PURPOSE: This randomized clinical trial was conducted to compare a fistulectomy and a fistulotomy with marsupialization in the management of a simple anal fistula. METHODS: Forty patients with simple anal fistula were randomized into two groups. Fistulous tracts were managed by using a fistulectomy (group A) while a fistulotomy with marsupialization was performed in group B. The primary outcome measure was wound healing time while secondary outcome measures were operating time, postoperative wound size, postoperative pain, wound infection, anal incontinence, recurrence and patient satisfaction. RESULTS: Postoperative wounds in group B healed earlier in comparison to group A wounds (4.85 +/- 1.39 weeks vs. 6.75 +/- 1.83 weeks, P = 0.035). No significant differences existed between the operating times (28.00 +/- 6.35 minutes vs. 28.20 +/- 6.57 minutes, P = 0.925) and visual analogue scale scores for postoperative pain on the first postoperative day (4.05 +/- 1.47 vs. 4.50 +/- 1.32, P = 0.221) for the two groups. Postoperative wounds were larger in group A than in group B (2.07 +/- 0.1.90 cm2 vs. 1.23 +/- 0.87 cm2), however this difference did not reach statistical significance (P = 0.192). Wound discharge was observed for a significantly longer duration in group A than in group B (4.10 +/- 1.91 weeks vs. 2.75 +/- 1.71 weeks, P = 0.035). There were no differences in social and sexual activities after surgery between the patients of the two groups. No patient developed anal incontinence or recurrence during the follow-up period of twelve weeks. CONCLUSION: In comparison to a fistulectomy, a fistulotomy with marsupialization results in faster healing and a shorter duration of wound discharge without increasing the operating time.
Follow-Up Studies ; Humans ; Outcome Assessment (Health Care) ; Pain, Postoperative ; Rectal Fistula ; Recurrence ; Sexual Behavior ; Wound Healing ; Wound Infection

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Objective: : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. Methods: : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy. Results: : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001. Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

BACKGROUND AND OBJECTIVES: Most studies in cardiac regeneration have explored bone marrow mesenchymal stem cells (BM-MSC) with variable therapeutic effects. Amniotic fluid MSC (AF-MSC) having extended self-renewal and multi-potent properties may be superior to bone marrow MSC (BM-MSC). However, a comparison of their cardiomyogenic potency has not been studied yet.METHODS: The 5-azacytidine (5-aza) treated AF-MSC and BM-MSC were evaluated for the expression of GATA-4, Nkx2.5 and ISL-1 transcripts and proteins by quantitative RT-PCR and Western blotting, respectively as well as for the expression of cardiomyogenic differentiation markers cardiac troponin-T (cTNT), beta myosin heavy chain (βMHC) and alpha sarcomeric actinin (ASA) by immunocytochemistry.RESULTS: The AF-MSC as compared to BM-MSC had significantly higher expression of GATA-4 (183.06±29.85 vs. 9.80±0.05; p<0.01), Nkx2.5 (8.3±1.4 vs. 1.82±0.32; p<0.05), and ISL-1 (39.59±4.05 vs. 4.36±0.39; p<0.01) genes as well as GATA-4 (2.01±0.5 vs. 0.6±0.1; p<0.05), NKx2.5 (1.9±0.14 vs. 0.8±0.2; p<0.01) and ISL-1 (1.7±0.3 vs. 0.9±0.1; p<0.05) proteins. The AF-MSC also had significantly elevated expression of cTNT (5.0×10⁴±0.6×10⁴ vs. 3.5 ×10⁴±0.8×10⁴; p<0.01), β-MHC (15.7×10⁴±0.9×10⁴ vs. 8.2×10⁴±0.6×10⁴; p<0.01) and ASA (18.6×10⁴±4.9×10⁴ vs. 13.1×10⁴±3.0×10⁴; p<0.05) than BM-MSC.CONCLUSIONS: Our data suggest that AF-MSC have greater cardiomyogenic potency than BM-MSC, and thus may be a better source of MSC for therapeutic applications in cardiac regenerative medicine.
Actinin ; Amniotic Fluid ; Antigens, Differentiation ; Azacitidine ; Blotting, Western ; Bone Marrow ; Female ; Humans ; Immunohistochemistry ; Mesenchymal Stromal Cells ; Regeneration ; Regenerative Medicine ; Therapeutic Uses ; Troponin T ; Ventricular Myosins

Antioxidant-the word itself is magic. Using the antioxidant concept as a spearhead in proposed mechanisms for staving off so-called "free-radical" reactions, the rush is on to mine claims for the latest and most effective combination of free-radical scavenging compounds. We must acknowledge that such "radicals" have definitively been shown to damage all biochemical components such as DNA/RNA, carbohydrates, unsaturated lipids, proteins, and micronutrients such as carotenoids (alpha and beta carotene, lycopene), vitamins A, B6, B12, and folate. Defense strategies against such aggressive radical species include enzymes, antioxidants that occur naturally in the body (glutathione, uric acid, ubiquinol-10, and others) and radical scavenging nutrients, such as vitamins A, C, and E, and carotenoids. This paper will present a brief discussion of some well- and little-known herbs that may add to the optimization of antioxidant status and therefore offer added preventive values for overall health. It is important to state at the outset that antioxidants vary widely in their free-radical quenching effects and each may be individually attracted to specific cell sites. Further evidence of the specialized nature of the carotenoids is demonstrated by the appearance of two carotenoids in the macula region of the retina where beta-carotene is totally absent.
Antioxidants ; Plant Preparations ; Plants, Medicinal

Convolvulus pluricaulis Choisy (C. pluricaulis) is a perennial herb that seems like morning glory. All parts of the herb are known to possess therapeutic benefits. The plant is used locally in Indian and Chinese medicine to cure various diseases. It is used in Ayurvedic formulation for chronic cough, sleeplessness, epilepsy, hallucinations, anxiety etc. Based on the comprehensive review of plant profile, pharmacognosy, phytochemistry, pharmacological and toxicological data on the C. pluricaulis, there will be more opportunities for the future research and development on the herb C. pluricaulis. Information on the C. pluricaulis was collected via electronic search (using Pub Med, SciFinder, Google Scholar and Web of Science) and library search for articles published in peer-reviewed journals. Furthermore, information also was obtained from some local books on ethnopharmacology. This paper covers the literature, primarily pharmacological, from 1985 to the end of 2012. The C. pluricaulis is an important indigenous medicine, which has a long medicinal application for liver disease, epileptic disease, microbial disease, cytotoxic and viral diseases, central nervous system (CNS) disease in Ayurvedic medicine, traditional Chinese medicine and other indigenous medical systems. The isolated metabolites and crude extract have exhibited a wide of in vitro and in vivo pharmacological effect, including CNS depression, anxiolytic, tranquillizing, antidepressant, antistress, neurodegenerative, antiamnesic, antioxidant, hypolipidemic, immunomodulatory, analgesic, antifungal, antibacterial, antidiabetic, antiulcer, anticatatonic, and cardiovascular activity. A chemical study of this plant was then initiated, which led to the isolation of carbohydrats, proteins, alkaloids, fatty acids, steroids, coumarins, flavanoids, and glycosides as active chemicals that bring about its biological effects. A series of pharmacognostical studies of this plant show that it is a herb, its stem and leaves are hairy, more over it has two types of stomata, anisocytic and paracytic. A herb, C. pluricaulis has emerged as a good source of the traditional medicine for the treatment of liver disease, epileptic disease, microbial disease, cytotoxic and viral diseases, and CNS disease. Pharmacological results have validated the use of this species in traditional medicine. All the parts of the herb are known to possess therapeutic benefits. Expansion of research materials would provide more opportunities for the discovery of new bioactive principles from C. pluricaulis.