Long-Term Prognosis and Predictors of Mortality in Patients Undergoing Transcatheter Aortic Valve Replacement: A Retrospective Analysis.

BACKGROUND: Aortic valve disease is a common and impactful disorder that imposes significant health burdens and is associated with increased mortality rates. Particularly noteworthy is the emergence of transcatheter aortic valve replacement (TAVR), a minimally invasive procedure that has revolutionized the management of aortic valve disease. However, there remain certain unresolved questions and ongoing research regarding the long-term effectiveness and suitability of TAVR in various patient populations, underscoring the need for further investigation and clinical scrutiny. OBJECTIVE: This retrospective analysis aimed to investigate the long-term outcomes and predictors of mortality in 500 patients who underwent transcatheter aortic valve replacement (TAVR). METHODS: This retrospective analysis included individuals who received transcatheter aortic valve replacement (TAVR) at Sri Venkata Sai (SVS) Medical College, Mahabubnagar, Telangana, India, between January 2020 and July 2023. Demographic characteristics, including age, gender, and comorbidities, were recorded, and long-term outcomes after TAVR were assessed, including the incidence of survival rates and major adverse cardiac events (MACE). Predictors of mortality were also identified using Cox proportional hazards regression analysis. RESULTS: The study group exhibited an average age of 75.6 years (standard deviation (SD): 6.8), with 58% male and 42% female patients. Hypertension (74%), coronary artery disease (CAD) (68%), diabetes mellitus (DM) (42%), and chronic kidney disease (CKD) stage ≥ 3 (36%) were prevalent comorbidities. The median follow-up duration was 5.2 years (interquartile range (IQR): 4.3-6.8 years). The overall long-term survival rate after TAVR was 73.5% (95% confidence interval (CI): 69.8%-77.1%). Additionally, MACE occurred in 21% of patients throughout the follow-up period. The cumulative incidence of MACE at one year, three years, and five years was 6.8% (95% CI: 4.2%-9.5%), 14.2% (95% CI: 10.6%-18.7%), and 21.8% (95% CI: 17.3%-26.7%), respectively. The study found that higher age (hazard ratio (HR): 1.08, 95% CI: 1.04-1.12, p < 0.001), male gender (HR: 1.48, 95% CI: 1.15-1.91, p = 0.002), and the presence of CAD (HR: 1.72, 95% CI: 1.29-2.30, p < 0.001) were linked to an elevated risk of mortality. Additionally, diabetes mellitus (HR: 1.39, 95% CI: 1.05-1.85, p = 0.022) and CKD stage ≥ 3 (HR: 1.96, 95% CI: 1.47-2.61, p < 0.001) emerged as notable predictors of mortality. Conversely, a history of prior coronary artery bypass grafting (CABG) (HR: 0.62, 95% CI: 0.46-0.84, p = 0.003) was associated with a reduced risk of mortality. No significant associations were found between mortality and hypertension (HR: 1.12, 95% CI: 0.88-1.43, p = 0.360) or prior percutaneous coronary intervention (PCI) (HR: 1.21, 95% CI: 0.88-1.67, p = 0.245). CONCLUSION: Age, male gender, CAD, DM, and CKD stage ≥ 3 were significant indicators of mortality risk in TAVR patients. Risk stratification and individualized management are crucial in optimizing long-term outcomes following TAVR procedures.

Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing the abnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affected area. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiate them. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads to early diagnosis of breast cancer. Methods: This work proposes a Crow search optimization based Intuitionistic fuzzy clustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First order moments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzy clustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is applied to choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken from the Mammographic Image Analysis Society (mini-MIAS) database. Results: CrSA-IFCM-NA effectively separated the masses from mammogram images and proved to have good results in terms of cluster validity indices indicating the clear segmentation of the regions. Conclusion: The experimental results show that the accuracy of the proposed method proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists in diagnosing breast cancer at an early stage.

Distributed ICSA Clustering Approach for Large Scale Protein Sequences and Cancer Diagnosis

Objective: With the over saturating growth of biological sequence databases, handling of these amounts of data has increasingly become a problem. Clustering has become one of the principal research objectives in structural and functional genomics. However, exact clustering algorithms, such as partitioned and hierarchical clustering, scale relatively poorly in terms of run time and memory usage with large sets of sequences. Methods: From these performance limits, heuristic optimizations such as Cuckoo Search Algorithm with genetic operators (ICSA) algorithm have been implemented in distributed computing environment. The proposed ICSA, a global optimized algorithm that can cluster large numbers of protein sequences by running on distributed computing hardware. Results: It allocates both memory and computing resources efficiently. Compare with the latest research results, our method requires only 15% of the execution time and obtains even higher quality information of protein sequence. Conclusion: From the experimental analysis, We noticed that the cluster of large protein sequence data sets using ICSA technique instead of only alignment methods reduce extremely the execution time and improve the efficiency of this important task in molecular biology. Moreover, the new era of proteomics is providing us with extensive knowledge of mutations and other alterations in cancer study.

Distribution based Fuzzy Estimate Spectral Clustering for Cancer Detection with Protein Sequence and Structural Motifs

Objective: In biological data analysis, protein sequence and structural motifs are an amino-acid sequence patterns that are widespread and used as tools for detecting the cancer at an earlier stage. To improve the cancer detection with minimum space and time complexity, Distribution based Fuzzy Estimate Spectral Clustering (DFESC) technique is developed. Methods: Initially, the protein sequence motifs are taken from dataset to form the cluster. The Distribution based spectral clustering is applied to group the protein sequence by measuring the generalized jaccard similarity between each protein sequences. To develop the clustering accuracy, soft computing technique namely fuzzy logic is applied to calculate membership value of each sequence motifs. Results: The outcome showed that the presented DFESC technique effectively identifies the cancer in terms of clustering accuracy, false positive rate, and cancer detection time and space complexity. Conclusion: Based on the observations, evaluation of DFESC technique provides improved result for premature detection of cancer using protein sequence and structural motifs.

Noninvasive type 2 diabetes screening: clinical evaluation of SCOUT DS in an Asian Indian cohort.

OBJECTIVE: This study evaluated the noninvasive, point-of-care diabetes screening device, Scout DS (VeraLight Inc., Albuquerque, NM) (SCOUT), in a native Asian Indian cohort. RESEARCH DESIGN AND METHODS: SCOUT is a tabletop, skin fluorescence spectrometer that reports a risk score following a 3-4-min noninvasive measurement of a subject's left volar forearm. SCOUT, fasting plasma glucose (FPG), and hemoglobin A(1c) (A1C) were compared for detection of abnormal glucose tolerance (AGT) in a cohort of 256 subjects without previous diagnosis of diabetes or impaired glucose tolerance in Chennai, India. After an overnight fast, a 75-g, 2-h oral glucose tolerance test was administered, and AGT was defined as a plasma glucose value ≥ 140 mg/dL (7.8 mmol/dL). Sensitivity, false-positive rate (FPR), and receiver-operating characteristics area under the curve for AGT detection were computed for SCOUT, FPG, and A1C. Intra-day reproducibility of SCOUT was assessed. RESULTS: SCOUT, FPG, and A1C (at respective thresholds of 50, 110 mg/dL, and 5.7%) exhibited sensitivities of 87%, 32%, and 86%, respectively, and FPR of 52%, 3%, and 58%, respectively. For the 177 subjects receiving a valid SCOUT Diabetes Score on both measurement attempts, the coefficient of variation was 5.8%, and the Pearson correlation was 0.91. A SCOUT score could be obtained on 91% of subjects after two attempts. CONCLUSIONS: The performance of SCOUT is similar to that of A1C, whereas FPG had a much lower sensitivity. SCOUT is an effective tool for AGT screening in Asian Indians.