The aim of the present study was to evaluate the effects of curcumin supplementation on inflammatory indices, and hepatic features in patients with non-alcoholic fatty liver disease (NAFLD). Fifty patients with NAFLD were randomized to receive lifestyle modification advice plus either 1500 mg curcumin or the same amount of placebo for 12 weeks. Curcumin supplementation was associated with significant decrease in hepatic fibrosis (p < 0.001), and nuclear factor-kappa B activity (p < 0.05) as compared with the baseline. Hepatic steatosis and serum level of liver enzymes, and tumor necrosis-α (TNF-α) significantly reduced in both groups (p < 0.05). None of the changes were significantly different between two groups. Our results indicated that curcumin supplementation plus lifestyle modification is not superior to lifestyle modification alone in amelioration of inflammation. IRCT20100524004010N24, this trial was retrospectively registered on May 14, 2018.

The high prevalence of antibiotic resistance in Helicobacter pylori has become a great challenge in Iran. The genetic mutations that contribute to the resistance have yet to be precisely identified. This study aimed to investigate the prevalence of antibiotic resistance and virulence markers in Iranian H. pylori isolates and to analyze if there is any association between resistance and genotype. Antibiotic susceptibility patterns of 68 H. pylori isolates were investigated against metronidazole, clarithromycin, amoxicillin, rifampicin, ciprofloxacin, levofloxacin, and tetracycline by the agar dilution method. The frxA, rdxA, gyrA, gyrB, and 23S rRNA genes of the isolates were sequenced. The virulence genotypes were also determined using PCR. Metronidazole resistance was present in 82.4% of the isolates, followed by clarithromycin (33.8%), ciprofloxacin (33.8%), rifampicin (32.4%), amoxicillin (30.9%), levofloxacin (27.9%), and tetracycline (4.4%). Overall, 75% of the isolates were resistant to at least two antibiotics tested and considered as a multidrug resistance (MDR) phenotype. Most of the metronidazole-resistant isolates carried frameshift mutations in both frxA and rdxA genes, and premature termination occurred in positions Q5Stop and Q50Stop, respectively. Amino acid substitutions M191I, G208E, and V199A were predominantly found in gyrA gene of fluoroquinolone-resistant isolates. A2143G and C2195T mutations of 23S rRNA were found in four clarithromycin-resistant isolates. Interestingly, significant associations were found between resistance to metronidazole (MNZ) and cagA-, sabA-, and dupA-positive genotypes, with p = 0.0002, p = 0.0001, and p = 0.0001, respectively. Furthermore, a significant association was found between oipA “on” status and resistance to amoxicillin (AMX) (p = 0.02). The prevalence of H. pylori antibiotic resistance is high in our region, particularly that of metronidazole, clarithromycin, ciprofloxacin, and MDR. Simultaneous screening of virulence and resistance genotypes can help clinicians to choose the appropriate therapeutic regime against H. pylori infection.

Alterations of the gut microbiome in cases of colorectal cancer (CRC) hint at the involvement of host–microbe interactions in the onset and progression of CRC and also, possibly, provide novel ways to detect and prevent CRC early. The aim of the present study was to evaluate whether the oral and fecal microbiomes of an individual can be suitable for CRC screening. Oral and fecal samples (n = 80) were gathered in Taleghani hospital, affiliated with Shahid Beheshti University of Medical Sciences, Tehran–Iran, from CRC stage 0 and I patients and healthy controls (HCs), who were screened for the first time. Microbial metagenomics assays were performed for studying microbiota profiles in all oral and fecal samples gathered. An abundance of top bacterial genera from both types of specimens (fecal and saliva samples) revealed a distinction between CRC patients and HCs. In saliva samples, the α diversity index was different between the microbiome of HCs and CRC patients, while β diversity showed a densely clustered microbiome in the HCs but a more dispersed pattern in CRC cases. The α and β diversity of fecal microbiota between HCs and CRC patients showed no statistically significant differences. Bifidobacterium was identified as a potential bacterial biomarker in CRC saliva samples, while Fusobacterium, Dialister, Catonella, Tennerella, Eubacterium-brachy-group, and Fretibacterium were ideal to distinguish HCs from CRC patients. One of the reasons for the heterogeneity of CRC may be the gastrointestinal (GI) tract microbiota, which can also cause systematic resistance to CRC. Moreover, an evaluation of saliva microbiota might offer a suitable screening test for the early detection of this malignancy, providing more accurate results than its fecal counterpart.

Results from different clinical trials on the effects of ginseng on prediabetes and type 2 diabetes (T2DM) are still inconsistent. To fill this knowledge gap, we investigated the overall effects of ginseng supplementation on improving cardiometabolic biomarkers among these patients. A systematic literature search was conducted on PubMed/MEDLINE, Scopus, Web of Science, and Cochrane library. A random-effect model was applied to estimate the weighted mean difference and 95% CI for each outcome. Overall, 20 eligible RCTs were included. Meta-analyses revealed that ginseng supplementation significantly reduced serum concentration of FPG, TC, IL-6, and HOMA-IR values. It also increased HR and TNF-α levels. Ginseng supplementation changed HOMA-IR and HDL-C significantly based on dose and changed HOMA-IR and LDL-C significantly based on study duration in a non-linear fashion. Furthermore, meta-regression analyses indicated a linear relationship between ginseng dose and absolute changes in HDL-C. Moreover, subgroup analyses showed that ginseng supplementation changed TC and LDL-C when the supplementation dose was ≥2 g/day. Our findings suggest that ginseng supplementation may be an effective strategy for improving cardiometabolic profiles in individuals with prediabetes and T2DM.