A Systematic Review and Meta-Analysis of Premenstrual Syndrome with Special Emphasis on Herbal Medicine and Nutritional Supplements

Sultana, Arshiya; Heyat, Md Belal Bin; Rahman, Khaleequr; Kunnavil, Radhika; Fazmiya, Mohamed Joonus; Akhtar, Faijan; Sumbul,; Mazón, Juan Luís Vidal; Rodríguez, Carmen Lili; Díez, Isabel de la Torre

doi:10.3390/ph15111371

Cited by 8 publications

(4 citation statements)

References 70 publications

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“…Married women aged 18–45 years who were diagnosed with any of the clinical features of uPID as per classical Unani texts [ 60 ] (abnormal vaginal discharge, adnexal and cervical motion tenderness, low backache, pelvic discomfort, dysuria, lower abdominal pain, dyspareunia) were included [ 7 ]. In addition, participants were also included based on presence of the one of the minimum criteria (lower abdominal pain, bilateral adnexal tenderness, and cervical motion tenderness) for diagnosis of PID as per Centre for Disease Control (CDC) guidelines.…”

Section: Methodsmentioning

confidence: 99%

Therapeutic Efficacy of a Formulation Prepared with Linum usitatissimum L., Plantago ovata Forssk., and Honey on Uncomplicated Pelvic Inflammatory Disease Analyzed with Machine Learning Techniques

et al. 2023

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A single-blind double-dummy randomized study was conducted in diagnosed patients (n = 66) to compare the efficacy of Linseeds (Linum usitatissimum L.), Psyllium (Plantago ovata Forssk.), and honey in uncomplicated pelvic inflammatory disease (uPID) with standard drugs using experimental and computational analysis. The pessary group received placebo capsules orally twice daily plus a per vaginum cotton pessary of powder from linseeds and psyllium seeds, each weighing 3 gm, with honey (5 mL) at bedtime. The standard group received 100 mg of doxycycline twice daily and 400 mg of metronidazole TID orally plus a placebo cotton pessary per vaginum at bedtime for 14 days. The primary outcomes were clinical features of uPID (vaginal discharge, lower abdominal pain (LAP), low backache (LBA), and pelvic tenderness. The secondary outcomes included leucocytes (WBCs) in vaginal discharge on saline microscopy and the SF-12 health questionnaire. In addition, we also classified both (pessary and standard) groups using machine learning models such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and AdaBoost (AB). The pessary group showed a higher percentage reduction than the standard group in abnormal vaginal discharge (87.05% vs. 77.94%), Visual Analogue Scale (VAS)-LAP (80.57% vs. 77.09%), VAS-LBA (74.19% vs. 68.54%), McCormack pain scale (McPS) score for pelvic tenderness (75.39% vs. 67.81%), WBC count of vaginal discharge (87.09% vs. 83.41%) and improvement in SF-12 HRQoL score (94.25% vs. 86.81%). Additionally, our DT 5-fold model achieved the maximum accuracy (61.80%) in the classification. We propose that the pessary group is cost-effective, safer, and more effective as standard drugs for treating uPID and improving the HRQoL of women. Aucubin, Plantamajoside, Herbacetin, secoisolariciresinol diglucoside, Secoisolariciresinol Monoglucoside, and other various natural bioactive molecules of psyllium and linseeds have beneficial effects as they possess anti-inflammatory, antioxidant, antimicrobial, and immunomodulatory properties. The anticipated research work is be a better alternative treatment for genital infections.

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Section: Methodsmentioning

confidence: 99%

Therapeutic Efficacy of a Formulation Prepared with Linum usitatissimum L., Plantago ovata Forssk., and Honey on Uncomplicated Pelvic Inflammatory Disease Analyzed with Machine Learning Techniques

et al. 2023

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“…In biology, the analytical and predictive capabilities of AI models have been empirically demonstrated across multiple research directions (Ukwuoma et al, 2022b;2022a;Fazmiya et al, 2024;Heyat et al, 2024;Sumbul et al, 2024). Based on this, we propose an innovative hybrid neural network model, STM-ac4C, designed to accurately identify N4-acetylcytidine (ac4C) modification sites on human mRNA.…”

Section: Model Architecturementioning

confidence: 99%

STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention

Yi,

Zhou,

Deng

2024

Front. Genet.

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N4-acetylcysteine (ac4C) is a chemical modification in mRNAs that alters the structure and function of mRNA by adding an acetyl group to the N4 position of cytosine. Researchers have shown that ac4C is closely associated with the occurrence and development of various cancers. Therefore, accurate prediction of ac4C modification sites on human mRNA is crucial for revealing its role in diseases and developing new diagnostic and therapeutic strategies. However, existing deep learning models still have limitations in prediction accuracy and generalization ability, which restrict their effectiveness in handling complex biological sequence data. This paper introduces a deep learning-based model, STM-ac4C, for predicting ac4C modification sites on human mRNA. The model combines the advantages of selective kernel convolution, temporal convolutional networks, and multi-head self-attention mechanisms to effectively extract and integrate multi-level features of RNA sequences, thereby achieving high-precision prediction of ac4C sites. On the independent test dataset, STM-ac4C showed improvements of 1.81%, 3.5%, and 0.37% in accuracy, Matthews correlation coefficient, and area under the curve, respectively, compared to the existing state-of-the-art technologies. Moreover, its performance on additional balanced and imbalanced datasets also confirmed the model’s robustness and generalization ability. Various experimental results indicate that STM-ac4C outperforms existing methods in predictive performance. In summary, STM-ac4C excels in predicting ac4C modification sites on human mRNA, providing a powerful new tool for a deeper understanding of the biological significance of mRNA modifications and cancer treatment. Additionally, the model reveals key sequence features that influence the prediction of ac4C sites through sequence region impact analysis, offering new perspectives for future research. The source code and experimental data are available at https://github.com/ymy12341/STM-ac4C.

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“…However, the dermatologist accuracy in detecting melanoma using dermoscopy images was lower than 80% in traditional clinical settings [ 6 , 7 , 8 ]. Using machine learning techniques [ 9 , 10 , 11 ], this analysis could be automated, leading to a framework in medicine that would provide experiential relevance, improve clinical accuracy, aid physicians in interacting objectively, reduce errors caused by human stress, and lower mortality rates [ 12 , 13 , 14 , 15 ]. One step in the right direction is the development of a machine-learning algorithm that can distinguish between malignant and benign lesions [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images

et al. 2023

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Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell multiplication. Melanoma detection is of the utmost significance in clinical practice because of the atypical border structure and the numerous types of tissue it can involve. The identification of melanoma is still a challenging process for color images, despite the fact that numerous approaches have been proposed in the research that has been done. In this research, we present a comprehensive system for the efficient and precise classification of skin lesions. The framework includes preprocessing, segmentation, feature extraction, and classification modules. Preprocessing with DullRazor eliminates skin-imaging hair artifacts. Next, Fully Connected Neural Network (FCNN) semantic segmentation extracts precise and obvious Regions of Interest (ROIs). We then extract relevant skin image features from ROIs using an enhanced Sobel Directional Pattern (SDP). For skin image analysis, Sobel Directional Pattern outperforms ABCD. Finally, a stacked Restricted Boltzmann Machine (RBM) classifies skin ROIs. Stacked RBMs accurately classify skin melanoma. The experiments have been conducted on five datasets: Pedro Hispano Hospital (PH2), International Skin Imaging Collaboration (ISIC 2016), ISIC 2017, Dermnet, and DermIS, and achieved an accuracy of 99.8%, 96.5%, 95.5%, 87.9%, and 97.6%, respectively. The results show that a stack of Restricted Boltzmann Machines is superior for categorizing skin cancer types using the proposed innovative SDP.

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A Systematic Review and Meta-Analysis of Premenstrual Syndrome with Special Emphasis on Herbal Medicine and Nutritional Supplements

Cited by 8 publications

References 70 publications

Therapeutic Efficacy of a Formulation Prepared with Linum usitatissimum L., Plantago ovata Forssk., and Honey on Uncomplicated Pelvic Inflammatory Disease Analyzed with Machine Learning Techniques

Therapeutic Efficacy of a Formulation Prepared with Linum usitatissimum L., Plantago ovata Forssk., and Honey on Uncomplicated Pelvic Inflammatory Disease Analyzed with Machine Learning Techniques

STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention

A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images

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