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Impact of preoperative patient information on anxiety and physiological responses in mandibular third molar surgery: A randomized controlled trial
No full textBackground: This study aimed to investigate the impact of different patient information modalities on anxiety levels and physiological responses during the surgical extraction of impacted mandibular third molars.
Material and methods: A prospective randomized clinical trial was conducted involving 97 individuals undergoing surgical removal of impacted mandibular third molars. Participants were allocated into three groups: Control, verbal, and visual. Anxiety was measured using the Modified Dental Anxiety Scale (MDAS) and the State-Trait Anxiety Inventory (STAI). Heart rate, blood pressure, and oxygen saturation (SpO2) were recorded at five surgical stages. Pain was assessed using a Visual Analog Scale (VAS).
Results: VAS scores did not differ between groups. Significant intergroup differences were observed in diastolic blood pressure after local anesthesia and in SpO2 during tooth extraction and postoperatively (p<0.05). All groups showed reductions in MDAS, STAI-T, and STAI-S from the preoperative to the postoperative period. MDAS decreased significantly in the control and visual groups, while STAI-T decreased in all groups (p<0.05). STAI-S showed no significant change.
Trial registration: ClinicalTrials.gov Identifier: NCT07018115; Registration date: 10 June, 2025.
Conclusions: Although no significant differences were found between information methods in reducing anxiety, providing preoperative information may enhance patient comfort during third molar surgery. Further studies are needed to determine the most effective strategies for patient education
RELAXING THE POSITIVITY ASSUMPTION ON THE SYMBOL OF A BERGMAN-TOEPLITZ OPERATOR
No full textWe examine Toeplitz operators on weighted Bergman spaces A(V)(p), 1 < p < infinity, on the unit disk of C with symbols satisfying the simple geometric condition that the values are contained in an angle with vertex in the origin and magnitude less than pi. The condition is used to relax the conventional positivity assumption of the symbol, yet it is possible to give characterizations of the boundedness and compactness of such Toeplitz operators. The radial weight V defining the space A(V)(p) may be doubling or exponentially decreasing, but the geometric condition depends only on the symbol and not on V. It is known that there are significant differences between doubling and exponential weights for example as regards to the boundedness of Bergman projections. Nevertheless, we give a unified approach which includes both weight classes.Funding agency : TUBITAK.
Grant number : 1059B192300654
Electrolyte Disorders: Insights from a Prospective, Multicenter, Observational Cohort Study on Fosfomycin s Impact
Full text linkObjective: Intravenous (IV) fosfomycin is widely used in combination therapies for resistant pathogens. This study aims to investigate the frequency and risk factors of electrolyte disorders (EDs) associated with IV fosfomycin in hospitalized patients. Methods: This was a prospective, multicenter, observational cohort study, conducted in six centers from February 2023 to February 2024. The Naranjo adverse drug reaction probability scale (NADRPS) was used to evaluate the relationship between IV fosfomycin use and EDs. Results: A total of 54 patients with a median age [interquartile range (IQR)] of 60 (35-73) years were included in the study. Most patients (38.8%) were admitted to the intensive care unit (ICU). The median IV fosfomycin dose was 12 g (IQR: 12-18), and the mean treatment duration was 13.2 +/- 7.2 days. Hypokalemia occurred in 28 patients (51.8%), and hypernatremia in 10 patients (18.5%). NADRPS scores were consistent with a probable association between fosfomycin use and EDs. The incidence of hypokalemia was significantly higher in ESBL-positive patients (63.4% vs. 30.7%, p=0.04). The incidence of hypernatremia was significantly higher among patients with hypokalemia than among those without hypokalemia (90% vs. 46.1%, p=0.013). Mortality was significantly higher in patients with EDs than in those without, with an odds ratio (OR) of 6.25 [95% confidence interval (CI): 1.82-20, p=0.002]. Furthermore, the presence of ESBL and positive fluid balance were associated with increased ED risk, with ORs of 3.77 (95% CI: 1.09-13.10, p=0.032) and 5.40 (95% CI: 1.12-26.04, p=0.03), respectively. Conclusion: The correlation between IV fosfomycin and electrolyte abnormalities requires careful consideration, especially in ICU patients. Clinicians must regularly monitor electrolyte levels while administering IV fosfomycin to minimize potential risks while ensuring patient safety
Novel convolutional neural network for bacterial identification of confocal microscopic datasets
No full textArtificial intelligence (AI), complex mathematical algorithms, is currently employed across various fields to perform tasks quickly and effectively. In this study, a novel deep-learning algorithm named (CM-Net) was developed to classify biological data obtained as images from Confocal Microscopy. The images were collected for two types of bacterial species: (Escherichia coli and Staphylococcus aureus), where the number of images was 300 for each class. To enhance the dataset, we divided each image (using the augmentation method) into a small number of images with 224 × 224 dimensions, resulting in a total of 7066 images for both classes. These augmented images were fed to CM-Net to ensure accurate results and avoid bias in the developed algorithms. The algorithm was trained and tested 30 times with a 5-K cross-validation for each time. The algorithm's performance was evaluated using seven metrics (accuracy, sensitivity, specificity, precision, NVA, F1-score, and MCC), where the respective results were 96.08%, 95.98%, 96.19%, 96.78%, 95.26%, 96.38%, and 92.11%, indicating the model's high accuracy and reliability. CM-Net drastically reduces bacterial identification time by automating large-scale data analysis, processing results in 8.9 min. The automation provided by CM-Net simplifies workflows, enabling non-expert workers to perform microbial identification without extensive training. The significant outcomes of applying CM-Net for bacterial identification revolve around its transformative impact on data analysis's speed, efficiency, and accuracy, making advanced analysis accessible to non-experts while minimizing human error
Postoperative complications of coronary artery bypass grafting: a narrative review on pathophysiology, management strategies, and the emerging role of artificial intelligence
No full textBackground and Objective: Coronary artery bypass grafting (CABG) remains a vital treatment option for high-risk patients with advanced coronary artery disease, especially those with multivessel disease, extensive left main disease, or refractory angina. While CABG effectively lowers long-term mortality and morbidity, it is still associated with many postoperative complications that can hinder recovery and affect quality of life. This review aims to thoroughly explore risk factors, prevention, and management strategies of major postoperative complications after CABG, categorized by physiological systems. Methods: A comprehensive literature review was conducted on PubMed and Google Scholar from January 1, 2005, to August 6, 2025, without applying filters, but only including English-language publications, to gather a wide range of studies. Full texts were chosen based on set criteria, followed by a qualitative analysis to identify common themes, results, and gaps. Key Content and Findings: Post-CABG complications span neurological, cardiac, pulmonary, renal, gastrointestinal/hepatobiliary, infectious, endocrine, and psychosocial domains. Across systems, consistently identified significant risk factors include advanced age, diabetes, renal dysfunction, prolonged cardiopulmonary bypass time, prior stroke, chronic obstructive pulmonary disease (COPD), and impaired left ventricular (LV) function. Effective preventive strategies included optimized glycemic control, early mobilization and rehabilitation, targeted use of anti-inflammatory and antioxidant therapies, prophylactic amiodarone or magnesium for atrial fibrillation (AF), strict infection-control measures, renal-protective protocols, and multimodal pain management. Recently, artificial intelligence (AI)-based tools, including machine learning models for predicting acute kidney injury, delirium, stroke, arrhythmias, and surgical-site infections, are emerging as promising adjuncts for earlier risk identification and personalized postoperative care. Conclusions: Post-CABG complications remain across organ systems, emphasizing the need for early risk identification and targeted prevention. Major risk factors include age, diabetes, renal dysfunction, and prolonged bypass time. Multidisciplinary care and emerging AI-based prediction tools may improve individualized risk assessment and postoperative outcomes
Knowledge of infertility risk factors and attitudes among reproductive-aged individuals in Turkey
Full text linkBackground: Public understanding of infertility risk factors is uneven, and attitudes toward infertility vary across sociodemographic groups. This study assessed knowledge and attitudes among reproductive-aged individuals in Turkey. Methods: This study was conducted as a web-based, descriptive, cross-sectional survey between February and March 2025 among 366 participants aged 18–49 years who were reached via WhatsApp and social media. Data were collected using the Demographic Information Form, the Infertility Risk Factors Questionnaire (IRFQ), and the Attitudes Toward Infertility Scale (ATIS). In data analysis, descriptive statistics were used to summarize item-level knowledge distributions from the IRFQ, while group differences in ATIS scores were examined using independent-samples t-tests and one-way analysis of variance (ANOVA) with Bonferroni-adjusted post-hoc tests. Effect sizes for pairwise comparisons were calculated using Cohen’s d, and the level of statistical significance was set at α = 0.05. Results: A total of 366 participants aged 18–49 years (mean age 30.7 ± 8.8) were included in the study; 55.7% were women, and 60.7% had a university-level education or higher. Awareness was relatively high for psychological (71.9%), hormonal (73.5%), and substance-related infertility risks (tobacco/alcohol/caffeine; 76.0%), and for sexually transmitted infections (66.7%), but lower for occupational (27.0%), environmental (27.9%), technological (32.8%), and lifestyle (20.5%) factors. ATIS scores differed significantly by gender (women > men, p = 0.021), age (younger > older, p = 0.041), marital status (single> married, p = 0.004), and income (higher> lower, p = 0.003); education showed no association (p = 0.293). Conclusions: While several infertility risks are widely recognized, important knowledge gaps persist—particularly in modifiable occupational, environmental, technological, and lifestyle domains. Attitudes vary by key demographics, underscoring the need for targeted, culturally sensitive fertility-literacy interventions
Tackling the interplay between the brain and kidneys: CYP2C19 mice as a preclinical tool for studying cognitive impairment in kidney disease?
No full textChronic kidney disease (CKD) is a global health issue, often associated with cognitive and behavioural disturbances. The cytochrome P450 enzyme CYP2C19 has previously been associated with neurobehavioural changes. Humanised transgenic CYP2C19 mice show emotional changes, and abnormalities in locomotion and in brain regions involved in memory and stress response. This study aimed to investigate whether cognitive impairments in CYP2C19 transgenic mice are related to impaired renal function or structure. Adult male and female wild-type and CYP2C19 mice (total N = 41) were included in the study. Behavioural phenotyping was performed by examining short-term memory in Novel Object Recognition Test (NORT) and social interaction in Social Recognition Test (SRT). After 24 h, urine was collected, animals were sacrificed, and blood samples and kidneys were collected and used for biochemical assays and histological assessment. NORT and SRT revealed cognitive deficits and possible social anxiety in CYP2C19 mice compared to wild-type controls, as no difference was observed in time CYP2C19 mice spent interacting with novel objects or unfamiliar animals. Biochemical analysis showed no significant differences in total protein, albumin, urea, creatinine and uric acid between experimental groups. Histological evaluation confirmed that there was no structural kidney damage in CYP2C19 mice. Our findings indicate that in CYP2C19 mice, cognitive and behavioural changes described here are independent of renal dysfunction. Therefore, CYP2C19 mouse model represents a valuable tool to study cognitive impairment without concomitant kidney disease and could serve as a suitable control in studies investigating the interplay between cognitive decline and CKD
Modeling and Optimization of NLOS Underwater Optical Channels Using QAM-OFDM Technique
No full textDue to increasing human activities underwater, there is a growing demand for high-speed underwater optical communication (UOWC) data links for security surveillance, environmental monitoring, pipeline inspection, and other applications. Line-of-sight communication is impossible under certain conditions due to misalignment, physical obstructions, irregular usage, and difficulty adjusting the receiver orientation, especially when used in environments with mobile users or submerged sensor networks. Therefore, non-line-of-sight (NLOS) optical communication is used in this study. Advanced modulation schemes—quadrature amplitude modulation and orthogonal frequency-division multiplexing (QAM-OFDM)—were used to transmit the signal underwater between two network nodes. QAM increases the data transfer rate, while OFDM reduces dispersion and inter-symbol interference (ISI). The proposed UOWC system is investigated using a 532 nm green laser diode (LD). Reliable high-speed data transmission of up to 15 Gbps is achieved over horizontal distances of 134 m, 43 m, 21 m, and 5 m in four different aquatic environments—pure water (PW), clear ocean (CLO), coastal ocean (COO), and harbor II (HarII), respectively. The system achieves effectively error-free performance within the simulation duration (BER < 10−9), with a received optical signal power of approximately −41.5 dBm. Clear constellation patterns and low BER values are observed, confirming the robustness of the proposed architecture. Despite the limitations imposed by non-line-of-sight (NLOS) communication and the diversity aquatic environments, our proposed architecture excels at underwater long-distance data transmission at high speeds
Homomorphic Cryptography in the Internet of Things Ensuring Data Security and Confidentiality
No full textVolume editors: Swaroop A., Virdee B., Correia S.D., Polkowski Z.
Conference name: International Conference on Data Analytics and Management, ICDAM 2025.The Internet of Things (IoT) expansion has increased in sectors like health care, industrial automation, and smart cities. But with this growth in devices comes growing worries about data security and privacy. However, traditional cryptographic methods tend to be inadequate for IoT environments that demand ciphertexts to be decrypted for processing, uncovering sensitive information in the process, thus expanding vulnerability in IoT networks. In this work, we investigate how to tackle these challenges using homomorphic encryption, particularly the Paillier cryptosystem, which enables computations on encrypted data without decryption. Our approach is to protect data in IoT from the point of collection until processing and storage. In this research, we extensively study the Paillier cryptosystem: its crucial generation, encryption, decryption mechanism, and unique homomorphic properties, which support secure operation on encrypted data. The encryption and decryption times, data transmission latency, and CPU and memory resource utilization were evaluated extensively in a simulated IoT environment. Comparisons with other cryptographic approaches show that Paillier encryption balances security and computational efficiency and is well-suited for resource-constrained IoT devices. Our results indicate that the Paillier cryptosystem improves data confidentiality and security in IoT networks and allows secure real-time data processing without incurring a performance penalty on the device. At the end of this study, we conclude that although Paillier encryption is a promising solution, additional optimization and the use of more powerful homomorphic schemes can allow it to be used in more comprehensive IoT security frameworks
Optimization of caper bud drying using the DT_LSBOOST model: A predictive approach to improve quality and efficiency
No full textCapparis spinosa L. buds undergo salting and drying to enhance their shelf life and organoleptic properties. This study evaluates the impact of four drying methods: oven drying (OD), vacuum drying (VD), freeze-drying (FD), and microwave drying (MD) on the physicochemical, antioxidant, and microbiological properties of dried caper buds. Salting reduced the initial moisture content from 508.50 % to 168.59 % (db), while drying further decreased it to approximately 9 %. Drying time varied significantly, with MD achieving the shortest duration (0.19–0.75h) and OD requiring the longest (reaching 49.66h). FD exhibited the highest energy consumption (60.77 kWh/kg), followed by VD, while OD and MD were the least energy-intensive (0.54–3.10 kWh/kg and 1.34–2.18 kWh/kg, respectively). FD preserved the most chlorophyll (193.63 μg/g DW) and total phenolic content (28.98 mgGAE/g DW), whereas MD at 200 W resulted in the lowest TPC (9.88 mgGAE/g DW). FD samples also showed superior antioxidant activities in both ABTS and FRAP assays. In contrast, OD and MD increased browning and degraded quality attributes. Multivariate analyses (PCA and clustering) highlighted FD as optimal for preserving quality, while MD was the most detrimental. Microbiological analysis confirmed that dried capers met food safety standards. A predictive model using Decision Tree coupled with Least Squares Boosting (DT_LSBOOST) achieved exceptional accuracy (R = 0.9999, RMSE = 0.0564, ESP = 0.2028, MAE = 0.0305), providing a reliable tool for optimizing drying parameters. Overall, freeze-drying emerged as the best method to retain nutritional and bioactive properties of capers, and the developed predictive model offers an innovative approach to enhancing caper processing efficiency