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Background/Aims Colonoscopy is widely used as a diagnostic and preventive procedure for colorectal diseases. The most recent guidelines advocate the use of a low-residue diet (LRD) for bowel preparation before colonoscopy. LRD duration varies considerably, with recommended 1-day and multiple-day regimens in clinical practice. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched multiple databases for randomized controlled trials (RCTs) and analyzed the outcomes using a fixed-effects model. Results Six RCTs with 2,469 subjects were included in this study. The rates of adequate bowel preparation for 1-day and >1-day were 87.2% and 87.1%, respectively. No statistically significant differences were observed between the 1-day and >1-day LRD in adequate bowel preparation (odds ratio [OR], 1.03; 95% confidence interval [CI], 0.76–1.41; p=0.84; I2=0%), polyp detection rate (OR, 0.91; 95% CI, 0.76–1.09; p=0.29; I2=16%), adenoma detection rate (OR, 0.87; 95% CI, 0.71–1.08; p=0.21; I2=0%), and withdrawal time (mean difference, –0.01; 95% CI, –0.25 to 0.24; p=0.97; I2=63%). Conclusions The efficacy of 1-day and multiple-day LRD is comparable in achieving satisfactory bowel preparation, highlighting their similar impact on the detection of polyps and adenomas during colonoscopy.

Background: Colorectal cancer (CRC) screening is essential to reduce incidence and mortality rates. However, participation in screening remains suboptimal. ColonFlag, a machine learning algorithm using complete blood count (CBC), identifies individuals at high CRC risk using routinely performed tests. This study aims to review the existing literature assessing the efficacy of ColonFlag across diverse populations in multiple countries.Methods: The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) were followed in reporting this systematic review. Searches were conducted on PubMed, Cochrane, ScienceDirect, and Google Scholar for English articles, using keywords related to CBC, machine learning, ColonFlag, and CRC, covering the first development study from 2016 to August 2023. The Cochrane Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias.Results: A total of 949 articles were identified during the literature search. Ten studies were found to be eligible. ColonFlag yielded Area Under the Curve (AUC) values ranging from 0.736 to 0.82. The sensitivity and specificity ranged from 3.91% to 35.4% and 82.73% to 94%, respectively. The positive predictive values ranged between 2.6% and 9.1%, while the negative predictive values ranged from 97.6% to 99.9%. ColonFlag performed better in shorter time windows, tumors located more proximally, in advanced stages, and in cases of CRC compared to adenoma.Conclusion: While ColonFlag exhibits low sensitivity compared to established screening methods such as the fecal immunochemical test (FIT) or colonoscopy, its potential to detect CRC before clinical diagnosis suggests an opportunity for identifying more cases than regular screening alone.

Artificial whitening agents available in the market can cause damage to melanocytes; therefore, a safe and naturally derived skin-whitening agent is needed. Avocado seeds (Persea americana Mill.) are often discarded as waste but possess numerous health benefits. One of the compounds in avocado seeds is catechin, a flavonoid metabolite. This compound has been reported to exhibit antioxidant activity and inhibit tyrosinase to prevent melanin formation, making it suitable for skin-whitening applications. This study aimed to conduct phytochemical screening, assess antioxidant activity using the DPPH method, analyze catechin content using HPLC, and perform cell viability tests using the MTT method from the ethyl acetate extract of avocado seeds. Phytochemical screening has revealed the presence of flavonoids, saponins, and steroids. The DPPH assay yielded an IC50 value of 89.47±0.73 ppm, indicating an intense antioxidant activity, and HPLC detected 0.09% catechin. The MTT test results yielded cell viability percentages ranging from 80-100% at test concentrations of 0.75-25 ppm, with an IC50 value of 61.7 ppm. In conclusion, our results indicate that the ethyl acetate extract from avocado seeds affects cell viability without toxicity, warranting further testing for tyrosinase inhibition in the mouse melanoma B16F10 cell line.

Background Germany is challenged by an increasing shortage in general practice services, especially in non-urban areas. Task shifting from general practitioners (GPs) to other health professionals may improve practice efficiency to address this mismatch.Objectives Exploring GPs’ motives and beliefs towards task shifting in non-urban Germany and identifying potential factors influencing these.Methods The cross-sectional survey was disseminated by mail in three waves between July 2021 and August 2022 among all GPs in non-urban Baden-Wuerttemberg, Germany. It included items on demographics and practice characteristics as well as 15 Likert-scale items addressing motives and beliefs towards task shifting, based on the Theoretical Domain Framework. Likert-scale items were analysed descriptively, influencing factors on motives and beliefs were identified using multiple linear regression.Results Response rate was 24.2% (281/1162), with respondents comparable in age and gender to all GPs in Baden-Wuerttemberg. GPs’ motives and beliefs towards task shifting are positive overall. The majority expects task shifting to reduce their workload (87.9%) and increase practice efficiency (74.7%). They are open to shift additional tasks to other professionals (69.1%), even in the currently prohibited form of substitution (51.2%). Motives and beliefs were significantly more positive among younger GPs and those participating in the GP-centred care programme.Conclusion This study describes GPs’ motives and beliefs towards task shifting in non-urban Germany. Identifying younger GPs and those participating in the GP-centred care programme as particularly endorsing may help design future interventions aiming to improve efficiency in general practice in non-urban Germany.

Banjir merupakan salah satu bencana alam yang sering terjadi di Indonesia, termasuk di Kota Samarinda dengan 18-33 titik desa terdampak dari tahun 2018-2021. Penggunaan machine learning dalam mengklasifikasi bencana banjir sangat penting untuk memprediksi kejadian di masa mendatang. Beberapa penelitian sebelumnya terkait klasifikasi data banjir dalam 3 tahun terakhir telah dilakukan. Namun, dari beberapa penelitian tersebut memunculkan masalah terkait dengan dataset high dimensional yang dapat menurunkan performa model klasifikasi dan menyebabkan overfitting. Selain itu, masalah lain juga muncul dalam hal imbalance data yang menyebabkan bias terhadap kelas mayoritas dan representasi yang tidak akurat. Oleh karena itu, permasalahan dataset high dimensional dan imbalance data merupakan tantangan spesifik yang harus diatas dalam klasifkasi data banjir Kota Samarinda. Penelitian ini bertujuan mengidentifkasi fitur-fitur yang diperoleh dari seleksi fitur Genetic Algorithm (GA) yang memiliki pengaruh terhadap akurasi klasifikasi data banjir Kota Samarinda menggunakan algoritma Support Vector Machine (SVM), serta meningkatkan akurasi klasifikasi data banjir di Kota Samarinda dengan mengimplementasikan algoritma SVM yang dikombinasikan dengan metode Synthetic Minority Oversampling Technique (SMOTE) untuk oversampling, seleksi fitur dengan GA dan optimasi menggunakan Particle Swarm Optimization (PSO). Teknik validasi yang digunakan adalah 10-fold cross validation dan evaluasi performa menggunakan confusion matrix. Data yang digunakan berasal dari BPBD (Badan Penanggulangan Bencana Daerah) dan BMKG (Badan Meteorologi, Klimatologi, dan Geofisika) Kota Samarinda pada tahun 2021-2023 terdiri dari 11 fitur dan 1.095 record. Hasil penelitian menunjukkan bahwa fitur-fitur penting yang terpilih melalui GA adalah temperatur maksimum, kecepatan angin maksimum, arah angin maksimum, arah angin terbanyak, lamanya penyinaran matahari dan kecepatan angin rata-rata. Dengan kombinasi metode SVM, SMOTE, GA dan PSO, akurasi klasifikasi data banjir mencapai 82,28%. Namun, penelitian ini juga menghadapi tantangan seperti kontradiksi hasil dengan penelitian lain terkait penggunaan SMOTE dan variasi hasil akibat karakteristik dataset serta metode pembagian data yang berbeda. Hasil penelitian ini dapat digunakan oleh pemerintah daerah dan badan penanggulangan bencana daerah Kota Samarinda untuk memprediksi kejadian banjir dengan lebih akurat, serta memungkinkan tindakan pencegahan yang lebih efektif. Penerapan hasil penelitian ini dapat meningkatkan efektivitas dalam mitigasi bencana banjir Kota Samarinda.

Black cumin (Nigella sativa) is a herbal plant that has been cultivated locally in Indonesia and is traditionally used for various diseases. Thymoquinone, one of the main components, is rich in biological activity. In several countries, topical application of its oil on human skin with vitiligo can stimulate skin repigmentation with minimal side effects. This study aims to determine the viability of B16F10 melanoma mouse cells against ethanol and n-hexane extracts of black cumin seeds through the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The ethanol extract (EE) yield was 14.306%, and the n-hexane extract (NHE) was 7.442%. Phytochemical screening of EE detected flavonoids, alkaloids, saponins, and steroids, and High-performance liquid chromatography (HPLC) detected 0.040% thymoquinone. The MTT test showed cell viability was >100% from EE at all treatment concentrations, namely 0.75-100 ppm and only 0.75-6.25 ppm for NHE. In conclusion, this study indicates that 96% EE of Nigella sativa is less toxic than NHE on B16F10 mouse melanoma cells and has potential as an alternative treatment for vitiligo, which needs to be proven in further research.

Abstract During the COVID-19 pandemic, lateral flow tests (LFTs) were used to regulate access to work, education, social activities, and travel. However, falsification of home LFT results was a concern. Falsification of test results during an ongoing pandemic is a sensitive issue. Consequently, respondents may not answer truthfully to questions about LFT falsification behaviours (FBs) when asked directly. Indirect questioning techniques such as the Extended Crosswise model (ECWM) can provide more reliable prevalence estimates of sensitive behaviors than direct questioning. Here we report the prevalence of LFT FBs in a representative sample in England (n = 1577) using direct questioning (DQ) and the ECWM. We examine the role of demographic and psychological variables as predictors of LFT FBs. We show that the prevalence estimates of the FBs in the DQ condition were significantly lower than the ECWM estimates, e.g., reporting a negative result without conducting a test: 5.7% DQ vs 18.4% ECWM. Moral norms, subjective norms, anticipated regret, perception of risk to self, and trust in government predicted some of the FBs. Indirect questioning techniques can help provide more realistic and higher quality data about compliance with behavioural regulations to government and public health agencies.