Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study ...design, and there is no straightforward way of calculating the effective sample size for reaching an accurate conclusion. Use of a statistically incorrect sample size may lead to inadequate results in both clinical and laboratory studies as well as resulting in time loss, cost, and ethical problems. This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Data generation in clinical settings is ongoing and perpetually increasing. Artificial intelligence (AI) software may help detect data-related errors or facilitate process management. The aim of the ...present study was to test the extent to which the frequently encountered pre-analytical, analytical, and postanalytical errors in clinical laboratories, and likely clinical diagnoses can be detected through the use of a chatbot.
A total of 20 case scenarios, 20 multiple-choice, and 20 direct questions related to errors observed in pre-analytical, analytical, and postanalytical processes were developed in English. Difficulty assessment was performed for the 60 questions. Responses by 4 chatbots to the questions were scored in a blinded manner by 3 independent laboratory experts for accuracy, usefulness, and completeness.
According to Chi-squared test, accuracy score of ChatGPT-3.5 (54.4 %) was significantly lower than CopyAI (86.7 %) (p=0.0269) and ChatGPT v4.0. (88.9 %) (p=0.0168), respectively in cases. In direct questions, there was no significant difference between ChatGPT-3.5 (67.8 %) and WriteSonic (69.4 %), ChatGPT v4.0. (78.9 %) and CopyAI (73.9 %) (p=0.914, p=0.433 and p=0.675, respectively) accuracy scores. CopyAI (90.6 %) presented significantly better performance compared to ChatGPT-3.5 (62.2 %) (p=0.036) in multiple choice questions.
These applications presented considerable performance to find out the cases and reply to questions. In the future, the use of AI applications is likely to increase in clinical settings if trained and validated by technical and medical experts within a structural framework.
Objectives
Cigarette consumption is common around the world and besides its negative effects on health, and its effects on periodontitis draw attention. Arginine metabolites are involved in the ...pathogenesis of several systemic inflammatory diseases' including cardiovascular diseases. Our aim was to determine periodontitis and healthy individuals' arginine metabolites and IL‐6 levels in saliva and serum and to evaluate those according to smoking status.
Materials and Methods
The study consisted of four groups: healthy individuals (control C; n = 20), smokers with healthy periodontium (S‐C; n = 20), nonsmokers with Stage‐III Grade‐B generalized periodontitis (P; n = 20) and smokers with Stage‐III Grade‐C generalized periodontitis (S‐P; n = 18). Periodontal parameters were measured. Analysis of methylated arginine metabolites was performed by LC–MS/MS, and IL‐6 levels were determined by ELISA kits.
Results
In nonsmokers, salivary concentrations of asymmetric dimethylarginine (ADMA) and symmetrical dimethylarginine (SDMA) were higher in the periodontitis than control (p < 0.001, p = 0.010). Smokers with periodontitis exhibited higher ADMA (p = 0.033, p < 0.001) and arginine (p = 0.030, p = 0.001) saliva concentrations than smoking and nonsmoking controls.
Conclusions
Our results demonstrated that salivary concentrations of ADMA and SDMA were associated with periodontitis. Smoking increased ADMA, SDMA and NG‐monomethyl L‐arginine (L‐NMMA) levels in serum only in periodontitis patients.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Alzheimer’s Disease (AD) is a progressively debilitating form of dementia that affects millions of individuals worldwide. Although a vast amount of research has investigated the complex interplay ...between gut microbiota and neurodegeneration, the metaproteomic effects of microbiota on AD pathogenesis remain largely uncharted territory. This study aims to reveal the role of gut microbiota in AD pathogenesis, particularly regarding changes in the proteome and molecular pathways that are intricately linked to disease progression. We operated state-of-the-art Nano-Liquid Chromatography Mass Spectrometry (nLC-MS/MS) to compare the metaproteomic shifts of 3-month-old transgenic (3M-ALZ) and control (3M-ALM, Alzheimer’s Littermate) mice, depicting the early onset of AD with those of 12-month-old ALZ and ALM mice displaying the late stage of AD. Combined with computational analysis, the outcomes of the gut–brain axis-focused inquiry furnish priceless knowledge regarding the intersection of gut microbiota and AD. Accordingly, our data indicate that the microbiota, proteome, and molecular changes in the intestine arise long before the manifestation of disease symptoms. Moreover, disparities exist between the normal-aged flora and the gut microbiota of late-stage AD mice, underscoring that the identified vital phyla, proteins, and pathways hold immense potential as markers for the early and late stages of AD. Our research endeavors to offer a comprehensive inquiry into the intricate interplay between gut microbiota and Alzheimer’s Disease utilizing metaproteomic approaches, which have not been widely adopted in this domain. This highlights the exigency for further scientific exploration to elucidate the underlying mechanisms that govern this complex and multifaceted linkage.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Objective
The aim of this study was to determine differences in GCF and serum levels of fractalkine/CX3CL1 and its receptor/ CX3CR1 between the patients with stage III/grade B periodontitis and ...periodontally healthy subjects.
Background
Fractalkine (CX3CL1), the only member of CX3C chemokine family, is involved in the pathogenesis of several systemic inflammatory diseases’ disorders including rheumatoid arthritis, cardiovascular diseases, tonsillitis, and diabetes mellitus. It has critical functions in inflammatory cell migration, adhesion, and proliferation.
Methods
20 stage III/grade B periodontitis (P) and 20 healthy individuals (control; C) were included in this clinical study (all never smokers and systemically healthy). Clinical periodontal parameters were measured. Serum and GCF levels of CX3CL1, CX3CR1, and IL‐1β were quantified by enzyme‐linked immunosorbent assay and reported as total amounts and concentration.
Results
The GCF concentrations and also total amount of CX3CL1, CX3CR1, and IL‐1β were statistically significantly higher in the patients with periodontitis compared with control group (P < 0.05). CX3CL1, CX3CR1, and IL‐1β levels in the GCF were significantly and positively correlated with all the clinical periodontal parameters (PI, PPD, BOP, and CAL; P < 0.01, P < 0.05). There was a significant correlation between IL‐1β, CX3CL1, and CX3CR1 concentrations in the GCF (respectively; r = 0.838 and r = 0.874, P < 0.01).
Conclusion
Fractalkine and its receptor may play role in mechanisms through the regulation of inflammation or on the pathogenesis of periodontal disease.
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CMK, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Background
Non‐invasive methods for periodontitis diagnosis would be a clinically important tool. This cross‐sectional study aimed to investigate the association between oxidative stress, glycation, ...and inflammation markers and periodontal clinical parameters in periodontitis and periodontally healthy patients with type 2 diabetes and corresponding systemically healthy controls.
Methods
Sixty‐seven periodontally healthy (DM‐H, n = 32) and periodontitis (DM‐P, n = 35) patients with type 2 diabetes, and 54 systemically healthy periodontitis (H‐P, n = 26) and periodontally healthy (H‐H, n = 28) controls were included. Clinical periodontal parameters, body mass index, fasting glucose, hemoglobin A1c (HbA1c), along with saliva and serum 8‐hydroxy‐2′‐deoxyguanosine (8‐OHdG), malondialdehyde (MDA), 4‐hydroxy‐2‐nonenal (4‐HNE), advanced glycation end products (AGE), AGE receptor (RAGE) and high sensitivity C‐reactive protein (hsCRP) levels were recorded and analyzed.
Results
Salivary 8‐OHdG levels were significantly higher in periodontitis compared to periodontally healthy patients, regardless of systemic status (P < 0.001). Salivary MDA levels were significantly higher in all disease groups compared to H‐H group (P ≤ 0.004). Serum AGE levels were significantly higher in diabetic groups than systemically healthy groups (P < 0.001) and in H‐P compared to H‐H (P < 0.001). Bleeding on probing (BOP) and clinical attachment level (CAL) strongly correlated with salivary 8‐OHdG and serum hsCRP (P < 0.001). In systemically healthy patients, salivary 8‐OHdG was the most accurate marker to differentiate periodontitis from controls (AUC = 0.84). In diabetics salivary 4‐HNE and RAGE were the most accurate (AUC = 0.85 for both).
Conclusion
Salivary 8‐OHdG alone or in combination with 4‐HNE, AGE and RAGE for diabetics, and salivary 8‐OHdG alone or in combination with MDA and hsCRP for systemically healthy persons, could potentially serve as non‐invasive screening marker(s) of periodontitis.
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BFBNIB, CMK, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Background
This study aimed to determine the effects of smoking on early (≤3 months) clinical outcomes and relevant molecular biomarkers following root coverage surgery.
Methods
Eighteen smokers and ...18 nonsmokers, status biochemically verified, with RT1 gingival recession defects were recruited and completed study procedures. All patients received coronally advanced flap plus connective tissue graft. Baseline and 3 month recession depth (RD), recession width (RW), keratinized tissue width (KTW), clinical attachment level (CAL), and gingival phenotype (GP) were recorded. Root coverage (RC) percentage and complete root coverage (CRC) were calculated. Recipient (gingival crevicular fluid) and donor (wound fluid) site VEGF‐A, HIF‐1α, 8‐OHdG, and ANG levels were determined.
Results
There were no significant intergroup differences for any baseline or postoperative clinical parameters (P > 0.05), except for whole mouth gingival index (increased in nonsmokers at 3 months; P < 0.05). Compared to baseline, RD, RW, CAL, KTW, and GP significantly improved postoperatively, without significant intergroup differences. There were no significant intergroup differences for RC (smokers = 83%, nonsmokers = 91%, P = 0.069), CRC (smokers = 50%, nonsmokers = 72%, P = 0.177), and CAL gain (P = 0.193). The four biomarker levels significantly increased postoperatively (day 7; P ≤ 0.042) in both groups and returned to baseline (day 28) without significant intergroup differences (P > 0.05). Similarly, donor site parameters were not different between groups. Strong correlations, consistent over time, were found between biomarkers implicated in angiogenesis (VEGF‐A, HIF‐1α, and ANG).
Conclusions
The early (3 month) clinical and molecular changes after root coverage surgery utilizing a coronally advanced flap plus connective tissue graft are similar between smokers and nonsmokers.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Recent literature suggests that the use of electronic cigarette (e-cigarette) is a substantial contributing factor to the unsuccessful outcomes of dental implant procedures. Our aim was to ...systematically review the effect of e-cigarette use on clinical (PI, PD, BOP), radiographic (bone loss), and immunologic (IL-1β) peri‑implant parameters.
Main search terms used in combination: electronic cigarette, peri‑implantitis, vaping.
An electronic search was undertaken for MEDLINE, EMBASE, COCHRANE, and SCOPUS databases between 2017 and 2023.
The study protocol was developed according to PRISMA guidelines, and the focus question was formulated according to the PICO strategy. No restriction was accepted regarding language or year to avoid selection bias; the initial database search yielded 49 publications. Following the selection process, only seven studies met the inclusion criteria. Seven studies were statistically analyzed via MedCalc program. A pooled effect was deemed statistically significant if the p-value was less than 0.05.
Electronic cigarettes cause an increase in probing depth, bone loss, and the level of IL-1β, one of the bone destruction mediators in the tissues around the implant, and a decrease in bleeding on probing.
E-cigarette is a potential risk factor for the healing process and the results of implant treatment, similar to cigarettes. Performing clinical research to evaluate the e-cigarette effect on peri‑implantitis in an age and gender-match population is needed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Kynurenine pathway was accelerated in axSpA patients.•IL-17, IL-23, and IFN-γ levels were significantly decreased in axSpA patients.•Accelerated kynurenine pathway may have a role in limiting the ...immune system activation in axSpA disease.•A positive correlation was found between age and IDO activity.•Kyn pathway may also become a new target in supportive treatments used at advanced ages.
Various studies reported that the kynurenine (Kyn) pathway plays a pivotal role in regulating the balance between activation and inhibition of the immune system. Proinflammatory cytokines can accelerate the Kyn pathway by altering indoleamine (2, 3)- dioxygenase (IDO) allosteric enzyme activity. Excessive cytokine release and immune system activation have essential roles in the pathogenesis of axial spondyloarthritis (axSpA). We aimed to investigate the relationship of the Kyn pathway with proinflammatory cytokines and with the severity of the disease in patients with axSpA.
The study included 104 patients with axSpA and 54 healthy volunteers. The severity of the disease was determined by Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). The Kyn pathway was evaluated by IDO activity calculated with Kyn/Tryptophan (Trp) ratio. Plasma Trp and Kyn concentrations were measured with tandem mass spectrometry. Serum IL 17/23 and IFN-γ concentrations were measured with ELISA. These groups were compared in terms of IDO, IL-17, IL-23, IFN-γ, and BASDAI.
Plasma IDO activity was significantly increased, however, serum IL-17, IL-23, and IFN-γ levels were significantly decreased in patients compared to healthy volunteers. While IFN-γ was positively correlated with the severity of the disease (p = 0.02), it also had a significant inverse correlation with IDO activity (p < 0.001). However, these correlations are weak.
As a result of this study, the Kyn pathway is accelerated and proinflammatory cytokine levels are decreased in patients with axSpA. All of these results with an indirect weak negative association between high IDO and low disease activity suggest that an accelerated Kyn pathway may limit the immune system activation in axSpA disease.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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