Solitary Fibrous Tumor of Head and Neck Region: A Series of Three Cases at an Uncommon Location With a Review of the Literature.

Solitary fibrous tumors (SFTs) uncommonly involve the head and neck region. Head and neck SFTs (HNSFTs) exhibit diverse histological features and can mimic several neoplasms with different treatment and behavior. Herein, we report the clinicopathological features of three cases of HNSFT. Case 1 was a 29-year-old female who presented with a nasal cavity mass measuring 3.5 cm. The patient underwent surgical excision. Microscopic examination revealed classic histological and immunohistochemical (IHC) features of SFT. Unusual histological features included epithelioid morphology, clear cells, and edematous change. She developed local recurrence after 11 months, which was also treated with surgery. Case 2 was a 55-year-old male who developed a 1-cm mass at the buccal mucosa. Surgical excision of the tumor was performed. The tumor was completely circumscribed microscopically. Characteristic histological and IHC features of SFT were identified. Unusual histological features observed were an adenomatous pattern, clear cells, and myxoid change. The patient was alive and disease-free at the 12-month follow-up. Case 3 was a 59-year-old female presenting with a medial canthus mass measuring 1.4 cm. The patient underwent surgical excision. Histological and IHC features observed were diagnostic for SFT. Unusual histological features identified were wavy nuclei and multinucleated stromal giant cells. The patient was alive and disease-free at the 124-month follow-up. Diagnosis of SFT can be challenging in unusual locations like the head and neck region. In addition, the histological spectrum of HNSFT is diverse. Therefore, knowledge about unusual histological features and classic IHC expression is essential for establishing correct diagnosis. Long-term follow-up is recommended because of the risk of recurrence in HNSFT.

Role of SOX10 Immunohistochemical Expression in Diagnosing Triple Negative Breast Cancer and Its Correlation With Clinicopathological Features.

BACKGROUND: Triple-negative breast cancer (TNBC) poses a diagnostic challenge for histopathologists due to the reduced frequency of breast-specific markers. SOX10 has emerged as a useful diagnostic marker for TNBC. The aim of our study was to determine the frequency of SOX-10 immunohistochemical (IHC) expression in our cohort and assess its correlation with clinicopathological and histological features. MATERIALS AND METHODS: We included 72 primary TNBC cases. Specimens included tru-cut biopsies and excision specimens. We stained whole slide sections of these specimens with SOX10 antibody and calculated its frequency (%) of expression and H-score. We applied the chi-square test to assess the correlation between SOX10 expression and clinicopathological and histological features such as the patient's age, specimen type, tumor size, histological type, histological grade, nuclear pleomorphism, mitotic count, tumor-infiltrating lymphocytes (TILs), necrosis, calcification, lymphovascular invasion (LVI), lymph node involvement, T stage, and N stage. RESULTS: SOX10 expression was observed in 42 (58.3%) cases with a median H-score of 57.5. The expression was significantly higher in tru-cut biopsy specimens as compared to excision specimens (73.5 vs 41.7%) and TILs negative tumors as compared to TILs positive tumors (64.3% vs 27.3). Metaplastic carcinoma showed reduced expression when compared with non-metaplastic tumors (35.7% vs 63.8%), but statistical significance was not achieved. No correlation was observed with the patient's age, tumor size, histological type, histological grade, nuclear pleomorphism, mitotic count, necrosis, calcification, LVI, lymph node involvement, T stage, and N stage. CONCLUSION: SOX10 was expressed in more than half of the TNBC cases of our study which not only highlights its diagnostic utility but advocated its application in combination with other breast-specific markers. The expression didn't correlate with the majority of clinicopathological and histological features, but correlation with tru-cut biopsy specimens and absence of TILs draws attention towards possible roles of proper fixation and host immunity, respectively.

Heteroscedasticity effects as component to future stock market predictions using RNN-based models.

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making. METHODS: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. RESULTS: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. CONCLUSION: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

Correction: Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.

[This corrects the article DOI: 10.1371/journal.pone.0287755.].

IoT based battery energy monitoring and management for electric vehicles with improved converter efficiency.

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs.

Ensemble learning for multi-class COVID-19 detection from big data.

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.

Solitary fibrous tumor of head and neck region; A clinicopathological study of 67 cases emphasizing the diversity of histological features and utility of various risk stratification models.

BACKGROUND: Head and neck SFT (HNSFT) exhibit diverse histological features and can mimic various neoplasms with different treatment and behavior. While risk stratification systems have been developed for this tumor at various anatomic sites, a specific scheme for head and neck tumors is lacking. Our aim was to describe the histologic patterns present in HNSFT cases as well as assess the utility of risk assessment models in this location. METHODS: A retrospective review of pathology reports and microscopy glass slides of HNSFT cases diagnosed between January 2010 and August 2022 was performed.STAT6 was additionally performed on selected cases if needed. Follow up was obtained and various risk stratification models were applied. RESULTS: Sixty seven cases of HNSFT were collected (age range from 11 to 87 years; median 42 years; M:F 1.6:1). Most common tumor sites were orbit (n = 21; 31.3 %), sinonasal tract (n = 18; 26.9 %), and oral cavity (n = 13; 19.4 %). Tumor size ranged from 1 to 16 cm (median 4cm). Apart from common histological features, tumor cells also showed focal epithelioid morphology, clear cell change and nuclear atypia in a subset of cases. Stromal findings included myxoid and lipomatous change, pseudoglandular spaces, pseudovascular spaces and multinucleated stromal giant cells. CD34 and STAT6 were expressed in 57/67 (85.1 %) and 56/56 (100 %) cases, respectively. Recurrence was observed in 4/26 (15.4 %) cases, while none (0/22) of the patients experienced distant metastasis (follow up 1-150 months; median 20.5 months). Clinical outcome was partially concordant with risk-categories of different risk stratification models. CONCLUSION: Knowledge about histological diversity of HNSFT is essential for establishing correct diagnosis. Current risk stratification models do not perfectly predict outcome, and larger studies are needed to develop more accurate criteria for aggressive behavior.

Exploring the therapeutic potential and bio-evaluation of muesli (Chlorophytum borivilianum) for the management of postprandial glucose management in healthy and diabetic female subjects.

Muesli and cereal based breakfast contain generous amounts of fiber that are helpful in the management of blood glucose levels. Muesli contains ß-glucans that ensure gradual rise in blood glucose levels. Muesli also limits the absorption of glucose by making it unavailable. This study explored the effect of muesli in the management of postprandial blood glucose levels. 15 healthy and 15 diabetic females were offered muesli meal after 8h fasting. Fasting blood glucose levels and blood glucose level 30 and 60 minutes after meal were measured. Results of study showed that muesli brought a gradual rise in blood sugar level. Healthy females showed fasting sugar (92.17±11.27), after 30 minutes (110.87±13.85) and after 60 minutes (114.25±15.67) while diabetic females showed fasting sugar (113.25±10.87), after 30 minutes (117.83±18.74) and after 60 minutes (118.26±17.85). The nutritional profile of muesli showed that it contained 202 kcal of energy, 32.7g of carbohydrates, 9g of fiber, 12.4g of protein and 2.5g of fats. It also contained 5.1g of ß-glucans. Muesli found to be effective in the management of postprandial blood glucose levels in healthy and diabetic populations.

Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.

A Case Report and Literature Review of Rectosigmoid Crohn's Disease: A Diagnostic Pitfall Ultimately Leading to Spontaneous Colonic Perforation.

Inflammatory bowel disease (IBD) is a chronic condition that affects the gastrointestinal tract, with ulcerative colitis (UC) and Crohn's disease (CD) as the two major entities. While these conditions share some similarities in clinical presentation, they have distinct histopathological features. UC is a mucosal disease affecting the left colon and rectum, while CD can affect any part of the gastrointestinal tract and all layers of the bowel wall. Accurate diagnosis of UC and CD is important for effective management and prevention of complications. However, distinguishing between the two conditions based on limited biopsy specimens or atypical clinical presentations can be challenging. We present a case of a patient diagnosed with UC based on a single endoscopic biopsy from the sigmoid colon, who later presented with colonic perforation and was found to have CD on the colectomy specimen. This case emphasizes the importance of clinical guidelines when dealing with any patient of suspected IBD, considering alternative diagnoses in patients with atypical presentations and the need for careful clinical, endoscopic, and histological evaluation to make an accurate diagnosis. Delayed or missed diagnosis of CD can lead to significant morbidity and mortality.

A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women.

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.

Image Encryption Scheme Based on Orbital Shift Pixels Shuffling with ILM Chaotic System.

Image encryption techniques protect private images from unauthorized access while they are being transmitted. Previously used confusion and diffusion processes are risky and time-consuming. Therefore, finding a solution to this problem has become necessary. In this paper, we propose a new image encryption scheme that combines the Intertwining Logistic Map (ILM) and Orbital Shift Pixels Shuffling Method (OSPSM). The proposed encryption scheme applies a technique for confusion inspired by the rotation of planets around their orbits. We linked the technique of changing the positions of planets around their orbits with the shuffling technique of pixels and combined it with chaotic sequences to disrupt the pixel positions of the plain image. First, randomly selected pixels from the outermost orbit are rotated to shift the pixels in that orbit, causing all pixels in that orbit to change their original position. This process is repeated for each orbit until all pixels have been shifted. This way, all pixels are randomly scrambled on their orbits. Later on, the scrambled pixels are converted into a 1D long vector. The cyclic shuffling is applied using the key generated by the ILM to a 1D long vector and reshaped into a 2D matrix. Then, the scrambled pixels are converted into a 1D long vector to apply cyclic shuffle using the key generated by the ILM. After that, the 1D long vector is converted into a 2D matrix. For the diffusion process, using ILM generates a mask image, which is then XORed with the transformed 2D matrix. Finally, a highly secure and unrecognizable ciphertext image is obtained. Experimental results, simulation analysis, security evaluation, and comparison with existing image encryption schemes show that it has a strong advantage in defending against common attacks, and the operating speed of this encryption scheme also performs excellently in practical image encryption applications.

Chondroblastoma of foot bones; a clinicopathological study of 29 cases confirming the diagnostic utility of H3K36M and H3G34W antibodies at an uncommon site.

OBJECTIVE: Chondroblastoma (CB) is a benign cartilaginous bone neoplasm which commonly occurs in long bones of adolescents. CB can uncommonly involve foot. Its mimics include both benign and malignant lesions. H3K36M immunohistochemical (IHC) stain is a helpful tool for establishing the diagnosis of CB in such challenging situations. In addition, H3G34W IHC stain helps to rule out giant cell tumor which is the closest differential of CB. Our objective was to describe the clinicopathological features and frequencies of H3K36M, H3G34W and SATB2 IHC stains in CB of foot. MATERIALS AND METHODS: We reviewed H&E slides and blocks of 29 cases diagnosed as "chondroblastoma" of foot at our institutions. RESULTS: Patient's age ranged from 6 to 69 (mean: 23.3 and median: 23) years. Males were almost 5 times more commonly affected than females. Talus and calcaneum were involved in 13 (44.8 %) cases each. Microscopically, tumors were composed of polygonal mononuclear cells and multinucleated giant cells and chondroid matrix. Other histological features included aneurysmal bone cyst-like (ABC-like) change (44.8 %), osteoid matrix (31 %), chicken-wire calcification (20.7 %), and necrosis (10.3 %). H3K36M was expressed in 100 % and SATB2 in 91.7 % cases. H3G34W was negative in all cases, where performed. One out of 11 patients with follow up information developed local recurrence after 48 months. CONCLUSION: CB in foot occur at an elder age and show more frequent ABC-like changes as compared to long bones. Males are affected ~5:1 as compared to 2:1 in long bones. H3K36M are H3G34W are extremely useful diagnostic markers for CB, especially elderly (aged or higher) patients and we report the largest series of foot CB cases confirmed by immunohistochemistry.

Extranodal Follicular Dendritic Cell Sarcoma of the Head and Neck Region: A Clinicopathological Study of 7 Cases.

Background. Follicular dendritic cell (FDC) sarcoma is a rare neoplasm arising from follicular dendritic cells (FDCs). It can be nodal or extranodal. Histological diagnosis of extranodal FDC sarcoma in the head and neck region is challenging and a significant percentage are misdiagnosed. Objectives. To report clinicopathological features of head and neck extranodal FDC sarcoma cases and discuss differential diagnoses. Methods. Seven head and neck extranodal FDC sarcomas were retrieved and clinicopathological features were noted. Results. Two tumors each involved parapharyngeal space and tonsil while remaining cases involved the parotid, soft tissue of neck and oropharynx. Age range was 12 to 79 years (mean and median age were 40 and 44 years respectively) and there was a male predilection (6 males: 1 female). All showed spindle to ovoid cells arranged in fascicles, whorls and/or storiform pattern. Mitoses ranged from 3 to 20/mm2. All tumors expressed CD21 and CD23. Two patients died of their disease at 9 and 16 months. Both had tumors larger than 5 cm with ≥10 mitoses/mm2. Three patients were alive at 12, 44 and 184 months. Conclusions. There was a distinct male predominance in our cohort. FDC sarcoma should be included in the differential diagnosis of spindle cell extranodal neoplasms in the head and neck with a whorled growth pattern and intratumoral lymphocytes. Head and neck region tumors show similar clinicopathologic characteristics as their counterparts at other locations with potential for aggressive behavior especially in tumors greater than 5 cm in size and with high mitotic rates.

Necrotizing Infection of the Breast: A Case Report on a Rare Presentation of Breast Carcinoma.

Necrotizing infection (NI) of the breast associated with underlying malignancy is a rare phenomenon characterized by necrosis of breast parenchyma, causing a delay in diagnosis and even leading to sepsis. We present a case of a 42-year-old female with NI of the right breast while on homeopathic treatment for a right breast lump for six months. Tissue culture showed a polymicrobial infection and histopathology established the diagnosis of breast carcinoma. After treating the NI, her breast cancer was managed as per standard guidelines.

Male Breast Cancer: The Three Decades' Experience of a Tertiary Care Hospital in a Lower-Middle Income Country.

Introduction Male breast cancer is uncommon and managed on the guidelines of female breast cancer due to tumor rarity. We sought to identify the incidence, clinicopathological features, and survival of all male breast cancer patients managed in our hospital. Methods A retrospective cross-sectional study was conducted at Aga Khan University Hospital (AKUH), Karachi, Pakistan, from January 1986 to December 2018. Demographic data, treatment records, and follow-up data of all male breast cancer patients who were treated at AKUH was reviewed. Results Thirty-eight out of 42 patients who presented over a period of 32 years were included. The mean age was 63 years. The most common tumor type and subtype were invasive ductal carcinoma (89.5%) and luminal A (73.7%), respectively. The majority (36.8%) of the patients presented at stage III. Among 30 (78.9%) patients who underwent surgery, mastectomy was performed in 30 (78.9%), upfront axillary clearance in 24 (63.2%), axillary sampling in five (15.1%) cases, and sentinel lymph node biopsy in one (2.6%) case. Neoadjuvant chemotherapy was given to 10 (26.3%) patients, and adjuvant chemotherapy to eight (21.1%) patients. Adjuvant hormonal treatment was administered to 22 (57.9%) patients, and 13 (34%) patients received adjuvant radiation to the chest wall. The five-year overall survival was 38.2% and the median survival was 36 months. The five-year disease-free survival (DFS) was found to be 33.7%. Conclusion Breast cancer in males presents at an advanced stage with poor survival. Multicenter studies are required to accurately identify incidence, prognostic factors, and outcomes in order to have a better understanding of its management.

Unsupervised Structural Classification of Dissolved Organic Matter Based on Fragmentation Pathways.

Dissolved organic matter (DOM) is considered an essential component of the Earth's ecological and biogeochemical processes. Structural information of DOM components at the molecular level remains one of the most extraordinary analytical challenges. Advances in determination of chemical formulas from the molecular studies of DOM have provided limited indications on structural signatures and potential reaction pathways. In this work, we extend the structural characterization of a wetland DOM sample using precursor and fragment molecular ions obtained by a sequential electrospray ionization-Fourier transform-ion cyclotron resonance tandem mass spectrometry (ESI-FT-ICR CASI-CID MS/MS) approach. The DOM chemical complexity resulted in near 900 precursors (P) and 24 000 fragment (F) molecular ions over a small m/z 261-477 range. The DOM structural content was dissected into families of structurally connected precursors based on neutral mass loss patterns (Pn-1 + F1:n + C) across the two-dimensional (2D) MS/MS space. This workflow identified over 1900 structural families of DOM compounds based on a precursor and neutral loss (H2O, CH4O, and CO2). The inspection of structural families showed a high degree of isomeric content (numerous identical fragmentation pathways), not discriminable with sole precursor ion analysis. The connectivity map of structural families allows for the visualization of potential biogeochemical processes that DOM undergoes throughout its lifetime. This study illustrates that integrating effective computational tools on a comprehensive high-resolution mass fragmentation strategy further enables the DOM structural characterization.