The existence of Naturally Occurring Radioactive Materials (NORM) such as K-40 was studied all over the world for their characteristics and effects on human and environment. K-40 exist in the earth crust with the concentration about 1.8 mg/kg or 481 Bq/g.. In this study, the level of K-40 in soil samples were measured using gamma spectrometer equipped with hyper pure germanium detector. The samples were collected from an oil palm cultivated area of Jengka 15, in Maran District, Pahang. The results show the level of K-40 activities at various locations. The activities of K-40 are in the range 52.9-150.5 Bq/kg and total potassium concentrations are 1.60-4.50%. There are no correlation between activities of K-40 with elevation i.e. R2= 0.0885.
Traditionally, transrectal ultrasound (TRUS)-guided biopsies are done for the diagnosis of prostate cancer (PCa) in Pakistan. The transperineal template-guided saturation biopsy (TTSB) approach has been recently introduced in Pakistan and we share diagnostic yields and pathological findings of specimens taken for PCa diagnosis in men with elevated serum total prostate specific antigen (PSA) and negative TRUS-guided prostate biopsies. In all, 16 patients investigated at the Department of Urology, Sindh Institute of Urology and Transplantation (SIUT), underwent TTSB. The mean age of patients was 67.8 ± 8.8 (range: 55 - 84) years. The median PSA was 9.5 (IQR: 7.9 - 19.8) ng/ ml. The duration of symptoms before biopsy ranged from 1 month to 144 months. The prostate was enlarged with mean weight of 73.5 ± 55.5 g. Histopathology revealed PCa in 5 of 16 (31.2%) cases. The Gleason score was 6 (3+3), 7 (3+4) and 8 (4+4) in 1 case each (6.3%) and 10 (5+5) in 2 cases (12.5%). At least two cores were positive in all positive cases. None of the patients required antibiotics post-procedure. In conclusion, the TTSB technique is a promising option for patients with elevated PSA level and negative transrectal prostate biopsies for the detection of PCa in our setting.
A cross-sectional comparative study was conducted to determine the nutritional status among physically active groups in Kota Bharu. The study population comprised 83 adult male athletes from 8 different types of sports (athlete group), 80 active men who exercised a minimum of 30 min per day for at least 3 times per week (exercise group), and 80 inactive men (sedentary group). All the respondents were aged between 18 to 44 years. Measurements taken from the respondents were anthropometric measurements, systolic (SBP) and diastolic (DBP) blood pressure, and serum total cholesterol (TC). The results showed that the combined prevalence of pre-obese (BMI 25.0-29.9) and obese (BMI ≥30.0) was 21.7% in athletes, 29.9% in exercise group, and 47.5% in sedentary group. The mean (± SD) percentage of body fat in athletes was 15.7 ± 5.4%, which was lower compared to the exercise (18.9 ± 5.5%) and sedentary (20.6 ± 5.8%) groups. The incidence of waist-to-hip ratio above 0.9 in athlete, exercise and sedentary groups was 9.6%, 18.7% and 31.3%, respectively. The incidence of hypertension (SBP ≥140 and/or DBP ≥90 mmHg) was 13.2% in athletes, 17.5% in exercise group and 42.5% in the sedentary group. The TC values showed that the prevalence of "high risk" individuals (TC ≥6.20 mmol/l) was also lower in athletes (20.5%), compared to the exercise (36.2%) and sedentary (47.5%) groups. The study revealed that individuals who are actively involved in physical activity, particularly in sport activities have better nutritional status compared to sedentary people. However, the nutritional status in the athlete and exercise groups was still unsatisfactory. The incidence of poor health status related to over nutrition in the active groups was rather high and needs attention from health professionals. Further studies are needed to determine nutritional practices among physically active groups.
Curcuma longa, Curcuma xanthorrhiza, and Curcuma manga have been widely used for herbal or traditional medicine purposes. It was reported that turmeric plants provided several biological activities such as antioxidant, anti-inflammatory, hepatoprotector, cardioprotector, and anticancer activities. Authentication of the Curcuma species is important to ensure its authenticity and to avoid adulteration practices. Plants from different origins will have different metabolite compositions because metabolites are affected by soil nutrition, climate, temperature, and humidity. 1H-NMR spectroscopy, principal component analysis (PCA), and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for authentication of C. longa, C. xanthorrhiza, and C. manga from seven different origins in Indonesia. From the 1H-NMR analysis it was obtained that 14 metabolites were responsible for generating classification model such as curcumin, demethoxycurcumin, alanine, methionine, threonine, lysine, alpha-glucose, beta-glucose, sucrose, alpha-fructose, beta-fructose, fumaric acid, tyrosine, and formate. Both PCA and OPLS-DA model demonstrated goodness of fit (R2 value more than 0.8) and good predictivity (Q2 value more than 0.45). All OPLS-DA models were validated by assessing the permutation test results with high value of original R2 and Q2. It can be concluded that metabolite fingerprinting using 1H-NMR spectroscopy and chemometrics provide a powerful tool for authentication of herbal and medicinal plants.
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.