Patient Treatment Outcomes Research Articles

P-1705. Infectious Diseases Pharmacist Curbsides: Questions Infectious Diseases Providers Ask Infectious Diseases Pharmacists

Abstract Background Infectious Diseases (ID) consult services, typically led by ID physicians, benefit significantly from the support of ID pharmacists. These pharmacists, with specialized training in ID pharmacotherapy through post-graduate residencies or fellowships, play a crucial role in advising on the selection, dosing, duration, and monitoring of antimicrobials. While the contribution of ID pharmacists to antimicrobial stewardship programs is well described, their impact in supporting ID consult services remains underdocumented. This report explores the clinical inquiries made by ID physicians and advanced practice providers (APP) to ID pharmacists on consult services, detailing how these interactions influence patient treatment and monitoring plans.Figure 1ID Pharmacist Curbside Question Categories (Categories with 2+ Submissions) Methods Ten ID residency- or fellowship-trained clinical pharmacists practicing in various practice settings documented questions they received from ID providers (attendings, fellows, and APPs) for patients on their respective ID consult service over a 6-month period. The impact of the conversations was documented if the pharmacist recommending a new drug, dose, duration, or monitoring plan which resulted in a change in care. Additional information was captured on the time spent answering questions, category of question, and pertinent microbiology or organism phenotype to describe areas of highest need for ID pharmacist support.Figure 2Questions Leading to Therapeutic Change Results Over the first three months of documentation, 782 questions were asked by ID providers to ID pharmacists. Total direct time spent answering questions was 8,937 minutes. The top three question categories were gram-negative resistance, antifungal therapy, and antimicrobial allergies. Seven hundred and three (89.8%) questions resulted in a clinical change in management as recommended by the ID pharmacist. The most common clinical change category was recommending a new dose which occurred in 57.4% of questions followed by new antimicrobial 50.4%, new monitoring plan 24.2% and new duration of therapy 8.8%. Conclusion ID pharmacists offer critical support that frequently changes management on ID consult services. Integrating their expertise into a multidisciplinary ID team is essential for achieving optimal patient safety and treatment outcomes. Disclosures Wesley D. Kufel, Pharm.D., BCPS, BCIDP, Merck & Co.: Grant/Research Support|Shionogi, Inc: Grant/Research Support Jennifer Ross, PharmD, BCIDP, Shionogi Inc.: Advisor/Consultant Frank Tverdek, PharmD, Merck: Advisor/Consultant

A Cross-Sectional Study on the Knowledge, Awareness, and Attitudes Toward Precision Orthodontics Among Orthodontists in India

Background Precision Orthodontics (PO) is a revolutionary concept that incorporates genetics into orthodontic practice. While many medical fields have adopted new genomic technologies, dentistry, particularly orthodontics, has been slower to adapt. PO involves the utilization of customized equipment and precise force delivery, coupled with the integration of genetic and proteomic data, to enhance the individualization of orthodontic therapies. With the emergence of precision medicine in healthcare, understanding the adoption and perception of PO is crucial in shaping the future of orthodontic practice. Objective This cross-sectional study aimed to assess the current state of knowledge, awareness, and attitudes toward PO among orthodontists in India. Methods A questionnaire-based survey was developed which was distributed online to orthodontists across India. The survey comprised three sections focusing on knowledge, awareness, and attitudes related to PO. The survey received 391 responses, representing 41.2% of the orthodontists surveyed. Results While 72.3% of surveyed orthodontists demonstrated awareness of PO, only 32% correctly defined it, revealing a notable knowledge gap. Furthermore, 66% of the respondents admitted a lack of knowledge regarding genomic concepts. Despite these gaps, a remarkably positive attitude was observed among orthodontists, with 98% expressing openness to adopting precision approaches. Conclusion This study underscores the need for targeted education in genomics for orthodontists to bridge knowledge gaps. To facilitate the integration of PO into routine practice, specific education and training may be required. The positive attitudes and willingness to embrace PO indicate a promising trajectory for its adoption, potentially enhancing patient care and treatment outcomes.

Application of artificial intelligence technologies in cardiovascular disease detection and management authors

Cardiovascular diseases (CVD) remain the leading cause of death worldwide, including in the Russian Federation. Early detection and continuous monitoring are crucial to reduce mortality and improve patient outcomes. This article examines the use of artificial intelligence technologies in the detection and treatment of cardiovascular diseases, emphasizing their potential for the development of the field of cardiology. A comprehensive literature search was conducted using, focusing on studies in which artificial intelligence was used to diagnose, treat, and monitor cardiovascular diseases. The review includes an analysis of various artificial intelligence methods, including machine learning and neural networks, and their effectiveness in detecting heart rhythm disorders using wireless sensors and wearable devices. The review highlights promising solutions using artificial intelligence developed both internationally and in the Russian Federation, and provides practical recommendations for their implementation. By addressing existing research gaps and offering directions for the future, the article aims to improve the understanding and application of artificial intelligence in cardiology, which ultimately contributes to improved patient care and treatment outcomes.

Overview on Analytical Methods for Simultaneous Estimation of Bisoprolol Fumarate and Telmisartan

This article reviews the development and validation of High-Performance Liquid Chromatography (HPLC) methods, particularly Reverse Phase HPLC (RP-HPLC), for the simultaneous estimation of Bisoprolol fumarate and Telmisartan in pharmaceutical formulations. Both drugs are essential in managing hypertension and cardiovascular diseases, and reliable analytical methods are crucial for quality control in their production. The review highlights various RP-HPLC methods with different chromatographic conditions, including stationary phases, mobile phase compositions, flow rates, and wavelengths. These methods have been validated for precision, accuracy, and regulatory compliance, ensuring efficient and cost-effective analysis. The development of such validated RP-HPLC methods supports the effective quality control of combination therapies, enhancing patient care and treatment outcomes.

Advancing healthcare through laboratory on a chip technology: Transforming microorganism identification and diagnostics

In a recent case report in the World Journal of Clinical Cases , emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes. Laboratory-on-a-chip (LOC) technology has emerged as a transformative tool in health care, offering rapid, sensitive, and specific identification of microorganisms. This editorial provides a comprehensive overview of LOC technology, highlighting its principles, advantages, applications, challenges, and future directions. Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics, epidemiology, and food safety. Comparative studies have underscored the superiority of LOC technology over traditional methods, showcasing improvements in speed, accuracy, and portability. The future integration of LOC with biosensors, artificial intelligence, and data analytics promises further innovation and expansion. This call to action emphasizes the importance of continued research, investment, and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.

Extramedullary plasmacytoma with the uvula as first affected site: A case report

BACKGROUND Extramedullary plasmacytoma (EMP) represents one of the rarer forms of plasma cell malignancies, capable of impacting a variety of tissues and organs throughout the body. The majority of EMP cases are predominantly found in the head and neck region, especially within the laryngopharynx, as well as in the gastrointestinal tract. While there have been documented instances of oropharyngeal involvement in EMP cases in the academic literature, it is important to note that EMP specifically affecting the uvula is exceedingly uncommon. Furthermore, it is noteworthy that over 60% of epithelial carcinomas in the upper respiratory tract and oropharynx tend to metastasize to the cervical lymph nodes, indicating a propensity for regional spread in these types of cancers. In this context, we present a rare case of extramedullary plasmacytoma where the uvula served as the initially affected site. This case emphasizes the need for heightened awareness among clinicians regarding such unusual comorbidities, as early recognition and diagnosis can significantly influence patient management and treatment outcomes. In addition, a review of the relevant literature is included to further educate and inform healthcare professionals about this rare presentation, ultimately aiming to enhance clinical understanding and improve patient care in similar situations. CASE SUMMARY A 51-year-old man was admitted to our hospital because of a slowly enlarging neck mass. A physical examination revealed a palpable left lymph node, and magnetic resonance imaging (MRI) of the oropharynx and the neck showed a soft tissue mass in the oropharynx and enlargement of multiple lymph nodes in the neck. The soft tissue mass was diagnosed as plasmacytoma by immunohistochemical analysis. Monoclonal immunoglobulins and bone marrow biopsy showed normal results. Therefore, we diagnosed that as EMP of the uvula. After four cycles of adjuvant chemotherapy dominated by bortezomib, MRI reexamination showed a significant reduction of the mass in the oropharynx and the cervical lymph nodes. Afterwards, the λ light chain returned to normal levels. There was no evidence of evolution to multiple myeloma. CONCLUSION We have reported a rare case of extramedullary plasmacytoma with the uvula as the first affected site and the relevant literature is reviewed to improve clinicians' awareness of such rare comorbidities.

Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity

This study develops predictive models for Chinese female patients with VL utilizing machine learning techniques. The aim is to create an effective model that can assist in clinical diagnosis and treatment of vaginal relaxation, thereby enhancing women’s pelvic floor health. In total, 1184 women with VL have been randomly selected and categorized into groups using the finger measurement method. Among them, there are 383 cases of mild VL, 405 cases of moderate VL, and 396 cases of severe VL. Concurrently, 396 healthy women without VL who underwent routine health examinations have been chosen at random and assigned to the non-VL group. Based on 1580 cases, we have established LightGBM, Random Forest, XGBoost, and AdaBoost models based on training dataset using 5-fold cross-validation and GridSearch, and analyzed the performance of the models on the hold-out test dataset. The confusion matrix, precision, recall, F1 score, overall accuracy, and ROC curve of the models on the hold-out test dataset are compared. The overall accuracy of LightGBM model, RF model, XGBoost model, and AdaBoost model are 0.8987, 0.8987, 0.8987, and 0.8457, respectively. The average AUC of LightGBM model is 0.976, the one of RF model is 0.9763, the one of XGBoost model is 0.9775, and the one of AdaBoost model is 0.928. The XGBoost model has the more comprehensive and reasonable performance among the four prediction models, which can accurately distinguish between healthy, mild VL, as well as moderate VL and severe VL, which can assist doctors in diagnosing persons’ conditions more accurately, devising personalized treatment plans, avoiding unnecessary surgeries, reducing persons’ psychological stress, improving patient compliance and treatment outcomes, thus enhancing overall treatment results.

Treatment strategies for soft tissue injuries associated with blunt brachial artery injury.

Blunt brachial artery injuries (BAI) require reconstruction with an extensive vein graft due to the wide area of arterial damage. In the upper arm, safe options for pedicle flaps are limited, and selecting recipient vessels for free-flap surgery is challenging, complicating the treatment of soft tissue injuries associated with blunt BAI. This study aimed to analyze the characteristics and soft tissue reconstruction of blunt BAI and propose treatment strategies for treating associated soft tissue injuries. Patients with blunt BAI who treated between August 2013 and April 2024 were included. Blunt BAI cases were classified into three groups (Type1A, Type1B, and Type2) based on soft tissue injury localization and elbow flexion reconstruction necessity. Patient demographics, treatment details, and treatment outcomes were investigated. Seven patients were included to this study. There were two cases of Type1A, three Type1B, and two Type2. Three cases of Type1B underwent soft tissue reconstruction using pedicled latissimus dorsi flap and concurrent reconstruction of the elbow flexion. One case of Type2 was treated with free latissimus dorsi flap using AV loop, and the remaining case of Type2 was performed forearm amputation. This study examined the treatment details of seven patients with blunt BAI. If a soft tissue injury is localized to the upper arm, it can be treated with a pedicle flap. However, if the damage extends beyond the forearm, a free flap is essential and some ingenuity is required to select the recipient blood vessel.

Improving Specialist Care for Patients in Out-of-Hospital Intensive Care through Regular Pulmonological Video Consultations: A Real-life Study

In recent years, there has been a significant increase in the number of patients requiring out-of-hospital intensive care. Although many of these patients are invasively ventilated, pulmonological care is often lacking. Additionally, up to 60-70% of these patients are suspected to have further potential for weaning. A telemedicine approach to assess weaning potential and provide pulmonological care has not yet been studied. From March 2021 to February 2024, we conducted telemedicine pulmonological consultations in four respiratory care groups. A medical history interview and the assessment of parameters such as ventilation measurements were performed via a video portal. In addition, a portable blood gas analysis (BGA) device and a digital stethoscope were used. Treatment recommendations were implemented by the primary care physicians. These data were compared with a control group from four respiratory care groups that did not receive pulmonological care. A total of 71 tracheotomized patients, regardless of their weaning potential, were included in the telemedicine group. Of these, 40 were breathing spontaneously and 31 were mechanically ventilated (weaning stages 3aII and 3cI respectively). The ventilation duration of 23/31 (74%) patients in the telemedicine group was successfully reduced: 5/31 (16%) required only nocturnal ventilation, and in 4/31 (13%), ventilation was completely discontinued. In a control group of 63 patients (3aII: n=34, 3cI: n=29), only 1/29 (3%) experienced a reduction in ventilation time.In the telemedicine group, 11/71 (15%) patients were admitted for decannulation (3aII: n=6, 3cI: n=5). Of these, 7/11 (64%) were successfully decannulated (3aII: n=3, 3cI: n=4), with an average hospital stay reduced to 9 days. In the control group, 3/63 (5%) patients were admitted for decannulation, but none were successfully decannulated. One patient was successfully decannulated during a hospital stay for another reason.In addition, 310 tele-pulmonological therapy modifications were made in the intervention group, corresponding to 978 patient months: 1/3 patients required a therapy modification per month. Tele-pulmonological care in out-of-hospital intensive care allows for the identification of weaning potential, the targeted planning of hospital stays for weaning, and the avoidance of unnecessary hospitalizations. Moreover, it improves patient treatment outcomes. Regular monitoring at individualized intervals is necessary for this approach.

Enhancing Real-Time Patient Monitoring in Intensive Care Units with Deep Learning and the Internet of Things.

The demand for intensive care units (ICUs) is steadily increasing, yet there is a relative shortage of medical staff to meet this need. Intensive care work is inherently heavy and stressful, highlighting the importance of optimizing these units' working conditions and processes. Such optimization is crucial for enhancing work efficiency and elevating the level of diagnosis and treatment provided in ICUs. The intelligent ICU concept represents a novel ward management model that has emerged through advancements in modern science and technology. This includes communication technology, the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data analytics. By leveraging these technologies, the intelligent ICU aims to significantly reduce potential risks associated with human error and improve patient monitoring and treatment outcomes. Deep learning (DL) and IoT technologies have huge potential to revolutionize the surveillance of patients in the ICUs due to the critical and complex nature of their conditions. This article provides an overview of the most recent research and applications of linical data for critically ill patients, with a focus on the execution of AI. In the ICU, seamless and continuous monitoring is critical, as even little delays in patient care decision-making can result in irreparable repercussions or death. This article looks at how modern technologies like DL and the IoT can improve patient monitoring, clinical results, and ICU processes. Furthermore, it investigates the function of wearable and advanced health sensors coupled with IoT networking systems, which enable the secure connection and analysis of various forms of patient data for predictive and remote analysis by medical professionals. By assessing existing patient monitoring systems, outlining the roles of DL and IoT, and analyzing the benefits and limitations of their integration, this study hopes to shed light on the future of ICU patient care and identify opportunities for further research.

Hemodynamic response of the aortic valve during dobutamine onset then progressive aortic banding.

An increasing number of procedures over the past two decades for aortic stenosis (AS) reflects the combination of an aging population and less invasive transcatheter options. As a result, the hemodynamics of the aortic valve (AV) have gained renewed interest to understand its behavior and to optimize patient selection. We studied the hemodynamic relationship between pressure loss (ΔP) and transvalvular flow (Q) of the normal AV as well as the impact of a variable supravalvular stenosis. Our mechanistic study included 11 healthy swine monitored during dobutamine stress and followed by acute aortic banding to simulate AS. Hemodynamics were continuously recorded, and transvalvular ΔP versus Q were analyzed using proportional and linear models. During dobutamine infusion, normal valves exhibited a highly linear relationship between ΔP and Q (median R2 of 0.93). Progressive aortic banding eventually displayed a highly linear relationship between an increasing ΔP and the decreasing Q, characterized by a constant systemic circulatory resistance (median R2 of 0.91). Consequently, a normal AV can be described by a single parameter: its resistance, median 0.37 Wood units [WU] in swine. During dobutamine stress and aortic banding, the systemic bed behaves like a constant and stable resistance, median 11.9 WU in swine. These findings carry significant implications for quantifying normal and diseased AV behavior, and potentially might improve patient selection and treatment outcomes.

Expression of some circulating microRNAs as predictive biomarkers for prognosis and treatment response in glioblastoma

Glioblastoma multiforme (GBM) is the most prevalent, treatment-resistant, and fatal form of brain malignancy. It is characterized by genetic heterogeneity, and an infiltrative nature, and GBM treatment is highly challenging. Despite multimodal therapies, clinicians lack efficient prognostic and predictive markers. Therefore, new insights into GBM management are urgently needed to increase the chance of therapeutic success. Circulating miRNAs (miRs) are important regulators of cancer progression and are potentially useful for GBM diagnosis and treatment. This study investigated how miR-29a, miR-106a, and miR-200a affect the prognosis of GBM patients. This study was conducted on 25 GBM patients and 20 healthy volunteers as a control group. The expression levels of target miRs were analyzed pre- and post-treatment using qRT-PCR and evaluated in relation to both clinical GBM criteria and the patient’s survival modes. The diagnostic efficacy of target miRs was assessed using the receiver operating characteristic (ROC) curve. MiRs levels showed significant differences among the enrolled participants. All investigated miRs were significantly elevated in GBM patients with non-frontal lesions. Only miR-200a showed a significant difference in GBM patients older than 60 years with a tumor size ≥ 5 mm. Regarding miR-106a, a significant difference was detected based on the surgical strategy and use of an Eastern Cooperative Oncology Group (ECOG) performance status equal to 2. For miR-29a, a significant upregulation was detected according to the surgical strategy. All post-treatment miRs levels in GBM patients were significantly downregulated. In conclusion, circulating miRs revealed a significant role in predicting GBM patient treatment outcomes providing valuable insights for personalized therapeutic strategies.

Industrial Automation using Artificial Intelligence

The pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes

Development Validation of UV Spectrophoto Meter Method for Determination of Polyherbal Powder

The pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes

3T dilated inception network for enhanced autism spectrum disorder diagnosis using resting-state fMRI data.

Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment. However, existing techniques have suffered from poor diagnostic outcomes, higher computational complexity, and overfitting issues. To address these challenges, this research work introduces an innovative framework called 3T Dilated Inception Network (3T-DINet) for effective ASD diagnosis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) images. The proposed 3T-DINet technique designs a 3T dilated inception module that incorporates dilated convolutions along with the inception module, allowing it to extract multi-scale features from brain connectivity patterns. The 3T dilated inception module uses three distinct dilation rates (low, medium, and high) in parallel to determine local, mid-level, and global features from the brain. In addition, the proposed approach implements Residual networks (ResNet) to avoid the vanishing gradient problem and enhance the feature extraction ability. The model is further optimized using a Crossover-based Black Widow Optimization (CBWO) algorithm that fine-tunes the hyperparameters thereby enhancing the overall performance of the model. Further, the performance of the 3T-DINet model is evaluated using the five ASD datasets with distinct evaluation parameters. The proposed 3T-DINet technique achieved superior diagnosis results compared to recent previous works. From this simulation validation, it's clear that the 3T-DINet provides an excellent contribution to early ASD diagnosis and enhances patient treatment outcomes.

Consistency between headache diagnoses and ICHD-3 criteria across different levels of care

BackgroundDiagnosing headache disorders poses significant challenges, particularly in primary and secondary levels of care (PSLC), potentially leading to misdiagnosis and underdiagnosis. This study evaluates diagnostic agreement for migraine, tension-type headache (TTH), and cluster headache (CH) between PSLC and tertiary care (TLC) and assesses adherence to the International Classification of Headache Disorders 3rd edition (ICHD-3) guidelines.MethodsA retrospective, cross-sectional analysis was conducted at Charité - Universitätsmedizin Berlin’s tertiary headache center. The patients’ self-reported diagnoses from the PSLC were compared with those in TLC and with ICHD-3 criteria. Cohen’s kappa (κ) and R² were used to assess diagnostic agreement.ResultsAmong 1,468 patients (43.4 ± 14.4 years; 74.5% women), 69.5% reported a diagnosis in PSLC, and 99.5% were diagnosed at their first TLC visit. Overall agreement between PSLC and TLC was 80% (κ = 0.55; R²=30%). Agreement between the PSLC and ICHD-3 was 77% for migraine, 82% for TTH, and 96% for CH (κ = 0.65; R²=41%). TLC diagnoses aligned with ICHD-3 in over 90%.ConclusionOur findings indicate a significant degree of diagnostic agreement across different levels of care according to the ICHD-3 guidelines. However, there remains insufficient reliability in clinical diagnostics, highlighting the need for continued efforts to improve the early recognition and diagnostic accuracy and consistency of primary headaches to optimize patient care and treatment outcomes in Germany.

Outcomes of Immunotherapy Treatment in Sinonasal Mucosal Melanoma.

Sinonasal mucosal melanoma has poor survival despite multimodality treatment. While the impact of immunotherapy (IT) on metastatic cutaneous melanoma is well-defined, there are relatively little data on sinonasal mucosal melanoma. We sought to define immunotherapy outcomes in patients with sinonasal mucosal melanoma. A retrospective cohort study evaluated patients treated with IT during their overall treatment strategy for SNMM. Patient demographics, treatment, and survival outcomes were recorded. 52 patients had IT treatment for SNMM from 2000 to 2022, with an average age of 69.1 ± 11.9 years. The most common treatment was surgery with radiation and IT (n = 26, 50%). Most regimens consisted of a combination of Nivolumab and Ipilimumab (n = 17, 32.7%) or pembrolizumab (n = 14, 26.9%). 44.2% of patients experienced reported complications. Overall survival at 1-, 2-, and 5 years was 86.9%, 74.1%, and 39.1%, respectively. Approximately half of patients will have a local response following immunotherapy, but it is rare to have improvement at metastatic locations. Further research within our group will assess optimal timing and markers that are predictive of response.

Nanoparticle-protein corona enhances accuracy of Ca-19.9-based pancreatic cancer classification.

Among the various types of pancreatic cancers, pancreatic ductal adenocarcinoma (PDAC) is the most lethal and aggressive, due to its tendency to metastasize quickly and has a particularly low five-year survival rate. Carbohydrate antigen 19-9 (CA 19-9) is the only biomarker approved by the Food and Drug Administration for PDAC and has been a focal point in diagnostic strategies, but its sensitivity and specificity are not sufficient for early and accurate detection. To address this issue, we introduce a synergistic approach combining CA 19-9 levels with a graphene oxide (GO)-based blood test. This non-invasive technique relies on the analysis of personalized protein corona formed on GO sheets once they are embedded in human plasma. Pairing CA 19-9 values with GO protein patterns from N = 106 donors significantly improved the ability to differentiate between non-oncological and PDAC patients (up to 92%), also boosting the classification of PDAC subjects by 50% compared to CA 19-9 testing alone. Overall, this study sought to bridge the existing gaps in PDAC detection by exploiting the complementary strengths of conventional biomarkers and cutting-edge nanotechnology. Exploration of this combined strategy holds promise for advancing the early detection of PDAC, ultimately contributing to improved patient prognosis and treatment outcomes.

Prognostic impact and correlation with disease activity of three‐dimensional oct biomarkers in vogt‐koyanagi‐harada patients

Aims/Purpose: Vogt‐Koyanagi‐Harada syndrome (VKH) is an autoimmune disease characterized by a chronic bilateral granulomatous panuveitis accompanied by extraocular manifestations, namely of the nervous, auditory, and integumentary systems. Retinal changes documented by optical coherence tomography (OCT) were found to have prognostic value, including detachments between retinal layers with fluid accumulation. The aim of this study was to estimate the total detachment volume (TDV) between retinal layers with fluid accumulation that characterizes the Vogt‐Koyanagi‐Harada syndrome.Methods: An OCT analysis framework was developed in Python which, after a parameter tuning process, automatically segments the 3D detachments between retinal layers and calculates the TDV. The pipeline processes each slice individually following a chain of image processing steps: image enhancement; binarization; size filter; and contour detection. To validate this strategy, this method was applied to one 3D OCT image of a patient with VKH, and the resulting masks were compared with the ground truth obtained after manual segmentation by an expert.Results: Our pipeline has revealed an Intersection of Union (IoU) score of 84%, demonstrating its ability to efficiently segment retinal detachments after a parameter tuning process. As a consequence of such capability, the relative deviation between the experimental and ground truth TDV was 8.3%.Conclusions: This semi‐automated approach offers a promising tool for clinicians to detect retinal changes in VKH patients. By enabling early and precise detection of fluid accumulation and retinal detachments, this method can enhance prognostic evaluations and potentially improve patient management and treatment outcomes. Future work should focus on validating this framework across a larger dataset and refining the processing pipeline to further improve segmentation accuracy and robustness.

Revolutionizing Dental Polymers: The Versatility and Future Potential of Polyetheretherketone in Restorative Dentistry.

Polyetheretherketone (PEEK) has emerged as a revolutionary material in modern dentistry because of its unique combination of mechanical strength, biocompatibility, and versatility. This literature review examines the current applications and future potential of PEEK in various dental disciplines. PEEK's favorable properties, including its low specific weight, high strength-to-weight ratio, and ability to be easily machined, have led to its adoption in prosthetics, implantology, and dental esthetic restorations. This material has shown promise for fabricating crowns, bridges, removable partial denture frameworks, and implant components. PEEK's radiolucency and bone-like elastic modulus make it particularly suitable for dental implants and abutments. Additionally, its resistance to degradation and compatibility with various surface treatments enhances its long-term performance in the oral environment. While challenges such as bonding to other dental materials and aesthetic limitations exist, ongoing research is addressing these issues through surface modifications and composite formulations. As the dental field continues to evolve, PEEK's adaptability and biocompatibility position it a key player in the development of next-generation dental materials and techniques, potentially transforming patient care and treatment outcomes in dentistry.