Multimodal Strategy Research Articles

Multi-mode operations of double-stator switched reluctance machine for electric vehicle

Purpose The purpose of this paper is to introduce a double-stator switched reluctance machine (DS-SRM) for electric vehicles (EVs) and to propose multi-mode operations for this machine. Design/methodology/approach Analysis of flux linkage distributions and torque characteristics using finite element method (FEM). Building a dynamic simulation model based on electromagnetic characteristics, mathematical equations and mechanical motion equations of the DS-SRM drive system. The paper proposes multi-mode operations (inner-stator excitation mode, outer-stator excitation mode and double-stator excitation mode) based on motor working regions. It also conducts simulation and experimental results to verify the effectiveness of the proposed multi-mode operations strategies and control schemes. Findings There is almost no electromagnetic coupling between the inner and outer stators due to the specially designed rotor structure and optimized windings polarity configuration. Analysis of flux linkage distributions and torque characteristics verified the independence of inner and outer stators. Proposal of multi-mode operations and corresponding control rules achieved the smooth switching between different modes. Originality/value The paper introduced the DS-SRM for EVs and proposed multi-mode operations, along with control rules, to optimize its performance. The specially designed rotor structure, optimized winding polarity configuration, and the proposed multi-mode operations contribute to the originality of the research.

Multimodal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O.

An important element of antibody-guided vaccine design is the use of neutralizing or opsonic monoclonal antibodies to define protective epitopes in their native three-dimensional conformation. Here, we demonstrate a multimodal mass spectrometry-based strategy for in-depth characterization of antigen-antibody complexes to enable the identification of protective epitopes using the cytolytic exotoxin Streptolysin O (SLO) from Streptococcus pyogenes as a showcase. We first discovered a monoclonal antibody with an undisclosed sequence capable of neutralizing SLO-mediated cytolysis. The amino acid sequence of both the antibody light and the heavy chain was determined using mass-spectrometry-based de novo sequencing, followed by chemical cross-linking mass spectrometry to generate distance constraints between the antibody fragment antigen-binding region and SLO. Subsequent integrative computational modeling revealed a discontinuous epitope located in domain 3 of SLO that was experimentally validated by hydrogen-deuterium exchange mass spectrometry and reverse engineering of the targeted epitope. The results show that the antibody inhibits SLO-mediated cytolysis by binding to a discontinuous epitope in domain 3, likely preventing oligomerization and subsequent secondary structure transitions critical for pore-formation. The epitope is highly conserved across >98% of the characterized S. pyogenes isolates, making it an attractive target for antibody-based therapy and vaccine design against severe streptococcal infections.

Anesthesiological provision in hip and knee joint replacement (a scientific literature review)

Effective pain management during hip and knee replacement ensures the efficient and fast recovery of patients. Joint replacement surgery is typically accompanied by the use of general anesthesia or spinal anesthesia. Spinal anesthesia, despite certain risks, demonstrates better effectiveness compared to general anesthesia in hip and knee replacements. In other words, spinal anesthesia entails fewer perioperative complications. The addition of sedation and anesthesia monitoring during joint replacements using spinal anesthesia is of particular importance. After a successful surgical procedure of joint replacement, achieving adequate pain relief in the postoperative period is crucial. This is achieved through intravenous patient-controlled analgesia or epidural analgesia. A multimodal analgesia strategy combines analgesics with different mechanisms of action to enhance pain management. Intraoperative periarticular administration of multimodal agents is one of the most important procedures in the perioperative pain control during total hip arthroplasty. Peripheral nerve blocks have emerged recently as an alternative analgesic approach. During hip and knee replacements, various nerve blocks are utilized, including lumbar plexus block, fascia iliaca block, femoral and sciatic nerve block, adductor canal block, quadratus lumborum block, erector spinae plane block. Given the entire perioperative process, the use of preventive and multimodal therapy along with peripheral nerve blocks can help reduce the intensity of postoperative pain, thereby improving the functional status and quality of life for patients.

BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis

Objective. To go beyond the deficiencies of the three conventional multimodal fusion strategies (i.e. input-, feature- and output-level fusion), we propose a bidirectional attention-aware fluid pyramid feature integrated fusion network (BAF-Net) with cross-modal interactions for multimodal medical image diagnosis and prognosis. Approach. BAF-Net is composed of two identical branches to preserve the unimodal features and one bidirectional attention-aware distillation stream to progressively assimilate cross-modal complements and to learn supplementary features in both bottom-up and top-down processes. Fluid pyramid connections were adopted to integrate the hierarchical features at different levels of the network, and channel-wise attention modules were exploited to mitigate cross-modal cross-level incompatibility. Furthermore, depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase in parameters to a great extent. The generalization abilities of BAF-Net were evaluated in terms of two clinical tasks: (1) an in-house PET-CT dataset with 174 patients for differentiation between lung cancer and pulmonary tuberculosis. (2) A public multicenter PET-CT head and neck cancer dataset with 800 patients from nine centers for overall survival prediction. Main results. On the LC-PTB dataset, improved performance was found in BAF-Net (AUC = 0.7342) compared with input-level fusion model (AUC = 0.6825; p < 0.05), feature-level fusion model (AUC = 0.6968; p = 0.0547), output-level fusion model (AUC = 0.7011; p < 0.05). On the H&N cancer dataset, BAF-Net (C-index = 0.7241) outperformed the input-, feature-, and output-level fusion model, with 2.95%, 3.77%, and 1.52% increments of C-index (p = 0.3336, 0.0479 and 0.2911, respectively). The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets. Significance. Extensive experiments on two datasets demonstrated better performance and robustness of BAF-Net than three conventional fusion strategies and PET or CT unimodal network in terms of diagnosis and prognosis.

Infection prevention and control programs in Armenian Psychoneurological Hospitals

Aim of the study: to characterize the integration and implementation of the main components of infection prevention and control (IPC), defined by the World Health Organization (WHO), in psychoneurological hospitals in Armenia. Materials and methods. The study was conducted over the period from 2019 to 2022 in all 8 psychoneurological hospitals in Armenia. The research tool was based on using the IPCAF (Infection Prevention and Control Assessment Framework) questionnaire translated into Armenian, consisting of 8 sections. In accordance with the WHO scoring methodology, each possible answer to the question was scored. The maximum total score for each core component could be 100 points, and the final score — 800. Descriptive epidemiology was used during the study. Results. The average final score for all IPCAF components for all neuropsychiatric hospitals in Armenia over the entire study period was increased by 1.46 times (from 345.0±10.7 points in 2019 to 502.5±32.5 in 2022; р 0.01) and in 2022 was characterized as an “intermediate level”. Analysis by individual IPC core components (CC) found that the highest average scores in 2022 were obtained for components CC2 (IPC guidelines) — 81.6 points, CC8 (Built environment, materials and equipment for IPC at the facility level) — 72.8 points, and CC5 (Multimodal strategies) — 70.0 points. The lowest average scores were obtained for the key components: CC4 (Health care-associated infection (HAI) surveillance) — 45.0 points, and for CC3 (IPC education and training) — 56.3 points and CC7 (Workload, staffing and bed occupancy) — 56.9 points. Conclusion. Our study demonstrated for the first time an opportunity for using the IPCAF questionnaire to evaluate IPC programs in neuropsychiatric hospitals and identify gaps in various areas of IPC. Information was received regarding the state of key IPC component implementation in psychoneurological hospitals in Armenia. Analysis by individual core components identified gaps that need to be addressed. It is recommended to implement continuous monitoring of compliance with various aspects of IPC in order to prevent weakening of control and deterioration of epidemiological situation.

Pericapsular Nervous Group Block versus Suprainguinal Fascia Iliaca Block Using the Same Injection Volume in Primary HIP Arthroplasty Prospective Observational Study

Objectives. To determine the pericapsular nerve group (PENG) block’s postoperative analgesic efficacy and safety compared to the suprainguinal fascia iliaca (SFI) block in patients undergoing primary hip arthroplasty using the same injectate volume. Material and Methods. Between January 2021 and March 2022, American Society of Anesthesiologists Physical Status (ASA-PS) classification I–III patients scheduled for hip arthroplasty were included in this study. After standard monitoring and subarachnoid anesthesia, an ultrasound-guided PENG or SFI block with 20 ml of 0.25% levobupivacaine was performed for postoperative analgesia. All patients were assessed with a numerical rating scale (NRS) at presurgery, upon arrival at the postanesthesia care unit (PACU), and in the postoperative period at 2, 4, 12, and 24 hours. The need for analgesic rescue and adverse effects was also assessed. Results. A total of 130 patients were included in the study (62 PENG block and 68 SFI block). Both blocks were equally effective in managing postoperative pain without any statistically significant differences except at 12 h (p=0.023), where the deviation found was not clinically relevant. The median total morphine consumption was 0 mg [0–2] in the PENG block group and 0 mg [0–2] in the SFI block group. A more significant motor block was found in the first 6 hours in the SFI block group (p=0.001). There was no significant difference in the ease of performing PENG (79%) or SFI (85%) blocks. No major complications were recorded in both groups, and patient satisfaction was high (83.9% for the PENG block group vs. 91.2% for the SFI block group). Discussion. Both blocks have been demonstrated to be effective for postoperative analgesia in hip arthroplasty and should be integrated as a multimodal analgesic strategy. The lesser degree of motor block recorded in the first hours with the PENG block makes it the most suitable option for early recovery. Both techniques were easy and safe to perform.

Multimodal Treatment of Metastatic Rectal Cancer in a Young Patient: Case Report and Literature Review

Metastatic colorectal cancer requires a multidisciplinary and individualized approach. Herein, we reported the case of a young woman diagnosed with metastatic rectal cancer who received an individualized multimodal treatment strategy that resulted in a remarkable survival. There were several particular aspects of this case, such as the early onset of the disease, the successful use of conversion therapy, the application of liquid biopsy to guide treatment, and the specific nature of the bone metastasis. To offer more insights for navigating such challenges in patients with metastatic colorectal cancer, we have conducted a literature review to find more data related to the particularities of this case. The incidence of early onset colorectal cancer is on the rise. Data suggests that it differs from older-onset colorectal cancer in terms of its pathological, epidemiological, anatomical, metabolic, and biological characteristics. Conversion therapy and surgical intervention provide an opportunity for cure and improve outcomes in metastatic colorectal cancer. It is important to approach each case individually, as every patient with limited liver disease should be considered as a candidate for secondary resection. Moreover, liquid biopsy has an important role in the individualized management of metastatic colorectal cancer patients, as it offers additional information for treatment decisions.

Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

Validation of Perioperative Troponin Levels for Predicting Postoperative Mortality and Long-Term Survival in Patients Undergoing Surgery for Hepatobiliary and Pancreatic Cancer.

Background: Hepatopancreato and biliary (HPB) tumors represent some of the leading cancer-related causes of death worldwide, with the majority of patients undergoing surgery in the context of a multimodal treatment strategy. Consequently, the implementation of an accurate risk stratification tool is crucial to facilitate informed consent, along with clinical decision making, and to compare surgical outcomes among different healthcare providers for either service evaluation or clinical audit. Perioperative troponin levels have been proposed as a feasible and easy-to-use tool in order to evaluate the risk of postoperative myocardial injury and 30-day mortality. The purpose of the present study is to validate the perioperative troponin levels as a prognostic factor regarding postoperative myocardial injury and 30-day mortality in Greek adult patients undergoing HPB surgery. Method: In total, 195 patients undergoing surgery performed by a single surgical team in a single tertiary hospital (2020-2022) were included. Perioperative levels of troponin before surgery and at 24 and 48 h postoperatively were assessed. Model accuracy was assessed by observed-to-expected (O:E) ratios, and area under the receiver operating characteristic curve (AUC). Survival at one year postoperatively was compared between patients with high and normal TnT levels at 24 h postoperatively. Results: Thirteen patients (6.6%) died within 30 days of surgery. TnT levels at 24 h postoperatively were associated with excellent discrimination and provided the best-performing calibration. Patients with normal TnT levels at 24 h postoperatively were associated with higher long-term survival compared to those with high TnT levels. Conclusions: TnT at 24 h postoperatively is an efficient risk assessment tool that should be implemented in the perioperative pathway of patients undergoing surgery for HPB cancer.

Enhancing diagnosis of benign lesions and lung cancer through ensemble text and breath analysis: a retrospective cohort study

Early diagnosis of lung cancer (LC) can significantly reduce its mortality rate. Considering the limitations of the high false positive rate and reliance on radiologists’ experience in computed tomography (CT)–based diagnosis, a multi-modal early LC screening model that combines radiology with other non-invasive, rapid detection methods is warranted. A high-resolution, multi-modal, and low-differentiation LC screening strategy named ensemble text and breath analysis (ETBA) is proposed that ensembles radiology report text analysis and breath analysis. In total, 231 samples (140 LC patients and 91 benign lesions [BL] patients) were screened using proton transfer reaction–time of flight–mass spectrometry and CT screening. Participants were randomly assigned to a training set and a validation set (4:1) with stratification. The report section of the radiology reports was used to train a text analysis (TA) model with a natural language processing algorithm. Twenty-two volatile organic compounds (VOCs) in the exhaled breath and the prediction results of the TA model were used as predictors to develop the ETBA model using an extreme gradient boosting algorithm. A breath analysis model was developed based on the 22 VOCs. The BA and TA models were compared with the ETBA model. The ETBA model achieved a sensitivity of 94.3%, a specificity of 77.3%, and an accuracy of 87.7% with the validation set. The radiologist diagnosis performance with the validation set had a sensitivity of 74.3%, a specificity of 59.1%, and an accuracy of 68.1%. High sensitivity and specificity were obtained by the ETBA model compared with radiologist diagnosis. The ETBA model has the potential to provide sensitivity and specificity in CT screening of LC. This approach is rapid, non-invasive, multi-dimensional, and accurate for LC and BL diagnosis.

Level of implementation of multimodal strategies for infection prevention and control interventions and prevalence of healthcare-associated infections in Northern Italy

BackgroundIn November 2022, Italy participated in the third edition of the European Centre for disease prevention and control (ECDC) point prevalence survey (PPS) of healthcare-associated infections (HAIs) in acute-care hospitals. A questionnaire based on the WHO infection prevention and control assessment framework (IPCAF) was included, which aims to investigate multimodal strategies for the implementation of IPC interventions.MethodsA PPS was conducted using the ECDC PPS protocol version 6.0. The Regional health authority of the region of Piedmont, in north-western Italy, chose to enlist all public acute-care hospitals. Data were collected within one day per each ward, within 3 weeks in each hospital, at hospital, ward and patient level. A score between 0–1 or 0–2 was assigned to each of the 9 items in the IPCAF questionnaire, with 14 points representing the best possible score. HAI prevalence was calculated at the hospital-level as the percentage of patients with at least one HAI over all included patients. Relations between HAI prevalence, IPCAF score, and other hospital-level variables were assessed using Spearman's Rho coefficient.ResultsIn total, 42 acute-care hospitals of the region of Piedmont were involved, with a total of 6865 included patients. All participant hospitals reported they employed multimodal strategies to implement IPC interventions. The median IPCAF overall score was 11/14 (interquartile range, IQR: 9.25–12). The multimodal strategy with the highest level of adherence was education and training, followed by communication and reminders. Strategies with the lowest level of adherence were safety climate and culture of change, and system change. Overall HAI prevalence was 8.06%. A weak to moderate inverse relation was found between IPCAF score and HAI prevalence (Spearman’s Rho -0.340, p 0.034). No other significant correlation was found.ConclusionsThis study found a high self-reported overall level of implementation of multimodal strategies for IPC in the region. Results of this study suggest the relevance of the multimodal approach and the validity of the IPCAF score in measuring IPC programs, in terms of effectiveness of preventing HAI transmission.

Prehabilitation in Adults Undergoing Cancer Surgery: A Comprehensive Review on Rationale, Methodology, and Measures of Effectiveness.

Cancer surgery places a significant burden on a patients' functional status and quality of life. In addition, cancer surgery is fraught with postoperative complications, themselves influenced by a patient's functional status. Prehabilitation is a unimodal or multimodal strategy that aims to increase a patient's functional capacity to reduce postoperative complications and improve postoperative recovery and quality of life. In most cases, it involves exercise, nutrition, and anxiety-reducing interventions. The impact of prehabilitation has been explored in several types of cancer surgery, most commonly colorectal and thoracic. Overall, the existing evidence suggests prehabilitation improves physiological outcomes (e.g., lean body mass, maximal oxygen consumption) as well as clinical outcomes (e.g., postoperative complications, quality of life). Notably, the benefit of prehabilitation is additional to that of enhanced recovery after surgery (ERAS) programs. While safe, prehabilitation programs require multidisciplinary coordination preoperatively. Despite the existence of numerous systematic reviews and meta-analyses, the certainty of evidence demonstrating the efficacy and safety of prehabilitation is low to moderate, principally due to significant methodological heterogeneity and small sample sizes. There is a need for more large-scale multicenter randomized controlled trials to draw strong clinical recommendations.

Hypervalent Iodine(III) Mediated Halogen Bonded Supramolecular Chiral System with Cholesteryl Naphthalimides.

Halogen bonding acknowledged as a noteworthy weak interaction, has gained growing recognition in the field of supramolecular chemistry. In this study, we selected structurally rigid diaryliodonium ions (I(III)) with two biaxial σ-holes as halogen-bond donors, to bind with three chiral acceptor molecules bearing cholesteryl and naphthalimides with distinct geometries. The abundant carbonyl oxygen atoms in side-arm substituents function as multiple acceptors for halogen bonding. The self-aggregation of chiral acceptor molecules demonstrates adaptiveness to solvent media, evidenced by the inversion of the Cotton effect and the morphological evolution from spherical to rod-like nanoarchitectures in different solvent systems. The distinct geometries of the acceptor molecules conferred various binding modes with I(III). The introduction of I(III) as a halogen-bond donor regulates the aggregation of the donors, achieving amplification of chiroptical signals and inheriting solvent responsiveness from the self-aggregated assembly. This study successfully utilized rational structural design and multimodal control strategies to achieve regulation of supramolecular chirality.

Enhancing psychiatric rehabilitation outcomes through a multimodal multitask learning model based on BERT and TabNet: An approach for personalized treatment and improved decision-making

Evaluating the rehabilitation status of individuals with serious mental illnesses (SMI) necessitates a comprehensive analysis of multimodal data, including unstructured text records and structured diagnostic data. However, progress in the effective assessment of rehabilitation status remains limited. Our study develops a deep learning model integrating Bidirectional Encoder Representations from Transformers (BERT) and TabNet through a late fusion strategy to enhance rehabilitation prediction, including referral risk, dangerous behaviors, self-awareness, and medication adherence, in patients with SMI. BERT processes unstructured textual data, such as doctor's notes, whereas TabNet manages structured diagnostic information. The model's interpretability function serves to assist healthcare professionals in understanding the model's predictive decisions, improving patient care. Our model exhibited excellent predictive performance for all four tasks, with an accuracy exceeding 0.78 and an area under the curve of 0.70. In addition, a series of tests proved the model's robustness, fairness, and interpretability. This study combines multimodal and multitask learning strategies into a model and applies it to rehabilitation assessment tasks, offering a promising new tool that can be seamlessly integrated with the clinical workflow to support the provision of optimized patient care.

Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI.

To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging. We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification. The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance. The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.

Women's Knowledge, Attitudes, and Perception on Personalized Risk-Stratified Breast Cancer Screening: A Cross-Sectional Study in Malaysia.

Breast cancer is commonest cancer among Malaysian women and screening is essential for the early detection. Therefore our study aimed at measuring the levels of knowledge, attitude and perception towards personalized risk stratified breast cancer screening in Malaysia. A cross-sectional study was carried out in Malaysia to assess the knowledge, perception and attitudes of the women in Malaysia. The study was conducted using an online questionnaire, and samples were obtained using convenience sampling. The questionnaire was distributed trilingual in English, Bahasa Malaysia and Chinese. The data was collected with content validated questionnaire. Data was analyzed with descriptive statistics and General Linear Model analysis in SPSS (Version 27). A total of 201 respondents' data were analyzed. From our study we were able to summarize that the women in Malaysia have a suboptimal knowledge towards personalized risk-stratified breast cancer screening as only 48.9% aware of the term for personalized risk-stratified breast cancer screening. Meanwhile, the majority of the respondents (96.7%) showed positive attitudes towards the importance of risk assessment and screening. Experience of participating in health education programmes about breast cancer and personalized risk-stratified screening was found to be significantly associated with knowledge, attitude and perception towards personalized risk-stratified breast cancer screening. General population's awareness of individualized risk-stratified breast cancer screening was insufficient despite their favourable attitude towards the disease. A multimodal strategy may be used to improve women's knowledge, attitude, and perception of individualized risk-stratified breast cancer screening.

MCT-VHD: Multi-modal contrastive transformer for video highlight detection

Autonomous highlight detection aims to identify the most captivating moments in a video, which is crucial for enhancing the efficiency of video editing and browsing on social media platforms. However, current efforts primarily focus on visual elements and often overlook other modalities, such as text information that could provide valuable semantic signals. To overcome this limitation, we propose a Multi-modal Contrastive Transformer for Video Highlight Detection (MCT-VHD). This transformer-based network mainly utilizes video and audio modalities, along with auxiliary text features (if exist) for video highlight detection. Specifically, We enhance the temporal connections within the video by integrating a convolution-based local enhancement module into the transformer blocks. Furthermore, we explore three multi-modal fusion strategies to improve highlight inference performance and employ a contrastive objective to facilitate interactions between different modalities. Comprehensive experiments conducted on three benchmark datasets validate the effectiveness of MCT-VHD, and our ablation studies provide valuable insights into its essential components.

Reliable human authentication using AI-based multibiometric image sensor fusion: Assessment of performance in information security

Reliable human authentication is essential for assuring the availability, confidentiality, and integrity of sensitive data and resources in the field of information security. Due to the vulnerabilities of conventional authentication techniques like passwords and PINs, interest in biometric-based authentication systems has developed. To get over this, we suggest an efficient voice and iris recognition-based multimodal biometric verification strategy for human authenticating tools. We first collect the voice and iris datasets from 150 people. Then, we used a median filter and high pass filter to preprocess the voice and iris data. The features of voice identification are extracted using the shifted delta cepstral coefficient (SDCC) and the Mel frequency discrete wavelet coefficient (MFCC), and these two coefficients are evaluated. The outcomes of the extraction of iris identification features using Local binary pattern (LBP) and Speeded robust features (SURF) are evaluated. The classifier Fine Tuned Cuckoo Search Optimized Convolutional Neural Network (FCSO-CNN) performs voice and iris recognition modalities. Voice and iris biometric systems may be combined into a single multimodal biometric system by fusing their respective feature sets and scoring algorithms. The results of the computer simulation demonstrate that for speech recognition, using the SDC and MFC coefficients produces better results, while for iris recognition, using the LBP and FCSO-CNN experiment produces superior outcomes. Additionally, the scores fusion works superior to other scenarios in the proposed multimodal biometrics system. The suggested system provides a dependable and strong solution for human identification, assisting in the improvement of security measures across a variety of industries, including financial institutions, governmental agencies, and the defense of key infrastructure.

Prognostic significance of pT3a staging subclassifications in renal cell carcinoma: Not all pT3a are equal

ObjectiveTo examine the prognostic significance of perinephric fat, renal sinus fat, and renal vein invasion in patients with pT3a renal cell carcinoma (RCC) by histologic type. MethodsA population-based retrospective cohort study of patients with pT3aN0M0 RCC was performed using Surveillance, Epidemiology, and End Results (SEER) data for the years 2010 through 2019. Cox proportional hazards models were used to examine the relationship between pT3a subclassification groups and cancer-specific survival (CSS) by histological subtype (clear cell, papillary, chromophobe, and other). ResultsThe cohort consisted of 10,170 patients with pT3a RCC, including 8,446 (83.0%) with clear cell RCC and 1,724 (17.0%) with nonclear cell RCC (nccRCC). Median follow up was 36 months. Differences in CSS by pT3a subclassification groups were observed in all histological subtypes but were most pronounced in nccRCC, specifically papillary RCC. Compared to perinephric fat (PF) invasion only, renal vein (RV) invasion (HR = 4.9, 95%CI: 2.5–9.3, P < 0.01), renal sinus fat invasion (HR = 3.0, 95%CI: 1.4–6.2), RV and PF invasion (HR = 7.5, 95%CI: 3.5–16.0), and combination of all three characteristics (HR = 4.4, 95%CI: 1.2–15.5) were associated with worse CSS in patients with papillary RCC. ConclusionWe examined the prognostic role of pT3a staging subclassifications in RCC by histologic subtype and observed survival differences, particularly in papillary RCC. Our findings highlight the need to refine pT3a staging criteria to help guide individualized, multimodal treatment strategies for locally advanced RCC.

The prevalence of cardiovascular diseases, chronic kidney disease, and obesity in patients with type 2 diabetes mellitus and the description of concurrent treatments: A two-center retrospective cross-sectional study in Saudi Arabia

BackgroundAtherosclerotic cardiovascular disease (ASCVD), heart failure (HF), chronic kidney disease (CKD), and obesity are associated with increased morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Nonetheless, their prevalence among patients with T2DM in Saudi Arabia (SA) remains unknown. As current guidelines recommend, these comorbidities require adding certain antidiabetic agents with cardiorenal benefits. However, the prescribers' adherence to these recommendations remains unclear. MethodsA two-center retrospective cross-sectional study was conducted including adult patients (≥18 years) with T2DM admitted to hospital or seen at outpatient clinics between January and December 2020. Patients were classified into two groups based on the presence or absence of ASCVD. Patients with no prior ASCVD history were further classified based on the 10-year ASCVD risk estimation. Endpoints of interest included the prevalence of ASCVD, HF, CKD, and obesity in patients with T2DM. We also evaluated the characteristics of the utilized antidiabetic agents, statin, and aspirin therapies.. ResultsOf the 1,218 included patients with T2DM, the majority were female (57.0 %), and aged 45–64 years (53.0 %) with a mean age of 59.3 ± 13.1 years. Hypertension and dyslipidemia were the most prevalent comorbidities (67.7 % and 69.0 %, respectively). Among all patients, 18.6 % had an established ASCVD and the prevalence of HF, CKD, and obesity were 5.1 %, 8.7 %, and 58.3 %, respectively. The most common types of ASCVD witnessed were revascularization (42.3 %), myocardial infarction (36.6 %), and stroke (33.9 %); with an increased prevalence of ASCVD as the age increases (52.8 % at age ≥ 65 years). In the non‐ASCVD group, the 10-year ASCVD risk was intermediate or high in 62.7 % of these patients. The rates of utilization of guidelines-recommended therapies were 83.6 % for metformin, 9.4 % for GLP-1 RA, 10.8 % for SGLT2i, 35.2 % for aspirin alone or in combination with clopidogrel, and 79.7 % for statin therapy. ConclusionsASCVD, HF, CKD, and obesity are common complications in patients with T2DM in SA, with low overall utilization of the recommended guidelines-recommended medical therapies. Multimodal strategies should be utilized to assess T2DM and its complications, and to improve prescribers' adherence to guidelines-recommended therapies.