study period, 13,250,145 individual fires were identified with an average size of 4.4 km2  at MODISosystems . For eacosystems  were useosystems .2, global extent and long length fills a unique gap in climate data. Using climatically aided interpolation, TerraClimate combines spatial climatology from the WorldClim dataset, with time-varying information from the coarser-resolution CRU Ts4.0 and Japanese 55-year Reanalysis (JRA55), to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapour pressure and solar radiation  was used to identify the relative importance of these for the different scenarios—(a) dry season in El Niño years (b) wet season in El Niño years (c) dry season in non-El Niño years (d) wet season in non-El Niño years. MARS is underpinned by the construction of smoothing functions (spline) based non-linear regression models. MARS follows the generic form:y is the response variable (the different fire variables) and x are the bioclimatic, land use and anthropogenic predictors. The predictors are incorporated into the MARS structure as part of a function (see f(x) is derived as follows:tion see  generati0 is the intercept of the model, βm is the coefficient of the m-th basis function, M is the number of basis functions in the model and Bm(x) is the m-th basis function. Each m(x)B takes one of the following two forms: (i) a hinge function of the form max  or max , where k is a constant value; (ii) product of two or more hinge functions that, therefore, can model the interaction between two or more predictors (x) (tors (x) .i.e., the number of times that each variable is included in a subset ; (ii) sqr-rss, i.e., the decrease in the sum of square errors (RSS) for each subset relative to the previous subset; and (iii) sqr-gcv, i.e., the decrease in the GCV value for each subset relative to the previous subset  nsubsets, s subset . The MARs subset . Residuas subset .While the four factors, month type, season type, landcover and forest structure individually had a significant impact on the different fire attributes, the interaction between these factors did not. The interaction between the month and season type was statistically significant for the different fire attributes  for the vs. the others for fire size and expansion. Fire expansion was also different between evergreen broadleaf forests and savannas. The role of year type, month type and forest structure and their interactions varied across the landcovers of different countries. The fire expansion in the grasslands of Colombia, the fire size and duration in the croplands of Brazil, fire size and duration in the savannas and grasslands of Bolivia were influenced by the year type, i.e., whether it was an El Niño year or not. Further the fire size and duration in the croplands of Peru and the fire duration in the evergreen broadleaf forests of Guyana were also influenced by the year type. Forest structure was a significant factor contributing to fire size across all the landcover classes (except croplands) in Brazil and for all the landcover classes in Colombia and Bolivia. Except for grasslands and woody savannas, the season and year type interactions were significant factors in influencing fire duration in Brazil while this interaction was only significant for the grasslands of Bolivia. The season type was a significant factor for influencing fire size across all landcovers except the evergreen broadleaf forests of Guyana.Post-hoc analysis revealed that the main differences existed between the fire dynamics of croplands vs. dry season) was significantly important  dry season in El Niño years (b) wet season in El Niño years (c) dry season in non-El Niño years (d) wet season in non-El Niño years. While precipitation and presence of grasslands are the most important determinants of fire size in the dry months of the El Niño years, grasslands alone were the most important determinant of fire size .Grasslands are the most important drivers of fire speed in both the wet and dry seasons of El Niño years as well, along with the wet non-El Niño months. While maximum temperature, precipitation and soil moisture are important drivers in explaining the variation in fire size in the dry season of the non-El Niño years, the maximum temperature did not explain the fire size variation in the non-El Niño years. The presence of dense and tall forest strata (strata 6) was an important driver for the non-El Niño years.Slope percentage and maximum temperature were important drivers of fire speed in dry seasons while precipitation and evergreen forests were important drivers of fire speed in the wet season of El Niño years. The maximum temperature was the least important .Grasslands were the most important drivers of fire speed for both the dry and wet seasons of the non-El Niño years, along with soil moisture and maximum temperature. In addition to grasslands, maximum temperature, the presence of evergreen broadleaf forests, human modification and precipitation too were important drivers of fire speed in the wet months of both El Niño and non-El Niño years. Precipitation was the most important driver of fire duration for all four scenarios. The maximum temperature was the most important driver of fire duration in the dry seasons of both the El Niño and non-El Niño years .The presence of savannas was an important determinant of fire duration for all cases. Presence of grasslands was an important determinant of fire expansion in all the scenarios while the presence of barren earth was an important determinant for the wet season of both the El Niño and non-El Niño years .Maximum temperature and soil moisture were the most important determinants of fire expansion in the dry seasons of both El Niño and non-El Niño years. The human modification did not impact forest fire expansion in any of the scenarios. The average fire expansion for the grassland land cover was 0.88 km/day in the dry months of the El Niño years and 0.4 km/day for all other ecosystems. For the wet months of the El Niño years, average fire expansion for the grassland land cover was 0.56 and 0.3 km/day for the non-grassland ecosystems. For the non-El Niño years, the average fire expansion was 0.9 km/day for grasslands and 0.42 km/day for non-grasslands in the dry season and 0.52 and 0.3 km/day respectively for the dry season.Partial dependence plots revealed that for all the scenarios, precipitation and soil moisture were inversely related to fire size and speed while the maximum temperature was proportionally related to fire size, meaning that lower precipitation, lower soil moisture and higher temperatures contributed to higher fire sizes. Precipitation was inversely related to fire duration in the El Niño and non-El Niño years (both dry and wet months). Soil moisture too was inversely related to fire dynamics, for both El Niño and Non-El Niño years. The high human modification index (>0.25) had a positive association with fire speed in the wet seasons of the El Niño years. Dense forest structures  had a negative association with fire dynamics such as fire speed and duration. See While the role of precipitation in increasing fire frequency in western Amazonia has been identified in previous research, our research findings indicate that precipitation can influence fire dynamics such as size, duration and speed across the Amazonian biome. For instance, fire size and duration were inversely related to precipitation for both the dry and wet months in the El Niño and non-El Niño years, indicating that lower precipitation resulted in larger fires that burnt for a longer time. Our findings are congruent with the existing body of literature. El Niño events are associated with decreased precipitation and increased temperature and drought severity  which haFor both bioclimatic and fire variables, the significance of the year type, seasonality, land cover and forest types and their interactions varied across the Amazonian countries under consideration. The interaction between climatic factors and fire occurrences varies across the Amazonian basin and is often mediated by anthropogenic factors, such as deforestation . Our anaThe role of natural and anthropogenic variables in influencing fire dynamics varies globally . FactorsIn addition to landcover types, we also included forest strata (structure in terms of canopy cover and intactness) as an explanatory variable. Dense and relatively undisturbed forests were found to have the lowest fire activity. More disturbed forested tend to have lower humidity retention and higher temperature  which arThe creation of croplands and grass-dominated ecosystems is a major perverse outcome of deforestation in Amazonia . In bothPrevious research has established that grass-dominated ecosystems have the highest rates of burning and more intense fires were observed in grasslands . This reBioclimatic variables such as precipitation are a major driver of forest fires across all the biomes of South America , includiThe El Niño phenomenon has a strong impact on the climatic patterns of the Amazon basin . Severe This study also establishes that bioclimatic and fire variables including characteristics such as x speed and size were not only influenced by whether it was an El Niño year or not but also by the interaction between the year types, season type and land use related variables. While El Niño events play an important role in influencing bioclimatic and fire dynamics, research indicates that changes in these dynamics may not always be directly linked to El Niño . PreviouThe findings of our research have notable conservation implications. Our research establishes the vulnerability of grass-dominated ecosystems and disturbed strata to fire dynamics. These findings are corroborated by existing literature  and fireOur research identifies the fire behaviour of different ecosystems across the different Amazonian countries and discovered that dense forests had lower fire incidences, and this is congruent with the existing body of literature; open ecosystems are more vulnerable to fires than dense forests . SignifiFurthermore, elevated anthropogenic disturbances result in elevated fire dynamics. This finding has been borne by a body of literature . ArguablAmazonian fire dynamics are influenced by a multitude of bioclimatic and landcover related factors and complex inter-linkages between these with the nature and depth of these interlinkages not completely understood. While our study and many other studies have considered El Niño as a static contribution factor , El NiñoWhile we have considered a subset of variables to explain the variation in fire dynamics, many other variables, related to the variables considered in our study influence fire dynamics. For instance, hydrological cycle too plays an important role in influencing fire dynamics . Hydroloi.e., Brazil, Bolivia, Peru, Colombia, Guyana and Ecuador. Fire characteristics  and their bioclimatic drivers  varied between dry and wet seasons of the Amazon biome from 2003–2016. Declining precipitation and increased temperatures have a strong impact on the fire characteristics for El Niño years, which also saw greater fire sizes and speeds as compared to non-El Niño years.While most studies on the Amazon basin focused on the Brazilian Amazon, this study established the variability of bioclimatic variables and fire dynamics in the different countries that have the Amazon rainforest within their boundaries, The presence of grass-dominated ecosystems was an important driver of the different fire dynamics and these ecosystems recorded higher fire sizes compared to dense canopied forests. These findings can help develop appropriate ecosystem specific interventions and management practices to control fires, especially in the El Niño years. We have only considered a subset of the most used drivers in our research. These by no means are not the only drivers that influence the fire dynamics in the Amazonian biome. Fire dynamics in the Amazonian biome is a deeply complex phenomenon influenced by a variety of other considerations such as the role of ocean currents in influencing El Niño to socioeconomic factors influencing landcover change. Covering all of these is beyond the scope of our research. However, the frameworks and methodology workflows developed in our research can facilitate the inclusion of other parameters in future studies and improve fire monitoring capabilities in the region.10.7717/peerj.12029/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.12029/supp-2Supplemental Information 2Click here for additional data file.